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The final capstone project is a series of written research-based assignments completed throughout this course that culminate in a final evidenced-based proposal paper and presentation. For this first assignment, you will select the topic for your evidence-based intervention project

Investigate a health care issue or problem in your area of disciple that can be improved by implementing an evidence-based intervention. The resolution of this problem or issue should support or improve patient care. Use the PICO format to develop a PICO question (clinical question) for your proposed evidence-based intervention project. Refer to the “PICO Guide” to help guide you through this process.

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In a formal paper of 500-750 words, address the following:

  1. Describe the health care issue or problem you selected. Explain why it is a problem in your discipline.
  2. Describe the target population for your intervention and explain how the population is affected by this issue or problem.
  3. Based on the results of your PICO formatting, draft one or two questions for your proposed topic. These questions will be the basis for your implementation plan.

You are required to cite to a minimum of three peer-reviewed or scholarly sources to complete this assignment. Sources must be published within the last 5 years and appropriate for the assignment criteria and health care setting.

PICO Guide

The PICOT question format is a consistent “formula” for developing answerable, researchable questions. When you write a good one, it makes the rest of the process of finding and evaluating evidence much more straightforward.

P: Population/Patient – age, gender, ethnicity, individuals with a certain disorder

I: Intervention/Indicator (Variable of Interest) – exposure to a disease, risk behavior, prognostic factor

C: Comparison/Control – could be a placebo or “business as usual” as in no disease, absence of risk factor, prognostic factor B

O: Outcome – risk of disease, accuracy of a diagnosis, rate of occurrence of adverse outcome

(T): Time – the time it takes for the intervention to achieve an outcome or how long participants are observed (Optional)

Example Questions:

For an intervention :

In _______(P), what is the effect of _______(I) on ______(O) compared with _______(C) within ________ (T)?

Diagnosis or diagnostic test :

Are (is) _________ (I) more accurate in diagnosing ________ (P) compared with ______ (C) for _______ (O)?

Prevention:

For ________ (P) does the use of ______ (I) reduce the future risk of ________ (O) compared with _________ (C)?

Predictions:

Does __________ (I) influence ________ (O) in patients who have _______ (P) over ______ (T)?

Meaning:

How do ________ (P) diagnosed with _______ (I) perceive ______ (O) during _____ (T)?

Intervention PICOT Question, an Intervention Example:

In adult patients with total hip replacements (Patient population) how effective is PCA pain medication (Intervention of interest) compared to prn IM pain medication (Comparison intervention) in controlling postoperative pain (Outcome) during the perioperative and recovery time? 

Therapy PICOT Question, a Nonintervention Example:

What is the duration of recovery (O) for patients with total hip replacement (P) who developed a postoperative infection (I) as opposed to those who did not (C) within the first 6 weeks of recovery (T)?

Diagnostic PICOT Question:

Is a PKU test (I) done on 2-week old infants (P) more accurate in diagnosis inborn errors in metabolism (O) compared with PKU tests done at 24 hours of age (C)? Time is implied in 2 weeks and 24 hours old.

Prevention PICOT Question:

In OR nurses doing a 5-minute scrub (P), what are the differences in the presence and types of microbes (O) found on natural polished nails and nail beds (I) and artificial nails (C) at the time of surgery (T)?

Prediction PICOT Question:

Does telemonitoring blood pressure (I) in urban African Americans with hypertension (P) improve blood pressure control (O) within the 6 months of initiation of the medication (T)?

Meaning PICOT Question:

How do pregnant women (P) newly diagnosed with diabetes (I) perceive reporting their blood sugar levels (O) to their health care providers during their pregnancy and 6 weeks postpartum (T)?

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Typing Template for APA Papers: A Sample of Proper Formatting for APA Style

Student A. Sample

College Name, Grand Canyon University

Course Number: Course Title

Instructor’s Name

Running head: ASSIGNMENT TITLE HERE

1

Assignment Due Date

Typing Template for APA Papers: A Sample of Proper Formatting for APA Style

This is an electronic template for papers written according to the style of the American Psychological Association (APA, 2020) as outlined in the seventh edition of the Publication Manual of the American Psychological Association. The purpose of the template is to help students set the margins and spacing. Margins are set at 1 inch for top, bottom, left, and right. The text is left-justified only; that means the left margin is straight, but the right margin is ragged. Each paragraph is indented 0.5 inch. It is best to use the tab key to indent, or set a first-line indent in the paragraph settings. The line spacing is double throughout the paper, even on the reference page. One space is used after punctuation at the end of sentences. The font style used in this template is Times New Roman and the font size is 12 point. This font and size is required for GCU papers.

The Section Heading

The heading above would be used if you want to have your paper divided into sections based on content. This is a Level 1 heading, and it is centered and bolded, and the initial word and each word of four or more letters is capitalized. The heading should be a short descriptor of the section. Note that not all papers will have headings or subheadings in them. Papers for beginning undergraduate courses (100 or 200 level) will generally not need headings beyond Level 1. The paper title serves as the heading for the first paragraph of the paper, so “Introduction” is not used as a heading.

Subsection Heading

The subheading above would be used if there are several sections within the topic labeled in a first level heading. This is a Level 2 heading, and it is flush left and bolded, and the initial word and each word of four or more letters is capitalized.

Subsection Heading

APA dictates that you should avoid having only one subsection heading and subsection within a section. In other words, use at least two subheadings under a main heading, or do not use any at all. Headings are used in order, so a paper must use Level 1 before using Level 2. Do not adjust spacing to change where on the page a heading falls, even if it would be the last line on a page.

The Title Page

When you are ready to write, and after having read these instructions completely, you can delete these directions and start typing. The formatting should stay the same. You will also need to change the items on the title page. Fill in your own title, name, course, college, instructor, and date. List the college to which the course belongs, such as College of Theology, College of Business, or College of Humanities and Social Sciences. GCU uses three letters and numbers with a hyphen for course numbers, such as CWV-101 or UNV-104. The date should be written as Month Day, Year. Spell out the month name.

Formatting References and Citations

APA Style includes rules for citing resources. The Publication Manual (APA, 2020) also discusses the desired tone of writing, grammar, punctuation, formatting for numbers, and a variety of other important topics. Although APA Style rules are used in this template, the purpose of the template is only to demonstrate spacing and the general parts of the paper. GCU has prepared an APA Style Guide available in the Student Success Center and on the GCU Library’s Citing Sources in APA guide (https://libguides.gcu.edu/APA) for help in correctly formatting according to APA Style.

The reference list should appear at the end of a paper. It provides the information necessary for a reader to locate and retrieve any source you cite in the body of the paper. Each source you cite in the paper must appear in your reference list; likewise, each entry in the reference list must be cited in your text. A sample reference page is included below. This page includes examples of how to format different reference types. The first reference is to a webpage without a clear date, which is common with organizational websites (American Nurses Association, n.d.). Next is the Publication Manual referred to throughout this template (APA, 2020). Notice that the manual reference includes the DOI number, even though this is a print book, as the DOI was listed on book, and does not include a publisher name since the publisher is also the author. A journal article reference will also often include a DOI, and as this article has four authors, only the first would appear in the in-text citation (Copeland et al., 2013). Government publications like the Treatment Improvement Protocol series documents from the Center for Substance Abuse Treatment (2014) are another common source found online. A book without a DOI is the last example (Holland & Forrest, 2017).

References

American Nurses Association. (n.d.). Scope of practice. https://www.nursingworld.org/practice-policy/scope-of-practice/

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

Center for Substance Abuse Treatment. (2014). Improving cultural competence (HHS Publication No. 14-4849). U.S. Department of Health and Human Services, Substance Abuse and Mental Health Services Administration. https://www.ncbi.nlm.nih.gov/books/NBK248428/

Copeland, T., Henderson, B., Mayer, B., & Nicholson, S. (2013). Three different paths for tabletop gaming in school libraries. Library Trends, 61(4), 825–835. https://doi.org/10.1353/lib.2013.0018

Holland, R. A., & Forrest, B. K. (2017). Good arguments: Making your case in writing and public speaking. Baker Academic.

Effectiveness of Physical Therapy Intervention in Decreasing the Risk for Fails in a Community-Dweliing Aging Population

Barbara Susan Rohinson | Joanne M. Gordon | Scott W. Wallentine | Michelle Visio

Purpose: This clinical investigation examined risk factors for falls and the effectiveness of physical therapy intervention to decrease the risk of falls in a community dwelling aging population.

Sample: A convenience sample of 25 community-dwelling elderly adults ages 65 and older participated.

Methods: Al! subjects participated in an extensive examination and evaluation to identify risk for falls and performance on selected measures of balance and mobility. Subjects were given the opportunity to participate in an exercise pro- gram designed to address impairments or functional disabilities. The control subjects received no intervention; however, they were encouraged to continue witb their normal activities, including exercise.

Findings: Significant differences were found between subjects classified as fall- crs and nonfallers in terms of their performance on selected balance and mobil- ity tests. After pbysical therapy intervention, subjects classified as fallers made significant improvements in their performance on the Berg Balance Scale.

Conclusion: Appropriately designed physical therapy intervention in the form of an exercise program can decrease the risk for falls among a community- dwelling aging population identified as having an increased risk of falls.

All authors work at Southwest Missouri State University in Springfield, Missouri.

Barbara Susan Robinson, DPT, PT, is an Assistant Professor in the Departtnent of Physical Therapy.

Joanne M. Gordon, PhD, RN, CS, is an Ass(xiate Professor in the Department of BionieJicitl Sciences.

Scott W. Wallentine, DPT, PT, is an Assistant Professor in the Departtnent of Physical Therapy.

Michelle Visio, PhD, is an As.sistant Professor in the Department of Psychology.

F alls may have devastating consequences for older adults. Each year, approximately one third of persons over tbe age of 65 experience a fall, or unexpected contact of a

body part with the ground or support- ing surface (Blake et al., 1988; Sattin, 1992). An individual who falls may experience a serious injury, including fracture, soft tissue injury, joint dislo- cation, and mobility impairment dur- ing 15-20^ of falls. Forty percent of hospital admissions for individuals over tbe age of 65 are tbe result of fall- related injuries (Sattin, 1990, 1992). Additionally, fall-related injuries and their consequences are associated

with declining function in activities of daily living (ADLs) (Tinetti et al., 1998) and are the leading cause of death from injuries for these individu- als (Sattin, 1992). Injuries from falls represent a significant public health problem witb annual expenditures of greater than $ 10 billion for the care of bip fractures alone {Sattin, 1992).

Research Question The purpose of this study was to iden- tify individuals at risk for falls, deter- mine specific risk factors for falling, and evaluate the effectiveness of phys- ical therapy intervention to decrease tbe risk of falls in a community- dwelling aging population. Tbe hy- potbeses were (1) tbat there are signif- icant differences between fallers and nonfallers in terms of specific risk fac- tors such as [lerfomiance on selected measures of balance and mobility, posture, preexisting health condi- tions, and the use of medication, and (2) that physical therapy intervention can modify risk factors for falling, and as a result of this intervention, sub- jects identified as fallers will demon- strate improvement on selected meas- ures of balance and mobility.

Review of the Literature Risk Factors for Falls and Characteristics of Fallers Falling is a complex problem witb many potential causes, yet it is a health condition that is highly pre- ventable. Risk factors for falls may be either intrinsic (specific to tbe individ- ual) or extrinsic (related to the envi-

Orthopaedic Nursing – {anuary/February 2002 – Volume 21 • Number 1 55

TaRnigthe Mystery Out of Research Quasi-experimental Design

A s orthopaedic nurses we knowthe reality: Imbalance = Fall = Fracture = Surgery = Dependence. And we know from experience that it takes time and effort to identify per- sons at risk for falling. Time we don’t always take. Sometimes it is just easi- er to lump all persons over the age of 65 as “potential fallers”and refer them to a physical therapist for instruction.

To separate out the differences between fallers and nonfallers becomes iristantly complicated by the multitude of compounding vari- ables within human study groups. It is much easier to study animal rat models confined within their labora- tory cages exercising on treadmills, eating premeasured caloric meals, and resting at predetermined inter- vals as the lab lights are dimmed. These rat subjects are free from dis- ability, fast foods diets, the stress of stock market declines, and baby sit- ting grandchildren. But rather than being overwhelmed by all these vari- ables, a study design exists that allows for these human variables: the quasi-experimental design.

We can classify designs into a simple tlireefold classification by ask- ing some key questions. First, does the design use random assignment to gmupa?

if random assignment is used, we call the design a randomized experi- ment or a true experiment. If ran- dom assignment is not used, then we have to ask a second question: Does the design use either multiple groups or multiple ways of measurement? If the answer is yes, we would label it a quasi-experimental design. If no, we would call it a nonexperimental design.

So a quasi-experimental design is one that looks a bit like an experi- mental design but lacks the key ingredient — random assignment. Some scientists refer to them as “queasy experiments” because they give the purists a queasy feeling. With respect to internal validity,

they often appear to be inferior to randomized experiments. But there is something compelling about these designs: taken as a group, they are easily more frequently implemented than their randomized cousins.

Probably the most commonly used quasi-experimental design is the nonequivalent groups design. In its simplest form it requires a pretest and a posttest for treated and com- parison groups. It’s identical to the Analysis of Covariance design except that the groups are not created through random assignment.

Authors Robinson et al. chose this design to execute their research questions and determine risk factors that orthopaedic nurses can include in their client assessment. These identifiers can help the nurse make appropriate referrals to a physical therapist.

The jury is still out as to whether quasi-experimental designs can ade- quately control selection bias. It Ls safe to conclude that experimental de- signs are superior in this critical respect. But the many problems asso- ciated with experiments render them impractical for many if not most eval- uations. Quasi-ex[X?ri mental designs are often the best practical approach to take for evaluations of health and education programs.

Do we really need more nursing research? Why bother when we have a nursing shortage and are all so fms- trated by health care cutbacks and insurance pressures? Maybe the answer to the question is that research gives us data to support cost- effective treatment plans that provide financial benefits in the long run.

Research results arm nurse man- agers with financial negotiating ammunition when dealing with the hospital administration. Without these results, the nurse manager is left to defend her patient care plans with anecdotal [>ersonai experiences. While these experiences might be tnae, they are disregarded as war sto- ries in business meetings.

The contrived conversation belowillustrates well the different per- ceptions of nurses and outside con- sultants when it comes to assessing the merits of a program.

Nurse manager: Why do we need to evaluate our physical therapy refer- ral program? We have a good han- dle on what’s going on with our program and our patients, and we know we are very successful.

Outside Consultant: Because you never know if it was your program or something else that produced the success you are claiming. , •

Nurse manager: Of course we know it’s our program. What else would cause all these people to fall less?

Outside consultant: Maybe the more motivated clients are more compU-, ant? •

Nurse manager: That’s nonsense. Anyway, we know we have to have our program evaluated. But why do we need to go through the trou- ble of finding a comparison group to do an evaluation?

Outside Consultant: Let’s say that 6 months after clients finish physical therapy, TOH. are falliiig less. Would you consider that proof your pro- gram is a success? What proportion of these individuals might be hav- ing fewer falls now if they hadn’t gone through the physical therapy instruction?

Nurse manager: I’m not sure, but it wouldn’t be as high as 7Wo, I can tell you that.

Outside Consultant: Well, you don’t really know that though. For all you know, S<yiiy might be falling less now if they hadn’t taken phys- ical therapy instruction.

Nur.se manager: No way. We are pro- viding a valuable service and are realty helping our clients.

Outside Consultant: That may be so.

56 Orthopaedic Nursing – |anuary/Febniary 2002 – Volume 21 ” Number 1

but you haven’t proven it. And the insurance companies need to know with certainty how successful you have been. They need to know they are getting bang for their buck.

Nurse manager: They are, I assure you.

Outside consultant: Okay, let’s say you are doing some good, that indi- viduals who go through physical therapy instruction are indeed falling less than if they hadn’t been instructed. How much of an effect are you having? Would half of them have fallen less anyway? One-third? Two-thirds? You can’t know that unless you do an evaluation that includes a comparable group of peo- ple who haven’t taken physical tlier- apy instmction.

Nurse manager: Even if half of them had fallen less without the instruc- tion, isn’t raising that proportion to 70’K) worth it?

Outside Consultant: 1 don’t know. What did it cost for that incremen- tal 20%? And how long will the effects of training last?

Nurse manager: Our program is well worth the money…

Nurses live and work with the patients every day. They care about their program and work hard to make it a success. They strongly believe they are doing a good job, and they understandably resent any implication that they are not.

Evaiuators usually have no con- nection with the program, and, more important, no stake in its survival (which sometimes leads to an under- estimation of the threat that evalua- tions can impose on program man- agement and staff). They know that managers are heavily invested in their program and that a manager’s assessment of the program — even one aided by reliable monitoring data — will not be accepted by pro-

gram sponsors as a valid test of whether the program is meeting its objectives and is worth what it costs. And they know that many different factors that are unrelated to the design of the program can affect the outcomes of any social program and can easily lead to unwarranted con- clusions about the program.

Many of us have experienced the “business consultants” who sweep through hospitals to evaluate the health care delivery system. We all know the results — layoff by attrition and hiring freezes throughout physi- cal therapy, nursing, laboratory, and radiology personnel. Research data proving cost-effectiveness is the only weapon we have to explain to the consultants how the health careJ team works. Nursing research results! explain the bottom line.

Beth Lucasey, MA, BSN, RN Nurse Clinician, Osteoporosis Clinic University of Kansas Medicai Center

ronment) and include characteristics such as decreased range of motion, inability to produce adequate joint torque, decreased proprioception, impairment of the visual or vestibular systems, or environmental hazards such as the presence of ice, low light, throw rugs, or lack of grab bars (Gill et al., 1999; Sattin et al., 1998).

Prescription medications are also known to increase the risk of falling, including drugs used to treat depres- sion, anxiety, and hypertension (Blake et al., 1988; CampbeU et al., 1989; Cumming 1998; Leipzig et al., 1999; Uu et al., 1995).

Mobility impairment has been reported in the literature as a major risk factor for falling (Graafmans et al., 1996). Age and physical health, in- cluding the presence of chronic med- ical conditions, musculoskeletal im- pairments, declining strength and joint flexibility, decreased sensation in the lower limbs, visual impairments, or the report of frequent stumbles are also factors that influence balance and may increase the risk for falls (Gehlsen & Whaley 1990b; Herndon et al., 1997; Ivers et al., 1998; Lord et al., 1991; Rohbins et al., 1989; Teno et al., 1990; Wegener et al., 1997).

In their analysis, Tmetd et al. (1995)

determined that cognitive impair- ment, low body mass index, the pres- ence of at least two chronic condi- tions, and impaired balance and gait were associated with increased risk of serious injury associated with a fall. Other studies indicate that gait changes in older adults may be pre- dictors of falling (Gehlsen & Whaley, 1990a; Maki, 1997). Most falls occur because of a complex interaction between intrinsic and extrinsic risk factors, and the risk for falls increases as the number of risk factors accumu- late (Lipsitz et al., 1991).

As a result of research on balance and falls, a number of assessment tools that focus on intrinsic factors for falls have been identified as predictive of fall risk in elderly persons, includ- ing the Berg Balance Scale (BBS), Functional Reach Test (FRT), and the Timed “Up and Go” Test (TU&GT) (Berg et al., 1989; Duncan et al., 1990; Podsiadlo & Richardson, 1991). These assessment tools assist in the identifi- cation of functional skills that can be modified by fall prevention programs.

Effects of Exercise or Balance Training on Falling Prior research supports the use of struc- tured exercise programs to improve

balance and mobility function, thus reducing the risk for falls or the fre- quency of falls (Lord et al., 1995; Province et al., 1995; Wolf et al., 1996). Shumway-Cook et al. (1997) found that a multidimensional exercise pro- gram can improve balance, mobUity, and decrease fall risk in older adults, as well as enhance their functional ability.

Judge et al. (1993, 1994) reported findings that support the hypothesis that an exercise program emphasizing postural control, moderate resistance training, and walking improves sin- gle-stance balance, although they considered these results to be prelimi- nary findings. A group exercise pro- gram was found to be an effective means to improve performance on several measures of fall risk in a group of elderly women over a period of 12 months (Lord et al., 2001, 1995).

Balance control can be taught to the elderly with evidence of improved functioning. Roberts (1989) reported changes in balance among older adults following a 6-week program of aerobic walking that he attributed to improvements in strength, coordina- tion, and flexibility. Topp et al. (1993) examined the relationship between a dynamic resistance strength-training program and static and dynamic

Orthopaedic Nursing – January/February 2002 – Volume 21 • Number 1 57

In a review of 11 randomized,

controlled trials, exercise was

found to be effective in lowering the

risk for falls in selected populations.

measures of balance. Their findings suggest that older adults may demon- strate improved measures of dynamic balance as a result of a 12-week inter- vention, but not at a statistically sig- nificant level.

A 9-week program of physical exer- cise performed twice weekly resulted in improved performance on balance assessments in 70- to 75-year-old sub- jects in a Swedish community (Ledin etal., 1990/91).

In a group of healthy 75 to 90 year- old individuals, balance training led to significant improvement in balance (Wolfson et al., 1996). By contrast, strength training using the same exper- imental group led to significant gains in lower extremity isokinetic strength and single limb stance time, but failed to show improvement in balajice out- come measures. In a review of 11 ran- domized, controlled trials, exercise was found to be effective in lowering the risk for fails in selected populations (Gardner et al., 2000).

Skeietai Alignment and Falling Previous studies that examined spinal curve measurements of elderly fallers and nonfallers demonstrated no signif- icant differences between the groups. W(X)dhall-McNeal (1992) reported a nonsignificant relationship between elderly women with a history of falling and measures of forward lean and tht> racic kyphosis. O’Brien et al. (1997) showed a weak relationship between measures of skeietai alignment in the sagittal plane and performance on the BBS, YKV, and TU&GT in elderly women. They noted that it was not

possible to determine whether the bal- ance impairments occurred as a result of skeletal deformities or if the skeletal deformities occurred in response to balance impairments.

Methods Subjects A convenience sample of 25 male and female subjects 65 years or older par- ticipated in this project. Participants were volunteers recruited from a com- munity senior center and a senior citi- zen apartment complex. Eligibility was determined by questionnaire that examined medical history, use of med- ications, cTjrrent home environment, activities, a self-reported history of falls during the previous 6 months, score on the Foistein Mini Mental State Exam (MMSE) (Foistein et al., 1975), and the ability to maintain standing with or without an assistive device.

Individuals were excluded from the study if they scored lower than 24 on the MMSE, were unable to stand independently for 60 seconds, or had a medical condition that might im- pact their ability to participate in the study {e.g., vesUbular or CNS patholo- gy, or recent orthopaedic, cardiac, or neurologic diagnosis).

Additionally, individuals who resided in an extended<are or assisted- living facility, or who were under 65, were excluded from the study. Three individuals, one control and two test subjects, withdrew after initiating the study because of health reasons unre- lated to participation in the study and are not included in data analysis.

Classification of Subjects as Fallers or Nonfallers Following evaluation and assessment, subjects in this study were identifieil as fallers or nonfallers, based on the following criteria. Identified as fallers were those subjects who had a self- reported history of one or more falls during the past 6 months or subjects who scored < 45/56 on the BBS or sub- jects who had less than 7 inches of extended reach on the FRT.

Due to the extensive time commit- ment required for participation in the exercise program, subjects were allowed to choose to participate as control subjects or in the exercise group and were not randomly assigned to these groups. Seventeen individuals completed the exercise program with 10 subjects classified as fallers and 7 subjects classified as nonfallers. Five individuals agreed to participate as control subjects, but were encouraged to continue with their normal activi- ties, including exercise. None of the control subjects were classified as fall- ers. Characteristics of the remaining 22 subjects {n =10 fallers and n = 12 nonfallers) are presented in Table 1.

This study used a quasi-exĵ wrimen- tal, nonequivalent control group design that has been reported to be an accept- able alternative to an experimental design when randomization is not pos- sible {Cxwk et al., 1979; Portney et al., 1993). Individuals parficipating in the physical tlierapy intervention attended group physical therapy exercise sessions two times a week for 6 weeks. Ad- ditionally, a home exercise program was designed for each subject in the exercise group. Subjects kept a daily log of exercises completed at iiome, as well as a record of falls for 6 montlis follow- ing completion of the program.

Test Procedures Protocol After receiving a thorough explanation regarding the research project, each volunteer signed an infonned consent statement, in accordance with the Human Subjects Review Committee at the sponsodng institution. The MMSE and questionnaire were then adminis- tered to determine eligibility as de- scribed under subjects. Subjects who participated in the physical therapy intervention were required to have the consent and approval of their physi- cian to participate.

58 Orthopaedic Nursing – |anuary/February 2002 – Volume 21 • Number 1

TABLE 1 Characteristics of the Subject Popuiation

Characteristic Fallers Nonfallers* (n=12)

Statistical test

Age distribution: 65-74 years 75-84 years 85-94 years Average age

Gender (proportion of subjects who are male)

Activity level Exercise < once per week Walk outside < once per week

Anthropometries Height (inches) Weight (pounds)

Home environment Live alone Assistance at home Stairs at home

Frequency of imbalance (by self-report) None Monthly Weekly Daily

Falls History of falls during the past 6 months Injured from fall (at any time)

60% 20% 20% 77.4

30%

60% 40%

64.98 180.7

60% 40% 50%

30% 50% 10% 10%

100% 30%

75% 25%

0 70.7

25%

58.3% 75%

64.33 165.42

50% 50% 75%

66.7% 8.3%

16.7% 8.3%

0 41.7%

x\2) = 2.64^ t (20) =-2.30*”

X\^) = O.79

X'(4) = 6.01 X^(4) ^ 5.67

t (20) = -0.47 t(20) =-0.88

X^(1) = 0.22 X^(l) = 0 22 X^(l) = 1.47

X'(4)=7.82

X^(1) = 5.87* X’O) = O.32

Nonfallers include individuals later identified as control subjects. x^ for age categories was not significant, t test for age means was significant. p<.05. **p<.01.

Individuais eligible to parhcipate in the study were asked to return for further evaluation of blood pressure, height, weight, range of motion, neu- rologic function (including lower extremity reflexes, lower extremity sensation, proprioception, and cere- bellar coordination), and vision. Sagittal plane posture of selected sub- jects was also measured. Subjects were further evaluated with three tests to evaluate balance and mobility during functional activities, the BBS, the FRT, and the TU&GT

Three investigators were involved

in collection of data. An adult nurse practitioner completed measures of blood pressure, height, weight, neuro- logic function, vision, the TU&GT, and the FRT. Two physical therapists com- pleted measures of cervical and lumbar active range of motion (AROM), BBS, sagittal plane posture, as well as lower extremity AROM and joint torque.

Ail tests and measures were com- pleted in the same order, designed to minimize the effects of fatigue on per- formance on the funrtional measures of balance. Subjects had a minimum of 10 minutes rest before completion of

lower extremity AROM and joint torque measurements. A variety of mus- culoskeletal assessments were made; however, it is beyond the sco[:)e of this article to discuss all of the results relat- ed to these assessments.

All subjects were reevaluated using identical test procedures carried out by the same investigators at the com- pletion of the 6-week physical therapy intervention. Subjects were asked to report any falls that occurred during this time and during a 6-month peri- od following the completion of the pbysical therapy intervention.

Orthopaedic Nursing – January/February 2002 – Volume 21 • Number 1 59

Balance and Mobility Assessment Berg Balance Scale The BBS involves 14 items, represent- ing common everyday functional tasks such as arising from a chair, bending, reaching, and transferring from a bed to a chair, and requires approximately 15-20 minutes to complete. A five- point ordinal scale is used to evaluate a subject’s performance (0-4), thus yield- ing a maximum score of 56 points (Berg et al., 1989). The quality of per- formance is rated on several functional tasks, while the time taken to complete a task is measured for other tasks.

A score of < 45 on the BBS has been shovm to be predictive of multiple falls in older adults, with subjects scor- ing below 45 more likely to experi- ence multiple falls (Berg et al., 1989, 1992). The BBS is more specific for identifying individuals who are not at risk for falls and less sensitive for iden- tifying those individuals who fall (Bogle Thorbabn & Newton, 1996).

The BBS has been shown to be a valid and reliable measure of balance in older adults (Berg et al., 1989, 1992). Shumway-Cook et al. (1997) developed a model to predict fall risk and concluded that a score on the BBS in combination witb a self-reported history of imbalance had a high level of sensitivity and specificity for com- munity-dwelling older adults.

Functional Reach Test Tbe FRT, developed by Duncan et al. (1990), is described as a dynamic measure of stability during a self-initi- ated movement and is a measurement of postural control and balance that takes approximately 5 minutes to complete. Using a yardstick secured to a wall at right shoulder height, the subject is asked to place his or her shoulder in forward flexion at 90 degrees, make a fist, and lean as far forward as they can without taking a step, or losing their balance.

Functional reach is defined as tbe difference in inches between an indi- vidual’s arm length (measured at the base of the third metacarpal) and his or ber maximum forward excursion while maintaining a fixed base of sup- port in standing.

Weiner et al. (1992) determined that individuals witb a functional reach of less than 7 inches demonstrated lim- itations in mobility skills, had the slow- est gait, were unable to perform one- footed stance or tandem walking, and

All subjects were reevaluated using identical test proce- dures carried out hy the same investigators at the completion of the 6-week physical therapy intervention.

were the most restricted in activities of daily living. This test is a reliable and valid measure of postural control and balance in tlie elderly and is highly sen- sitive to change over time as a result of balance training (Duncan et al., 1990, 1992; Weiner et al., 1992, 1993).

Timed “Up and Go” Test The final measure of balance was a modified, timed version of the “Get Up and Go” Test described by Mathias et al. (1986). The Timed “Up & Go” Test (TU&GT) takes approximately 1-2 minutes to complete and measures the time an individual needs to rise to standing from an armchair, walk a dis- tance of 3 meters, turn, walk back to the chair, and sit down (Podsiadio & Richardson, 1991). No physical assis- tance is given during the test; howev- er, individuals are allowed to use an assistive device if tbey normally use such a device when walking.

Podsiadio & Richardson (1991) found that individuals who are able to complete the TU&GT in less than 20 seconds have been sliown to be inde- pendently mobile. Those who score greater than 30 seconds tend to need the assistance of others tor many mobility tasks and typically score in tbe middle or lower third of the BBS. Individuals who take between 20 and 29 seconds to complete the test require additional testing to clarify their functional level. The TU&GT is considered a reliable test and corre- lates well with more extensive meas- ures of balance such as the BBS.

Analysis of Sagittal Plane Posture The Life Mechanics Institute Mid- Sagittal Contour Gauge (Salem, UT) is designed to outline the curvature of the spine in the mid-sagittal plane. It consists of a rectangular, airtight, chamber that measures 97cm x 36cm X 3cm in height, width, and depth, respectively.

Encased within the chamber are 124 hollow alumijium rods, the ends of which are covered witb soft latex tips. Under resting conditions, each of tbe rods extends slightly out of the chamber through a precisely machined exit hole, and are aligned one above the other vertically.

Under 1-2 psi of nitrogen gas intro- duced into the chamber, the rods are deployed against the subject’s soft tis- sue overlying the spinous processes of the vertebral column in the mid-sagit- tal plane. A scanner is then moved ver- tically along tbe full length of the chamber face and measures tbe dis- tance of deployment of each of tbe 124 rods, transferring that information into a computer.

The average radius of each of the three spinal airves, tbe amount of cur- vature for eacb curve, the sagittal index, and the degree of forward lean are cal- culated. Measurement procedures were followed as outlined by the manufac- turer of the device. Reliability and valid- ity studies are currently underway.

Physical Therapy Intervention All subjects were given the opportunity to participate in a 6-week fall preven- tion program consisting of group phys- ical therapy exercise sessions. Ad- ditionally, participants in the exercise program received individualized exer- cise programs designed to address spe- cific impairments or functional deficits tbat were identified during their initial evaluation and assessment.

Exercises were performed twice a week in group physical tlierapy exer- cise sessions and daily by the subjects in their home, as reported on a daily log. Attendance was high, witb all subjects attending an average of 10.6 of the 12 exercise sessions scheduled with >50% of the subjects exercising an additional 2-3 times per week in their homes. Only one subject attend- ed fewer than 8 sessions.

60 Orthopaedic Nursing – lanuary/February 2002 – Volume 21 • Number 1

Each group physical therapy exer- cise session lasted for approximately 50 minutes and included ail of the foi- lowing: exercises to improve postural alignment and axial extension, thera- peutic exercise designed to address strength deficits and to improve bal- ance skills, and flexibility exercises for those individuals shown to have impairments in spinal or lower extremity range of motion. The exer- cise sessions also incorporated static and dynamic balance activities.

All exercises could be completed sitting or standing to facilitate the subjects’ ability to complete similar exercises in their home. Cervical spine and shoulder girdle flexibility, tho- racic extension, weight shifting, and trunk rotation exercises were complet- ed in a sitting position. These were fol- lowed by ankle flexibility and sitting thigh extension exercises.

Standing hip extension exercises were performed unilaterally with sub- jects stabilizing their positions by holding onto tbe hack of a chair. Unilateral hip abduction exercises fol- lowed. Both of these exercises empha- sized the ability to shift weight from side to side while still maintaining balance. Spinal alignment was em- phasized during these exercises to encourage improved posture with increased lumbar lordosis, decreased thoracic kyphosis, and decreased for- ward lean.

Subjects then moved to an area that allowed them to use a wail for sup- port and completed wall slides to strengthen bilateral quadriceps mus- cles. Subjects then stood approximate- ly 12 inches from the wall (facing the wall) with palms against the wall shoulder width apart and leaned for- ward while rising on their toes and looking up. This exercise allowed stretching of bilateral hip flexors and strengthening of bilateral plantarflex- ors; it also promoted lumbar extension.

Exercises completed in standing position were concluded with stretch- es for plantarflexors. Dynamic gait activities followed, requiring the sub- jects to alter the size of their base of support and increase awareness of the position of their feet during tandem walking, braid walking, and walking forward, backward, and sideways.

During the exercise sessions, sub- jects often requested information regarding activities that they found increasingly difficult with age or were

During the exercise sessions, subjects often requested information regarding activities that they found increasingly difficult with age or were no longer able to complete.

no longer able to complete. As a result, several exercises were incorporated into the program, including the task of rising from a prone or supine position on the floor to a standing position without assistance. All participants, many of whom stated that they had been unable to get up off the floor independently for the last 2 years, were able to leam to rise from the floor.

Another task that was identified as challenging included rising from over- stuffed couches or chairs. Subjects were taught basic transfer techniques to move from sitting to standing with the addition of using the legs and hips to move to the front of the sitting surface.

Other activities that were difficult and often led to loss of balance included reaching into upper level kitchen cabinets. Participants were taught how to use counters as sup- port, rising on the toes of one foot, and extending the opposite arm to reach. Education followed regarding the safe use of step stools, nightlights, and grab-bars, as well as the potential hazards of throw rugs.

Data Analysis Statistical analysis was performed using SPSS for Windows, version 10.0. Descriptive statistics were used to describe all demographic data (see Table 1). For the analyses conducted to examine risk factors for falls, the group identified as nonfallers {/; = 12) includ- ed the control subjects (n = 5) who did not participate in the exercise program as well as subjects identified as non-

fallers (H = 7) who subsequently partic- ipated in the exercise program. These two groups were combined for the risk factor analyses because subject assign- ment to nonfaller exercise group and control group was made after preexer- cise measures were taken. Other than history of falls, statistical tests con- firmed that no significant differences existed in the demographic data between subjects identified as fallers and nonfallers.

Additional analyses were conduct- ed to examine risk factors for falls and the effectiveness of the intervention in improving scores on selected tests of balance and mobility and changing forward lean. A probability value of less than .05 was considered statistically significant unless otherwise indicated.

To assess the effectiveness of the intervention in modifying risk factors for falls, preexercise and postexercise measures were examined using paired-samples t tests and analysis of variance (ANOVA). For these analyses, the nonfaller preexercise group was split into two groups, nonfallers (n = 7) who participated in the exercise group (nonfallers exercise group) and control subjects (n = 5) who did not participate in the intervention.

Results Risk Factors for Falls Prior Medical History and Medication Use Only one of the assessed 29 medical conditions emerged as significant between faUers and nonfallers. All of

Orthopaedic Nursing – January/February 2002 – Volume 21 • Number 1 61

TABLE 2 Percent of Medical Conditions Reported at Pretest

by Participants Within Each Group

Medical history

Neurologic Stroke

Orthopaedic Neck pain Low back pain Fracture Arthritis loint replacement Fibromyalgia

Cardiopulmonary system High blood pressure Peripheral vascular disease

Endocrine Diabetes

Other Sinus disorder Surgery Cancer tumor

Croup Fallers (n=10)

30%

30% 70% 40%

100% 10% 0

60% 10%

20%

50% 90% 30%

Nonfallers^ (n=12)

16.7%

25% 58.3% 41.7% 41.7%

0 0

58.3% 8.3%

16.7%

16.7% 66.7% 33.3%

x’

0.55

0.07 0.32 0.01 8.56** 1.26 –

0.01 0.02

0.04

2.79

0.03

Note: df= 1 for all tests. * Nonfallers Include individuals later identified as control subjects.

*p<.05. – p < . 0 1 .

the fallers reported arthritis compared to only 41.7% of the nonfallers (see Table 2).

A significantly greater proportion of fallers {33.3’M)) reported using pre- scription analgesics when compared to nonfallers (O’K.) (see Table 3). Although frequency of use between the two groups of the remaining med- ications did not differ significantly at a probability level of .05 level, two of the categories were significant at a probability level of p < .10. A greater proportion of fallers reported using over-the-counter pain, sleep, and/or sinus medications (66.7*Mi) than non- fallers (25’/f)). Similarly, a greater pro- portion of fallers (22.2’K>) reported using some other over-the-counter medication when compared to the nonfallers (0%).

Neurologic Examination No significant differences emerged between fallers and nonfallers in tests of proprioception, reflexes, sensation, and cerehellar coordination.

Balance and Mobility Assessments Alttiougii Individuals classified as fail- ers required longer to complete the TU&GT, no statistically significant dif- ferences were found between the fall- ers and nonfaliers on the preexercise measure (see Table 4).

Significant differences were found between the two groups on the preex- ercise BBS and the FRT On the BBS, fallers scored significantly lower than the nonfallers, with the mean of 48.80 approaching the cut off score of 45 to be classified at increased risk for falls.

nonfallers were able to reach signifi- cantly farther on the FRT than fallers. The mean functional reach for fallers, prior to intervention was 6.62 inches, slightly below the 7 inches required to be classified as a nonfaller.

Berg Baiance Scaie There were no statistically significant differences between the nonfallers exercise group or the control subjects on the preexercise and postexercise BBS. However, there was a statistically significant difference in preexercise and postexercise BBS scores for the fallers. As shown in Figure 1 and Table 5, the mean score for the fallers increased from 48.80 to 52.90.

One-way ANOVAs were conducted to examine differences among the groups for postexercise measures and revealed no significant differences among the groups for the postexercise BBS scores, tlius further supporting the positive effect of physical therapy intervention for fallers. TWo of the 17 subjects who participated in the exer- cise program scored less than 45 points on the BBS, scoring 38 and 39 points, placing them at increased risk for falls hefore participation in the exercise program. After 6 weeks of physical therapy intervention, their scores im- proved to 56 and 45, respectively.

Functional Reactt Test No statistically significant differences were found between the preexercise and postexercise FRT scores for fallers or nonfallers exercise group. There was a statistically significant difference in the preexercise and postexercise scores for the control group. The mean score for the controls increa.sed from 10.56 inches to 13.89 inches, as shown in Figure 2 and Table 5. Because of the small number of subjects in this group (n = 5), caution is warranted for inter- preting this increase. A practice effect may explain the increase.

A one-way ANOVA revealed that fallers and nonfallers exercise group differed significantly from controls when measured postexercise, with the control subjects able to reacii signifi- cantly farther than both groups. Six of the 17 subjects who participated in the exercise program demonstrated a functional reach of less than 7 inches. After completion of the exercise pro- gram, only 3 of the subjects had a functional reach of less than 7 inches.

62 Orthopaedic Nursing – |anuary/February 2002 – Volume 21 • Number 1

TABLE 3 Percent of Medications Used at Pretest by Participants Within Each Group

Hypertension

Respiratory Diabetes

Analgesic

Anticoagulant Seizure

Psychotropic Hypnotic Optic

Herbal

Vitamins/minerals Over-the-counter – pain/sleep/sinus Other prescription

Other over-the-counter

Fallers Cn = 9)

66%

22.2% 22.2%

33.3% 11.1%

0 11.1% 11.1% 11.1%

22.2% 66.7% 66.7%

55.6% 22.2%

Croup Nonfallers’ {n=12)

66.7%

8.3% 16.7% 0

8.3%

8.3% 8.3% 0

8.3%

8.3% 66.7%

25.0% 33.3% 0

0.00

0.81 0.10 4.67*

0.05

0.79 0.79 1.40 0.05

0.81 0.00

5.45t 1.04

2.95t

Note: df= 1 for all tests. ^ Nonfallers include individuals later identified as control subjects. tp<.10. *p<.05.

Measure

TABLE 4 Preexercise Baiance and Mobiiity Assessments

Berg Balance Scale Timed “Up and Go” Test Functional Reach Test

*p< .05 . * *p< .01 .

M

48.80 11.50 6.62

Fallers (n=10)

5 4 2

Croup

fD

.85

.30

.23

Nonfallers (n=12)

M

54.83 9.67

10.43

SD

1.59 2.27 2.12

3 -1 4

t

.17**

.28

.11**

Timed “Up and Go” Test No statistically significant differences were found among the scores for fallers, nonfallers exercise group, or the control subjects in the preexercise or postexer- dse mean scores on the TU&GT. Post- exercise comparisons of the groups revealed fallers and nonfallers exercise group each required significantly longer to complete the test tlian did control subjects (see Figure 3 and Table 5). Only

one subject was unable to perform the TU&GT in less than 20 seconds before the exercise program; however, she was able to complete the test in 15 seconds during the follow-up assessment.

Analysis of Sagittal Plane Posture No statistically significant differences were found between the faller and nonfallers before the intervention in terms of forward lean as measured in

Clinical Implications Fall-related injury is a major cause of disabilit)’ and even death in the elderly. Falling is a complex prob- lem with many potential causes, yet is a health condition that is highly preventable. Identifying those indi- viduals at risk for falls and imple- menting fall prevention programs can help decrease the risk for falls among a community-dwelling aging population.

Individuals at increased risk for falls can be identified by using assessment tools such as the Berg Balance Scale, Functional Reach Test, and Timed “Up and Go” Test. These tests are relatively quick and easy to administer in a variety of settings, require minimal equip- ment, and help to provide a com- prehensive assessment of fall risk.

Research has demonstrated that a variety of exercise interventions, including muscle strengthening, flexibility exercise, and balance training can reduce the risk for falls. Individuals who participated in a 6- week fall prevention program, with exercises performed twice a week in group physical therapy exercise ses- sions and individual exercises per- formed 2 to 3 times a week by sub- jects in their homes, demonstrated a decreased risk for falls.

Nurses and physical therapists can help the elderly maintain their independence and reduce their risks for falls by teaching them cor- rect transfer techniques when mov- ing from a sitting to standing posi- tion, rising from the floor inde- pendently, and modifying activities of daily living.

Collaboration between nurses and physical therapists can be used effectively to identify those at risk for falls and provide intervention to reduce that risk. Nurses, who have more contact with the general pop- ulation than pliysical therapists, could administer balance assess- ments, or recommend that a patient be examined further based on their findings during a patient interview.

Orthopaedic Nursing – lanuary/February 2002 – Volume 21 • Number 1 63

Berg Balance Scale

Preexercise

Postexercise

Controls (n = 5) Fallers ( n = 10)

Subjects

Nonfallers EG (n = 7)

FIGURE 1

A statistically significant difference was found in the preexercise and post- exercise scores of fallers, ((9)=-2.47, p < .05. There were no statistically significant differences for the control or nonfaller exercise groups. There was no statistically significant difference among the groups for postexercise BBS

scores.

Functional Reach

10 –

10-

8

6

4

I

n-

_ —^ 1_• Hffl

u•L

Preexercise

Postexercise

Controls (n = 5) Fallers (n = 9)

Subjects

Nonfallers EC (n = 7)

FIGURE 2

The control group showed a statistically significant difference in functional reach preexercise and postexercise, t (4) = -4.30, p < .05. No statistically significant differences were found in those fallers and nonfallers who partic- ipated in the exercise program. Fallers and nonfallers in the exercise group were significantly different from controls when measured postexercise, F(2,18) = 11.62, p < . 0 1 .

ii,n = 7.80, – 5.68,

degrees 3.31; M = 2.15, /

However, individuals catego- rized as fallers tended to have a higher degree of forward iean with a larger standard deviation than nonfallers. In ali other measures of sagittal plane pos- tiire including cervical, thoracic and lumbar depth to length ratio and the ratio of length of the thoracic spine length to lumbar spine length, there were no sig- nificant differences between the preintervention faller and non- faller groups.

There was a statistically sig- nificant decrease {p < .05) for the nonfallers exercise group for- ward lean postexercise. There was no significant difference in fallers postexercise or the con- trol subjects (see Figure 4 and Table 5).

Model for Fall Classification A predictive model for fall risk was constmcted by conducting a forward stepwise logistic regres- sion analysis. The variables con- sidered for the model were deter- mined from the analysis of indi- vidual risk factors that were found to show significant differ- ences between fallers and non- fallers {i.e., history of arthritis, use of prescription pain medica- tion, BBS, FRT, history of falls).

Clearly, the variables used to classify subjects as fallers and nonfallers (BBS, FRT, history of falls) should emerge as impor- tant predictors for fall risk; how- ever, this model was constructed to examine if other subject char- acteristics may also be impor- tant in predicting fall risk. History of falls was not included in model selection because his- tory of falls predicted fall risk perfectly and thus was not of interest as a variable.

The final model, shown in Table 6, included both the both the BBS and the FRT. History of arthritis and use of prescription analgesics did not emerge as sta- tistically significant variables in the model.

64 Orthopaedic Nursing – January/February 2002 – Volume 21 • Number 1

Discussion Timed Up and Co Test

It is evident from our results that fallers take significantly more prescription analgesics than non- fallers. The need for prescription analgesics may be due to the increased frequency of a diagno- sis of arthritis or degenerative jointdisease (100̂ )̂ ) of the fallers). Wegener et al. (1997) noted that balance deteriorates in individu- als with knee osteoarthritis.

Typically, individuals with arthritis modify their activity lev- els secondary to complaints of pain associated with this diagno- sis. Decreased activity levels may subsequently lead to decreased lower extremity strength and range of motion. Such changes may have affected the perform- ance of subjects classified as fall- ers on the BBS, FRT, and TU&GT

Additionally, individuals who were fallers had a tendency to take more medications than nonfallers. This finding is con- sistent with the literature. Gen- erally, fallers have been found to take more medications than nonfallers (Lipsitz et al., 1991). Leipzig et al. (1999) noted that individuals taking more than three to four medications have been shown to have increased risk for recurrent falls.

The nonfallers who were members of the exercise group (n =7) made the most clinically significant change by decreasing their degree of forward lean. These subjects may have been able to achieve a more upright position secondary to the physi- cal therapy intervention.

Alternatively, they may have simply tried harder to “stand up straight” at the postexercise assessment. Failure of the faller group to decrease forward lean may be associated with this group’s high report of arthritis (100%) and degenerative joint disease, which could hinder their ability to increase spinal mobility.

The mean forward lean of nonfallers after physical therapy intervention was 5.2 degrees, while fallers had a mean forward lean of 7.7 degrees. The relation- ship between forward lean and risk for falls should be explored

• Preexercise

D Postexercise

Controls (n = 5) Fallers (n = 10) Nonfallers EC (n = 7)

Subjects

FIGURE 3

When preexercise and postexercise scores were analyzed, no statistically significant differences were found for controls, fallers, or the nonfallers in the exercise group for the Timed Up and Go Test. Postexercise, fallers and nonfaliers differed significantly from controls with both groups taking a longer time to complete the test, F (2,19) = 9.07, p < .01.

Forward Lean

Preexercise

Postexercise

Controls (n = 4) Fallers (n = 8)

Subjects Nonfallers EG (n = 5)

FIGURE 4

Preexercise and postexercise measures of forward lean for nonfallers in the exer- cise group were statistically significantly different, t (4) = 3.64, p < .02. There were no statistically significant differences for the controls or fallers on the preex- ercise and postexercise measures of forward lean. There were no statistically significant differences among the three groups postexercise, although the failers and nonfallers in the exercise group had a greater degree of forward lean.

Orthopaedic Nursing – January/February 2002 – Volume 21 • Number 1 65

TABLE 5 Balance and Mobility Assessments and Forward Lean

Measure Preexercise Postexercise

Group M SD M SD

Fallers NonfallersEG Controls

Berg Balance Scale

48.80 5.85 54.86 2.04 54.80 0.84

52.90 55.43 55.60

3.11 0.79 0.55

-2.47* -0.66 -2.14

1.15 1.17 0.67

Fallers NonfallersEG Controls

Timed “Up and Go” Test

11.50 4.30 12.70^ 2.63 10.57 2.44 10.86” 2.34 8.40 1.34 7.40^ 0.89

-0.85 -0.55 1.12

1.60 1.47 0.27

Fallers NonfallersEG Controls

Functional Reach Test

6.66 2.36 10.34 1.84 10.56 2.69

7.30=” 10.17^ 13.89″

2.74 1.76 2.76

-1.08 0.22 -4.30*

1.20 1.25 0.61

Failers NonfallersEG Control

Forward Lean

7.80 3.31 6.10 2.46 5.15 1.89

7.70 ••&M:

3.93

3.74

2.31

0.14 3.64* 0.98

0.03 0.34 0.60

Note: NonfallersEG = Nonfallers Exercise Group. ‘”‘One-way ANOVAs were conducted to examine differences among groups for postexercise measures. Different superscripts indicate

significant differences among groups; Berg Balance Scale f (2,19) = 3.82, p > .05; Timed “Up and Go” Test f (2,19) ^ 9.07, p < .01; Functional Reach Test F (2,18) = 11.62, p < .01; Forward Lean f (2,15) = 2.55, p> .05. Degrees of freedom for Berg Balance Scale, Timed “Up and Go” Test, Functional Reach Test t tests = df fallers = 9. df nonfallersEG = 6. df controls = 4. Degrees of freedom for Forward Lean (test ^ df fallers = 7. d/” nonfallersEG = 4. df controls = 3.

* p < . 0 5 . * * p < . 0 1 .

in more detail with a larger subject population.

During the postintervention assess- ment, all subjects scored 45 or above on the BBS, indicating tbat they are less likely to experience a fall. Ad- ditionally, the standard deviarion for fallers decreased preexercise and pos- texercise signifying a more homoge- neous group. Increased BBS scores for fallers may be due to the effects of exercise on lower extremity flexibility or strength. Studies to evaluate these effects are ongoing.

After the intervention, the mean funcrionai reach for fallers increased from 6.6 inches to 7.3 inches, indicat- ing that as a group they are less likely to demonstrate limitations in mobili- ty or restrictions on activities of daily living. Three individuals remained

classified as fallers based on their FRT. The average age of these individuals was 87.3 years, possibly indicating greater difficulty with this skill with aging. Duncan et al. (1990) noted an influence of age on functional reach with a .7 inch decrease in functional reach with a 10-year increase in age. Additionally, the forward lean of the two oldest members (89 years and 92 years) of the faller group averaged 9.5 degrees, already stressing their limits of stability, and perhaps preventing them from reaching greater than 7 inches during the FRT.

Results of this study demonstrate that physical therapy intervention can reduce the risk for falls for those elderly individuals who are consid- ered to be at increased risk for falls based on scores on the BBS, FRT, and

history of falls. Before the physical therapy intervention, 10 subjects were classified as fallers. After the fall pre- vention program, only four individu- als met the criteria to be classified as fallers, based on their scores on the FRT and their history of falls during the 6 months following completion of the program.

Sixty percent of subjects classified as fallers before the 6 week physical therapy intervention held twice week- ly demonstrated improvements that enabled them to no longer be classi- fied as fallers. It is possible that a longer length of intervention may have decreased tbe risk for falls for a larger percentage of participants. The ideal frequency and duration of an exercise program, as well as the ideal structure of such a program, required

66 Orthopaedic Nursing – lanuary/February 2002 – Volume 21 • Number 1

Risk Factor

Functional Reach Test

Berg Balance Scale

Constant

TABLE 6 Logistic Regression Model

Model Coefficient

-0.79

6.57

62.78

for Faiis

Standard Error

0.34

2.94

33.81

P

.02

.03

.06

for individuals with impairments in balance to demonstrate improvement on balance measures has not been established. However, significant improvements have been noted with exercise programs lasting from 6 to 24 weeks with 1-3 sessions per week {Ledin et al., 1990/91; Shumway- Cook et al., 1997; Taaffe et al., 1999).

After completion of the exercise program, subjects were interviewed by a researcher not involved in the exer- cise program and asked to comment on their perceptions regarding the value of the physical therapy interven- tion. The majority of participants stat- ed the exercise program was of great value to them and has allowed them to remain more active, as well as com- plete tasks that they have not been able to do, in some cases, for over 2 years.

During a 6-month follow-up, 21 of the 22 subjects completing the study were contacted. Two additional sub- jects who had withdrawn early in the study were also contacted. Only one of these subjects had experienced a fall that she attributed to slipping on the ice. Many of the participants in the exercise program provided anec- dotal testimony regarding the impact of the fall prevention program, report- ing increased confidence that they would not fall and the feeling that if they did fall they would be able to independently return to standing.

The camaraderie and supportive interaction among the members of the exercise group appeared to gener- ate interest in participation and con- tinuation of the exercise program. Continuation of the twice-weekly exercise sessions was organized by one participant. These sessions were held in the exercise facility of the senior cit- izen apartment complex. Over 75’Ki of the original participants participated in these group sessions.

Tlie majority of the participants

The majority of partici- pants stated the exercise program was of great value to them and has allowed them to remain more active, as well as complete tasks that they have not been able to do, in some cases, for over 2 years.

stated that they continued to partici- pate in exercise in some capacity at least 2-3 times a week. One subject, identi- fied as a faller during the initial assess- ment who subsequently withdrew from the program secondary to an unrelated health problem, noted a dra- matic change in her frequency of stum- bles that she attributed to learning, dur- ing her short enrollment period, to walk with a wider base of support.

The team efforts between a profes- sional registered nurse and physical therapists allowed the expertise of both groups of practitioners to be used to enhance the functional ability of the participants. A collaborative approach with role interaction between nurses and physical therapists may be useful in identifying individuals who have increased risk for falls.

The BBS, FRT, and TU&GT are easy and quick to administer, require little equipment, and help provide a com- prehensive assessment of fall risk (Whitney et al., 1998). Functional

tests of balance have the advantages of ease of administration, low cost, and more directly interpretable func- tional relevance. However, they may not offer as much precision or ability to measure subclinical balance impair- ments (Bergetal., 1992).

Nurses, who have more contact with the general population than phys- ical therapists, could administer bal- ance-screening tests in a variety of set- tings, or recommend that a patient be further evaluated based on their find- ings during the patient interview. As a result of these screenings, more individ- uals who are at increased risk for falls could be given the opportunity to par- ticipate in fall prevention programs conducted by physical therapists.

Limitations of the Study A major limitation of the study was the small number of subjects in each group. Because participation in the study required active interest of the subjects, individuals may have self- selected into the fall prevention pro- gram because of a history of falling, frequent stumbling, or the perception that their physical strength or mobili- ty had declined. Therefore, members of the intervention group may consti- tute a select group of the elderly.

Further investigation with larger numbers of subjects and random assignment of subjects to test groups will aid in determining the efficacy of physical therapy intervention in the reduction of an individual’s risk for falling.

A second limitation of the study was that portions of the preinterven- tion and postintervention evaluation for each subject were performed by the physical therapist primarily responsi- ble for the group physical therapy ses- sion, thus introducing the possibility of evaluator bias. At the time of the

Orthopaedic Nursing – January/February 2002 – Volume 21 • Number 1 67

postintervention evaluation, none of the preintervention scores were avail- able to the investigators, reducing the possibility of evaluator bias.

Implications for Future Research Future research using a larger study population will allow for more gener- alization of the results of this study. Several factors may have influenced the results of this study, including changes in lower extremity AROM and joint torque production, as well as changes in spinal mobility in all planes of movement that may have occurred because of the physical ther- apy intervention.

Further examination of spinal and lower extremity active range of motion measurements and lower extremity joint torque is currently ongoing. An examination of the relationship be- tween sagittal plane posture as meas- ured on a reliable device with a larger subject population may indicate a relationship between the degree of forward lean and risk for falls, as well as accurately describe the spinal cur- vature of an elderly population.

Conclusion The identification of specific risk fac- tors for falls may allow health care professionals to more easily identify individuals at increased risk for falls and provide appropriate physical therapy intervention to prevent falls in a community-dwelling aging popu- lation, ft is possible that an exercise program tbat combines strengthening and dynamic balance activities pro- vides the most appropriate interven- tion to decrease the likelihood of falls in this population.

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Orthopaedic Nursing ~ |anuary/February 2002 – Volume 21 • Number 1 69

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Journal of Health Economics 57 (2018) 60–74

Contents lists available at ScienceDirect

Journal of Health Economics

jo u r n al homep age: www.elsev ier .com/ locate /econbase

ealth care expenditures, age, proximity to death and morbidity: mplications for an ageing population

aniel Howdona,∗, Nigel Riceb,c

Department of Economics, Econometrics and Finance, University of Groningen, Duisenberg Building, Nettelbosje 2, 9747AE Groningen, Netherlands Centre for Health Economics, University of York, York YO10 5DD, UK Department of Economics and Related Studies, University of York, York YO10 5DD, UK

r t i c l e i n f o

rticle history: eceived 18 April 2016 eceived in revised form 10 October 2017 ccepted 1 November 2017 vailable online 15 November 2017

EL classification: 51

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a b s t r a c t

This paper uses Hospital Episode Statistics, English administrative data, to investigate the growth in admitted patient health care expenditures and the implications of an ageing population. We use two samples of around 40,000 individuals who (a) used inpatient health care in the financial year 2005/06 and died by the end of 2011/12 and (b) died in 2011/12 and had some hospital utilisation since 2005/06. We use a panel structure to follow individuals over seven years of this administrative data, containing estimates of inpatient health care expenditures (HCE), information regarding individuals’ age, time-to- death (TTD), morbidities at the time of an admission, as well as the hospital provider, year and season of admission. We show that HCE is principally determined by proximity to death rather than age, and that

19

eywords: ealth care expenditures geing ime-to-death

proximity to death is itself a proxy for morbidity. © 2017 Elsevier B.V. All rights reserved.

orbidity

. Introduction

There is concern that the demographic pressures of population geing will lead to an unprecedented rise in public expenditures o levels unsustainable under current financing arrangements. In he UK in 2013 approximately 17% of the population (11 million ndividuals) were aged 65 years or over. This represents a rise of 7.3% in this age group on a decade earlier. Projections suggest hat by 2050 this group will have increased disproportionately to ounger age groups, accounting for approximately 25% of the pop- lation (Cracknell, 2010). The growth in the proportion of older

ndividuals is partly due to increased longevity and partly due to he age structure of the population, particularly the ageing of the eneration of baby boomers of the post war period to the early970s. Health care expenditures in the UK have also risen substan- ially over time, both in real terms and proportional to economic rowth. Close to the inception of the National Health Service (NHS),

∗ Corresponding author at: Department of Economics, Econometrics and Finance, niversity of Groningen, Duisenberg Building, Nettelbosje 2, 9747AE Groningen, etherlands.

E-mail address: [email protected] (D. Howdon).

ttps://doi.org/10.1016/j.jhealeco.2017.11.001 167-6296/© 2017 Elsevier B.V. All rights reserved.

net expenditure (net of patient charges and receipts) on the UK NHS in 1950/51 was £11.7b (GBP, in 2010/11 prices), representing 3.5% of Gross Domestic Product (GDP). This had risen to £121.3b by 2010/11, approximately 8.2% of GDP. Over the twenty-five year period from 1999/00 to 2014/15, expenditure in England almost doubled to £103.7b (2010/11 prices), with an average expenditure per head of population of £1900 (Harker, 2012). Abstracting from issues such as technological innovation, the concern is that as the share of the population at older ages rises, the economic burden of providing healthcare will become increasingly unsupportable.

Interest in the link between ageing populations and health care expenditures can be traced back 25 years, when the International Monetary Fund (IMF) asserted that ‘demographic pressures [in the UK] of an aging population will be associated with increased demand for medical services’, and presented descriptive statistics from various countries, showing that older patients, on average, had greater health care costs than younger patients (Heller et al., 1986). A report by the Organisation for Economic Co-operation and Development (OECD) predicted that across Europe population age-ing will create a rise in age-related social expenditures from around 19% of GDP in 2000 to around 26% by 2050. Old-age pension pay- ments and expenditure on health and long-term care was deemed responsible for approximately half this increase (Dang et al., 2001).https://doi.org/10.1016/j.jhealeco.2017.11.001http://www.sciencedirect.com/science/journal/01676296http://www.elsevier.com/locate/econbasehttp://crossmark.crossref.org/dialog/?doi=10.1016/j.jhealeco.2017.11.001&domain=pdfmailto:[email protected]https://doi.org/10.1016/j.jhealeco.2017.11.001

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D. Howdon, N. Rice / Journal of

pproaches to predicting expenditure growth vary, but in a simple orm this consists of computing observed expenditures per head or different age-sex groups, and multiplying by projections of the umber of people expected to fall into each group. This approach, owever, fails to consider the underlying drivers of heath care xpenditures and the relative role of age, or, as has been suggested, roximity to death, or underlying levels of disability and ill-health,

n determining expenditures and its likely growth (see Gray, 2005). Additional to projections of population ageing is the poten-

ial change in the health profile of the population over time. An expansion of morbidity’ hypothesis has proposed that the ‘net con- ribution of our successes has actually been to worsen the people’s ealth’, as improvements in health care tend to lengthen the lives f those living with illness disproportionately to the effect of such mprovements on the lifespan of those living without (Gruenberg, 005). Should population ageing occur alongside a deterioration f health at older ages, then this will exacerbate impacts on pub- ic expenditures. While subsequent academic research into these laims – notably, research in the ‘compression of morbidity’ and red herring’ strands of literature – have given reason to suggest hat such concerns may have been misplaced or exaggerated, con- ern over the impact of an ageing population on HCE has persisted. ndeed, even in 2012, the UK’s then-Secretary of State for Health laimed that the fact that ‘the number of people aged over 85 in his country will double in the next 20 years’ was one of two factors n ‘costs . . . rising at an unaffordable rate’ (Lansley, 2012). He fur- her argued that ‘age is the principal determinant of health need’,1

nd that local NHS budgets should be recalibrated to be based n this, as a result (Williams, 2012). This paper uses UK admin- strative data from Hospital Episode Statistics (HES), and deaths ata from the Office for National Statistics (ONS), to consider two elated research areas. The first, in line with the ‘red herring’ thesis dvanced by Zweifel et al. (1999), is to explore the determinants f inpatient health care expenditures, with particular attention to he role played by age, time-to-death (TTD), and morbidity. We do his in a unique way by following samples of individuals who died n England, over seven years of HES data from 2005/06 to 2011/12, nd constructing a panel on individual health care expenditures and orbidity over this period. We show that TTD dominates age as a

ey driver of health care expenditures and morbidity characteris- ics dominate TTD. This finding extends the ‘red herring’ literature y showing that TTD is itself a ‘red herring’ and acts as a proxy for orbidity. This links to a second area of research by locating the odelling of health care expenditures for individuals close to death ithin the broader literature on prospective prediction of hospital se to inform resource allocation, particularly those based on indi- idual level data and which incorporate information on morbidity for example, see Iezzoni et al., 1998; van de Ven et al., 2003; Pope t al., 2004; Dixon et al., 2011).

. Literature review

.1. Compression of morbidity

The ‘compression of morbidity’ strand of literature beginning ith Fries (1980) suggests that, ‘[i]n its simplest form, “the age

t first appearance of symptoms of aging and chronic disease can ncrease more rapidly than life expectancy”’ (Fries et al., 2011).ries (2005) identifies three separate ‘eras’ of illness and well-being xperienced during the 20th Century and beyond: an era of infec- ious disease, followed by an era of chronic disease, followed by an ra described by the author as ‘directly related to the process of

1 Emphasis ours.

h Economics 57 (2018) 60–74 61

senescence, where the aging process itself, independent of specific disease, will constitute a major burden of disease’. Senescence – the process of ageing – is characterised by the ‘decline of maximal function of [all] vital organs’, beginning before any chronic disease takes hold: deaths where this function declines below a level nec- essary to sustain life, in the absence of any disease occasioning this, may be termed ‘natural deaths’ (Fries, 2005).

The implications for HCE of an ageing population become less clear in the light of compression of morbidity, and there are two aspects to this which deserve attention. First, as the “age at first appearance of symptoms of aging and chronic disease” increases, individuals can be said to age more healthily: the implications of this for HCE are considered below. Second, the compression of mor- bidity thesis takes for granted an increase in life expectancy. The implications of this for HCE can be considered at a population level for any given year of spending. Setting aside the causal process for this health ageing (again, considered below), as the average person ages more healthily, they require lower HCE at any given age. As more people live to very old age – for instance, 90 years old – each individual requires lower health spending at that age. The overall picture for HCE is however ambiguous: a larger number of people requiring lower HCE may require greater overall costs at a popula- tion level than a smaller number of people requiring higher HCE. Similarly, an individual, who dies at age 90 and requires lower HCE at any given age than they would had they been born into an ear- lier cohort, may require greater cumulative HCE over their lifespan than they would had they aged less healthily and died at the age of 70. The implications for HCE in the presence of healthy ageing and increased lifespan may differ at an individual level to a population level.

Freedman et al. (2002), in a systematic review covering research that had been conducted between 1990 and 2002, found that many measures of disability and limitations in old age had seen declines in recent years: in particular, a change of −1.55% to −0.92% per year in those reporting any disability during the late 1980s and 1990s. Romeu Gordo (2011) observes a cohort-on-cohort fall in the num- ber of individuals with high levels of disability-related functional problems in their everyday life for those born between 1924 and 1947 in the US. Cutler et al. (2013), using Medicare records from the US, present evidence of an increase in disability-free life between 1991 and 2009. The authors conclude that ‘The major question raised by our results is why this has occurred. How much of this trend is a result of medical care versus other social and environ- mental factors?’.

Cross-country international evidence on the changing pat- terns of disability rates across nine OECD countries is provide by Jacobzone et al. (2000). Consistent with the above literature, they report evidence of significant falls in severe disability rates. The importance of this issue for forecasting HCE depends upon how changes in mortality, changes in morbidity, and changes in dis- ability occur and interact with each other. If the onset of chronic conditions – those imposing large costs on health systems – can be postponed out of an individual’s lifetime, then health care costs may fall as later cohorts enjoy a longer lifespan, with a reduced level of necessary treatment for chronic conditions. Dormont et al. (2006), for instance, find that improvements in morbidity profiles in France between 1992 and 2000 have caused reductions in HCE that more than offset the rise in HCE induced by an ageing population.

The morbidity and disability profile of individuals, according tothis research, at any given age has improved over time, leading to health problems being experienced later in life and more closely to death. In the illustrated case (Figs. 1 and 22), individuals live

2 Adapted from Fries (1980) and http://www.aei.org/files/2008/06/27/20080626 WashingtonAEI.pdf.http://www.aei.org/files/2008/06/27/20080626_WashingtonAEI.pdfhttp://www.aei.org/files/2008/06/27/20080626_WashingtonAEI.pdfhttp://www.aei.org/files/2008/06/27/20080626_WashingtonAEI.pdfhttp://www.aei.org/files/2008/06/27/20080626_WashingtonAEI.pdfhttp://www.aei.org/files/2008/06/27/20080626_WashingtonAEI.pdfhttp://www.aei.org/files/2008/06/27/20080626_WashingtonAEI.pdfhttp://www.aei.org/files/2008/06/27/20080626_WashingtonAEI.pdfhttp://www.aei.org/files/2008/06/27/20080626_WashingtonAEI.pdfhttp://www.aei.org/files/2008/06/27/20080626_WashingtonAEI.pdfhttp://www.aei.org/files/2008/06/27/20080626_WashingtonAEI.pdfhttp://www.aei.org/files/2008/06/27/20080626_WashingtonAEI.pdf

62 D. Howdon, N. Rice / Journal of Healt

Fig. 1. Stylised change in survival curves.

u o o h a e g g i T m t t c c

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Fig. 2. Stylised change in health profiles.

p to a longer observed maximum age (indicated by the shift out f the survival curve from S1 to S2 in Fig. 1), and have a higher bserved level of health at all ages (indicated by the shift out of the ealth status curve from H1 to H2 in Fig. 2). Both survival curves nd health status curves have become increasingly rectangular. The ffect on health care expenditure (HCE) is ambiguous, given that enerally more healthy ageing – a decrease in morbidity at any iven age – puts downward pressure on HCE, while an increase n life expectancy, ceteris paribus, puts upward pressure on HCE. he actual relationship between health care costs and changes in orbidity and mortality profiles at every given age depends upon

he changing shape of these two curves, and also the extent to which he changes in each are due to or caused by the healthcare that reates these HCE. The use of age per se in predicting future health are costs should be approached with caution, as a result.

.2. Age, time-to-death and healthcare expenditures

The ‘red herring’ strand of literature further gives empiricaleason to suggest that claims of steeply-rising future HCE due to opulation ageing3 may have been exaggerated, potentially owing o morbidity being concentrated in later years of life. Zweifel et al.

3 HCE may rise due to technological change brought about by new expensive nnovations in health care treatments, or due to shifting patterns of morbidity.

h Economics 57 (2018) 60–74

(1999), using Swiss sickness fund data, find that no effect of age on health care expenditures existed after controlling for TTD, i.e. the time from any given point of observation to death for an individual. Owing to the number of individuals with zero HCE, a two-step model (with a probit first stage and OLS second stage) was employed, with only deceased patients included in the model. Such work was criticised on the grounds of potential endogene- ity, with time-to-death affected by both present, previous (and, due to the nature of how TTD must be measured) future HCE. In a subsequent paper, Zweifel et al. (2004) seek to test for such prob- lems, finding that while TTD is endogenous, their results were ‘fairly robust’ to the error this induces. Werblow et al. (2007) find that age is a small (but statistically significant) determinant of HCE after controlling for TTD for patients using long-term care (LTC), such as those in care homes, and is not associated with HCE for non- LTC patients. More complicated methods, such as those employing generalised linear models, have since been used, for example by Werblow et al. (2007), in order to deal with the non-normal prop- erties (such as positive skewness) exhibited in the distribution of HCE. These papers have corroborated results obtained using probit and OLS two-step models. Felder et al. (2010), in a recent paper in this series, first predict individuals’ survival based on observed HCE and socioeconomic characteristics (in early waves), before using predicted values based on this as an instrument for TTD in explain- ing HCE in later waves. The authors find that, while TTD cannot be deemed exogenous, any effect of age on HCE becomes insignificant when TTD (or instrumented TTD) is included in the model. Further- more, results regarding the relative importance of TTD compared to age have also been corroborated in a disease-specific study carried out by Wong et al. (2011).

While use has been made of morbidity markers in models of long-term care expenditures (LTCE) (see de Meijer et al., 2011), such use has not been made in models explicitly investigating the link between HCE and population ageing. One possibility is that TTD is itself a red herring, in that it is simply a proxy for morbidity, unob- served in existing HCE models in the red herring strand of literature. Such a theory has been provisionally borne out empirically in liter- ature related to economic evaluation of healthcare, with Gheorghe et al. (2015) finding that quality of life (as measured by SF-6D scores) declines with proximity to death, and also by biological and medical literature. Indeed, Dalgaard and Strulik (2014), proposing an alternative life cycle model of ageing, note that previous work in the ‘red herring’ strand of literature is consistent with biolog- ical and medical research (see, inter alia Mitnitski et al., 2002a,b, 2005; Rockwood and Mitnitski, 2006, 2007), showing that concep- tions of ageing focusing on time-from-birth (such as that inherent in Grossman (1972)) are erroneous. This model conceptualises the human body as a system which has substantial inbuilt redundancy (that is, an ability to function at a level well over and above that required to sustain life) in youth, but redundancy which declines as ‘deficits’ (a decline in function of individual parts of the body) are accumulated. Ageing depends not upon a ‘biological clock’, but is a process of increasing frailty which is the outcome of investments in health, available health technology, the lived environment, and a physiological ‘force of aging’ parameter. According to such a model, health is predicted to decline at an increasing rate when the individ- ual’s health status is lower. The authors note that existing research in the ‘red herring’ strand of literature, in line with this, ‘suggests that health status (e.g., frailty), and not the year on the birth certifi- cate, is what matters to health investments’. The latent assumption here is that a TTD variable proxies for this health status, which declines as individuals become more morbid as they approachdeath, and with ever-increasing levels of health investment to (partly) offset this decline in health status and postpone death.

This seems intuitively plausible: in the years before death, it is likely that morbidity will increase, leading to more treatment,

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the HES dataset, which has been published for each financial year since 1989/90 and is available for admitted patient care, outpa- tient, accident and emergency and maternity cases. The admitted

D. Howdon, N. Rice / Journal of

nd that comorbidities complicating the treatment of the disease ringing about the hospital episode will also increase. Shwartz et al. 1996), in work predating the original red herring hypothesis, note hat the inclusion of variables for comorbidities increase substan- ially the explanatory power of models. It seems likely that, as algaard and Strulik (2014) suggest, variables incorporating ‘time-

o-death’ in more recent models of HCE are picking up, in large part, hese comorbidities, which are not included in existing HCE models n the red herring literature. Indeed, de Meijer et al. (2011) conclude hat time-to-death ‘largely approximates disability’ in models of TCE. Dixon et al. (2011), in proposing individual-level formulae or resource allocation in the UK’s National Health Service (often ermed ‘Person-Based Resource Allocation’, or PBRA) include indi- idual level morbidity markers, finding that these have a ‘powerful ffect. . . in predicting individual level expenditure’.

The process generating HCE is clearly not a simple function of hose explanatory variables used in existing ‘red herring’ research: he actual data-generating process behind these health care expen- itures is unlikely to be characterised accurately by a simple use of ge, historical time and time-to-death. In addition to the aforemen- ioned problems surrounding TTD and age as a proxy for morbidity, s Breyer et al. (2014) note, many existing models are likely to e characterised with substantial endogeneity problems, which

ead to potential bias in the estimation of the change in HCE as n individual ages or approaches death. The authors control for otential endogeneity introduced by differential treatment based n a physician’s view of the patient’s expected health benefits from reatment, proxied by actuarial tables of life expectancy conditional n age. If physicians expect individuals to respond differently to reatment, this may cause those who are more likely to respond to reatment to be treated more intensely than those who are not, hus increasing expected HCE for individuals who are younger, urther-from-death or with fewer comorbidities because of physi- ian selection. Conversely, HCE for older individuals – or, more ikely, individuals in the final years of life – may rise as intensity f treatment becomes stronger with heroic efforts to save an indi- idual’s life, possibly motivated by ethical ‘rule of rescue’ concerns hen faced with an identifiable, gravely sick individual (Jonsen,

986). Breyer et al. (2014) jointly estimate this possible physician election based on life expectancy alongside a model for health are expenditures, incorporating both age and time-to-death as xplanatory variables. They find that increasing survival rates for he elderly in Germany have positive impacts on HCE, arguing that his is explained by physician selection: treating patients more ntensively if they expect positive results from treatment over a onger time span.

Datasets used within the ‘red herring’ literature are, in general, ickness fund datasets, with only Seshamani and Gray (2004) using opulation-level (for users of NHS treatment) data, the Oxford ecord Linkage Study, a longitudinal dataset of all individuals ithin an area of Oxfordshire, England. We believe our paper to

e the first to use a sample of individuals from a comprehensive ational-level dataset of health care users.

The extent to which ‘red herring’ and related issues are of inter- st depends upon the intended use of such research. Much existing iterature focuses on projections of future health care costs given an geing population, with the headline results of some papers (such s Stearns and Norton (2004) and Seshamani and Gray (2004)) eing the overestimation of expected costs for a given future year hen TTD is an omitted variable. This is due to the collinearity

etween TTD and age for a given individual: an individual who gets ne year closer to death also gets one year older, and so the impactf TTD is picked up by age in such models. The inclusion of morbid- ty markers in addition to, or replacing, TTD would allow greater recision of future estimates where reliable estimates of morbidity revalence, and the cost of treatments, conditional on age and TTD

h Economics 57 (2018) 60–74 63

were known. Certainly, if the compression of morbidity hypothesis holds, and individuals are able to postpone the onset of chronic dis- eases – with associated higher HCE – to a time period closer to their death, or even indefinitely, explicitly considering morbidity rather than proxying this by age or TTD becomes ever more important.

We build upon the compression of morbidity and red her- ring strands of existing literature, seeking to further examine the relationship between ageing, time-to-death and health care expenditures. The original red herring hypothesis is that, once time- to-death is included in models of HCE, age per se does not explain changes in HCE. While models intended for resource allocation (Dixon et al., 2011) have already included morbidity as an explana- tory variable in HCE for the general population, other applications of models of HCE have not – in particular, those focusing explicitly on ageing populations, or costs in the years approaching death.

While hospital inpatient care forms only one part of health and social care incurred later in life, it is responsible for a large pro- portion of such expenditures. While it may seem that expenditures from other categories of health-related expenditures such as that arising from general practice, prescriptions, and even long-term care should be considered in the aggregate, much richer, more uni- versal and more reliable individual-level administrative data is at our disposal for the UK for hospital expenditures than for other types of expenditure, and information regarding the relationships examined in this paper are likely to be informative for specific avenues of NHS budget-setting. Furthermore, conflicting evidence exists regarding end-of-life long-term care expenditures and their functional relationships with age and TTD (de Meijer et al., 2011; Karlsson and Klohn, 2014), and the UK’s institutional structure and its resultant incentives regarding (predominantly privately- financed) long-term care and (predominantly socially-financed) hospital care is such that different relationships for each that are particular to the UK may well be expected. Finally, hospital expen- diture is a major contributor to end-of-life health care costs: French et al. (2017) find that hospital expenditure is dominant in the final year of life with non-hospital expenditure, of which long-term care is a major component, playing a greater role in periods prior to this. Indeed, they estimate that for England 11.6% of hospital expendi- ture occurs in the last year of life.

This paper seeks to bridge the gap between the red herring strand of literature and models of resource allocation, treating mor- bidity measures as omitted variables in models of current health care expenditure, and examining what the relationship between age, TTD and HCE is once morbidity is included in these models (see, for instance, Aragon et al., 2016).

3. Data

3.1. Data sources

Information on patient-level hospital use and associated refer- ence costs for treatment are derived from the Hospital Episodes Statistics (HES) dataset, published by the Health and Social Care Information Centre (HSCIC). This is complemented with small-area data on years of potential life lost (YPLL) published by the ONS, and individual level mortality information, jointly published by the HSCIC and the ONS.

We use successive years (financial years 2005/06 to 2011/12) of 4

patient (commonly, ‘inpatient’) care HES dataset that we use pro-

4 In the UK, the financial year runs from April to March.

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ides information on individual-level patient characteristics and iagnoses and procedures undergone for all patients admitted to ospitals in England.5

Information regarding inpatient spells is used to associate ref- rence costs to each spell. Reference costs are based on each NHS rovider’s estimates of their own costs for each patient spell, cat- gorised by Healthcare Resource Group (HRG, the NHS’s system of rouping clinically-similar events with comparable resource use). hese reference costs are derived from accounting costs for each RG, submitted by each organisation providing secondary care in ngland (Department of Health, 2012). The NHS Costing Manual rovides guidance to all providers to support the calculation of ref- rence costs and to enforce more uniform standards for costing ethodologies. We use the estimate provided by the hospital pro-

iding treatment as our estimated cost for the patient’s episode. The H’s Reference Cost data is submitted on a full absorption basis –

hat is, taking account of all direct and indirect costs relating to he activities in question, as well as a proportion of an estimate of ll overhead costs relating to the overall running of the provider. urther, to account for the fact that costs will vary even within RGs, hospitals are required to provide per diem costs for longer dmissions that exceed a given ‘trim point’, which differs by each RG. This trim point is defined as the upper quartile of length of

tay, plus 1.5 times the inter-quartile range for length of stay for hat HRG (Department of Health, 2012). Moreover, we augment the tandard costs incurred in each episode with the ‘unbundled’ costs here recorded for the episode. This represents one or more extra xed costs associated with the episode where additional, unusual, igh-cost treatment or procedures were involved. Even within the ame primary HRG, costs are not identical but differ according to he patient’s length of stay. An estimate of costs for each inpatient pell is obtained by matching data on costs for that provider in the eference Costs database to HRG for each episode in the relevant ear’s HES data.

HES contains diagnostic data, categorised (since 1995/96) ccording to the tenth revision of the World Health Organiza- ion’s International Classification of Diseases (ICD-10). Details of rocedures and interventions are recorded according to the fourth evision of the Office of Population, Censuses and Surveys’ Classifi- ation of Intervention and Procedures (OPCS-4) (Health and Social are Information Centre, 2013).

HES is broken down by completed “episode” – each record onsists of a continuous period of care at a single provider of reatment under the same consultant. A new record is generated hen a patient is either transferred to the care of either a new

onsultant, transferred to a new provider, or is discharged from ospital. Although individuals are not identifiable, individuals can e tracked across episodes by an anonymised identification num- er. The costing of a patient’s time in hospital and the recording of heir diagnoses and procedures undergone are made at the episode evel.

Patients can be tracked across different years of the HES dataset, hich enables the creation of a panel structure for the data. Infor- ation within the HES dataset – most commonly, information

egarding diagnosis, treatment and age of the patient – is used o apply the most appropriate Healthcare Resource Group (HRG) ategorisation to the dataset. We use the Health and Social Care nformation Centre’s Consultation ‘Grouper’ software in order toarry out this first step. We use the most recent version of this rouper – for the 2011/12 financial year – for all seven of the years e use, to categorise patients into HRGs. HRGs are used to cate-

5 This dataset includes both day cases (patients without an overnight stay) as ell as patients who have at least one night’s stay in hospital. Our use of ‘inpatient’

hroughout this text includes both types of patient.

h Economics 57 (2018) 60–74

gorise patient spells not only by broad diagnosis, but by the type and complexity of the patient’s spell, into one of over 1400 group- ings. This allows us to apply the current best-practice methods for grouping patients into HRGs based on the information available. We apply available estimates of hospital costs for each inpatient spell, using reference costs data for the relevant financial year.

We add information regarding an individual’s death from linked HES-ONS mortality data. The latest version of this data provides information on deaths to the end of the 2012 calendar year, and therefore provides information on some individuals whose deaths are known to have occurred after the end of the final wave in our dataset. Where individuals are known to have died, they are included up to and including the final quarter of their life, and not included in the panel in following years. TTD can only be measured – for decedents – retrospectively, using information available at the time of the individual’s death. We observe individuals for a maxi- mum of seven years (from 2005/06 to 2011/12) or 28 quarters and code TTD from 1 to 28, with TTD = 1 denoting the final quarter in which death occurs.6

We adopt a strategy that employs two complementary sampling procedures, each incorporating approximately 40,000 individuals. The first draws a sample of individuals who died in 2011/12, the final year of our analysis, and who had at least one quarter of recorded positive HCE in the 28 quarters of our data. The second draws a sample of individuals who had at least one quarter of recorded positive HCE in 2005/06, and died in or before 2011/12. We believe that each of these sampling procedures has advantages and disadvantages but that, together, they can be used to establish a clear conclusion on our research question.

Our first sample for analysis consists of a random sample of 39,381 individuals (18,690 men and 20,691 women) aged 50 years and older, taken from those with at least one inpatient episode between 2005/06 and 2011/12, and whose death was recorded by the ONS in the financial year 2011/12. Our second sample consists of a random sample of 39,796 individuals (19,673 men and 20,123 women) aged 50 years and older, taken from those with at least one inpatient episode in 2005/06, and whose death was recorded by the ONS after this point, and by the end of the financial year 2011/12. Sample size was selected to enable computations not to become burdensome, and the age cut-off was selected to ensure sufficient deaths were observed in the data to make meaningful inference. We follow all sampled individuals across all quarters until their death to observe their subsequent inpatient health care use and associated morbidity characteristics.

We collapse all inpatient episodes for each individual from HES for a given quarter into a single observation in our data. This obser- vation contains the sum of all hospital costs incurred in all episodes finishing in that quarter, as well as diagnostic information con- tained in the ICD-10 codes for those episodes in that quarter. In principle, the ICD-10 classification allows for up to 14,400 dif- ferent diagnoses. To make these more manageable for analysis, however, we collapse this information using the US Agency for Healthcare Research and Quality’s Clinical Classifications Software (CCS) method to convert ICD-10 codes to CCS codes (US Agency for Healthcare Research and Quality, 2009). This reduces the number of different groupings to a more manageable 260 mutually-exclusive, and clinically meaningful, categories.7 Where individuals do not have any episodes in a given quarter, we separately adopt two dis-tinct methods in order to deal with such cases. In one approach, they are recorded as having zero hospital costs, and as having zero observed morbidities arising from diagnostic information. In the

6 Coding TTD in this way is akin to assuming all deaths occur at the end of a quarter.

7 A full list of these CCS groupings is provided in Appendix A.

Health Economics 57 (2018) 60–74 65

a h i a m i a c f m

i d i t n

I ( d o u o E 3 S

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d p p o (

i p

n

Table 1 Summary statistics (quarter 1, men, first year sample).

Variable Mean Std. Dev. Min Max

HCE [missing treated as zero] 475.60 1740.26 0 82,901.09 log(HCE) [missing treated as zero] 1.57 3.00 0 11.32 log(HCE) [missing treated as missing] 7.19 1.01 3.42 11.32 Quarters to death (QTD) 9.53 7.77 0 27 log(QTD) 2.02 0.88 0 3.33 Age 75.03 10.24 50 105.66 YPLL (IMD 2007) 65.50 15.71 33.80 180.8

Table 2 Summary statistics (quarter 1, women, first year sample).

Variable Mean Std. Dev. Min Max

HCE [missing treated as zero] 504.42 1629.34 0 45,095.81 log(HCE) [missing treated as zero] 1.56 3.03 0 10.71 log(HCE) [missing treated as missing] 7.30 0.99 3.39 10.71 Quarters to death (QTD) 9.86 7.89 0 27 log(QTD) 2.05 0.89 0 3.33 Age 78.11 10.93 50 111.15 YPLL (IMD 2007) 65.85 15.54 33.30 191.5

Table 3 Summary statistics (quarter 1, men, final year sample).

Variable Mean Std. Dev. Min Max

HCE [missing treated as zero] 220.74 1339.96 0 66,770.92 log(HCE) [missing treated as zero] 0.61 2.05 0 11.11 log(HCE) [missing treated as missing] 7.28 1.08 3.85 11.11 Quarters to death (QTD) 25.57 1.13 24.00 27.00 log(QTD) 3.28 0.04 3.22 3.33 Age 72.93 9.82 50 100.83 YPLL (IMD 2007) 64.14 15.09 33.80 162.90

Table 4 Summary statistics (quarter 1, women, final year sample).

Variable Mean Std. Dev. Min Max

HCE [missing treated as zero] 213.89 1310.72 0 64,392.08 log(HCE) [missing treated as zero] 0.56 1.98 0 11.07 log(HCE) [missing treated as missing] 7.36 1.08 3.34 11.07 Quarters to death (QTD) 25.59 1.13 24.00 27.00

D. Howdon, N. Rice / Journal of

bsence of additional information on the gravity of any residual ealth problem, this assumes that such health issues are insignif-

cant relative to those leading to a hospitalisation. In a second pproach, we recognise that the recording of zero morbidities ight be unrealistic for patients observed to have hospitalisations

n recent periods and for whom there is likely to exist an underlying, lbeit less grave, health problem. Consequently, we model these ases in our second approach under the assumption that episodes or which no information is available represent non-informative,

issing data. While we include a sum of all hospital costs for episodes ending

n the quarter in question, we include only a maximum of three iagnoses for each individual, for a maximum of five episodes end-

ng in that quarter. Using the merged mortality data, we are able o add a variable for the individual’s time-to-death, measured in umber of quarters to death.

In addition, we make use of the Office for National Statistics’ ndices of Multiple Deprivation (IMD), by Lower Super Output Area LSOA) in order to construct an instrument for TTD. LSOAs are efined at the time of the UK’s decennial Census and are made up f similarly-sized small areas of the country. HES data, for the years sed in our dataset, provides information on the individual’s LSOA f residence at the time of the 2001 Census. At this time, LSOAs in ngland consisted of 32,482 areas of populations between 1000 and 000, with between 400 and 1200 households (Office for National tatistics, 2011).

Indices of Multiple Deprivation, at this LSOA level, are measures f the levels of deprivation in those small areas. Although made p of seven domains (income, employment, health and disability, ducation, housing, living environment and crime (Department for ommunities and Local Government, 2011)), we primarily make se of one of the indicators that forms part of the health and dis- bility IMD score: years of potential life lost per 1000 people. This onsists of a standardised measure of premature mortality calcu- ated using information for all individuals to have died before the ge of 75, as described in Blane and Drever (1998).8,9 Although the SOAs themselves are defined every ten years at the time of the K’s census, statistics for each domain are collected and published

or these areas more regularly: we make use of those published in 007 (produced using data from 2001 to 2005 inclusive), and 2010 produced using data from 2004 to 2008 inclusive) (Department or Communities and Local Government, 2008, 2011). For each of hese years, we use LSOAs as defined in the 2001 UK Census. While hese figures are comparable within years, the data collector (the K’s Department for Communities and Local Government) caution gainst using this data for trend analysis. These measures are highly orrelated with TTD and, by virtue of being calculated at an aggre- ate level, exogenous in a model of HCE. That is, while the level of PLL at an LSOA level is a strong predictor of an individual’s TTD, his YPLL level is not influenced by the HCE for a given individual.

e therefore include at least one wave of this measure separately s instruments.

Tables 1 and 2 present descriptive statistics for the sample of ecedents from the first wave of data, under our strategy of sam- ling from the first year of observations (2005/06). Tables 3 and 4 resent descriptive statistics from the first wave of data, underur strategy of sampling from the final year of observations 2011/12).

8 The Office for National Statistics, however, use 75 rather than 65 years, in their mplementation of this method, as the age at which mortality is considered to be remature (Department for Communities and Local Government, 2011). 9 Details of the method employed by the ONS were obtained in personal commu- ication with the study’s author, Chris Dibben.

log(QTD) 3.28 0.04 3.22 3.33 Age 76.80 10.02 50 105.58 YPLL (IMD 2007) 64.78 15.07 33.80 180.80

As is usual, the distribution of HCE is positively skewed, with this skewness reduced somewhat when we take a logarithmic transformation.10 As would be expected due to their longer lifes- pan, on average, the average age of women in the sample is somewhat higher than that for men. Similarly, women are observed for, on average, slightly more waves. HCE, with missing waves treated as zero-(log)-cost observations, is on average higher when sampling from the first financial year of data than when sam- pling from those who died in the final year of analysis. This is as expected: the former is drawn from those with an inpatient episode in 2005/06, whereas the latter is drawn from those with an inpa- tient episode in any of the seven financial years of analysis. Indeed, HCE is approximately similar when missing waves are treated as missing observations.

Diagrams, presented in Figs. 3 and 4, based on descriptive statis- tics from a sample of 9,957,084 individual quarterly observationsin HES, provide some illustration of the existing red herring the- sis. HCE appear to increase with age (top-left panel): this is the usual age-expenditure curve that is used to infer rising costs with

10 Due to log(0) being undefined, we add a value of one to such observations in our modelling strategies that include zero-cost quarters.

66 D. Howdon, N. Rice / Journal of Health Economics 57 (2018) 60–74

by age

p l o h e h m ( t a a t T H i a

b

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Fig. 3. Healthcare expenditures

opulation ageing, with the assumption being that as the popu- ation ages, the curve continues to rise as an extrapolation of the bserved trend.11 The observation that expenditures rise with age, owever, is an artefact of a compositional effect. The naïve age- xpenditure curve is composed of individuals who are known to ave died during the period of observation (the sample used in esti- ation) – who have, on average, high expenditures for this period

top-right panel) – and individuals who are known to have survived o at least the end of the period of observation who have, on aver- ge, lower expenditures for this period (bottom-left panel).12 The verage expenditures for individuals observed to have died during he sample period are far greater than for individuals who survive. his suggests an important role for time-to-death in explainingCE. As the proportion of the full population who are decedents

ncreases with age, the näive observed relationship between age nd expenditure displays an increasing trend. Note, however, that

11 We set aside here the drop in expenditures at very high ages, as this is likely to e due to the substantially lower sample sizes observed here. 12 While some of these survivors will be closer to death than other and therefore ould be classed as decedents over a longer observation period, such an effect would

ias us against finding a visual difference in these graphs. We therefore consider this o be strong evidence of a different age profile of HCE for decedents and survivors.

and proximity to death, males.

average expenditures for both decedents and survivors display a flatter profile than that depicted for the full population suggest- ing a less important role for age. Indeed, expenditure on decedents generally decrease, with this decrease particularly pronounced for women. Expenditure on survivors generally increase, but with a shallower gradient than observed for the full population, and at a lower average cost.

When we focus on decedents, and consider average HCE by proximity to death, we observe a large increase in costs in termi- nal quarters – particularly in the year immediately before death. Fig. 7 in Appendix A shows a similar relationship between expen- ditures and TTD for men at selected ages. In general, expenditure in quarters preceding the final three average around £500 (although there is variation). In the final three quarters, and particularly the final quarter, we observe a large increase in expenditure. With the exception of 50 year olds, there is a clear gradient of health expen- ditures rising most dramatically in the final quarter of life with average increases over the penultimate quarter ranging from £460 for 55 year olds to £1099 for 90 year olds.

The relationship between HCE and TTD in levels is nonlinear.Fig. 5 shows that the relationship is approximately linear on the logarithmic scale and in the modelling that follows logarithms of both HCE and TTD are used throughout.

D. Howdon, N. Rice / Journal of Health Economics 57 (2018) 60–74 67

Fig. 4. Healthcare expenditures by age and proximity to death, females.

Fig. 5. Average health care expenditures according to quarters to death (log scale for x- and y-axes).

68 D. Howdon, N. Rice / Journal of Health Economics 57 (2018) 60–74

al ind

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information about a patient’s morbidities at the time of their hos- pital stay. We estimate each of these models with random effects, representing unobserved heterogeneity.

Fig. 6. Change in HCE according to time-to-death and age, hypothetic

. Econometric model

We follow the general strand of the red herring literature and pecify a baseline model of HCE, including only age as an explana- ory variable.

log(HCEit) = ̨ + ˇageageit + �it + �i + εit, i = 1, . . ., N, t = 1, . . ., Ti, (1)

here �it is a vector of control variables (year and season of admis- ion, and hospital provider dummies) �i is an individual-specific nobserved effect and εit is an idiosyncratic error term. Although his model is not estimated in existing papers, it is claimed that uch a model would not adequately explain HCE. TTD is claimed toe an omitted variable in these models, giving rise to models such s:

og(HCEit) = ̨ + ˇageageit + ˇTTD log(TTDit) + �it + �i + εit . (2)

ividual dying at 75 (top – men, bottom – women; log HCE on y-axis).

We argue that individual morbidity is an omitted variable in this type of model, where TTD functions as a proxy for such morbidity.13

Accordingly, we augment the model as follows:

log(HCEit) = ̨ + ˇageageit + ˇTTD log(TTDit)

+ 260∑

j=1 ˇCCSj CCSjit + �it + �i + εit, (3)

where CCSn represents a recorded morbidity of CCS type n (n = 1, . . ., 260). We exploit the available data in HES to include detailed

13 And, furthermore, that such a proxy relationship may change over time in the presence of a compression of morbidity.

Health Economics 57 (2018) 60–74 69

l o T H T t t t i e e u

u e i F a p T a l T u i h d s fi a e

5

l s m r T i i m a r t t m

e c d

a a o T

t w o c s t l

Table 5 Results, final wave sampling.

Model Missing observations treated as missing

(1) (2) (3) AGE ONLY AGE TTD AGE TTD MORBS

Men Age −.01459** −.01274* −.00518

(.00654) (.00652) (.00526) Age2 .00010** .00009** .00003

(.00004) (.00004) (.00003) log(TTD) −.42375*** −.14454***

(.01467) (.01206) Morbidities Included

Women Age −.00068 .00081 −.00038

(.00588) (.00585) (.00474) Age2 .00004 .00003 .00001

(.00004) (.00004) (.00003) log(TTD) −.34305*** −.13276***

(.01458) (.01218) Morbidities Included

Model Missing observations treated as zeros

(1) (2) (3)

Age .00130 .00204 .00289*

(.00180) (.00181) (.00156) Age2 0.00000 −.00001 −.00002**

(.00001) (.00001) (.00001) log(TTD) −.33712*** −.10645***

(.00679) (.00560) Morbidities Included

Women Age .00983*** .01087*** .00559***

(.00189) (.00189) (.00154) Age2 −.00005*** −.00006*** −.00003***

(.00001) (.00001) (.00000) log(TTD) −.27927*** −.09789***

(.00604) (.00520) Morbidities Included

*

D. Howdon, N. Rice / Journal of

Modelling HCE as a function of TTD suffers from potential prob- ems of endogeneity. Existing literature suggests that conditional n other covariates, being further from death – i.e. having a high TD – in time period t is likely to lead to lower levels of HCE in t. igher levels of HCEit, however, are likely to lead to high levels of TDit: if the hospital activity that generates health care expendi- ures is effective in improving health then the individual is likely o enjoy a longer remaining lifespan as a result. We therefore posit hat actual TTD at time period t has been determined in part by HCE n that time period as well as other time periods. Consequently, if ndogeneity does pose problems in this analysis, the coefficient stimate on TTD (when treated as exogenous) is likely to be an nderestimate of the true ‘effect’ of TTD.

Other models in the red herring strand of literature model HCE, sing TTD and age as explanatory variables, but highlighting this ndogeneity problem. Various attempts are made to purge TTD of ts endogeneity in HCE (Zweifel et al., 2004; Werblow et al., 2007; elder et al., 2010). We propose the use of a component of the Health nd Disability Index of Multiple Deprivation by Lower Super Out- ut Area – years of potential life lost (YPLL) – as an instrument for TD under the assumption that such measures are exogenous in

model of HCE but highly correlated with TTD. That is, while the evel of YPLL at an LSOA level is a strong predictor of an individual’s TD, this YPLL level is not influenced by the HCE for a given individ- al. Accordingly, where possible, we reestimate models (2) and (3)

nstrumenting TTD by YPLL.14 Such an instrumented approach is, owever, possible only in the case of our second sampling proce- ure, where TTD is not pre-determined by the construction of the ample. In our former sampling procedure, all individuals die in the nal four quarters (i.e., final financial year) of the sample, and thus ny relevance of variation across areas in deprivation would not be xpected.

. Results

All versions of our different sampling and modelling strategies ead to qualitatively similar results. In short, a weak (and often tatistically insignificant) relationship is observed when costs are odelled as a function of age alone. Confirming the overall red her-

ing results, a strongly significant relationship is observed between TD and HCE, when TTD is added as an explanatory variable. This s in line with our descriptive diagrams (Figs. 3 and 4), demonstrat- ng that the naïvely-estimated relationship between age and HCE is

uted when conditioning on TTD. When morbidities are included s explanatory variables, the relationship between TTD and HCE is educed (in all cases, the coefficient is reduced by approximately wo-thirds). When, where possible, instrumenting TTD, the rela- ionship between TTD and age becomes larger, with the addition of

orbidities again reducing the size of the TTD coefficient.15

Table 5 presents the results of various specification of a randomffects panel data model of log(HCE) on age, log(TTD) and morbidity haracteristics for the sub-sample of decedents, when a sample is rawn from those who died in 2011/12. The first column of results

14 While HCE is a function of morbidity, morbidity itself will be a function of age, nd TTD is likely to be a function of morbidity. Indeed, our hypothesis is that TTD is

proxy for morbidity. Accordingly, we expect supplementing (3) with information n morbidity will temper the effect of both age (remaining after conditioning on TD) and TTD on HCE. 15 In the interests of consistency, all results presented here employ one wave of he YPLL instrument. Where both instruments appear as relevant at the first stage, e estimated the models using both YPLL waves in order to carry out a Hansen J test

f the validity of overidentifying restrictions. In all cases, we observe large p-values onsistent with failing to reject the null-hypothesis (between 0.3354 and 0.6317), uggesting evidence in favour of the exogeneity of our chosen instruments. Fur- hermore, our second stage results suggest very similar coefficients and confidence evels, such that none of our conclusions drawn below are affected.

p < 0.05. ** p < 0.01.

*** p < 1.

(model 1) shows a weak and generally non-significant relationship between age and inpatient costs. These results represent, as far as we are aware, the first reported results in the red herring strand of literature of whether hospital costs increase with age in the aggre- gate, even before control is made for other factors such as TTD and morbidities. Existing research broadly states that this is the case, but refer merely to population-level descriptive statistics. In a ran- dom effects model (2) including TTD and age, we observe a highly significant relationship with TTD. This result is in line with those in the red herring strand of existing research. As an individual gets 1% closer to death, HCE increases by between 0.34% and 0.42% for men (between 0.28% and 0.34% for women), depending on the modelling strategy adopted.16

Conditioning on morbidity markers, we find a reduced role for TTD in explaining HCE, using both sampling strategies. Our esti- mate of the TTD elasticity of HCE falls by approximately two-thirds in almost all (non-IV) cases when we condition on the individ- ual’s observed morbidity in the current time period (i.e., whenwe move from model 2 to model 3). In all models, in excess of 90% of the estimated coefficients for the morbidity indicators are significant at the 1% level, yielding a p-value of 0.0000. We inter-

16 Because we aggregate costs by quarter and consequently use discrete values of TTD for each individual in each wave, this elasticity can only be considered as an approximation.

70 D. Howdon, N. Rice / Journal of Health Economics 57 (2018) 60–74

Table 6 Results, first wave sampling.

Model Missing observations treated as missing

(1) (2) (3) (4) (5)

Men AGE ONLY AGE TTD AGE TTD MORBS AGE TTDIV AGE TTDIV MORBS

Age −.01800* −.00454 .00290 −.00953 −.01124 (.00932) (.00896) (.00746) (.02617) (.01087)

Age2 .00013** .00003 −.00002 .00007 .00007 (.00006) (0.00006) (.00005) (.00018) (.00008)

log(TTD) −.31565*** −.10098*** −.35626 −.13120 (.00655) (.00616) (.36994) (.30968)

Relevance F-statistic 16.38 13.24 Morbidities Included Included

Women Age −.00269 .01198 .00065 .08939 −.01118

(.00832) (.00813) (.00696) (.06773) (.01124) Age2 .00004 −.00005 −.00001 −.00059 .00007

(.00005) (.00005) (.00004) (.00047) (.00009) log(TTD) −.26423*** −.09307*** −1.82773 −.12894

(.00663) (.00612) (1.2711) (.3239383) Relevance F-statistic 66.95 11.67 Morbidities Included Included

Model Missing observations treated as zeros

(1) (2) (3) (4) (5)

Men Age −.06500*** .10191*** −.00691 .28057*** .03547

(.02465) (.02265) (.01462) (.09001) (.06062) Age2 .00049*** −.00088*** .00001 −.00363*** −.00036

(.00016) (.00015) (.00010) (.00113) (.00058) log(TTD) −1.19842*** −.18669*** −2.05176*** −.31020

(.01205) (.00745) (.38267) (.23856) Relevance F-statistic 80.44 71.30 Morbidities Included Included

Women Age .06863*** .08155*** .02635*** .61590 .05830

(.00928) (.00934) (.00806) (.40329) (.05937) Age2 −.00039*** −.00048*** −.00016*** −.00393 −.00038

(.00928) (.00006) (.00005) (.00261) (.00040) log(TTD) −.15705*** −.03723*** −6.43066 −.64920

(.00801) (.00716) (4.95179) (.8581702) Relevance F-statistic 13.09 11.81 Morbidities Included Included

p u m i o 7 r c s

d H f w t b t

a s

expected, first-stage regressions show a negative and significant relationship between YPLL and TTD and an F-test of these instru-

* p < 0.05. ** p < 0.01.

*** p < 1.

ret this as indicating that TTD does indeed serve as a proxy for nobserved morbidity. The estimated coefficients for age when orbidity markers are included see similar falls. This is illustrated

n Fig. 6 which shows the difference in log(HCE) from the quarter f death to preceding quarters for an individual who dies at age 5 for the alternative specifications of the model.17 The combined elationship of time-to-death and age is severely muted when we ondition on current morbidity markers as seen by the lines repre- enting RE AGE TTD MORBS and RE AGE TTD (Table 6).

We anticipate hospital costs to rise as individuals approach eath, and as such expect a negative relationship between TTD and CE. For the sampling strategy where this is possible – sampling

rom the first calendar year – we instrument for TTD in order to deal ith the potential endogeneity of TTD in HCE, which would mean

hat a naïve estimate of the ‘effect’ of TTD on HCE was likely to be iased towards zero (i.e. that naïve estimates would be expected o be less negative). In a further pair of models, we instrument

17 While our instrumented model including morbidity markers appears to show mildly negative relationship between age and HCE, this arises from the use of a mall and non-significant negative age coefficient in construction of this graph.

TTD with LSOA-level YPLL measures, our small-area measure of premature mortality.

When we instrument using YPLL measures – model (4) – the estimated coefficient of log(TTD) rises (in absolute terms) in all cases. While we confirm the findings of Zweifel et al. (2004) that ‘the proximity of death rather than age [being] a main determinant of HCE is fairly robust to endogeneity error,’ our results also suggest that failing to account for the endogeneity of TTD in these models may lead to a large underestimate of the true ‘effect’ of TTD in mod- els that do not include morbidity markers.18 This is also illustrated in Fig. 6, which shows the large divergence in estimated costs for these two models for an individual who dies at the age of 75. As

ments suggests their relevance as a predictor of TTD according to

18 While our point estimates rise, in most cases, however, the TTD coefficients are no longer significant when we carried out our instrumented regressions. We do not rely on these results in our conclusions regarding the relationship between TTD, morbidity, and HCE, and present these results to demonstrate its consistency with existing research.

D. Howdon, N. Rice / Journal of Health Economics 57 (2018) 60–74 71

by pr

t 1

6

p a o p t t m o l l o o t ( t m A f g

b i b ‘ t m i – i o a f a a p

Fig. 7. Healthcare expenditures

he commonly used Stock-Yogo ‘rule of thumb’ of an F-statistic of 0 in all cases.

. Conclusions

Ageing populations pose a substantial problem for public service rovision, particularly for health and social care. Estimates of how n ageing population will impact HCEs vary considerably. Devel- ping credible predictions is a core component of health systems lanning as is allocating resources efficiently and equitably to meet he health care needs of the population. Whilst it is undeniable hat health care costs will rise as the baby-boomers age, the impact

ight not be quite as large as models based on a simple extrap- lation of a crude age-expenditure curve suggests. As individuals ive longer, all other things equal, they may generate larger cumu- ative life-time costs. The extent to which this becomes a burden n the health care sector will depend on how morbidity profiles f cohorts change over time. Should a compression of morbidity hesis hold, Fries (1980), Freedman et al. (2002) and Romeu Gordo 2011), on average individuals can expect to live longer and delay he onset of morbidity into later years. This will have the effect of

oving the age-expenditure curve to the right as populations age. n expansion of morbidity would have more severe consequences

or HCEs with individuals living longer, but also experiencing a reater number of years in ill-health.

Our findings support other literature that it is not age per se, ut time-to-death (TTD), particularly the final year of life, that

s a strong driver of HCEs. Our results regarding the relationship etween age, TTD and HCE are in line with existing results in the

red herring’ strange of literature. We extend this existing literature o show that, in line with the economic implications of biological

odels of ageing as drawn out by Dalgaard and Strulik (2014), TTD n large part proxies for morbidity in explaining HCE. Our results

showing a weak relationship between HCE and age when TTD is ncluded – fall in line with existing research into the determinants f HCE for ageing populations. However, while TTD clearly plays n important role in explaining HCEs, it is unhelpful in forecastinguture expenditure needs. At an individual level TTD is unknown nd hence to forecast future expenditure growth assumptions bout the proportions of decedents and survivors together with rojections of populations within age groups is required. By extend-

oximity to death, males by age.

ing the modelling of HCE to include morbidity characteristics we show that the impact of TTD is diminished indicating that it acts as a proxy for underlying health status. This is important to allow the planning of future resource requirements and in developing appro- priate models for budgets to be allocated equitably across providers of care in response to population health care need. Our results are robust to problems of endogeneity that exist between HCE and TTD.

Our results strengthen the need to include measures of mor- bidity in models of HCE. Merely including TTD is insufficient in predicting future HCE. To accurately forecast future expenditure needs, information on changes to profiles of morbidity are required. The existence of a compression of morbidity, along with a tendency for increased life expectancy, suggests competing and opposing pressures on HCE. While increases in life expectancy suggests that a greater number of individuals will be alive at any given age, with associated upward pressure on HCE, a compression of morbidity will tend to, on average, provide downward pressure on HCE for any given individual at any given age.

This work has focused on determinants of the demand for inpatient health care services at an individual level via age, time- to-death and morbidity characteristics. Clearly there is also a substantial role for supply-side impacts on expenditure growth notably through technological advances in health care interven- tions and the way in which health care services are organized and delivered. We do not address these issues here, but are areas that warrant further investigation at an aggregate level. Inpatient hos- pital care is one of a number of services provided by the National Health Service in England and other expenditure should also be taken into account when assessing the overall impact of an ageing population, as should costs placed on the Government by long- term care services predominantly accessed by older age groups. The increasing ability to link administrative sources of data provides a potentially valuable resource for future research in this area.

Acknowledgements

This is an independent report commissioned and funded bythe Policy Research Programme in the Department of Health from the Economics of Social and Health Care Research Unit (ESHCRU). ESHCRU is a joint collaboration between the University of York, London School of Economics and University of Kent. The views

7 Health Economics 57 (2018) 60–74

e f n ( c

A

T C

Table A1 (Continued)

CCS code Description

61 Sickle cell anemia 62 Coagulation and hemorrhagic disorders 63 Diseases of white blood cells 64 Other hematologic conditions 65 Mental retardation 66 Alcohol-related mental disorders 67 Substance-related mental disorders 68 Senility and organic mental disorders 69 Affective disorders 70 Schizophrenia and related disorders 71 Other psychoses 72 Anxiety; somatoform; dissociative; and personality disorders 73 Preadult disorders 74 Other mental conditions 75 Personal history of mental disorder; mental and behavioral

problems; observation and screening for mental condition 76 Meningitis (except that caused by tuberculosis or sexually

transmitted disease) 77 Encephalitis (except that caused by tuberculosis or sexually

transmitted disease) 78 Other CNS infection and poliomyelitis 79 Parkinson’s disease 80 Multiple sclerosis 81 Other hereditary and degenerative nervous system conditions 82 Paralysis 83 Epilepsy; convulsions 84 Headache; including migraine 85 Coma; stupor; and brain damage 86 Cataract 87 Retinal detachments; defects; vascular occlusion; and

retinopathy 88 Glaucoma 89 Blindness and vision defects 90 Inflammation; infection of eye (except that caused by

tuberculosis or sexually transmitted disease) 91 Other eye disorders 92 Otitis media and related conditions 93 Conditions associated with dizziness or vertigo 94 Other ear and sense organ disorders 95 Other nervous system disorders 96 Heart valve disorders 97 Peri-; endo-; and myocarditis; cardiomyopathy (except that

caused by tuberculosis or sexually transmitted disease) 98 Essential hypertension 99 Hypertension with complications and secondary hypertension 100 Acute myocardial infarction 101 Coronary atherosclerosis and other heart disease 102 Nonspecific chest pain 103 Pulmonary heart disease 104 Other and ill-defined heart disease 105 Conduction disorders 106 Cardiac dysrhythmias 107 Cardiac arrest and ventricular fibrillation 108 Congestive heart failure; nonhypertensive 109 Acute cerebrovascular disease 110 Occlusion or stenosis of precerebral arteries 111 Other and ill-defined cerebrovascular disease 112 Transient cerebral ischemia 113 Late effects of cerebrovascular disease 114 Peripheral and visceral atherosclerosis 115 Aortic; peripheral; and visceral artery aneurysms 116 Aortic and peripheral arterial embolism or thrombosis 117 Other circulatory disease 118 Phlebitis; thrombophlebitis and thromboembolism 119 Varicose veins of lower extremity 120 Hemorrhoids 121 Other diseases of veins and lymphatics 122 Pneumonia (except that caused by tuberculosis or sexually

transmitted disease) 123 Influenza 124 Acute and chronic tonsillitis 125 Acute bronchitis

2 D. Howdon, N. Rice / Journal of

xpressed are those of the authors and may not reflect those of the unders. Daniel Howdon acknowledges PhD funding from the Eco- omic and Social Research Council under the Large Grant Scheme RES-060-25-0045). The authors gratefully acknowledge the highly onstructive remarks of two anonymous peer reviewers.

ppendix A.

See Fig. 7 and Table A1.

able A1 linical classifications software (CCS) groupings.

CCS code Description

1 Tuberculosis 2 Septicemia (except in labor) 3 Bacterial infection; unspecified site 4 Mycoses 5 HIV infection 6 Hepatitis 7 Viral infection 8 Other infections; including parasitic 9 Sexually transmitted infections (not HIV or hepatitis) 10 Immunizations and screening for infectious disease 11 Cancer of head and neck 12 Cancer of esophagus 13 Cancer of stomach 14 Cancer of colon 15 Cancer of rectum and anus 16 Cancer of liver and intrahepatic bile duct 17 Cancer of pancreas 18 Cancer of other GI organs; peritoneum 19 Cancer of bronchus; lung 20 Cancer; other respiratory and intrathoracic 21 Cancer of bone and connective tissue 22 Melanomas of skin 23 Other non-epithelial cancer of skin 24 Cancer of breast 25 Cancer of uterus 26 Cancer of cervix 27 Cancer of ovary 28 Cancer of other female genital organs 29 Cancer of prostate 30 Cancer of testis 31 Cancer of other male genital organs 32 Cancer of bladder 33 Cancer of kidney and renal pelvis 34 Cancer of other urinary organs 35 Cancer of brain and nervous system 36 Cancer of thyroid 37 Hodgkin’s disease 38 Non-Hodgkin’s lymphoma 39 Leukemias 40 Multiple myeloma 41 Cancer; other and unspecified primary 42 Secondary malignancies 43 Malignant neoplasm without specification of site 44 Neoplasms of unspecified nature or uncertain behavior 45 Maintenance chemotherapy; radiotherapy 46 Benign neoplasm of uterus 47 Other and unspecified benign neoplasm 48 Thyroid disorders 49 Diabetes mellitus without complication 50 Diabetes mellitus with complications 51 Other endocrine disorders 52 Nutritional deficiencies 53 Disorders of lipid metabolism 54 Gout and other crystal arthropathies 55 Fluid and electrolyte disorders 56 Cystic fibrosis 57 Immunity disorders58 Other nutritional; endocrine; and metabolic disorders 59 Deficiency and other anemia 60 Acute posthemorrhagic anemia

126 Other upper respiratory infections 127 Chronic obstructive pulmonary disease and bronchiectasis 128 Asthma 129 Aspiration pneumonitis; food/vomitus 130 Pleurisy; pneumothorax; pulmonary collapse

D. Howdon, N. Rice / Journal of Health Economics 57 (2018) 60–74 73

Table A1 (Continued)

CCS code Description

131 Respiratory failure; insufficiency; arrest (adult) 132 Lung disease due to external agents 133 Other lower respiratory disease 134 Other upper respiratory disease 135 Intestinal infection 136 Disorders of teeth and jaw 137 Diseases of mouth; excluding dental 138 Esophageal disorders 139 Gastroduodenal ulcer (except hemorrhage) 140 Gastritis and duodenitis 141 Other disorders of stomach and duodenum 142 Appendicitis and other appendiceal conditions 143 Abdominal hernia 144 Regional enteritis and ulcerative colitis 145 Intestinal obstruction without hernia 146 Diverticulosis and diverticulitis 147 Anal and rectal conditions 148 Peritonitis and intestinal abscess 149 Biliary tract disease 150 Liver disease; alcohol-related 151 Other liver diseases 152 Pancreatic disorders (not diabetes) 153 Gastrointestinal hemorrhage 154 Noninfectious gastroenteritis 155 Other gastrointestinal disorders 156 Nephritis; nephrosis; renal sclerosis 157 Acute and unspecified renal failure 158 Chronic renal failure 159 Urinary tract infections 160 Calculus of urinary tract 161 Other diseases of kidney and ureters 162 Other diseases of bladder and urethra 163 Genitourinary symptoms and ill-defined conditions 164 Hyperplasia of prostate 165 Inflammatory conditions of male genital organs 166 Other male genital disorders 167 Nonmalignant breast conditions 168 Inflammatory diseases of female pelvic organs 169 Endometriosis 170 Prolapse of female genital organs 171 Menstrual disorders 172 Ovarian cyst 173 Menopausal disorders 174 Female infertility 175 Other female genital disorders 176 Contraceptive and procreative management 177 Spontaneous abortion 178 Induced abortion 179 Postabortion complications 180 Ectopic pregnancy 181 Other complications of pregnancy 182 Hemorrhage during pregnancy; abruptio placenta; placenta

previa 183 Hypertension complicating pregnancy; childbirth and the

puerperium 184 Early or threatened labor 185 Prolonged pregnancy 186 Diabetes or abnormal glucose tolerance complicating

pregnancy; childbirth; or the puerperium 187 Malposition; malpresentation 188 Fetopelvic disproportion; obstruction 189 Previous C-section 190 Fetal distress and abnormal forces of labor 191 Polyhydramnios and other problems of amniotic cavity 192 Umbilical cord complication 193 OB-related trauma to perineum and vulva 194 Forceps delivery 195 Other complications of birth; puerperium affecting

management of mother 196 Normal pregnancy and/or delivery 197 Skin and subcutaneous tissue infections 198 Other inflammatory condition of skin 199 Chronic ulcer of skin 200 Other skin disorders 201 Infective arthritis and osteomyelitis (except that caused by

tuberculosis or sexually transmitted disease)

Table A1 (Continued)

CCS code Description

202 Rheumatoid arthritis and related disease 203 Osteoarthritis 204 Other non-traumatic joint disorders 205 Spondylosis; intervertebral disc disorders; other back

problems 206 Osteoporosis 207 Pathological fracture 208 Acquired foot deformities 209 Other acquired deformities 210 Systemic lupus erythematosus and connective tissue disorders 211 Other connective tissue disease 212 Other bone disease and musculoskeletal deformities 213 Cardiac and circulatory congenital anomalies 214 Digestive congenital anomalies 215 Genitourinary congenital anomalies 216 Nervous system congenital anomalies 217 Other congenital anomalies 218 Liveborn 219 Short gestation; low birth weight; and fetal growth retardation 220 Intrauterine hypoxia and birth asphyxia 221 Respiratory distress syndrome 222 Hemolytic jaundice and perinatal jaundice 223 Birth trauma 224 Other perinatal conditions 225 Joint disorders and dislocations; trauma-related 226 Fracture of neck of femur (hip) 227 Spinal cord injury 228 Skull and face fractures 229 Fracture of upper limb 230 Fracture of lower limb 231 Other fractures 232 Sprains and strains 233 Intracranial injury 234 Crushing injury or internal injury 235 Open wounds of head; neck; and trunk 236 Open wounds of extremities 237 Complication of device; implant or graft 238 Complications of surgical procedures or medical care 239 Superficial injury; contusion 240 Burns 241 Poisoning by psychotropic agents 242 Poisoning by other medications and drugs 243 Poisoning by nonmedicinal substances 244 Other injuries and conditions due to external causes 245 Syncope 246 Fever of unknown origin 247 Lymphadenitis 248 Gangrene 249 Shock 250 Nausea and vomiting 251 Abdominal pain 252 Malaise and fatigue 253 Allergic reactions 254 Rehabilitation care; fitting of prostheses; and adjustment of

devices 255 Administrative/social admission 256 Medical examination/evaluation 257 Other aftercare 258 Other screening for suspected conditions (not mental

disorders or infectious disease)259 Residual codes; unclassified 260 E Codes: All (external causes of injury and poisoning)

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  • Health care expenditures, age, proximity to death and morbidity: Implications for an ageing population
    • 1 Introduction
    • 2 Literature review
      • 2.1 Compression of morbidity
      • 2.2 Age, time-to-death and healthcare expenditures
    • 3 Data
      • 3.1 Data sources
    • 4 Econometric model
    • 5 Results
    • 6 Conclusions
    • Acknowledgements
    • References
    • References

Age and Ageing 2018; 47: 638–640 doi: 10.1093/ageing/afy014 Published electronically 23 February 2018

© The Author(s) 2018. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: [email protected]

COMMENTARIES

Deprescribing: the emerging evidence for and the practice of the ‘geriatrician’s salute’

SARAH N. HILMER1, DANIJELA GNJIDIC2

1Kolling Institute of Medical Research, Sydney Medical School, Sydney University and Royal North Shore Hospital, St Leonards, New South Wales 2065, Australia 2Faculty of Pharmacy and Charles Perkins Centre, University of Sydney, New South Wales 2006, Australia

Address correspondence to: S. N. Hilmer. Tel: +612 9926 4481; Fax: +612 9926 4053. Email: [email protected]

Abstract

The process of a health professional withdrawing medicines for which the current risk may outweigh the benefit in the indi- vidual patient has been given a variety of names including the colloquial ‘geriatrician’s salute’, ‘de-intensification’ and increas- ingly ‘deprescribing’. The rise of deprescribing as a word with a definition, evidence base and implementation plan, reflects the changing environment in which we practice. In particular, the emphasis on evidence-based medicine and the need to care for our expanding ageing populations, which requires application of components of geriatric evaluation and manage- ment by a wider range of health care practitioners. However, there are still significant challenges related to research on the safety, efficacy and implementation of deprescribing. In this commentary, we discuss the current evidence on the effects of deprescribing, emergence of implementation tools to embed deprescribing into the clinical care of older adults, as well as efforts to develop guidelines to improve health care practitioners’ awareness and self-efficacy of deprescribing. Ultimately, judicious prescribing and deprescribing, across a wide range of health care settings, ought to enable older people to use medicines to support their achievable ageing goals.

Keywords: deprescribing, prescribing, geriatric evaluation and management, older people

Judicious, frequent, goal-oriented medication review is a core component of the practice of geriatric medicine. As geriatricians, we ensure that our patients are not denied treatments that may help them because they are considered ‘too old’, while minimising iatrogenesis, which includes adverse effects of medicines. One way to achieve this is by ceasing medicines for which the current risk is thought to outweigh the benefit in the individual patient. This process has been given a variety of names: the colloquial ‘geriatri- cian’s salute’, ‘de-intensification’ and increasingly ‘depre- scribing’, which is defined in the literature as, ‘the process of withdrawal of an inappropriate medication, supervised by a health care professional with the goal of managing polypharmacy and improving outcomes’ [1].

The rise of deprescribing as a word with a definition, evidence base and implementation plan, reflects the chan- ging environment in which we practice. Geriatricians cannot possibly evaluate and manage every older person in our ageing population. Therefore, we need to objectively define and describe some of our strategies, including medication

review, so that they can be performed by other health care practitioners, reserving our face to face clinical expertise for the most complex patients. Emergence of evidence-based medicine to guide prescribing has resulted in calls for comparable evidence to guide deprescribing. As evidence on the effects of deprescribing grows, tools of implementation science for behaviour change are simultaneously being applied to embed deprescribing into the routine clinical care of older adults.

In the era of evidence-based medicine, clinicians report that a lack of evidence of the safety and efficacy of deprescrib- ing is a major barrier to practicing deprescribing [2] and there are significant efforts to address this. Systematic reviews sug- gest that deprescribing certain medication classes may reduce adverse events and improve quality of life [3]. Deprescribing has been consistently shown to be safe: it appears to improve survival in non-randomised studies and does not reduce sur- vival in randomised studies [4]. While there is high-grade evidence that deprescribing of psychotropics reduces falls [4], there is limited evidence of the impact of deprescribing targeting polypharmacy in general on global health outcomes

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that are critical for successful ageing, such as physical and cognitive function. This is also the case for most prescribing interventions. While there is an absence of evidence at pre- sent, it is plausible that deprescribing, which tackles only one part of geriatric evaluation and management, may not have a big impact on multifactorial geriatric syndromes. However, since adverse effects of medications are one of the most reversible causes of geriatric syndromes, it is important that they are addressed.

Research on how to deprescribe is emerging concurrently with the evidence on its safety and efficacy. Enablers and bar- riers to deprescribing have been described for practitioners [2] and for patients [5]. For practitioners, in addition to lack of evidence, these include problem awareness, inertia because of lower perceived value of stopping than continuing medicines, self-efficacy and feasibility [2]. Key factors identified by patients are the appropriateness of cessation, the need for a process for cessation, previous experiences, influence of health care practitioners, family and friends, fear of cessation and dis- like of medications [5]. Researchers are striving to define and test a ‘deprescribing process’ for routine care to address polypharmacy, including the five step patient-centred depre- scribing process, the CEASE protocol (Current medications, Elevated risk, Assess, Sort, Eliminate), and the Good Palliative-Geriatric Practice algorithm [6]. A number of clin- ical trials have assessed the feasibility of deprescribing benzo- diazepines in older patients and have yielded success rates between 27% and 80% targeting patients and/or different health care practitioners in a range of settings [7]. While most studies demonstrate that benzodiazepine withdrawal is feasible and safe in the older population, the clinical impact and sus- tainability of various interventions is yet to be established. Similarly, a recent Cochrane review which assessed the benefits and harms of deprescribing long-term proton pump inhibitor therapy in adults reported a reduction in pill burden, an increase in gastro intestinal symptoms and insufficient evi- dence in relation to long-term benefits and harms [8].

Guidelines are being developed that aim to improve health care practitioners’ awareness and self-efficacy of deprescribing. However, while rigorous methodology has been developed and utilised to generate the guidelines [9], the recommenda- tions are rarely supported by a strong evidence base because of the limited evidence on safety and efficacy of deprescribing. The Deprescribing Guidelines in the Elderly group, based in Canada have developed evidence-based deprescribing guidelines for a range of medication classes including proton pump inhi- bitors (PPIs), benzodiazepines, antipsychotics, cholinesterase inhibitors and memantine, with tools to support implementa- tion. While preliminary evidence suggests the guidelines and tools may reduce the use and cost of certain medication such us proton pump inhibitors [10], no randomised trial to date has assessed impact of rolling out guidelines on a population level on prescribing, patient-centred or clinical outcomes.

Other tools have been developed to facilitate deprescrib- ing in practice. Implementation of deprescribing is greatly assisted by access to non-pharmacological therapies. Efforts

are being made internationally to consolidate the evidence for and improve the availability of these therapies, through projects such as the European Union funded SENATOR- ONTOP (Optimal Evidence-Based Non-drug Therapies in Older People) series. A wide range of tools to help identify medications that are likely to be candidates for deprescrib- ing have been developed and validated, such as the STOPP criteria, Beers criteria and Drug Burden Index [6]. Most recently computerised decision support systems have been developed to facilitate using these tools to identify medica- tions for which risk is likely to outweigh benefit and prompt deprescribing in practice [11]. Use of the tools, and collab- oration between medical practitioners, pharmacists and nurses [12] may enable wider deprescribing practice and reach a broader group of older people than those who can access geriatricians.

Deprescribing occurs most often in patients during their last year of life, as it becomes clearer that a person’s care goals focus on comfort, and the time to benefit from most pre- ventative medications is limited. For example, it has been demonstrated that withdrawal of statins in patients with life limiting illness is safe [13], and analysis of national data from New Zealand found that statins were discontinued in the last year of life in 70.4% of people with cancer [14]. There are also efforts to apply deprescribing to specific patient populations such as people with chronic kidney disease [15].

There are opportunities for deprescribing across all health care settings where a comprehensive and, where pos- sible, multidisciplinary review can be performed. These include on admission to hospital where a new diagnosis or change in prognosis may become apparent, on transition to a nursing home, and during regular review by a practitioner who knows the patient well in the community. Healthcare for older people is frequently fragmented between settings and practitioners, and rather than ‘passing the buck’ for the responsibility of medication review, a collaborative approach with excellent communication is required [12]. The patient’s goals and priorities are central to deprescribing, and the vast majority of patients report that they would like to stop a medicine if their doctor said that they could [5].

Despite increasing international efforts, clinicians and researchers still face significant challenges in relation to depre- scribing research to generate evidence on safety, efficacy and implementation. We need to move away from conducting pilot deprescribing studies, which test feasibility in relation to pre- scribing outcomes, but are not powered to evaluate clinical outcomes. In addition, the choice of study design and interven- tion needs to be carefully selected to ensure that outcomes of deprescribing trials are reproducible, beneficial to patients and cost effective. The optimal study design will depend on the study population, outcomes of interest (e.g. short versus long- term), setting and health care system. Improving the evidence base for deprescribing, educating health care practitioners, and increasing public awareness are essential for application of deprescribing to clinical care and translation to policy. The ultimate aim is that through careful, informed prescribing and

Deprescribing: the emerging evidence for and the practice of the ‘geriatrician’s salute’

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deprescribing, across a wide range of health care settings, older people will use medicines to support their achievable ageing goals.

Key points

• Judicious, frequent, goal-oriented medication review is a core component of the practice of geriatric medicine.

• Deprescribing is the process of supervised withdrawal of a medication that aims to improve patient outcomes.

• Research on how to deprescribe is emerging concurrently with the evidence on its safety and efficacy.

Conflict of interest

None declared.

Funding

D.G. is supported by the Australian National Health and Medical Research Council Dementia Leadership Fellowship (1136849).

References

1. Reeve E, Gnjidic D, Long J, Hilmer S. A systematic review of the emerging definition of ‘deprescribing’ with network analysis: implications for future research and clinical practice. Br J Clin Pharmacol 2015; 80: 1254–68.

2. Anderson K, Stowasser D, Freeman C, Scott I. Prescriber barriers and enablers to minimising potentially inappropriate medications in adults: a systematic review and thematic syn- thesis. BMJ Open 2014; 4: e006544.

3. van der Cammen TJ, Rajkumar C, Onder G, Sterke CS, Petrovic M. Drug cessation in complex older adults: time for action. Age Ageing 2014; 43: 20–5.

4. Page AT, Clifford RM, Potter K, Schwartz D, Etherton-Beer CD. The feasibility and effect of deprescribing in older adults

on mortality and health: a systematic review and meta-analysis. Br J Clin Pharmacol 2016; 82: 583–623.

5. Reeve E, To J, Hendrix I, Shakib S, Roberts MS, Wiese MD. Patient barriers to and enablers of deprescribing: a systematic review. Drugs Aging 2013; 30: 793–807.

6. Scott IA, Hilmer SN, Reeve E et al. Reducing inappropriate polypharmacy: the process of deprescribing. JAMA Intern Med 2015; 175: 827–34.

7. Reeve E, Ong M, Wu A, Jansen J, Petrovic M, Gnjidic D. A systematic review of interventions to deprescribe benzodiaze- pines and other hypnotics among older people. Eur J Clin Pharmacol 2017; 73: 927–35.

8. Boghossian TA, Rashid FJ, Thompson W et al. Deprescribing versus continuation of chronic proton pump inhibitor use in adults. Cochrane Database Syst Rev 2017; 3: CD011969.

9. Farrell B, Pottie K, Rojas-Fernandez CH, Bjerre LM, Thompson W, Welch V. Methodology for developing depre- scribing guidelines: using evidence and GRADE to guide recommendations for deprescribing. PLoS One 2016; 11: e0161248.

10. Thompson W, Hogel M, Li Y et al. Effect of a proton pump inhibitor deprescribing guideline on drug usage and costs in long-term care. J Am Med Dir Assoc 2016; 17: 673 e1–4.

11. Alagiakrishnan K, Wilson P, Sadowski CA et al. Physicians’ use of computerized clinical decision supports to improve medica- tion management in the elderly—the Seniors Medication Alert and Review Technology intervention. Clin Interv Aging 2016; 11: 73–81.

12. Gnjidic D, Le Couteur DG, Kouladjian L, Hilmer SN. Deprescribing trials: methods to reduce polypharmacy and the impact on prescribing and clinical outcomes. Clin Geriatr Med 2012; 28: 237–53.

13. Kutner JS, Blatchford PJ, Taylor DH Jr. et al. Safety and benefit of discontinuing statin therapy in the setting of advanced, life-limiting illness: a randomized clinical trial. JAMA Intern Med 2015; 175: 691–700.

14. Nishtala PS, Gnjidic D, Chyou T, Hilmer SN. Discontinuation of statins in a population of older New Zealanders with limited life expectancy. Intern Med J 2016; 46: 493–6.

15. Whittaker CF, Fink JC. Deprescribing in CKD: the proof is in the process. Am J Kidney Dis 2017; 70: 596–8.

Received 2 January 2018; editorial decision 4 January 2018

S. N. Hilmer and D. Gnjidic

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  • Deprescribing: the emerging evidence for and the practice of the ‘geriatrician’s salute’
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