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Kane RL, Talley KMC, Shamliyan T, et al. Common Syndromes in Older Adults Related to Primary and Secondary Prevention [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2011 Jul. (Evidence Syntheses/Technology Assessments, No. 87.)
Common Syndromes in Older Adults Related to Primary and Secondary Prevention [Internet].
Show detailsOur analytical framework includes target population, syndromes, and patient mortality, morbidity, disability, and institutionalization. Our conceptual model (Figure 5) outlines the pathways from the development of the syndromes to patient outcomes, including health care interventions related to screening and prevention. However, health care interventions were beyond the scope of this project.
Figure 5
Conceptual Model.
Figure 5 also provides information about the following research questions:
- Key Question 1.
What is the prevalence of syndromes?
- Key Question 2.
What is the epidemiology of syndromes?
- Key Question 3.
What is the association between syndromes and patient outcomes?
- Key Question 4.
What models report mortality and morbidity in association with the syndromes?
Selection of Eligible Syndromes
The TEP selected geriatric syndromes for this review. We sent a list of 21 syndromes to nine TEP members, asking them to indicate the extent to which the presence of each syndrome in an older person would affect their enthusiasm for recommending each of four prevention strategies. Prevention strategies included simple (e.g., immunization) and complex (e.g., weight loss program) primary prevention or simple (e.g., visual screening) and complex (e.g., colonoscopy) secondary prevention. The TEP used 0 to indicate no effect, 1 to indicate a very mild effect, and 9 to indicate a very strong effect. We collected responses and calculated the mean score for each syndrome. For this review we selected syndromes with a mean score >4. The TEP members responded (eight responses) that presence of each syndrome in an older person would not greatly affect their enthusiasm for recommending simple primary or secondary prevention (mean scores were <4). TEP members responded that eight syndromes would affect their decision about complex primary and secondary prevention in an elderly population (Figure 6). We defined these eight syndromes as eligible for this review.
Figure 6
Results of Ranking Exercise. Bars represent mean scores, based on ranking of each syndrome on a scale of 0 to 9 by eight TEP members. Horizontal axis shows the mean score for each syndrome.
Search Strategy
We sought studies from a wide variety of sources, including MEDLINE via Ovid and PubMed, Cochrane databases, manual searches of reference lists from systematic reviews and other relevant publications, and the Centers for Disease Control and Prevention (CDC) Web site that lists all publications from the Longitudinal Study of Aging. The search strategies for the three research questions are described in Appendix A. Exact search strategies were developed through consultation with qualified librarians. We developed a priori search strategies based on relevant medical subject headings terms, text words, and weighted word frequency algorithms to identify related articles. We documented each recommended, included, and excluded study in the reference library. We limited our search to studies published in English from January 1, 1990 to April 25, 2010.
Excluded references are shown in Appendix B. All work was conducted under the guidance of a TEP, whose members are identified in Appendix C.
Eligibility
We included studies that were original epidemiologic population-based surveys and cohorts and well designed systematic reviews and meta-analyses published in English from January 1, 1990 to April 25, 2010. The studies had to report the prevalence or incidence of the eligible geriatric syndromes or the association geriatric syndromes had with frailty, disability, or mortality. The study sample had to include community-dwelling adults ages 65 years or older. We defined young-old as ages 65–80 years, elderly as ages 80–90 years, and very old as ages 90 years and older. We also used the same definitions of age categories used in the original studies. We reviewed results from the Survey of Income and Program Participation, the National Long-Term Care Survey, and the Medicare Current Beneficiary Survey.
Exclusion Criteria
Studies were excluded from the review if any of the following conditions were met:
- Study participants are in a hospital or long-term care facility setting.
- Study participants were recruited in hospital settings and followed after discharge.
- Study participants are a disease-specific population (i.e., all participants have congestive heart failure).
- Study does not report prevalence of the syndromes or the relative measures of the association with outcomes.
- Study reports the mean value of a continuous measure of the geriatric syndrome (i.e., muscle strength, blood levels of biomarkers, scores of cognitive function or functional decline) rather than prevalence of the syndromes.
- Study is an intervention to prevent eligible syndromes and/or progression of such.
- Study is a screening intervention to reduce morbidity and mortality.
- Study assesses the cost-effectiveness of screening and prevention strategies.
- Study reports diagnostic values and psychometric evaluations of geriatric assessment tools.
For key question 3, we included statistical and decisionmaking models that report mortality based on geriatric syndromes. We used the Social Security Administration’s 2007 Period Life Table (available at http://www.ssa.gov/OACT/STATS/table4c6.html) to estimate life expectancy for participants with eligible syndromes when compared to the general population. We excluded articles that described methods for quantifying frailty, calibration of depression symptoms, and methods differing in handling survival data.
Data Extraction
Evaluations of the studies and data extraction were performed manually and independently by five researchers. The data abstraction forms are shown in Appendix D. We abstracted the information relevant to the PICOTS (population, intervention, comparator, outcomes, time, and settings) framework for each question (Table 4). Errors in data extractions were assessed by comparison with the established ranges for each variable and the data charts from the original articles. Any discrepancies were detected and discussed without formal double entry or statistical evaluation of inter-rater reliability. We abstracted exact definitions of the outcomes from the studies. We analyzed sampling strategies and inclusion of disabled or institutionalized participants in the primary studies. We abstracted the sample size and prevalence of the syndromes to calculate 95 percent confidence intervals of the prevalence using Meta-Analyst software (Tufts Medical Center, Boston, MA).200 We abstracted adjusted relative measures of the association between syndromes and outcomes with 95 percent confidence intervals, descriptive information about populations, definitions of the syndromes, outcomes, and time to measure outcomes. We abstracted all variables that were included in multivariate adjusted models.
Table 4
Population, Intervention, Comparator, Outcomes, Time, and Settings for Each Research Question (PICOTS Framework).
Data Synthesis
For key question 1, results of individual studies (expressed as crude and age-adjusted prevalence estimates) were summarized in evidence tables to analyze prevalence, depending on the definitions of the syndrome. We categorized operational definitions of the syndromes as:
- Abnormal categories of individual biomarkers or diagnostic tests.
- Composite measures of the same syndrome.
- Composite measures of more than one syndrome (e.g., malnutrition and chronic inflammation).
We synthesized the evidence regarding homeostenosis, chronic inflammation, and malnutrition following definitions from the guidelines (Table 5) and from the original studies. We defined homeostenosis as homeostatic dysregulation.201 We categorized generally similar cutoffs of anthropometric and diagnostic tests as well as biomarkers into the same groups. For example, we categorized the studies with increased C-reactive protein (CRP) levels of >2.8mg/L,174 >3 mg/L,87,177 or in the highest quartile124,202 into one group of chronic inflammation.
Table 5
Definitions of Homeostatic Dysregulation in Older Persons, Modified From Kuchel.
We defined comorbidity and multimorbidity according to the National Institute on Aging Task Force on Comorbidity.203 Comorbidity was defined as co-occurrence of preexisting age-related health conditions or diseases in reference to an index disease. Multimorbidity was defined as the co-occurrence of two or more diseases or active health conditions (e.g., aggregate of coequals) that may or may not be linked by a causal relationship or with no consistent dominant index disorder. Both definitions ignore severity of the diseases or conditions and quality of health care managing the diseases. We analyzed previously validated composite comorbidity weighted indices that take into account the number and seriousness of comorbid diseases.204 We analyzed the prevalence of polypharmacy because it reflects comorbidity and treatment utilization.205,206 We analyzed poor self-perceived health in relation to mortality because it reflects morbidity and well-being.15,207 We focused on poor self-reported health because this category has been associated with health problems and physical functioning in older individuals.208
We used the framework proposed by the Interventions on Frailty Working Group to identify criteria of frailty in epidemiologic studies (Table 6).209 We categorized the definitions of frailty into two groups: phenotype and accumulation of deficits. When the studies accepted the biologic syndrome model of frailty, with five major criteria, including weight loss, fatigue and exhaustion, weakness, low physical activity and slowness, and mobility impairment, we categorized the estimates into phenotype definitions of frailty.23 When the studies accepted the burden model of frailty, including symptoms, diseases, conditions, and disability, we categorized the estimates into the accumulation of deficits definition of frailty.22
Table 6
Reported Components of Frailty Syndrome By the Interventions on Frailty Working Group.
We synthesized the evidence about disability using two operational definitions: measures of basic activities of daily living (BADLs) and instrumental activities of daily living (IADLs). We defined BADL disability as difficulty with or requiring help with any number of the following activities: dressing/hygiene, bathing, toileting, transferring, ambulating, feeding, and grooming. We defined IADL disability as having difficulty with or needing help with using the telephone, shopping, preparing meals, housekeeping, transportation, medication management, and financial management. Since each study used a different combination of items or scoring system to define BADL or ADL disability, a new set of categorical definitions was created to organize and compare the study findings on disability prevalence and incidence. The new categorical definitions represent a hierarchy of disability; for example, BADL disabilities are more life limiting than IADL disabilities. For categorical operational definitions of IADLs, the indicators “any,” “moderate,” or “severe” represent severity of a given type of IADL disability. For example, studies that used a cutoff score of one or more BADL present were labeled as “any BADL disability.” Moderate IADL disability was designated when the study indicated that one or two items were used to define disability. Severe disability was designated if three or more items were used to define disability. Some studies used a continuous measure of IADL disability, and these study results are reported separately from categorical definitions. A few studies used a measure that combined BADL and IADL disability, which are also reported separately. Table 7 summarizes how ADL disability definitions were recategorized.
Table 7
Summary of Basic and Instrumental ADL Disability Definitions.
Results from studies with the same operational definition of the geriatric syndrome were pooled to estimate prevalence and incidence.210 Meta-analysis was used to assess the consistency of the association between syndromes and outcomes with random effects models.200 Chi-square tests were used to assess consistency in study results.211,212 Significant heterogeneity means that estimates of prevalence and association were not consistent in the studies (not replicable results). We used Stata 10.1 software (StataCorp, College Station, TX) to calculate pooled prevalence and association estimates with random effects models. All calculations were conducted at a 95 percent confidence level.
We synthesized the evidence in the total samples and then in age, race, and gender categories when possible. We synthesized the evidence answering the research question about prevalence of all syndromes and then the association with morbidity, mortality, and health care utilization.
For key question 3, we used published criteria to appraise cost-effectiveness models213 and criteria from the British Medical Journal economic submissions checklist.214 We also estimated the number of deaths among 1,000 older persons with each syndrome using calculations based on the simulation algorithm.195 We calculated population attributable risk of mortality or institutionalization using prevalence and risk estimates from pooled analyses when available or from individual studies.215 We estimated remaining life expectancy for those with each syndrome from CDC United States Life Tables (available at http://www.cdc.gov/nchs/data/nvsr/nvsr56/nvsr56_09.pdf) and relative risks of all cause mortality in older populations with each syndrome. Life expectancy was estimated as the area under the survival curve. In such estimations we could not address the length of having syndromes, interaction with other syndromes, and health care interventions. We used the mortality rates of the general population, which also contains people at risk of the syndromes. When available in the studies, sex and race specific regression coefficients were applied.
Quality Assessment
We included original epidemiologic studies that employed strategies to reduce bias in observational research. We evaluated quality of individual studies and level of evidence using the following USPSTF criteria.216
- Do the studies have the appropriate research design to answer the key question(s)?
- To what extent are the existing studies of high quality (i.e., what is the internal validity)?
- To what extent are the results of the studies generalizable to the general U.S. primary care population and situation (i.e., what is the external validity)?
- How many studies have been conducted that address the key question(s)? How large are the studies (i.e., what is the precision of the evidence)?
- How consistent are the results across the studies?
- Are there additional factors that assist in drawing conclusions (e.g., presence or absence of dose-response effects, fit within a biologic model)?
We defined the nationally representative population based surveys and prospective cohort studies having the highest applicability.
Rating the Body of Evidence
We assessed study quality and strength of evidence using guidelines from the Agency for Healthcare Research and Quality.217 The strength of evidence was judged according to the domains of risk of bias, consistency, and precision for each major outcome.217 When appropriate, presence of confounders that would diminish an observed effect and strength of association were also included. We graded the quality of evidence as follows:
| Grade | Definition |
|---|---|
| High | High confidence that the evidence reflects the true effect. Further research is very unlikely to change confidence in the estimate of effect. |
| Moderate | Moderate confidence that the evidence reflects the true effect. Further research may change confidence in the estimate of effect and may change the estimate. |
| Low | Low confidence that the evidence reflects the true effect. Further research is likely to change confidence in the estimate of effect and is likely to change the estimate. |
| Insufficient | Evidence either is unavailable or does not permit a conclusion. |
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