Reducing the Impact of Dementia in America A Decadal Survey of the Behavioral and Social Sciences (2021) / Chapter Skim
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8 Strengthening Data Collection and Research Methodology
8 Strengthening Data Collection and Research Methodology
Pages 223-262
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Innovations and improvements in research methodology to address the challenges associated with quantitative dementia research could significantly increase the potential for research to reduce the negative impacts of dementia in the coming decade. This chapter reviews those challenges and then explores opportunities and challenges in four areas: data infrastructure, measurement, study design, and integration of evidence from varied sources to yield stronger conclusions (meta-research)
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Some of these difficulties arise in other fields as well, but in the context of dementia they have hindered progress in identifying effective preventive measures, managing disease, improving quality of life for people with dementia and their families, and even fully quantifying dementia's social impact. One issue in dementia research is that the analytic approaches and study designs applied have been predominantly observational, sometimes longitudinal -- designs that draw on covariate-controlled regression models.
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For example, research is constrained by the data sources available and the exposure and outcome measures that can be extracted from those sources. Novel data sources and linkages can both expand the measures available for dementia research and support the inclusion of populations FIGURE 8-1 Domains for enhancing research methodology.
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. Similarly, new data infrastructure can support novel study designs to improve identification, and more rigorous study designs to estimate biases and heterogeneity across populations will support stronger population effect estimates.
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Developing New Data Sources and Adding Items to Existing Sources New data sources with potential relevance to dementia are rapidly emerging. These include "found" data, such as digital health data, phone records, and social media, as well as increasingly well-documented administrative sources, such as comprehensive electronic health records, claims records for health service billing, and the Minimum Data Set reported by nursing homes (Nicholas et al., 2021)
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Dementia researchers also must grapple with identifying the most relevant data sources and needed data infrastructure. These determinations will be important for research investigating such emerging questions as how physical isolation affects people (e.g., whether electronic tools can partially or fully replace in-person interactions)
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In 2017, for example, the Panel Study of Income Dynamics added the AD8 dementia screen into the national household study. The National Longitudinal Study of Youth is adding cognitive measures that match those used in the Health and Retirement Study, and the National Longitudinal Study of Adolescent to Adult Health has incorporated various cognitive measures over the years and is adding more detailed neurocognitive assessments in planned future waves.
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An example of an innovative approach that can open up new research opportunities is linking sources 3 https://hrs.isr.umich.edu/data-products/restricted-data/available-products 4 In the basic sciences, this type of linkage is also creating powerful study designs, such as two-sample Mendelian randomization analyses (in which genetic variants established in one sample as influencing a phenotype of interest are used as instrumental variables to estimate the effect of that phenotype on an outcome in a second sample) or research on gene expression profiles (in which information on genetic variants is scored based on tissue-specific data about how each variant relates to RNA or protein levels)
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Credit histories may reveal cognitive risk years before a dementia diagnosis, with implications for research design, public policy, and clinical care. For example, linking data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel with Medicare outcomes data made it possible to identify this connection and quantify how early it was detectable.
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The lack of representation of many groups in dementia research has cascading effects that limit the relevance of scientific findings and in some cases may directly exacerbate health disparities that have adversely affected communities of color, individuals from disadvantaged socioeconomic backgrounds, and people living in certain geographic regions (see Chapters 2 and 5)
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Social and behavioral researchers will need to be fully engaged to challenge narrow conceptualizations of relevant risk factors and consider the implications of alternative measurement approaches. The challenge of measuring biologically meaningful and patient- and family-centered outcomes in dementia research is relevant to nearly every topic addressed in this report, but one example will illustrate the issues involved.
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Passive cognitive measures (discussed below) may be helpful with respect to equivalence across groups and would support the collection of data from large samples, thus facilitating rigorous study designs.
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Building out older datasets with risk information from earlier in the life course for individuals who are now older adults is another approach to help address the problem of measuring exposure (e.g., by following up old studies to incorporate late-life cognitive assessments)
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Defining outcomes related to quality of life for people living with dementia, their caregivers, and other loved ones is another key goal for dementia researchers. In some cases, imperfect measurement is inevitable, but it is possible to use statistical tools to quantify the measures and correct for bias.
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. Alternative methodologies applied to observational data, such as difference-in-difference, interrupted time series, regression discontinuity, or instrumental variables methods, are much less commonly used, possibly because these methods must be modified or adapted for dementia outcomes.
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Quasi-experimental methods (including instrumental variables methods, regression discontinuity, and others noted above) can be powerful complements to conventional observational analyses because they often depend on entirely different assumptions.
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. Creating Opportunities for Quasi-Experimental Discovery, Including Leveraging Instrumental Variables Analyses In many cases, major programs or interventions are rolled out with limited understanding of their likely impacts.
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Instrumental variables analyses applied to RCTs are typically uncontroversial but have not been widely adopted in health research (Glymour et al., 2017)
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For example, complex, high-touch, expensive intervention programs may be difficult to implement at a scale that will have substantial population health impact. Considerations of scalability should lead researchers -- even people working on theory-building interventions -- to focus efforts on particular types of risk factors and exposures amenable to scaling.
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Because there is currently no effective treatment for dementia and many individuals will survive for many years after diagnosis, interventions to improve quality of life are particularly important (Karlawish, 2021) .8 Like other interventions, those designed to improve quality of life require rigorous study designs that integrate qualitative and quantitative methods to improve quality and allow for adaptation of programs to accommodate context-specific constraints and modifications based on what is learned.
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Many such insurance plans already have discounted membership relationships with health clubs, which could be the perfect vehicle for implementing pragmatic trials designed to deliver exercise and other social support programs to older persons at risk of cognitive impairment. These kinds of efforts are likely to be become more common as the line between medical and nonmedical health services blurs, making it possible to evaluate rigorously the effectiveness of these kinds of services.
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Augmenting intent-to-treat analyses with analyses based on instrumental variables or identifying adherent subgroups based on prerandomization characteristics can yield additional relevant effect estimates. Thus, even though sample sizes will be very large, and the population of providers and patients will be much more representative than is the case in researcher-based trials, the challenges of implementing pragmatic trials can be considerable.
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Researchers will need training in these domains as well as expertise in the substantive problems in dementia research to take advantage of these methods. Formalizing Study Design Approaches A formal framework is useful for understanding how study design should guide the selection of acceptable exposure and outcome measures.
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Currently, researchers have a very ad hoc strategy for selecting study designs and are driven as much by the convenience of an approach or access to data as by scientific priority based on the available evidence and limitations of prior studies. Figure 8-2 illustrates these trade-offs.
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. Research triangulation is an intentional process of proposing new study designs that address the weaknesses of prior work, for example, by using negative controls, instrumental variables, or study populations with very likely different patterns of confounding.
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Meta-analyses are thus of limited utility in evidence integration, which focuses on combining results from varied study designs. Meta-analyses can be especially challenging in dementia research because the studies
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Most meta-analyses give limited attention to evaluating the biases implicit in various study designs and the extent to which different studies address those biases. Better tools are therefore needed for triangulating evidence and quantifying how findings from different study designs fit together.
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. In addition, Medicare beneficiaries would likely face approximately $11,500 in coinsurance costs, or approximately 40 percent of the median annual income of Medicare beneficiaries.
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. Simulations often depend on valid input assumptions from other study designs.
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Moreover, the lack of racial/ethnic and socioeconomic diversity in the health research workforce has been widely recognized as a major barrier to progress and is equally problematic in dementia research. Efforts to build researchers' capacity to design and run pragmatic trials, such as the IMbedded Pragmatic Alzheimer's disease and Alzheimer's disease-related dementias Clinical Trials Collaboratory (IMPACT)
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RESEARCH DIRECTIONS This is a moment of opportunity for investments in strengthening research methods for the study of dementia in four areas: data sources that support new, more inclusive, and more rigorous research; measurement of factors that influence both dementia risk and progression, of patientand family-centered outcomes related to dementia, and of outcomes that are appropriate for diverse populations and settings; study designs that offer greater rigor and relevance to diverse populations; and meta-research methods with which to integrate disparate sources of evidence and guide decision making. These opportunities are interrelated: better data sources will support the development of more rigorous study designs; innovations in measurement will make previously nonviable data sources potentially useful; and thinking about the weaknesses implicit in any single study will help researchers better integrate the available evidence to provide guidance for the development of systems, policies, and interventions to prevent dementia, improve prognosis and quality of life for people living with dementia, and ameliorate the adverse consequences of the disease for families and society.
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TABLE 8-1 Detailed Research Needs 1: Expansion of Data • Fostering of new applications for existing data sources, Infrastructure including those from beyond the health care context (e.g., by improving the extraction of relevant constructs from existing data sources) • Development of new tools for data linkage across health care system, payer, and community-based data systems, including standards for variables and protocols • Funding to support documentation, archiving, and sharing of existing datasets and follow-up of previously abandoned datasets, including RCTs that evaluated relevant treatments but did not originally include dementia-relevant outcomes • Creation of a "reference database" by linking multiple data sources that researchers can use for comparison against localized samples and selection of settings for new studies • Evaluation of biases in who is selected into research studies; how data are collected; and how measurements are made, including algorithmic bias • Increased focus and accountability related to inclusion in research participation and development of a testable scientific framework for conducting inclusive research 2: Improved • Evaluation of the correspondence between evolving Measurement biomarkers and clinical/functional outcomes of relevance to people living with dementia • Development of standardized definitions/tools for identifying dementia and capturing stages of disease • Development of reliable and valid early markers of subsequent disease -- potentially based on novel, nontraditional data sources -- to foster research on causes of disease • Research to improve measurement validity across disease stages, outcome domains, and assessment modes • Expanded inclusive measurement strategies to yield valid measures for diverse populations, including heterogeneous cultural settings, racial/ethnic identities, gender/sexual minority groups, and linguistic backgrounds • Tools to support valid crosswalks between assessments from different instruments or modalities • Fostering of exploratory research to evaluate novel exposures that may influence disease risk across the life course • Development of improved measures of consent to participate in research
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5: Improved Inclusion and • Bolstering of training efforts to accommodate changing Representation Among standards of living, continue to foster diversity and Research Participants and representation in the research workforce, and emphasize the Researchers interdisciplinary skills noted elsewhere
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. Quasi-ex perimental study designs series -- Paper 1: Introduction: Two historical lineages.
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. Study designs for extending causal inferences from a random ized trial to a target population.
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. You say tomato, I say radish: Can brief cognitive assessments in the US Health Retirement Study be harmo nized with its International Partner Studies?
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. Embedded pragmatic trials in dementia care: Realizing the vision of the NIA IMPACT Collaboratory. Journal of the American Geriatrics Society, 68(Suppl 2)
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. Financial presentation of Alzheimer disease and related dementias.
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. Target validity and the hierarchy of study designs.
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. The impact of changes in population health and mortality on future prevalence of Alzheimer's disease and other dementias in the United States.
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