A volume devoted to the demography of aging does well to recognize the importance of innovative data collection and data sharing. The field has been a leader in developing new designs and measurement approaches, and these studies in turn have shaped and pushed the research frontier for the study of aging. The strong commitment to data sharing with open access to the data for the scientific community—and the resulting high volume of research—has begun to influence other related fields. The National Institute on Aging (NIA), and in particular its branch of Behavioral and Social Research, has been the primary force behind this empirical infrastructure, providing funding, scientific leadership, and encouraging collaboration across the studies it supports and beyond.
The demography of aging is fundamentally about the changes people and societies experience as they age. Thus, virtually all important aging studies are longitudinal, to observe changes with age over time. Design choices arise about which part of that aging process to include directly or to query indirectly. Moreover, individuals are situated in couples, households, families, neighborhoods, schools, workplaces, social networks, and so on. A fully comprehensive study at all levels is infeasible, so studies must choose. The NIA has been successful both at creating studies in pursuit of
1 University of Michigan.
2 University of Chicago.
3 University of Texas Medical Branch.
desired goals and at leveraging studies created for other purposes to obtain unique design features.
We begin this chapter with a review of several of the more significant studies with unique design features, both within and outside the United States. The collection of studies showcases how the incorporation of new technologies has greatly improved the measurement of traditional concepts and has provided an expanded toolkit for demographic research. The next section presents an annotated summary of current developments in measurement and design, grouped in large categories of demographic and psychosocial measures, biological and functional measures of health, including cognitive function measures, and other important developments in genetics, administrative and medical record linkages, and mortality ascertainment. After a brief description of data sharing practices, the final section sheds light on what data collection scholars foresee as measurements and designs for the future of the demography of aging, while pointing out possible challenges and ethical issues derived from the vast reach of new data collection technologies that have recently emerged.
STUDY DESIGNS: ADVANTAGES AND CHALLENGES
This section provides a summary of several of the more significant studies, highlighting their unique design features. We begin with three studies, initially conceived for other purposes, that have become important to the study of aging and in particular the influence of early-life conditions on later-life outcomes, before turning to studies designed expressly for aging research (see Table 13-1).
The Wisconsin Longitudinal Study (WLS) is based on a 1-in-3 sample of high school graduates in the state of Wisconsin in 1957, subsequently augmented with a sample of siblings (Herd et al., 2014). Its design is unique
TABLE 13-1 Currently Active U.S.-based Demography of Aging Projects
|1957||Wisconsin Longitudinal Study (WLS)|
|1969||Panel Study of Income Dynamics (PSID)|
|1992||Health and Retirement Study (HRS)|
|1995||Midlife in the United States (MIDUS)|
|2005||National Social Life, Health and Aging Project (NSHAP)|
|2011||National Health and Aging Trends Study (NHATS)|
|2017||Add Health Parents Study|
in the long-term follow-up and direct observation of late adolescence, including cognitive ability, in a sample now in their 70s. The study has been innovative in the use of biomarkers, including genetic data and, more recently, samples of the microbiome (Hauser and Weir, 2010; Herd et al., 2014). While not national in scope, it captures most of the variation in the non-Hispanic White population of its cohort.
Project Talent is a large, nationally representative study of high school students in 1960 designed by John Flanagan of the American Institutes for Research. A two-day battery of cognitive and personality tests was conducted with 377,000 students in 1,200 schools. The sample had only very limited follow-up until recently. A project aimed at studying twin and sibling pairs is under way (Prescott et al., 2013).
The Panel Study of Income Dynamics (PSID) is the world’s longest running national panel study. Begun in 1968 as a nationally representative sample of 5,000 families to study the war on poverty, PSID has expanded its content on health and demographic topics over the past 50 years (McGonagle et al., 2012). With its long-panel design, PSID is well situated to study factors over the life course that influence later-life health and mortality. In addition, because adult children are invited to join the study when they form their own households, the PSID supports studies of health and well-being and transfers across generations. The study’s main funders are the National Science Foundation, the NIA, and the Eunice Kennedy Shriver National Institute of Child Health and Development (NICHD).
Midlife in the United States (MIDUS) began in 1995 with funding from the MacArthur Foundation. Its subsequently became a program project and then a cooperative agreement with the NIA. Its sample is derived from a variety of sources, including some sibling and twin pairs (Brim et al., 2004). It combines extensive psychosocial measurement with biomarkers collected in overnight clinic visits (Dienberg Love et al., 2010).
The National Social Life, Health, and Aging Project (NSHAP) began as an NIA-funded project focused on the social relationships of persons 57 and older, including sexual relationships (Suzman, 2009). Its design was initially individual-based but subsequent waves brought spouses and partners into the study. Social relations are queried through innovative survey methods (Cornwell et al., 2009). It has also been innovative in pursuing biomarker acquisition and prescription drug information during in-home interviews.
The National Health and Aging Trends Study (NHATS) was funded by the NIA as a platform for understanding both population-level trends in late-life disability and individual-level dynamics (Kasper and Freedman, 2014). The NHATS sample is drawn from Medicare records and refreshed periodically. Its centerpiece is a validated disability protocol (administered in person annually) that has reengineered traditional measures to capture changes in how activities are carried out by older adults in their daily lives
(Freedman et al., 2011). Detailed information is collected about residential care settings in which older adults live and assistance received. Unpaid and family caregivers are also interviewed periodically as part of the National Study of Caregiving. NHATS also permits analysis of long-term disability trends when used in conjunction with its predecessor survey, the National Long-Term Care Survey (Manton, et al., 1993).
The National Longitudinal Study of Adolescent to Adult Health (Add Health) began in 1994 with funding from the NICHD to study children in grades 7–12, with some information about their parents and families of origin. Rich in biomarker measurement, the study has followed its cohort periodically into middle age (Harris, 2013). Beginning in 2015, the NIA began supporting a study of the parents of Add Health participants, using a reciprocal design that enables studies of parent-child relationships as the parents age.
The Health and Retirement Study (HRS) was created by the NIA explicitly to provide a foundation for research on aging (Juster and Suzman, 1995). It has expanded from a single cohort in 1992 to represent the entire population 51 years and older, with oversamples of minorities and a steady-state sampling design to refresh the study with new cohorts to maintain its age coverage as the cohorts age (Sonnega et al., 2014). It also moved from primarily a telephone survey to a multimode study with biomarkers, administrative linkages, and other enhancements (Weir, 2008). It includes both of the members in coupled households, enabling studies of marital relationships as couples age. Respondents provide information about exchanges with parents and children, but those other family members are not included directly. Administrative linkages to Social Security and Medicare provide information about earnings, employment, health, and health care utilization.
The success of the HRS design has encouraged the development of harmonized sister studies now encompassing more than half of the world’s population (Table 13-2). The first was the Mexican Health and Aging Study (Wong et al., 2017), followed by the English Longitudinal Study of Ageing (ELSA; Steptoe et al., 2013), and the multicountry Survey of Health, Ageing and Retirement in Europe (SHARE, Börsch-Supan et al., 2013). A number of studies then developed in Asia (National Research Council, 2012), including the Korean Longitudinal Study of Aging (Park et al., 2007), the adaptation of the ongoing Indonesia Family Life Survey to the HRS model, the Japanese Study of Aging and Retirement, the China Health and Retirement Longitudinal Study (Zhao et al., 2014), and the Longitudinal Aging Study for India (Arokiasamy et al., 2012). The Study of Global Ageing and Adult Health (Kowal et al., 2012) includes Asian and African countries, while South Africa is the location of the Health and Aging in Africa study.
Other recent additions include the Irish Longitudinal Study of Aging (Kearney et al., 2011) in the Republic of Ireland, the Northern Ireland
TABLE 13-2 International Data Projects on the HRS Model
|1993||Indonesia Family Life Survey (IFLS)|
|2001||The Mexican Health and Aging Study (MHAS)|
|2002||The English Longitudinal Study of Ageing (ELSA)|
|2004||The Survey of Health and Retirement in Europe (SHARE)|
|2006||Korean Longitudinal Study of Aging (KLoSA)|
|2006||Study of Global Ageing and Adult Health (SAGE)|
|2007||Japanese Study of Aging and Retirement (JSTAR)|
|2010||The Irish Longitudinal Study of Aging (TILDA)|
|2011||China Health and Retirement Longitudinal Study (CHARLS)|
|2015||Health and Aging in Africa (HAALSI)|
|2016||Northern Ireland Cohort for Longitudinal Study of Aging (NICOLA)|
|2016||Brazilian Longitudinal Study of Aging and Well-Being (ELSI)|
|2017||Longitudinal Aging Study in India (LASI)|
Cohort for Longitudinal Study of Aging, and the Brazilian Longitudinal Study of Aging and Well-Being (Lima-Costa et al., 2018). Both Irish studies include clinic-based health assessments as part of the core study (Cronin et al., 2013). Another longitudinal aging study with rich clinical health data but less harmonization to the HRS model is the Canadian Longitudinal Study of Aging (Raina et al., 2009). NIA funding has been important to many of the HRS sister studies, particularly for early development, although on average the great majority of funding is from other sources. The encouragement and support to the collaborative network has been essential, including support for data harmonization for analysts through the University of Southern California’s Gateway to Global Aging Data.4
The ability to collect population-based measures in aging research has accelerated as mentioned, but the pace has varied widely across the globe, in particular in the poorer developing nations. Longitudinal studies with large national cohorts of older adults are just beginning to proliferate in middle- and low-income countries. The approach is costly and requires political and social commitment to data collection that can be difficult to achieve, especially in societies with scarce resources. Often there may be long time gaps between the study follow-up waves, making the data collection efforts more challenging as study subjects move away or die.
In the case of the Mexican Health and Aging Study, two waves were collected in 2001 and 2003, with a long hiatus thereafter. Nine years later, wave 3 was collected with surprisingly high follow-up response rates. This success was achieved by the fieldwork operations personnel implementing a unique strategy. Approximately 3–4 months prior to the home visits by interviewers, a preliminary “sweep” or visit was conducted to every household in which a follow-up person resided. This preliminary contact resulted in either (a) recontact at the original address, (b) obtaining a new address and recontact at the new address, or (c) notice of death and location of a possible next-of-kin for the final interview. Then, interviewers were sent out to collect their follow-up interviews. After the 9-year gap from the second wave, a successful 88 percent recontact rate was accomplished. Similar strategies can be implemented in other countries, depending on the infrastructure and budget available.
CURRENT MEASUREMENT DEVELOPMENTS
Demographic and Psychosocial Measures
People are connected to others in a variety of ways, from kin relationships to socializing to exchanges. Social networks are created by webs of connections among groups of people, such that the social network of an individual includes that person’s connections to others and the connections of those other people to each other (Cornwell et al., 2009). There are many ways to define social networks and many ways to measure them. NSHAP pioneered collection of social network data in older adults by focusing on their discussion networks: the people with whom they talk about things that are important to them. The respondent names these people, called “alters,” and the relationship of each of them to the respondent (referred to as “ego” in social network research) is ascertained. How are they related? Are they kin? How old are they? Do they live with ego? And does ego talk to them about health? Then the respondent is asked in detail about the connection, if any, between each of the pairs of alters named. Do they know each other? Are they related? How often are they in contact? How close is their relationship? This innovation allows researchers to look closely at the links between all of those in the network, including flows of information and affection (Cornwell et al., 2009).
But social networks are not cast in stone; they change as the situations of the people in them change. The second wave of NSHAP obtained the social network as described 5 years after the first time the social network was measured for respondents. The Wave 2 social network module asked specifically
about losses and additions to the network and reasons for them (Cornwell and Laumann, 2013). Network loss over 5 years has been found to be greater for older Blacks and those of low socioeconomic status (Cornwell, 2015). The study of social networks is poised to benefit from recent leaps in social connectivity and the technology that supports it. Facebook and other social media platforms both change people’s networks and make them easier to trace. New methods and software are being developed for analyzing these types of data, especially changes in egocentric networks over time. It is critical to distinguish between important contacts on these platforms and those that are casual or fleeting, but this can be difficult to do.
Sexuality is an important component of both individual health and social functioning throughout the life course. A report of the U.S. Surgeon General points to sexuality as essential to well-being, with calls to attend to sexual health (Office of the Surgeon General, 2001). But serious research consideration of sexual behavior and attitudes, especially among older adults, is relatively recent. From the perspective of the demography of aging, sexuality can be conceptualized as a component of well-being, as a social indicator, and as a predictor or consequence of other dimensions of health (Galinsky and Waite, 2014; Waite et al., 2009). Because of the increasing recognition by researchers in the demography of aging of the importance of understanding sexuality, detailed measures of sexual behavior, attitudes, beliefs, functioning, and well-being have been included recently in important national surveys of health, including ELSA and NSHAP, and new measures are appearing in other health surveys, including the National Health and Nutrition Examination Survey (Laumann et al., 2008). The inclusion of both partners in some longitudinal surveys of older adults, together with questions on sexuality asked of each respondent individually, have allowed researchers to study the contribution of each partner to the sexuality of the dyad (Galinsky and Waite, 2014; Kim and Waite, 2014).
The study of sexuality at older ages encompasses multiple dimensions, typically assessed through self-report. These include sexual desire or interest, sexual activity or behavior, sexual functioning, and sexual health (Lee et al., 2016). Especially at older ages, it is important to define sexual activity with a partner quite broadly, as the activities that couples engage in shift away from vaginal intercourse toward touching, cuddling, and kissing (Waite et al., 2009), and sexual inactivity among those with a partner increases with age (Lindau et al., 2007).
Future directions in the research of sexuality among older adults might include in-depth study of health at older ages among lesbian, gay, bisexual, and transgender individuals. New cohorts moving into older ages have
very different sexual and relationship histories than earlier cohorts, with implications for their sexuality, partnership, and health at older ages. These younger cohorts were also exposed to different medical practices, including vaccines for human papilloma virus, treatments for HIV/AIDS, and medications to improve sexual function. At the same time, instability in relationships, the obesity epidemic and concomitant rise in the prevalence of diabetes, and increases in death rates for middle-aged men (Case and Deaton, 2015) all make the landscape of sexuality at older ages different than it was several decades ago.
Time Use and Experienced Well-Being
At all stages of the life course, time is arguably among the most fundamental of human currencies. Either time can be spent for some immediate purpose (work, household tasks, family care, leisure) or it can be invested for some later benefit (e.g., learning or physical activity to improve health). Participation in valued activities constitutes an important domain in the disablement process (National Research Council, 2009; also see the chapter by V. Freedman on late-life disability in this volume). Time use influences demographic processes such as marriage, fertility, and survival, and it is in turn influenced by demographic and health processes.
How individuals allocate time to the activities and experiences of daily life also shapes enjoyment and other emotions experienced through the day. Conceptually, this notion of “experienced well-being” is distinct from evaluations of life as a whole (National Research Council, 2013). Evaluations of well-being, such as reports of life satisfaction, typically require an individual to appraise their situation, often relative to an unspecified standard. Evaluative well-being often is thought to be more strongly related to more enduring aspects of quality of life such as one’s health, job, partner, family, and so on. By contrast, experienced well-being reflects how one is feeling in the moment and is therefore more responsive to immediate circumstances.
As with many of the measurement innovations in aging studies, time use had been traditionally measured in separate surveys before ongoing national panel studies such as HRS, PSID, NHATS, and the National Study of Caregiving supplement to NHATS, among others, began to incorporate time-use measures and in some cases associated measures of experienced well-being. The three most common forms of time assessment are experiential sampling methods (Csikszentmihalyi and Larson, 1987), time diary methods (Juster and Stafford, 1991), and stylized reports (Juster and Stafford, 1985). The three approaches differ in the type of data they produce, in their cognitive demands, and in accuracy (Juster et al., 2003).
Experiential sampling methods typically involve contacting respondents randomly multiple times per day. They have the highest accuracy of the
three approaches because reports are provided in real time. In time-diary measures, respondents are asked a chronology of events, typically for the previous day, with follow-up descriptors similar to those used in experiential sampling. Diaries have good accuracy for respondents at older ages but can take 20–25 minutes to administer with descriptors (Freedman et al., 2013). Moreover, there is variability across days in how individuals spend their time (Kalton, 1985), making longitudinal comparison more difficult. In stylized reports, a respondent (or informant) aggregates time use over a specified reference period (e.g., a week or month) and reports typical amounts of time devoted to a particular class of activities, such as working for pay or housework (Juster and Stafford, 1985; Juster et al., 2003). The stylized reports are brief, have good reliability, and can be reported by knowledgeable proxies.
The time-use measurement methods also differ in their ability to incorporate measures of experienced well-being. Experiential sampling and time diaries, but not stylized time-use questions, can be used to reinstantiate memories of the day prior to asking about emotional experiences. Use of time diaries with follow-up questions that ask about emotion is also referred to as the Day Reconstruction Method (Kahneman et al., 2004). Briefer instruments that review activities the day before the interview in less detail before asking about emotions have also been developed, and preliminary evidence suggests that the measurement properties for the resulting experienced well-being measures are similar to the full Day Reconstruction Method (Lucas et al., 2018).
Biological and Functional Measures of Health
Biomeasures are objective measures of health, reflecting aspects of physiology that may not be known to a survey respondent. They provide information on biological aging prior to the development of age-related disease and physical dysfunction, as well as the progression of disease processes and functioning of multiple organ systems in the aging population. Although some aging studies do incorporate clinic visits, most surveys use biomeasures, obtained by trained interviewers in the home, that have been modified from and validated against clinical measures (O’Doherty et al., 2014). A review by McClintock et al. (2016) contains an extensive list, by dimension of health, of survey items and biomeasures, together with specification of their coding and validation against other national data.
Anthropometric measures generally include height, weight, waist circumference, and sometimes hip circumference, which is correlated with waist circumference. Height and weight can be measured objectively or by respondent report. While self-reports are generally accurate, small biases among older persons toward overstating height and understating
weight can lead to significant shifts in the distribution of BMI categories (Weir, 2008).
Blood pressure (systolic and diastolic), pulse, and heart rate variability are basic health indicators obtained in virtually every medical encounter. They are also safe and easy for field interviewers to collect using automated cuff machines with digital readouts. This makes them standard for virtually any biomeasure panel in a survey that claims to measure health.
Physical Function and Performance
Physical functioning is the hallmark of successful aging: someone who cannot walk across a room, whose balance is poor, who is weak and tired a good deal of the time cannot be considered to be in good health. Many studies now routinely include both self-reported and performance-based measures of physical function, and international comparisons are now possible (Payne et al., 2017; Capistrant et al., 2014). Because such measures have been included repeatedly, researchers have been able to examine trajectories (Pettee et al., 2017). Researchers have also studied the consequences of poor physical functioning for subsequent disability onset, trajectories, and well-being (Martin et al., 2017; Freedman et al., 2017). Details on advances in conceptualization and measurement of physical function and its role in the broader disablement process are provided in the chapter by V. Freedman in this volume.
Many studies, including NSHAP, NHATS, and HRS, collect all or part of the Short Physical Performance Battery, which includes measures of mobility, balance, and lower body strength (Guralnik et al., 1994). Measures of physical functioning in surveys have often been designed to allow for the assessment of frailty, a condition rather than a disease, which signals increased vulnerability to dysregulation of organ systems, leading to hospitalization, disability, and death. Frailty is typically measured in surveys as an accumulation of health deficits such as poor balance, weakness or exhaustion, unintentional weight loss, slow walking speed, poor grip strength, low physical activity, limited lung capacity, and poor leg strength (Fried et al., 2001; Huisingh-Scheetz et al., 2014). Some frailty indexes include more and different measures, but evidence to date suggests that the association of these various indexes with poor health outcomes is robust to the differences in frailty (Searle et al., 2008). Measures of physical performance are key to measuring frailty and have predictive value on their own. Those most often used in surveys include timed walk, chair stands, balance, grip strength, and most recently, physical activity (Huisingh-Scheetz et al.,
2014). These can be combined with self-reported exhaustion, unintentional weight loss, falls, activities of daily living, instrumental activities of daily living (IADLs), depressive symptoms, and other survey measures to create a variety of frailty indexes (Searle et al., 2008).
The development and popularity of activity tracking devices, which are worn on the wrist, ankle, or hip and are often used by individuals to track their daily exercise, have made it possible to measure characteristics of sleep and daytime activity for survey respondents (Lauderdale et al., 2014). These actigraph devices measure and record movement using an accelerometer, which enables the identification of periods of sleep, awakenings during periods of sleep, sleep latency (measured as time to fall asleep), and other characteristics of sleep (Lauderdale et al., 2014). The actigraph measures of sleep can be used in conjunction with self-reports of sleep and sleep problems. Comparison of self-reported sleep with that measured by actigraph suggests fairly low correspondence between the two (Chen et al., 2015). Recent research has used actigraph measures of activity during nonsleep periods to identify sedentary behavior, a characteristic of frail older adults, as well as periods of moderate and vigorous activity (Huisingh-Scheetz et al., 2017).
A more recent development is the direct measurement of sensory function, which is increasingly recognized as essential to physical and social functioning and as a possible early indicator of neurodegeneration (Correia et al., 2016; Welge-Lüssen, 2009). Deficits in sensory function become common with age and predict disability, cognitive decline (Adams et al., 2017), and mortality (Genther et al., 2014).
The first nationally representative survey to include measures of the five classical senses—vision, taste, touch, smell, and hearing—was NSHAP, which measured all but hearing (assessed by self-report) (McClintock et al., 2016). Hearing has now been successfully measured in ELSA and in HRS.
Advances in field methods for use in countries that are less developed have been translated successfully to social surveys (Williams and McDade, 2009). The most prominent of these is dried blood spots, which can be collected by field interviewers; dried, stored, and mailed without much difficulty; then assayed for various blood-borne markers of biological func-
tion. New assays of dried blood spots continue to come into use, but the most common include hemoglobin, hemoglobin A1c, C-reactive protein, and Epstein-Barr virus, which are markers of blood iron, glucose metabolism, systemic inflammation, and immune function, respectively. Recently, cholesterol and high-density lipoprotein (HDL) have been assayed from dried bloods spots, and genotyping has been successfully accomplished. One substantial drawback of biomarkers collected through dried blood spots is the cost of the assays, which can run to $25 per assay per person, which adds up quickly given the number of markers to be assayed and the number of respondents. Another drawback is the variability across laboratories and between venous blood and dried blood assays of identical samples (Crimmins et al., 2014). Wave 2 of NSHAP collected a microtainer of blood at the same time as the dried blood spots, which permitted assay of a much wider range of biomeasures (O’Doherty et al., 2014). Outside the United States, in developing countries, intravenous blood collection is also common, with a phlebotomist or equivalent personnel conducting the sample collection at home. The experience is positive in general, as health personnel tend to be well received in home visits, which tends to raise the response rates in household surveys on aging (see, for example, Arokiasamy et al., 2012, for India; Zhao et al., 2014, for China; Wong et al., 2017, for Mexico).
Easy to collect, with high rates of completion by respondents, saliva samples allow endocrine and cortisol assessment (Kozloski et al., 2014). HRS and WLS have established their genetic repositories based on saliva samples collected via Oragene kits. Such kits stabilize DNA but thereby make samples unusable for other analytes.
Cognitive function is among the most important abilities to measure in studies of aging—and the most challenging. It varies across individuals from at least four major sources of variation: innate ability; the impact of formal education and life experience; decline due to “normal” aging; and pathological changes in the brain due to Alzheimer’s disease, vascular disease, or other dementing conditions. Some would add plasticity to this list: the ability to recover brain function.
Cognition is multidimensional, and age-related changes affect different domains of function differently in different people. In aging studies, the focus has often been on the emergence of dementia because of the magnitude of its impact on individuals, families, and society. Recent estimates
suggest nearly half of 65-year-olds will develop dementia in their lifetime, with the other half dying younger due to other conditions. The disease costs about $200 billion per year, about half of which is for paid formal health care and half is the estimated burden on families for unpaid care (Hurd et al., 2013). But dementia is not the only reason to measure cognitive function. Early stages of decline, long before dementia would be diagnosed, can impair judgment and lead to problems managing finances or health care.
The measurement of cognitive abilities comes from two very different scientific disciplines: the clinical neurological approach, which seeks to identify organic brain disease in vivo through performance testing and other observations, and the psychometric approach, which sees cognitive ability measurement as an extension of intelligence measurement. Surveys have tended to draw more on the former, reflecting both the importance attached to dementia and the development of short scales for clinical use.
One of the earliest studies to include a measure of cognition was the National Long Term Care Survey, which used the Pfeiffer short portable mental status questionnaire (Pfeiffer, 1975) and later the Mini-Mental State Examination (MMSE; Folstein et al., 1975). The MMSE has been widely used, but aggressive copyright practices in recent years have made it less appealing to large surveys. The HRS faced a particular challenge in that its design called for a mix of telephone and in-person surveys, requiring it to identify measures that could be administered comparably in both modes. The basis for the HRS measures is the Telephone Interview for Cognitive Status (TICS; Brandt et al., 1988), augmented by a version of the CERAD5 immediate and delayed word recall tasks.
Most of the longitudinal studies of aging are harmonized with the HRS. The international family of HRS studies has adapted the HRS measures in a variety of ways that, with caution, allow integrated analysis (e.g., Rohwedder and Willis, 2010). WLS is unique in having good measures of cognitive ability in high school paired with harmonized measures at older ages. MIDUS relies primarily on the Brief Test of Adult Cognition by Telephone (Tun and Lachman, 2006), which has similar measures of orientation, word recall, category fluency, and fluid intelligence, along with a measure of processing speed. NHATS uses TICS and word recall measures, with an additional test of executive function that requires in-person administration. NSHAP is the least harmonized of the major NIA-supported studies, relying most on the Montreal Cognitive Assessment, a public-use screening tool for mild cognitive impairment (Nasreddine et al., 2005). In addition to the direct measurement of cognitive abilities, most studies assess difficulty with IADLs, which are tasks affected by cognitive decline.
5 The acronym “CERAD” derives from the Consortium to Establish a Registry for Alzheimer’s Disease.
Another critical issue for longitudinal studies of aging is how to maintain participation of persons experiencing cognitive decline. These studies are cognitively demanding. HRS and a number of other studies allow for interviews with proxy respondents when a participant is unwilling. This eliminates what would otherwise be a substantially lower participation rate by the cognitively impaired (Weir et al., 2011). However, it poses a different challenge for the measurement of cognitive status and comparability with direct assessment. HRS relies mainly on proxy-reported IADL difficulties and the IQCODE6 instrument designed for informant reporting of cognitive status (Jorm, 1994). NHATS uses the AD8, an informant dementia screen that asks about changes in behaviors (Galvin et al., 2005). PSID recently added the AD8 to identify families with an older adult with memory-related behavior issues. Various approaches have been used to combine proxy reports with direct assessments (Crimmins et al., 2011).
Large studies representative of national populations can also serve as sampling frames for more in-depth studies of cognition. HRS did this with its substudy, the Aging, Demographics, and Memory Study (ADAMS; Langa et al., 2005; Plassman et al., 2007). More recently, HRS in conjunction with most of its international sister studies developed a new assessment termed the Harmonized Cognitive Assessment Protocol, a flexible structure harmonized to ADAMS and other major U.S. studies but also to the assessment used in developing countries in the 10/66 studies (Prince et al., 2011) and adaptable to many different national populations.
Other Important Developments in Measurement and Study Design
An important area of recent development is genetics, fueled by the improving technology and falling cost of generating statistical data from DNA samples (genotyping). The social sciences were somewhat slow to respond to these opportunities, due in part to the bad reputation of the so-called “eugenics” movement of the early 20th century, which linked most genetic study to the study of racial differences (and the justification of differential treatment and outcomes). Modern human genetics mostly avoids examining differences between races and is highly concerned with eliminating the influence of ethnic (ancestral) differences (population stratification) from inferences about genetic influences (Pritchard and Rosenberg, 1999). Eugenics and related strands of scientific racism derive from a particularly naïve deterministic view about the role of genes. Both biomedical and
behavioral scientists have at times been lured by the prospect of single-gene or other simple determinants of complex outcomes; they have been inevitably disappointed by evidence to the contrary. In fact, complex organisms, and human beings in particular, are highly adaptable in the course of an individual life to respond to opportunities and challenges in their environments, including social environments. DNA plays a part in this as well, with some genes regulating the expression of other genes (Boyle et al., 2017). As a result, the genetic associations with outcomes of interest are complex and extremely difficult to infer from even relatively large samples. From stale debates over “nature versus nurture,” the field has moved toward increasingly sophisticated models of gene-environment interactions.
The large aging studies have an extremely valuable role to play in the development of good genetic research. Large samples are important, as are good observations on outcomes and on environments that might modify outcomes or the relationship between genetics and outcomes (Boardman et al., 2013). ELSA began providing data on candidate genes: a small number of polymorphisms identified in prior research as related to outcomes of interests. In part because early findings often proved spurious in replication and in part because of the complex interactions created by the regulation of gene expression, the field moved toward genome-wide approaches covering millions of polymorphisms (e.g., genome-wide association studies). HRS was the first of the large aging studies to build a large database of genomic data, using saliva samples and a dedicated consent that those samples would be used for genetic research (Weir, 2008). ELSA and WLS followed soon after, using harmonized technology to maximize the potential for joint work. Most studies collect DNA through either saliva samples or blood draws, although dried blood spots have also been used.
A particularly promising approach for bringing genomic science to bear on social and health questions is the use of polygenic scores (Belsky et al., 2013). These involve applying genetic relationships estimated in large, pooled studies to the genetic data on individuals to produce a quantitative estimate of relative risk for an outcome of interest. As these scores depend on the state of scientific knowledge about genetic relationships, they require frequent updating. HRS, ELSA, and WLS have worked together to construct similar polygenic scores. Such scores are particularly useful for studying the interactions of genes and environments because they greatly reduce the dimensionality of the genetic side of the interactions.
Surveys rely on participants to report on the entire range of objective conditions and subjective perceptions. Aging surveys often also seek retrospective information on life experiences before recruitment. Administrative records
can provide valuable supplementary information to augment the scope of survey coverage, improve the accuracy of information, and reduce the burden on respondents and interviewers. Linkages to administrative records can either be direct linkage at the individual level, which generally requires consent of the individual, or indirect linkage through geographic location or some other characteristic. Indirect linkages through geographic areas are a valuable source of information on the social and physical environments in which people live. Both types have implications for the confidentiality of participants, and these implications require careful attention to data security throughout the process of consent, linkage, and data distribution.
For studies of aging and health, the records of the Medicare program are a rich source of individual information on health care use, health conditions and events, and cost. The Centers for Medicare & Medicaid Services (CMS) has an extensive system for managing the research uses of its data. The NIA has supported the development of a private third-party organization to facilitate linkage of data from its surveys to Medicare data. HRS has obtained high rates of consent for linkage to Medicare and high research use of the linked data. The NHATS sample is generated from a Medicare list, greatly facilitating linkage. PSID has also linked to Medicare records. Valuable as they are, Medicare records are not a gold standard that should always override self-reported information (Sakshaug et al., 2014; Wolinsky et al., 2014; St. Clair et al., 2017). A further significant limitation of Medicare records is that managed care systems, which currently cover 20 percent or more of Medicare beneficiaries, do not report individual information comparable to that generated by fee-for-service billing. At present there is also no comparable linkage resource for the population under age 65, and no standardized system of electronic medical records that would permit the study of health and not just health care billing.
The other major federal record system for older populations is the Social Security Administration (SSA). Unlike the CMS, the SSA is not configured for general support of outside research linkages and must determine a specific value and purpose to justify record sharing. This has been done for HRS. The linkage provides information on employment and earnings over the full life course, as well as applications for, and receipt of benefits for, retirement, disability, and supplemental security income. Consent for linkage to SSA is more variable than that for Medicare (Sakshaug et al., 2012). Repeated requests for consent are valuable in improving the overall linkage rate.
Mortality ascertainment is critically important to longitudinal studies of aging. Mortality is itself a crucial outcome to assess for evaluating
determinants of health and disparities in health. Moreover, failure to accurately capture deaths makes it difficult to properly define the surviving study population at risk for any outcome, including mortality, leading to potentially biased findings. Longitudinal studies have two primary sources for ascertaining vital status: the study’s own efforts to recontact respondents (tracking) and linkage or search of vital registration records. The latter, when successful at reasonable cost, can extend the life of a study past its active interviewing phase to follow survival and study mortality differentials long after, as has been done for several studies managed by the National Center for Health Statistics (NCHS)(Office of Analysis and Epidemiology, 2017). The NCHS also manages the National Death Index (NDI), the only national source of mortality registration. NDI combines death certificate information on dates and causes of death obtained from state vital registration offices, and regulates research access to them.
Weir (2016) evaluated mortality ascertainment in HRS. Mortality rates were statistically identical to national life table rates, for periods and for cohorts. The only exception was the early years of the oldest-old cohorts, who were initially sampled from the community-dwelling population, excluding the higher-mortality nursing home residents. Study tracking and NDI linkage agreed in almost all cases. For continuing panel members, study tracking found slightly more deaths missing in the linkage than vice versa. For study dropouts, linkage is the only source of vital status information after the last contact. Thus, the higher the rate of study attrition, the more important linkage becomes to maintain complete ascertainment.
Outside the United States, opportunities for linkage vary widely. In the United Kingdom, ELSA is linked to the National Health Service Central Register of mortality records. China and Mexico do not have national registration systems that would support a linkage. Europe has a diverse array of record systems and legal restrictions. To date, only Denmark has created a linkage to SHARE participants. Thus, for many studies, imputation of missing vital status will be important. Simple reliance on those with known vital status to do imputation implies assumptions about missing-at-random data that are almost certain to be violated, even with extensive covariate controls. Conversely, using national life table rates as constraints on the overall imputation could add valuable information.
The commitment to measurement innovation in the demography of aging has been matched by the commitment to public sharing of data. This runs throughout the range of studies, from HRS, which was designed for the express purpose of creating data for the research community, through to MIDUS and NSHAP, which have had extensive analytic aims as part
of their core. Many studies manage their own web-based distribution systems while some use data archives, such as that managed by the Inter-University Consortium for Political and Social Research. All studies must strictly protect respondent confidentiality, which typically involves multiple levels of data release from public access up to restricted access. HRS has recently pioneered the use of remote access through secure encrypted dual-authenticated channels to both protect data on its own servers and expand access to more researchers.
MEASUREMENT ISSUES AND DESIGNS FOR THE FUTURE
As mentioned previously, the future of data collection for demography of aging research is expected to be driven largely by information technology, which implies that this future is highly uncertain. Nevertheless, current developments in approaches allow a glance into the future. Mixed-mode collections are bound to provide the most comprehensive assessments and will continue to be pursued. In the short-to-medium run, the likely mode will be a combination of survey data, data from administrative/medical records, and monitoring data from mobile device applications, to provide a picture more complete than researchers have ever been able to collect from just one or even two of these modes. Survey data will provide individual self-reports, administrative and medical records will provide objective historical measures on individuals, and mobile devices will provide monitoring of individuals’ functionality and activities. The personalized medicine movement has emphasized the use of medical records and monitoring devices, combined with genetic and other biomarkers of disease, in order to support the design and implementation of large clinical trials. Data-driven science systems are currently aiming to develop ways to extract insights from data in several forms, similar to data mining. These approaches are supported, again, by technological innovations likely to become more and more common, and the data collection for demographic aging research will benefit in parallel.
The nature of clinical encounters and clinical assessments will drastically change through advances in technology, including mobile sensors, smart voice technology, smart homes, the collection of ambulatory data from the real environment, and the incorporation of these data streams into individuals’ electronic medical records. Further, technology will make it easier for older adults to reconnect to resources such as friends, family, caregivers, health care providers, and information that they often struggle to reach due to physical limitations.
There is a long-standing call in the research community for wider coverage of the large cohort studies, expanding their samples to include diverse, minority groups in the population. Future directions in the research
of racial/ethnic groups among older adults might include in-depth study of health at older ages among lesbian, gay, bisexual, and transgender individuals. But this may only be possible with the expansion of coverage of the large national studies. The personalized medicine movement is looking to include diverse groups; hence the ability to expand demographic research on aging to these groups will be positioned to benefit as well.
The study of social networks for older adults is bound to benefit from technology developments supporting social media platforms. In this regard, as more data are collected through social media, it will be critical to distinguish between important contacts on these platforms and those that are occasional or irrelevant, which may be difficult to do. Advances in graphical user interfaces can help respondents in identifying and describing their networks. These approaches enable faster, complete data collection and facilitate the measurement of network change. Mobile devices such as cell phones will increasingly provide the opportunity for passive collection of network data and for tracking the movement of respondents. Internet panels such as NORC’s AmeriSpeak could be used to collect network data for mapping exchanges across networks, changes in network membership, and different types of networks. Furthermore, new methods and software are being developed for analyzing these types of data, especially changes in egocentric networks over time. These changes will increase the value of network data in panel studies.
Cellular phones, wearable devices with sensors, and cloud clusters will increasingly enhance the ability to gather data, as service providers can leverage the technology and offer users a service such as health monitoring and corresponding health alerts sent to the individual when certain markers reach critical levels. In exchange, there would be consent to continuous data gathering on individuals (Topol, 2010; Schatz, 2016). Participants’ health markers (such as heart rate and rhythm, lung function), health activities (such as steps taken, gait speed, swimming laps), locations where activities are conducted in daily life (such as supermarkets, restaurants, pharmacies, and others), and details on networks’ size and frequency of contacts are only the beginning of the spectrum of data that can be collected (Van Remoortel et al., 2012). In the future, it will be possible to measure stress and other common markers (Ertin et al., 2011), and mobile devices combined with online applications will be able to apply certain questionnaires periodically to obtain longitudinal assessments of self-reports as well. It will be important to assess the quality of the data gathered, as well as the likelihood that studies using these approaches can represent the overall population, sick and healthy, active and sedentary, and can represent disadvantaged groups and persons who are either unable to, or prefer not to, use these mobile electronic devices.
As a tool for data collection for national populations, telephones seem to offer the current advantage over wearable devices and Internet surveys,
as many adults in the developed and developing worlds carry a phone and the simpler phones will soon have the current capabilities of smartphones. While the advantages of collecting massive amounts of data appear to be obvious and the need to monitor their health may be the most compelling reason for individuals to agree and consent to data being collected, the essence of the scheme is monitoring or continuous observation. Data generated by social media, commercial interactions, and other activity not designed for research, which are possible to obtain even now, will only increase as a potential for research use in the future. These modes of data collection bring up the thorny issues of personal privacy and data confidentiality, which will need to be addressed as new information technologies continue to be developed and applied.
There are many foreseeable ways in which the current momentum of progress in data science can directly benefit research in the demography of aging, and the coming decades may see leaps in progress in this regard. Large data analytics will be another future challenge for the scientific community. The previous generation of studies, with the features of data sharing and collaborative multidisciplinary teams, served to prepare the stage for the challenges to come.
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