111.Longitudinal survey planning and execution
V. Conclusions and lessons future longitudinal studies
for
The future of any longitudinal sample survey is precarious in a period of high Federal deficits, domestic program budget cuts, and laissez-faire attitudes. The trials and tribulations of SIPP as it migrated from one organization to another before finding a permanent home at the Census Bureau and the aborted HUD-DHEW longitudinal survey should be sufficient proofof that. Higher level administrators who do not appreciate issues such as sample heterogeneity may argue for the use of aggregate data. even though such data may result in mislead-ing analyses and misdirected policies. The availability of cross-sectionalsurveys such as the National Health Interview Survey und the Current Population Survey ensure that data will be at hand on many issues that a dynamic survey would address.
Therefore, it is essential to utilize existing longitudinal surveys, including RHS, NLS, SIPP, BLSA, and Framingham, as fully as possible to study facets of aging which are relevant to research and policy. It is also important that, when temporal data are necessary in the future, the samples from such studies
as NH IS, NHANES, and CPS continue to be given strong consideration as the basis. as is being done in the NHANES I Followup Survey (Comoni-Huntley et al., 1983).
Since longitudinal studies involve costly surveys which require large initial samples, considerable resources for fol-lowup, and an appreciation of analytic techniques, the lessons to be learned from past studies are extremely important. Lon-gitudinal sample surveys have both relative advantages and disadvantages compared to other data sets. In the data collec-tion and disseminacollec-tion phase. they are similar in many respects to cross-sectional surveys except for the temporal loss of respondents; this loss requires starting with a larger baseline sample, holding the study team together, and taking changing technical and social measurements into account in the question-naire and in the analysis. This is necessary to ensure that the terminal sample size is adequate for statistical estimation purposes and that the measured items are equivalent.
Longitudinal surveys provide precise details of individual changes in behavior and other characteristics over time, but this greater specificity of information than is possible through a single study with recall questions is exacted at a cost of maintaining the original sample minus dropouts. The popula-tion lost to followup also can provide invaluable information on the heterogeneity of the survivors and decedents that is not available in other data. The information provided. can be used with a great deal of confidence for research and policy-related issues in aging where individual distinctions, such as risk factors for certain diseases or characteristics which determine program eligibility, are important. Among the im-portant issues for which longitudinal information is now avail-able to researchers and policy makers are streams of income, wealth, and assets; health status, disability, and long-term care arrangements; retirement and employment; living arrange-ments under changing family and household structure: risk factors for death and illness. especially from chronic diseases;
and adjustments to changing personal circumstances and social conditions (Alexander, 1974; IreIan and Schwab, 1982; Kagan et al., 1974: Kannel and Gordon, 1980: Manton and Wood-bury, 1983; McGee, 1973; Newhouse, 1974: Phillips et al.,
1974; Quinn, 1979; Rogot, 1974: Tissue and McCoy, 1981).
Given the success of previous studies and limited re-sources, what lessons can be learned from the existing studies with regard to committing more resources to new longitudinal survey samples? Perhaps the most critical element in the plan-ning of a study is identifying the goal of the survey in the context of an important, long-term policy issue which will justify the commitment of funds and personnel over an ex-tended period. In the past, numerous health and social pro-grams have been able to do this. Yet, one recently planned longitudinal sample survey (the HUD–DHEW study of the housing adjustments of the elderly) has failed in the pilot stage because of inadequate support to continue the study.
Another (SIPP) has gone through administrative purgatory and appears to have a design that does not utilize information on the behavior of individuals as fully as it might have.
The heterogeneity of individuals should be emphasized as important to poIicy. The analysis must take advantage of the inherent time dimension and individual diversity of
169
the observations. The survey instrument must be planned with the analysis of the collected data and the goal of the project in mind. For example, SIPP has a very elaborate analysis plan. The baseline analyses of RHS suggest that a wide array of papers was anticipated. Even though there were plans for analyses of RHS. there have been more papers published than would have been planned because the public data files have been analyzed by the wider research community. The external benefits derived from making the data available to the public cannot be estimated beforehand, but should not be minimized. Public use data sets should be a part of the data preparation and analysis plans for every longitudinal survey.
Alternatives to initiating new longitudinal studies, such as using cross-sectional or aggregate time-series data, do not provide information on the same level as longitudinal sample surveys do. As discussed earlier, static analyses of such data can be misleading regarding dynamic events and behavior.
For most policy-related issues, agencies need to know the specific characteristics of individuals to determine “who wins”
and “who loses” when a policy change is considered.
The possible alternative of using linked administrative and census records has a number of positive features, including a much lower program cost, and could be used effectively if the variables of interest are found within an administrative data set (Rogot et al., 1983). For the elderly population, this could involve Medicare data sets and Social Security files. However, to provide public use data files maybe impossi-ble if the data set contains certain legally restricted administra-tive information (e. g., Internal Revenue Service data) or does not contain information essential to the study (see Scheuren, Chapter 11). Thus. it would seem that there may continue to be a need for these surveys as long as the Federal Govern-ment has programs which deal in different ways with persons of different Characteristics.
The future of longitudinal studies for aging policy is not as bright as it’should be for several reasons. These studies are seen as extremely costly, which they are in the short term. However, the potential benefits in terms of the potential program savings often far outweigh these costs. The political time horizon in a period of severe budget constraints would tend to prohibit planning and executing any new longitudinal sample survey. Nonetheless, the results of past surveys have provided invaluable data for health and social policies and programs after years of planning, fieldwork, and analysis.
In summary, there are six lessons we should have learned from past longitudinal sample surveys:
1.
7-.
. .3
Longitudinal surveys should be used to examine dynamic events where changes in individual characteristics and outcomes associated with aging are the issues under study.
New longitudinal sample surveys should start with an existing data base such as CPS or NHIS, be linked to administmtive records, and use the National Death Index for followup of nonrespondents.
Expert consultants from academia and government agen-cies should be involved early in the planning process to ensure that the survey instrument and methodology are appropriate to achieve the goals of the study.
4.
5.
6.
Analyses should use methods, including stochastic mod-els, which deal with the temporal and heterogeneous nature of the observations in such surveys.
When used appropriately, longitudinal sample surveys provide information for the research community and policy makers which cannot be acquired by other types of studies. Therefore, these data sets should be considered as valuable research and policy resources and be made available in well-documented public use tapes through a national archival system.
Cross-sectional data and aggregate time series are useful for comparative static analyses, especially where mean and total values are the measures of interest; to use lon-gitudinal data for such studies is inefficient and may be inappropriate when the numbers are too small.
In the final section. recommendations for future kmgitudi-nal studies are presented.
VI. Recommendations
There are several recommendations which naturally flow from the discussion in this paper. These include the excellent recommendations on data sharing made by the Committee on National Statistics (Fienberg, Martin, and Straf, 1985).
These recommendations are intended to stimulate further dis-cussion of the value of longitudinal sample surveys in relation to other methods used to obtain statistics needed to analyze problemsin an aging society.
Recommendations in the area of planning and execution include:
Any new longitudinal sample survey should justify its longitudinal nature and hence higher cost by showing how temporal dynamics of heterogeneous individuals are necessary to the research or policy issues which otherwise might be addressed by either aggregate time-series or annual cross-sectional data to attain the goals of the study.
Ongoing cross-sectional studies, such as the CPS, NHIS, and NHANES, should be explored as sources for establish-ing new longitudinal studies of agestablish-ing and the aged.
All federally funded surveys should routinely be linked to appropriate administrative records and the National Death Index. This will require legislative mandate to make such records available for research purposes.
SIPP and the NHANES I Followup Study should follow the same individual for longer continuous periods to cap-ture more behavioral and health changes due to aging that are likely to be seen by following each cohort longer.
The socioeconomic risk factor data in ongoing longitudinal health surveys should be expanded if it can be done within the goals of the study and without disrupting estab-lished relationships between the subjects and the survey staff.
Longitudinal studies should be well documented and com-puter-readable copies and codebooks archived in a
nation-7.
8.
1.
2.
. .3
ally recognized distribution network within a year of the first data runs of the study.
The w.xdemic research community should be involved early as advisors in the planning of any new longitudinal surveys.
Administrative records expunged of personal identifiers should be linked to survey data on a continuing basis to strengthen the subjective data and extend the temporal coverage of individuals in the survey, as RHS has done.
Recommendations in the area of analysis include:
Analyses of longitudinal data should employ methods appropriate for exploring hypotheses concerning temporal dynamics of heterogeneous populations, such as the appli-cation of models involving hazard functions and Markov processes.
Statistical techniques involving repeated measures should be utilized and refined for longitudinal surveys involving rmnrandom nonresponse.
Dissemination of public dara sets to the research communi-ty should be given a higher prioricommuni-ty by the Federal agencies responsible for maintaining longitudinal local studies so that fuller exploration of this information can proceed
4.
5.
in a manner similar to that which has occurred with the information from national] y representative surveys.
Analyses should compare results of longitudinal sample survey data collected in the different local studies to dem-onstrate the consistency of the results of these surveys as input to social and health policies.
Analyses should compare results of longitudinal sample survey data and similar cross-sectional and aggregate tinle-series data to demonstrate the value of the longitudinal sample survey data forpolicy and research.
In summary, longitudinal sample surveys have been ex-tremely valuable in expanding knowledge of social behavior, health changes, and other individual responses to aging. Lon-gitudinal studies should not be considered either obsolete or too expensive to be used now. The recommendations in this paper are broad and should be widely applicable, though they do not attempt to address the specific details of a given study. In today’s environment, we must have information to determine the value of policy-related issues such as retire-ment age changes, pension and insurance proposals. disease prevention activities, and health service utilization; we must have longitudinal sample surveys.
171
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