Leveraging Longitudinal Data in Developing Countries Report of a Workshop (2002) / Chapter Skim
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Report: Leveraging Longitudinal Data in Developing Countries
Report: Leveraging Longitudinal Data in Developing Countries
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Panel studies, cohort studies, and longitudinal community studies have proved particularly important in developing countries that lack vital registration systems and comprehensive sources of information on the demographic and health situation of their populations. Research using data from such studies has led to scientific advances and improvements in the well-being of individuals in developing countries.
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Longitudinal community studies, also known as population laboratories or demographic surveillance studies, systematically collect data (generally on fertility, mortality, and in- and out-migration) from allindividuals (at least all individuals of interest in all households)
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The number of longitudinal community studies also has increased dramatically (Kahn and Tollman, 19981. A few of these studies have their roots in the 1950s and 1960s such as the Matlab study in Bangladesh (Aziz and Mosley, 19971; the Khanna (Wyon and Gordon, 1971)
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Workshop participants addressed a wide range of scientific, practical, and strategic issues, concentrating on longitudinal community studies, panel studies, and cohort studies. Africa received special emphasis for two reasons: (1)
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The first section considers the benefits of longitudinal data in general. The section that follows compares the advantages and disadvantages of panel studies, cohort studies, and longitudinal community studies and presents considerations for determining the best approach.
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BENEFITS OF LONGITUDINAL DATA Throughout the workshop discussion, participants noted the strengths of longitudinal research, even though identifying the advantages of longitudinal studies relative to those of cross-sectional studies was not an objective of the workshop.3 Yet while mentioning the virtues of longitudinal efforts, they continually noted that the use of longitudinal data and the specific approach adopted depend on the research question at hand. For many time-dependent research questions, synthetic cohorts (using crosssectional data in a way that builds on age groups, representing birth cohorts, to examine how events of interest change over time)
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Scientifically, the availability of longitudinal data has allowed researchers to better understand human, social, and economic development processes, to test more dynamic and complex theories of social and health behaviors, and to refine their understanding of causal relationships. Studies have illuminated the health, social, and economic needs of individuals, communities, or subgroups of populations; evaluated the effectiveness of a range of programs and interventions; and enabled policy makers and planners to set priorities based on evidence.
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RLMS (Russia,if Privatization's effect on poverty: major expansion of long term poor Increased gender and economic inequality Longitudinal Community Studies Rufiji, Tanzania Bandim, Guinea-Bissau Matlab, Bangladesh Location of health facilities, use of health services, and infant and child health Mortality burden of malaria (especially for children) Risks of Diptheria, Pertussis, and Tetanus (DPT)
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on the impact of AIDS on mortality. Longitudinal studies undertaken to evaluate interventions aimed at reducing HIV infection (including community trials)
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Stephen Tollman, in discussing the contributions of longitudinal community studies to science and policy, pointed to specific findings (see Table 1) that have emerged from longitudinal community studies and the overall process of serendipity and feedback that makes a critical contribution to such studies.
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COMPARISON OF DIFFERENT APPROACHES TO LONGITUDINAL DATA Panel studies, cohort studies, and longitudinal community studies are, of course, not equivalent in their objectives or their ability to answer specific social sciences questions. As noted earlier, the research question and the goals of the research project, along with context, funding, and other considerations, must be taken into account in determining the best study approach.
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For measurement, or description, of demographic and health processes or patterns, the major differences among the three approaches stem primarily from the differences in the target populations. Panel studies generally cover a broad population, whereas cohort and longitudinal communities focus on specific subpopulations (based on a common characteristic for cohort studies and on a specific community or geographic area for longitudinal community studies)
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In terms of enhancing community participation and strengthening local research capacity, longitudinal community studies outperform the other longitudinal approaches. The primary objectives of longitudinal community studies extend beyond their potential to advance immediate knowledge.
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sample I of a con characte class me informa cohort r Capacity strengthening Generally work with established institutions and researchers in country Offer temporary jobs, mostly for data collecting, data entry, and coding Generally work with researchers and policy makers at the national level Ethical concerns Protecting confidentiality of individual respondents, particularly when sharing data Obligations to respondents to provide results that may be produced from the data years after data collection General .
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Generally work with established institutions and researchers in country Offer temporary jobs, mostly for data collecting, data entry, and coding Generally work with researchers and policy makers at the national level Protecting confidentiality of individual respondents, particularly when sharing data Data are generally collected at more frequent intervals than for cohort or panel studies, although on a smaller range of topics. Communities are the primary unit of observation (though data come from individuals)
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and changes in these measures and patterns over time. Advantages Most likely to be representative of large-area population Represe particul; Greater depth and breadth in socioeconomic and health measures Disadvantages Need to refresh panels to remain representative over time Often have few observations of rare events Greater soc~oecc Not rep to gene Often h events
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by establishing research infrastructure Allows precise estimation of mortality through larger (person-year) samples and greater focus on rare events Utilize greater periodicity of measurement Not necessarily representative of other .
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May have insufficient sample sizes in communities where interventions are placed to study program impacts. Research goal: str?
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. Because of the population coverage, generally include information necessary for a panel study and a cohort study opening the possibility for a range of longitudinal studies and approaches.
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, comprehensive approaches to capacity strengthening and community participation have been concentrated within longitudinal community studies. At several points in the discussion, workshop participants expressed the sentiment that, though particular methods of longitudinal data collection and analysis may better suit particular research questions, the potential for knowledge multiplies when different longitudinal approaches are used in conjunction (this point is discussed later in the section on "Strengthening Longitudinal Efforts".
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Additional problems relate to the mobility of respondents and change in the communities under study, as well as the ethical considerations unique to this type of research. Five challenges to working with longitudinal data are described in this section: (1)
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The long germination period required by many longitudinal studies was demonstrated with two examples at the workshop. First, Barry Popkin and Linda Adair cited David Barker's work on the effects of the prenatal and early-natal environment on adult health outcomes as an example of how significant returns to longitudinal studies may emerge years after study initiation (Adair et al., 2001; Barker, 1998; Popkin et al., 19961.
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. On the other hand, longitudinal community studies have a strong long-term presence in a specific (generally relatively small)
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People are added to the study population through in-migration and births and removed from study populations and samples through out-migration, deaths, and refusals to participate. Current demographic patterns in many developing areas suggest that longitudinal researchers need to be aware of these changes; however, information is often insufficient to allow researchers to anticipate specific changes.
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stay. For example, research using the Indonesian Family Life Survey shows that long-distance migrants, short-distance migrants, and nonmigrants differ in educational attainment and earned income levels.5 Examining longitudinal household surveys from Bolivia, Kenya, and South Africa, Alderman and colleagues (2001)
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Effects of Changes in Longitudinal Study Protocols The existence of a research infrastructure (for example, trained interviewers, research center, baseline data) provided by longitudinal studies, especially long-term ones such as longitudinal community studies, may provide an incentive for others to locate independent studies or programs in the area or for researchers to add variables or types of data to their protocols (for example, the existing research infrastructure and baseline information may facilitate evaluation)
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Ethical Research Practices More rigorous institutional requirements for research on human subjects and greater knowledge about how research practices can affect individuals and communities have led to a heightened emphasis on ethics in demographic and health research. Yet, growing concerns about reducing the costs of longitudinal data collection and increasing public accessibility to the data often come in direct conflict with ethical concerns.
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Third, the longer life of longitudinal data generates additional considerations related to researchers' obligations to respondents (for example, providing research results to participants who may move during or after the study or offering treatment for illnesses identified with the research)
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Two discussions at the workshop on incorporating biomedical testing and GIS into social research in developing countrieshighlight some of the ethical dilemmas (see Box 21. STRENGTHENING LONGITUDINAL EFFORTS Because many longitudinal research efforts tend to be resource intensive and difficult, maximizing the returns to these efforts is a worthy aim.
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Flexibility and adaptability are features of longitudinal research. The power of longitudinal data lies not just in repeated measures of the same variables, but also in the ability to add a rich set of non-time-varying covariates for analysis in new domains as well as contextual information for prespecified hypotheses.
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Increasing Variables and Types of Data The addition of more or different variables to longitudinal data collection can increase the potential and use of longitudinal data. This is especially true when the added variables substantially improve researchers' ability to incorporate more accurate measures of important factors or test more complex models.
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By networking and collaborating across sites and study approaches, researchers can easily leverage longitudinal data, increasing their value. Networking of researchers involved in various studies facilitates the development of comparable datasets across study sites.
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These efforts can strengthen individual sites as well as promote standardized datasets and increase the potential for further cross-site comparisons and multisite research. Recognizing the limitations of longitudinal community studies (primarily in terms of their limited geographical coverage)
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The generation of common variables in multiple clatasets through geococling has already been cliscussecl. Developments to ease the extensive technological demands of large and complex datasets, such as linked data, are presented in the later section "Increasing Data Access and Use Through Computer Science Innovations and Technology." Strengthening Research Capacity Efforts to expand the participation of developing country scientists in research and strengthen their analytical skills improves the quality of current research and research capacity in developing regions.
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Most current longitudinal community studies explicitly incorporate local research development into their projects; indeed, strengthening local research capacity is often a key objective guiding these studies (as discussed earlier in the section "Relationships with Communities and Respondents"~. Researchers carrying out panel and cohort studies have generally worked with research institutions in developing countries rather than individual scientists.
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. Second, Cheikh Mbacke argued that, "because they entail long-term involvement with communities in geographically defined areas, longitudinal studies have great potential for building both individual and institutional capacity." Speaking specifically about longitudinal community studies in Africa, he highlighted the need for explicit strategies for capacity strengthening in order to exploit the opportunities for capacity development inherent in these projects.
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The issues raised in this list highlight a core issue in data sharing: successful data sharing requires balancing the potentially competing interests of three interest groups the data controllers (collectors and primary users) , the data users, and the data subjects (or respondent community)
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Yet, respondents also have an interest in receiving the benefits of the research which is encouraged and expedited through data sharing. These issues again highlight the distinctions between cohort and panel studies, on the one hand, and longitudinal community studies, on the other.
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These different datasharing practices arise largely from the different features of these studies. As discussed in previous sections, longitudinal community studies often work with massive amounts of data that are continually collected, and they have goals and relationships with communities that differ greatly from those associated with other longitudinal studies thus data collectors involved with those studies face multiple demands.
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Once the intensive data collection and management requirements are reduced, data collectors can spend more time analyzing data. Many longitudinal community sites and the INDEPTH network are currently developing mechanisms for more efficient data storage and management.
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Currently, software to automate construction of data management systems building on consistency logic and basic data types is being developed in the field by experienced users. Simplifying data management of single sites is an important goal for software design; however, a main objective is to develop templates and other mechanisms that facilitate standardized data formats (particularly definitions of key variables and data storage logic)
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The same is true for the goals of networking and data sharing, dissemination of results back to the community, and collaborative work. Not only will these strategies enable researchers and funding agencies to better realize their own objectives, but incorporating aspects of interest into the funding mechanisms will help researchers determine which longitudinal approaches are most appropriate given the set of objectives on the table.
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This report has highlighted how these approaches compare in confronting several challenges faced by longitudinal researchers and in adding value to existing and future longitudinal efforts. A clear theme of the workshop was the importance of using longitudinal approaches that best fit the research questions being asked or the overall goals of the project, which may include aspects of community strengthening and local investment along with the scientific objectives.
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Pp.28-53 in Prospective Community Studies in Developing Countries, M Das Gupta, P
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Mosley, W.H. 1989 Population Lalooratoriesfor Community Health Research.
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Pp. 101-132 in Prospective Community Studies in Developing Countries, M
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