PART I
REPORT



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PART I REPORT

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Leveraging Longitudinal Data in Developing Countries INTRODUCTION Longitudinal data collection and analysis are critical to social, demographic, and health research, policy, and practice. They are regularly used to address questions of demographic and health trends, policy and program evaluation, and causality. 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. Yet questions remain about the usefulness of these studies relative to their expense (and relative to cross-sectional surveys) and about the appropriate choice of alternative longitudinal strategies in different contexts. For these reasons, the Committee on Population convened a workshop to examine the comparative strengths and weaknesses of various longitudinal approaches in addressing demographic and health questions in developing countries and to consider ways to strengthen longitudinal data collection and analysis. This report summarizes the discussion and opinions voiced at that workshop. The term longitudinal studies encompasses all studies in which a defined population is interviewed over an extended period of time. This workshop focused only on studies that gather information from the same

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respondents in two or more waves of data collection.1 Therefore, the discussion in this report may not be applicable to longitudinal studies that select respondents in each wave from a common sample pool (such as a community or other sampling unit) rather than follow the same individuals. The workshop distinguished three types of longitudinal studies. Panel studies2 are usually broad-based in sample and topical coverage, and frequently use the household as the sampling unit. In that case, information is collected from all or a sample of members of the selected households. Cohort studies, a subset of panel studies, follow a sample of people selected on the basis of a common age- or time-specific characteristic (such as birth year, age, or class membership). In some cases, the households to which cohort members belong may be included in the study. 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 all individuals (at least all individuals of interest in all households) in geographically demarcated communities. Such studies usually collect data at more frequent intervals than cohort or panel studies, although on a smaller range of topics. The most important distinction, however, relates to the focus of these studies on the community: data are collected from individuals, but the actual unit of observation is the community. Thus in general new people enter the sample as they move into the community, but those who leave the community are not followed. Background Information on population and health issues in developing countries during the first half of the 20th century was based on the few censuses that included relevant questions and on a few intensive longitudinal studies. Well-known examples of these studies include the Instituto de Nutrición de Centro América y Panamá (INCAP), which addressed child nutrition in Guatemala, (for a review, see Scrimshaw and Guzman, 1997) and the 1   A paper by Andrew Foster presented at the workshop and reproduced in Part II explores other longitudinal designs. 2   Barry Popkin presented definitions of the three types of longitudinal studies considered at the workshop.

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Khanna study of health and fertility in India (Wyon, 1997; Wyon and Gordon, 1971). Beginning in the 1960s, individual research institutions initiated multicountry programs of household surveys that greatly expanded the availability of developing country demographic and health information. These programs, which included the Knowledge, Attitude, and Practice (KAP) surveys on contraception (in the 1960s and 1970s), the World Fertility Surveys (1972 -1984), the Demographic and Health Surveys (DHS, which began in 1984 and continues today), and other survey series sponsored by individual research institutions, generally provide nationally representative, widely accessible, and comprehensive data. Most of these efforts were cross-sectional (data were collected at only one point in time) and focused on the fertility and health status of women and children. Capitalizing on the explicit goal to develop comparable information for a wide range of countries over time, researchers currently use these data widely. The data have proven especially useful for cross-national comparisons. The coverage of household surveys has increased to the point where, by mid-2001, the DHS database alone contained over 100 datasets for 68 countries. Increased general use of DHS (and other) data can be credited to the development of standard recode files. Moreover, the speed with which survey data are made available to the public and with which analytic studies are conducted and their results published has increased remarkably. The DHS surveys are currently available on the World Wide Web. The number of longitudinal community studies also has increased dramatically (Kahn and Tollman, 1998). A few of these studies have their roots in the 1950s and 1960s such as the Matlab study in Bangladesh (Aziz and Mosley, 1997); the Khanna (Wyon and Gordon, 1971), Singur (Garenne and Koumans, 1997), and Narangwal (Taylor and De Sweemer, 1997) studies in India; The Medical Social Research Project at Lulliani in Pakistan (Garenne and Koumans, 1997); and the ORSTOM (l’Institut Français de Recherche Scientifique pour le Développement en Coopération) study in rural Senegal (Garenne and Cantrelle, 1997; Garenne and Cantrelle, 2001; Cantrelle, 1969). However, most began in the late 1980s and 1990s (Alderman et al., 2001; Mosley, 1989). Many of these studies were undertaken to evaluate specific interventions such as family planning programs, vaccine-trials, or treatments for specific diseases (Das Gupta et al., 1997). However, research inquiries were often broadened beyond their original intent, extending the life of the study long after

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the original questions were addressed and enhancing their value for other researchers. Some critics believe these studies, which are time-consuming and expensive because of the repeated collection of data at short intervals, have resulted in findings and publications that may be too limited to justify adding new sites and, in some cases, continuing existing efforts. The current situation is therefore one in which the number of studies of various types (including but not limited to longitudinal studies and cross-sectional surveys) is large and growing. Yet, at the same time, policy makers are increasingly demanding rapid analysis and policy-relevant findings, and new analytical tools are expanding the ways in which data from studies of different types can be used. In addition, an increasing number of institutions and data collection sites are requesting access to limited funds in circumstances of growing competition and often more costly research environments. Purpose of the Workshop In this context, the Committee on Population convened a Workshop on Leveraging Longitudinal Data in Developing Countries in Washington, D.C., in June 2001. The primary goals of the workshop were to examine the comparative strengths and weaknesses of several longitudinal approaches in addressing demographic and health questions in developing countries and to consider ways to strengthen longitudinal data collection and analysis. 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) the ongoing information crisis in the region and (2) the interest of funding agencies in evaluating the potential of expanded investment in longitudinal studies for addressing issues currently crucial to the region. The intention of the Committee on Population was to provide an arena for discussion among researchers with diverse topical interests, disciplinary backgrounds, experience with longitudinal methods and approaches, and motivations for conducting longitudinal research; it was not to make recommendations about the best longitudinal approaches for various research questions or in various settings. This report provides a summary of the invited presentations and short papers, the discussants’ comments, and the general discussion. Two commissioned papers are reproduced in Part II of this report. The technical discussion ranged broadly from comparison of the approaches themselves, to examples of longitudinal studies, to data col-

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lection and data management issues, to the relevant innovations in computer science. It also covered additional important and diverse issues, including ethics, collaboration and networking across studies, funding mechanisms, data sharing, capacity building, and researcher/participant/ community relations. The workshop agenda is in Appendix A and a list of participants is presented in Appendix B. At this point, it is important to clarify what topics were not covered at the workshop and therefore are not covered in this report. To maximize the time devoted to comparing longitudinal approaches, workshop participants did not address topics related to cross-sectional data, including a comparison of longitudinal and cross-sectional approaches, and to techniques such as synthetic cohort analysis and retrospective data, which can be used with cross-sectional data to simulate longitudinal data. Other aspects of collecting and analyzing longitudinal data also were beyond the scope of the workshop. Relevant topics that were not adequately addressed include: tracking respondents (methods or costs); dealing with “split-offs” or changes in the sample produced when members of a household in the study leave the household (such as adult children moving out to establish their own household and separations and divorces); “refreshing” a sample (adding new respondents for those who drop out); changing survey questions if a better approach is developed over the course of a study; deciding on the optimal interval between interviews; analyzing longitudinal data (strategies and techniques); keeping data users informed about features of the data (e.g., if oddities or errors are discovered in the data); and including retrospective data collection in the first wave of a study. Organization of the Report This report has two parts. Part I includes an overview of the presentations and discussion at the workshop presented in four sections. 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. The third section examines challenges to longitudinal research, highlighting those associated with funding, relationships with respondents, attrition and population change, research biases, and ethics. The final section explores several ways in which longitudinal research efforts can be strengthened to increase returns to researchers, respondents, policy makers, and the scientific community. Part II of the

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report includes two of the papers presented at the workshop. The first paper, by Andrew Foster, compares panel, cohort, and longitudinal community studies in low-income countries from a methodological perspective. The second paper, by Richard A. Cash and Tracy L. Rabin, presents an overview of ethical issues in developing country research with special reference to longitudinal data. The workshop agenda and the list of workshop participants are included as appendixes. 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 cross-sectional data in a way that builds on age groups, representing birth cohorts, to examine how events of interest change over time) or retrospective data from cross-sectional studies may be equally or even more useful and have the additional benefits of lower cost and time intensity. Even when the research question demands longitudinal data, without sufficient time and money for follow-up, longitudinal efforts may be futile. The benefits of longitudinal research discussed at the workshop can be grouped into two general areas: (1) contributing to scientific knowledge and (2) promoting careful research practices and designs. Contributing to Scientific Knowledge Workshop participants suggested that longitudinal research contributes to understanding causal relationships by collecting more accurate and detailed information on the timing and sequence of various events than might otherwise be obtainable. It also seems to permit greater accuracy by4: 3   This section is based on presentations by Linda Adair, Ties Boerma, Andrew Foster, Barry Popkin, and Stephen Tollman. 4   This section is based on the presentation by Andrew Foster.

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examining changes in various behaviors and related events over time with observations close to the time of the change or event addressing selectivity problems (such as the background characteristics of respondents that may confound the relationship between the variables of interest) in statistical analyses with fewer assumptions studying programs or sources of change in which there are lags between the introduction of an intervention and its possible effects. The advantage of longitudinal data relates specifically to researchers’ ability to look at change (e.g., before and after differences) for a given individual while, in the process, netting out the effect of (unobserved) characteristics of the individual that do not change over time. With cross-sectional data, researchers compare different individuals at a point in time and must be concerned that differences along the dimension of interest might be due to other (unobserved) differences among individuals. Longitudinal research of various types has led to a substantial body of scientific and policy-relevant findings. 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. Table 1 pulls together some specific examples of study findings that were mentioned at the workshop. In the opinion of several workshop participants, the contributions of longitudinal research to science have been much greater than those to policy to date. The benefits of longitudinal research can become clearer when research is related to a particular topic, and Ties Boerma did just that in his presentation on the human immunodeficiency virus (HIV) and other sexually transmitted diseases (STDs). Through longitudinal studies of HIV/STDs, researchers now better understand the complex interactions among the biomedical and social determinants of HIV and other STDs. These studies include investigations of the trends and determinants of HIV infection; the impacts of HIV and other STDs on fertility, adult and child mortality, and population size; and the interactions among demographic factors, such as age and migration, and socioeconomic, cultural, and biological factors in the acquired immunodeficiency syndrome (AIDS) epidemic. Longitudi-

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TABLE 1 Examples of Lessons Learned from Longitudinal Data Presented at the Workshop Study Findings Cohort Studies INCAP (Guatemala) Inter-relationships of diet, nutritional status, and infectiona (particularly the sequencingb) Khanna (India) Breastfeeding alone is insufficient to supply the calories needed by infants six months and older; supplementary foods are required for infants to fight common intestinal and respiratory diseases Cebu (Philippines) Long-term effects of stuntingc INCAP Economic, health, and developmental effects of key developmental patternsd Rationale for multipurpose child care focus in development Fetal programming (Barker hypothesis): adult health outcomes affected by prenatal and early postnatal environment Panel Studies IFLS (Indonesia) Household adjustments to macroeconomic shocks CHNS (China)e Increased malnutrition among rural poor (leading to government policies to lower food prices and initiate anti-poverty efforts) RLMS (Russia)f Privatization’s effect on poverty: major expansion of long-term poor Increased gender and economic inequality Longitudinal Community Studies Rufiji, Tanzania Location of health facilities, use of health services, and infant and child health Mortality burden of malaria (especially for children) Bandim, Guinea-Bissau Risks of Diptheria, Pertussis, and Tetanus (DPT) vaccination for young infants (less than 3 months old); importance of when vaccinations are administered Matlab, Bangladesh DPT vaccinations among children between three and five months and decreased mortality

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Study Findings Manhica, Mozambique Rapid increase in mortality of children under age five in year 2000 (possibly as a consequence of the stress of the January-February 2000 floods) Ifakara, Tanzania Insecticide treated bednets and reduced under-five mortality SOURCE: Based on presentations by Linda Adair, Barry Popkin, Stephen Tollman, and INDEPTH information. NOTE: This table reflects the experience of particular workshop presenters rather than a comprehensive or systematic picture of the field. It should be viewed as illustratious of the range of rich findings generated through longitudinal analyses of various types. INCAP=Instituto de Nutrición de Centro América y Panamá; IFLS=Indonesian Family Life Survey; CHNS=China Health and Nutrition Survey; RLMS=Russian Longitudinal Monitoring Survey. aMartorell et al. (1990). bRamakrishnan (1999a); Schroeder et al. (1999); Martorell (1995); Martorell et al. (1995); Ruel et al. (1995). cMendez and Adair (1999) and Adair and Guilkey (1997). dRamakrishnan et al. (1999b). eGuo et al. (2000) and Bell et al. (2001). fLokshin et al. (2000); Lokshin and Popkin (1999); Popkin and Mroz (1995). nal studies have provided some information (based on verbal autopsies) on the impact of AIDS on mortality. Longitudinal studies undertaken to evaluate interventions aimed at reducing HIV infection (including community trials) have yielded important information on HIV and sexually transmitted diseases. The future of HIV/STDs research is likely to continue to be dominated by intervention studies (albeit focused on various aspects of transmission, prevention, and cure). Boerma foresees that more gains in knowledge about HIV/STDs and related population and health issues will require more studies using population-based samples (as opposed to clinic clients or other ad hoc groups) and larger comparison populations. He expects to see more

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serving the desires of secondary users, including the public, to get the maximum use and benefit out of the data? Christine Bachrach identified some of the benefits and costs associated with data sharing. Six benefits of data sharing were discussed, specifically how data sharing: advances science promotes timely analysis and dissemination of data increases the speed with which results get out facilitates linking independent datasets increases efficient use of scarce resources promotes hands-on training opportunities. The costs, or drawbacks, of data sharing include four issues: Data sharing can pose a threat to the perceived intellectual property rights of investigators. Data sharing reduces the control of the principal investigator and the scientific community over the use of data, increasing the possibility for misuse, misleading results, or bad research based on the data. The process of preparing, documenting, disseminating, and supporting data incurs monetary costs. Data sharing can pose potential risks to privacy of research participants. 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). In his presentation, Kobus Herbst outlined the different interests and roles of these three groups (see Figure 1). Data controllers, those who collect and maintain the data, are obliged to produce high-quality data, invest in the local community, attract and keep quality researchers, and protect respondents. These obligations and the nontrivial investments that data controllers make give them the right to use the data before others have access to it. Allowing some time for collectors to work with the data is important for ensuring the quality of the data, incorporating measures to protect the security of respondents, and developing the necessary codebooks and instructions for using the data.

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FIGURE 1 Interests of data collectors, data users and data subjects in longitudinal research projects. SOURCE: Based on presentation by Kobus Herbst. Data users, including secondary users, policy makers, and the general public, are interested in recognizing the benefits of data as quickly as possible. Broad use of data reinforces scientific inquiry, a diversity of perspectives of approaches, and investigations other than those planned by the data collectors. Data sharing serves the interests of this group by increasing the knowledge base in a timely manner. The interests of data subjects include both ethical dimensions and the rights to information. Confidentiality, privacy, and safety are key concerns of data subjects and their communities. Data sharing can compromise the ability of data collectors to ensure these protections. 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. Whereas cohort and panel studies in developing countries tend to be fairly

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accessible to secondary and public users within a few years of data collection, longitudinal community studies have remained under the tight grip of the researchers and institutions collecting the data. These different data-sharing 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. Again, because they are located in small physical areas, researchers working with longitudinal community studies face added challenges in protecting their respondents. Many workshop participants agreed that the dilemma of competing interests can be resolved by adopting explicit strategies and creative models that reflect the various needs and concerns and that are appropriate for the topics and goals of studies. Workshop participants then discussed the various approaches to data sharing now in use (generally from most accessible to most restrictive): Datasets are available on the Internet. Data can be acquired through a formal request process (with or without a charge). Variables that could be used to identify respondents (ID variables) or communities (such as geocodes) can be removed. Data can be made available to secondary users who come to multipurpose data centers or enclaves. Subsets of data in the form of specific modules or variables can be made available Data at various levels of aggregation can be made available. Users can request summary tables or specific analyses that are then provided by data custodians. Thus, depending on the research interests and critical issues associated with a particular dataset or study, different strategies of data sharing can be developed to promote wider use of data without compromising the confidentiality of respondents, the property rights of primary users, or the quality of research. To date, removing identifying variables from shared data, especially public access samples, is the most common approach. Data collectors also have responsibilities for facilitating use of their data. The Demographic and Health Surveys are currently available on the

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Internet to any interested users that complete a short form. Martin Vaessen described issues that the DHS has addressed in making these data widely available. Specfiically, Vaessen pointed out that data should be provided in usable formats, and multiple formats when possible, to be most useful. Users also need the tools required to use the data, including software, training, and documentation. The documentation should address information about the contents and general features of the data, the history of its collection and use, and its nuances. Determining when data should be made available is a critical consideration that requires a compromise between the various interests just described. Having a clearly set schedule that details when data will be more broadly available and the procedures for accessing them is perhaps the most important consideration. Several workshop participants mentioned that often data-sharing practices are determined by the funding agencies of a project; expectations about when the data will enter the public domain is included in the original agreement. Christine Bachrach suggested that funding agencies become more involved with creative models to encourage data sharing and that they work data-sharing expectations into the granting process (as is currently the case with many panel studies supported by the U.S. National Institutes of Health). Increasing Data Access and Use Through Computer Science Innovations and Technology Many issues of data sharing, particularly the tension between primary and secondary users, can be addressed through improved technologies for collecting, maintaining and using longitudinal data.11 Although these issues are not specific to longitudinal data, in view of the amount of data produced in longitudinal studies, the technology and processes for managing and analyzing data are particularly salient. 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. As primary users shift their emphasis from data handling to analysis with better data management and 11   This section is based on presentations by Bruce MacLeod and Sam Clark.

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analysis software, their returns increase and the prospects of data sharing are enhanced. Reducing the time and costs associated with data storage and retrieval is critical for the long-term success of longitudinal community sites. Bruce MacLeod calculated that data storage and retrieval currently account for 10-40 percent of research budgets. These figures are likely to go up if biomarkers or extra security measures are included. 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) across studies, thereby supporting cross-country comparisons and linkages between datasets. A basic building block of this project is a relational data model, described by Sam Clark, with the capacity for self-generation (and change) of relational variables with minimal syntax. The Structural Population Event History Register (SPEHR) is an example.12 Requests for summary tables or specific analyses are filled by data custodians. Workshop participants discussed seven design considerations for relational data model for longitudinal population data: standards and comparability: compatible data definitions and storage structures across datasets flexibility and extensibility: ability to manage data with a wide range of realities around time dimension, structure dimension (marriages, households, residences), and relationships self-documentation: management system that automatically generates descriptions of data, logic, and metadata. 12   The SPEHR is a relational data model being developed by Sam Clark and his colleagues. The model is built on several components: events (birth, death), multievent processes, item-episodes (residency at place X from time 1 to time 2, marital union from date of union to separation), experiences (the manner in which an item-episode is affected by an event or vice versa), shared experiences, and attributes that change over time. A working demonstration is available at http://www.samclark.net/SPEHR/SPEHR.htm.

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easy maintenance: automated day-to-day maintenance tasks that include adding, editing, and deleting data and data structures; verifying data integrity; and generating operational reports security: system that includes partitioning of data into secured units and legally inaccessible units and centralized control over data validity: no duplication of data and checks for validity analysis-friendly: user-friendly software based on intuitive design, easily recognizable views of the data, and defined summary measures and analytical building blocks, and defined output data formats The ultimate goal of relational models and supportive software is to facilitate the sharing and comparing of data from different populations to improve the scientific and research base on demographic and health issues. Strengthening Longitudinal Research Through Funding Mechanisms A recurrent and important theme emerged throughout the discussions of each topic covered in this section of the report. For each topic, workshop participants recognized the importance of support from funding agencies to fully realize the objectives identified. If sustainability, capacity building, data sharing, community involvement, linking, and networking are worthy goals, funding agencies should encourage and support them through more precise and explicit strategies in proposals and award criteria. For example, sustainability strategies, including a fixed time limit for funding and a plan to shift to local funding mechanisms, should be built into original proposals to encourage in-country support and less reliance on international funding agencies. If capacity strengthening is a priority, plans for strengthening capacity, including a clear definition of what it entails, should be explicit in the proposals, funding criteria, and evaluations of the project. 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. CONCLUSION The main goal of this workshop was to compare the strengths and weaknesses of different longitudinal approaches—specifically, panel, cohort,

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and longitudinal community studies. These approaches differ in their objectives for research and community participation, the study populations and samples, the potential for addressing various research questions, and the ethical concerns with which researchers must grapple. 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. A second major theme that emerged was the importance of multiple research approaches to enhance scientific progress and improve the well-being of individuals through effective policies. Workshop participants identified how the weaknesses in one approach could be easily offset by linking the data collected using that approach with other data collected using similar or different approaches. Issues of access to data—clearly a critical need in increasing the use (and value) of longitudinal data—resurfaced throughout the workshop as an area that needs more careful and critical attention, particularly as it affects the ability to protect the confidentiality of respondents and communities. Overall, the workshop emphasized the importance of careful designs that provide the best science and information while protecting respondents and the communities in which they live, whatever the approach. REFERENCES Aaby, P. 1997 Bandim: An unplanned longitudinal study. Pp. 276-296 in Prospective Community Studies in Developing Countries, M. Das Gupta, P. Aaby, M. Garenne, and G. Pison. Oxford: Clarendon Press. Adair, L.S., C.W. Kuwaza, and J. Borja. 2001 Maternal energy stores and diet composition during pregnancy program adolescent blood pressure. Circulation 104:1034-1039. Adair, L.S., and D.K. Guilkey 1997 Age-specific determinants of stunting in Filipino children. Journal of Nutrition 127:314-320. Alderman, H., J.R. Berhman, H.-P. Kohler, J.A. Maluccio, and S.C. Watkins 2001 Attrition in longitudinal household survey data. Demographic Research 5(4):80-124.

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