Literature DB >> 28651610

A training manual for event history data management using Health and Demographic Surveillance System data.

Philippe Bocquier1,2, Carren Ginsburg3,4, Kobus Herbst5,6, Osman Sankoh6,7,8, Mark A Collinson2,6,9.   

Abstract

OBJECTIVE: The objective of this research note is to introduce a training manual for event history data management. The manual provides a first comprehensive guide to longitudinal Health and Demographic Surveillance System (HDSS) data management that allows for a step-by-step description of the process of structuring and preparing a dataset for the calculation of demographic rates and event history analysis. The research note provides some background information on the INDEPTH Network, and the iShare data repository and describes the need for a manual to guide users as to how to correctly handle HDSS datasets.
RESULTS: The approach outlined in the manual is flexible and can be applied to other longitudinal data sources. It facilitates the development of standardised longitudinal data management and harmonization of datasets to produce a comparative set of results.

Entities:  

Keywords:  Event history analysis; Health and Demographic Surveillance System; Longitudinal data management

Mesh:

Year:  2017        PMID: 28651610      PMCID: PMC5485641          DOI: 10.1186/s13104-017-2541-9

Source DB:  PubMed          Journal:  BMC Res Notes        ISSN: 1756-0500


Introduction

The International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) was founded in 1998 and represents a group of currently 47 Health and Demographic Surveillance System (HDSS) sites located in 18 low- and middle-income countries in Africa, Asia and the Pacific. Following its establishment, the Network has endeavoured to build a standardised set of data management protocols pertaining to HDSS data [1]. One of the key challenges of HDSS data management relates to the effective and efficient means of storing and maintaining longitudinal data on health, socio-economic and demographic dynamics that are prospectively updated within a geographically-defined population. These longitudinal datasets capture the dynamic sets of events and episodes pertaining to every individual and household under surveillance (including migration into and out of the demarcated HDSS area). They require sound database structures and protocols for data management and storage. The HDSS platforms form the backbone for high-quality data analysis for scientific enquiry and embedding research projects. One of the research priorities of the INDEPTH Network is to facilitate comparative demographic analyses across HDSSs. The Multi-centre Analysis of the Dynamics of Internal Migration And Health (MADIMAH) is an INDEPTH project that commenced in 2011 with the aim of producing a set of comparative analyses across HDSSs on questions concerning migration and health [2]. The first phase of MADIMAH involved a study of migration, urbanisation and human capital using datasets from eight HDSSs in Burkina Faso, Kenya, South Africa and Mozambique [3]. Thereafter, a multi-centre study examining the migration effect on mortality across nine sub-Saharan African HDSSs was conducted [4]. These studies illustrate the use of standardised longitudinal data management and harmonization of HDSS data across multiple centres. They further illustrate the scientific potential that may be realised by following a uniform analytical framework to produce a comparative set of results across different locations. The objective of this research note is to introduce a training manual for event history data management which proposes a set of versatile procedures aimed at producing standard data structures for use in longitudinal event history analysis (EHA).

Main text

Data

The INDEPTH iSHARE2 data repository1 went online in 2014 and provides a unique resource of high-quality, fully documented HDSS longitudinal datasets available for download to a wide range of users, including HDSS-linked scientists and analysts, researchers and students [5]. The repository, which is growing over time, holds amongst others, core micro datasets describing the key demographic events of more than 25 HDSS populations and unique data on cause specific mortality [5]. Recently, the first of a series of multi-centre core micro datasets attached to the MADIMAH project has been released and is structured to examine determinants of in- and out-migration, particularly the education status of the migrant [6].

Methods

The efficient use of these micro datasets requires that users are able to handle HDSS data structures (such as the residency episode files) and understand the range of core events that alter residency status in the HDSS, especially, in- and out-migration, births and deaths. These data structures and properties form the necessary foundation for the statistical analyses of population dynamics. In order to address these requirements, the MADIMAH group developed a manual based on the group’s experiences of conducting comparative analyses across multiple HDSS sites, and of training HDSS data scientists and analysts in these methods. The intention was to provide data managers and analysts who manage raw questionnaire data with a step-by-step description of the process of structuring and preparing a dataset for the calculation of demographic rates and EHA. The approach was to create a common language and set of codes that can create synergies and enable communication across larger communities of data managers and analysts working with longitudinal research designs.

Results: training manual

The training manual is available on-line as Additional file 1 to this note. It provides a general introduction to event history data management. The manual leads the user through all the procedures necessary to format and analyse longitudinal data. It demonstrates how to create a core residency file suitable for EHA and how to check for inconsistencies in the data. The approach is flexible and covers the calculation of basic demographic rates, as well as more complex determinants analysis through the addition of individual and household attributes. The manual illustrates how to enrich the database with new events with precise or imputed dates of occurrence. Finally, the manual explains how to create duration events of several types. The methods outlined in the manual are implemented in detailed coding using Stata software. All sections start with an example of an output file, followed by a check-list and conclude with further examples or programmes needed to solve specific technical issues. Longer, more detailed Stata programmes are available in Additional file 1: Appendix. This manual is the first comprehensive guide to HDSS longitudinal data management and has become a standard for INDEPTH member HDSS Centres. It can be implemented on longitudinal data from other sources, including register-based, retrospective, or cohort data. It forms the first part of a two-part series. The second manual will guide analysts through the computation of demographic rates and the analysis of determinants and outcomes of demographic processes, using the longitudinal dimension in the data.

Limitations

The procedures outlined in the training manual are most comprehensively applied to HDSS data because these data are inclusive of all entry and exit events in a geographically defined population. In other data sources some entry or exit events might not be relevant, e.g. in-migration for cohort data, or death for retrospective data. Nonetheless, the procedures described in this manual will remain valid and only minor changes to the programming codes will be necessary to apply these methods to such study designs.
  4 in total

1.  The INDEPTH Data Repository: An International Resource for Longitudinal Population and Health Data From Health and Demographic Surveillance Systems.

Authors:  Kobus Herbst; Sanjay Juvekar; Tathagata Bhattacharjee; Martin Bangha; Nidhi Patharia; Titus Tei; Brendan Gilbert; Osman Sankoh
Journal:  J Empir Res Hum Res Ethics       Date:  2015-07       Impact factor: 1.742

2.  Healthy or unhealthy migrants? Identifying internal migration effects on mortality in Africa using health and demographic surveillance systems of the INDEPTH network.

Authors:  Carren Ginsburg; Philippe Bocquier; Donatien Béguy; Sulaimon Afolabi; Orvalho Augusto; Karim Derra; Kobus Herbst; Bruno Lankoande; Frank Odhiambo; Mark Otiende; Abdramane Soura; Marylene Wamukoya; Pascal Zabré; Michael J White; Mark A Collinson
Journal:  Soc Sci Med       Date:  2016-06-23       Impact factor: 4.634

3.  The INDEPTH Network: filling vital gaps in global epidemiology.

Authors:  Osman Sankoh; Peter Byass
Journal:  Int J Epidemiol       Date:  2012-06       Impact factor: 7.196

4.  Health and demographic surveillance systems: contributing to an understanding of the dynamics in migration and health.

Authors:  Annette Gerritsen; Philippe Bocquier; Michael White; Cheikh Mbacké; Nurul Alam; Donatien Beguy; Frank Odhiambo; Charfudin Sacoor; Ho Dang Phuc; Sureeporn Punpuing; Mark A Collinson
Journal:  Glob Health Action       Date:  2013-07-11       Impact factor: 2.640

  4 in total
  6 in total

1.  Are health and demographic surveillance system estimates sufficiently generalisable?

Authors:  Philippe Bocquier; Osman Sankoh; Peter Byass
Journal:  Glob Health Action       Date:  2017       Impact factor: 2.640

2.  A training manual for event history analysis using longitudinal data.

Authors:  Philippe Bocquier; Carren Ginsburg; Mark A Collinson
Journal:  BMC Res Notes       Date:  2019-08-14

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Journal:  Global Health       Date:  2022-01-06       Impact factor: 4.185

Review 4.  Health and demographic surveillance systems in low- and middle-income countries: history, state of the art and future prospects.

Authors:  Kobus Herbst; Sanjay Juvekar; Momodou Jasseh; Yemane Berhane; Nguyen Thi Kim Chuc; Janet Seeley; Osman Sankoh; Samuel J Clark; Mark A Collinson
Journal:  Glob Health Action       Date:  2021-10-26       Impact factor: 2.640

5.  Cohort Profile: South African Population Research Infrastructure Network (SAPRIN).

Authors:  Mark A Collinson; Taurayi Mudzana; Tinofa Mutevedzi; Kathleen Kahn; Eric Maimela; F Xavier Gómez-Olivé; Thobeka Mngomezulu; Dickman Gareta; Chodziwadziwa W Kabudula; Rathani Nemuramba; Joseph Tlouyamma; Stephen Tollman; Kobus Herbst
Journal:  Int J Epidemiol       Date:  2022-08-10       Impact factor: 9.685

6.  Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization.

Authors:  Matthias Templ; Chifundo Kanjala; Inken Siems
Journal:  JMIR Public Health Surveill       Date:  2022-09-02
  6 in total

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