Data and Code for: Depression and Loneliness Among the Elderly in Low- and Middle-Income Countries

We combine data from longitudinal surveys in seven low- and middle-income countries (plus the United States for comparison) to document that depressive symptoms among those aged 55 and above are prevalent in those countries and increase sharply with age. Depressive symptoms in one survey wave are associated with a greater decline in ability to carry out basic daily activities and a higher probability of death in the next wave. Using additional data from a panel survey we conducted in Tamil Nadu with a focus on elderly living alone, we document that social isolation, poverty, and physical health challenges are three of the leading correlates of depression. We discuss potential policy interventions in these three domains, including some results from our randomized control trials in the Tamil Nadu sample. 

Geographic Coverage:  United States, Malawi, China, Brazil, South Africa, Mexico, India, Costa Rica

Time Period(s):  2014 – 2016 (US HRS survey); 2015 – 2015 (Brazil ELSI survey); 2011 – 2013 (China CHARLS survey); 2005 – 2007 (Costa Rica CRELES survey); 2017 – 2017 (India LASI survey); 2008 – 2010 (Malawi MLSFH survey); 2001 – 2003 (Mexico MHAS survey); 2017 – 2019 (South Africa HAALSI survey); 2019 – 2021 (Tamil Nadu survey)

Collection Dates: 2019 – 2021 (Waves 1 and 2 of Tamil Nadu survey)

Universe:  For most surveys, nationally representative sample of older adults not living in institutions. The exceptions are (1) South Africa, which sampled a single community in Agincourt, (2) Malawi, which sampled adults in rural communities across three districts in Malawi, and (3) Tamil Nadu data, which sampled adults living in the state of Tamil Nadu in India.

Data Types:  survey data

Data Sources:  

Berkman, Lisa. Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa [HAALSI]: Agincourt, South Africa, 2015-2019. Inter-university Consortium for Political and Social Research [distributor], 2020-11-05. https://doi.org/10.3886/ICPSR36633.

Bloom, D.E., Sekher, T.V. & Lee, J. Longitudinal Aging Study in India (LASI): new data resources for addressing aging in India. Nat Aging 1, 1070–1072 (2021). https://doi.org/10.1038/s43587-021-00155-y

Duflo, Esther, Abhijit Banerjee, Madeline McKelway, Frank Schilbach, Garima Sharma, and Girija Vaidyanathan, 2022, "Tamil Nadu Aging Panel", https://doi.org/10.7910/DVN/SXEYFW, Harvard Dataverse, V9, UNF:6:CjZ32wP6uOhj8pkiN15eWw

Health and Retirement Study, (RAND HRS Longitudinal File 2018 (V2)) public use dataset. Produced and distributed by the University of Michigan with funding from the National Institute on Aging (grant number NIA U01AG009740). Ann Arbor, MI, (2022).

RAND HRS Longitudinal File 2018 (V2). Produced by the RAND Center for the Study of Aging, with funding from the National Institute on Aging and the Social Security Administration. Santa Monica, CA (July 2022).

Kohler, Hans-Peter et al. (2013). “Cohort Profile: The Malawi Longitudinal Study of Families and Health (MLSFH).” Population Studies Center, University of Pennsylvania,Working Paper 2013-06. URL: http://repository.upenn.edu/psc_working_papers/46.

Lima-Costa MF, de Andrade FB, de Souza PRB Jr, Neri AL, Duarte YAO, Castro-Costa E, de Oliveira C. The Brazilian Longitudinal Study of Aging (ELSI-Brazil): Objectives and Design. Am J Epidemiol. 2018 Jul 1;187(7):1345-1353. doi: 10.1093/aje/kwx387. PMID: 29394304; PMCID: PMC6031009.

Rosero-Bixby, Luis, Fernández, Xinia, and Dow, William H. CRELES: Costa Rican Longevity and Healthy Aging Study, 2005 (Costa Rica Estudio de Longevidad y Envejecimiento Saludable). Inter-university Consortium for Political and Social Research [distributor], 2013-02-13. https://doi.org/10.3886/ICPSR26681.v2

The Longitudinal Ageing Study in India 2017-18 - national report. International Institute for Population Sciences, National Programme for Health Care of Elderly, Ministry of Health and Family Welfare, Harvard T. H. Chan School of Public Health, and the University of Southern California; 2020.

Wong, Rebeca, Alejandra Michaels-Obregon, Alberto Palloni, Cohort Profile: The Mexican Health and Aging Study (MHAS), International Journal of Epidemiology, Volume 46, Issue 2, April 2017, Page e2, https://doi.org/10.1093/ije/dyu263

Zhao, Yaohui, Yisong Hu, James P Smith, John Strauss, Gonghuan Yang, Cohort Profile: The China Health and Retirement Longitudinal Study (CHARLS), International Journal of Epidemiology, Volume 43, Issue 1, February 2014, Pages 61–68, https://doi.org/10.1093/ije/dys203

Unit(s) of Observation:   Individual per survey wave