Laboratory

Demographic Data

At a Glance Projects Publications Team

Project

Mortality and Mortality Disparities in Low- and Middle-Income Countries

Conducted by Dmitri A. Jdanov; Domantas Jasilionis, Moradhvaj Dhakad; in Collaboration with Nandita Saikia (International Institute for Population Sciences, Mumbai, India), László Németh (Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany), Rasul Afza (Lahore College for Women University, Pakistan), Özer Bakar (Hacettepe University, Turkey)

Detailed Description

The majority of low- and middle-income countries are currently undergoing substantial epidemiological transformations manifested by large declines in child mortality and the growing role of chronic noncommunicable diseases. However, the ongoing health transitions are highly uneven and complex. In many cases, these countries enter a new stage of the health transition amid a) a persistently high or even reemerging burden of communicable diseases and b) peaking health and mortality inequalities across sociodemographic groups and regions. In addition, sustainable health progress is threatened by new epidemics at the global, regional, and national levels.

To adequately monitor health progress and health challenges, internationally comparable, data-driven mortality estimates are needed at the global, regional, national, and subnational levels. Currently, the existing evidence for the majority of low- and middle-income countries is almost entirely based on surveys that primarily focus on child and maternal mortality. The international estimates that are available are based on heavy modeling assumptions and substantial adjustments, and they are hardly comparable and interpretable. The new epidemiological realities and emerging threats call for more reliable, data-driven, and transparent mortality estimates, especially for adult and older age groups.

This project focuses on producing internationally comparable mortality estimates at the national and subnational levels for a selected set of developing countries. It benefits from leveraging and sharing the experiences of the Human Mortality Database (HMD, www.mortality.org) in data collection, harmonization, adjustment, and distribution. The extrapolation of the methodological advances and the main principles of the HMD to countries with problematic data will be the major challenge. However, success in implementing at least some of the methodological solutions and principles could help to fill data and knowledge gaps.

We first focus on national and subnational mortality estimates for India and Turkey. In collaboration with the International Institute for Population Sciences (Mumbai, India), the project team conducted a systematic quality assessment and harmonization of the Sample Registration System (SRS) and the National Family Health Survey data. Special methodological solutions and alternative data sources have been tested to adjust for child and old-age mortality. Together with Özer Bakar from Hacettepe University (Turkey), we did a thorough data quality assessment and tested the HMD methods using recent Turkish data. In collaboration with Afza Rasul from Lahore College for Women University (Pakistan), the project investigated age-heaping patterns in DHS data from Pakistan and other low- and middle-income countries.

Publications

Rasul, A.; Nasir, J. A.; Jasilionis, D.; Jdanov, D. A.:

Quality of reported ages: a robust re-modification in total modified Whipple’s index

PLOS One 20:8, 1–18. (2025)    

Kühn, M.; Díaz-Venegas, C.; Jasilionis, D.; Oksuzyan, A.:

Gender differences in health in Havana versus in Mexico City and in the US Hispanic population

European Journal of Ageing 18:2, 217–226. (2021)    

Oksuzyan, A.; Singh, P. K.; Christensen, K.; Jasilionis, D.:

A cross-national study of the gender gap in health among older adults in India and China: similarities and disparities

The Gerontologist 58:6, 1156–1165. (2018)    

Singh, P. K.; Jasilionis, D.; Oksuzyan, A.:

Gender difference in cognitive health among older Indian adults: a cross-sectional multilevel analysis

SSM-Population Health 5, 180–187. (2018)