INDEPTH Network

INDEPTH is a pioneer in health and population research, providing robust answers to some of the most important questions in development.

We are a global network of health and demographic surveillance systems (HDSSs) that provide a more complete picture of the health status of communities. Since they collect data from whole communities over extended time periods, they more accurately reflect health and population problems in low- and middle-income countries (LMICs). The HDSSs increasingly link population and health facility data to implement the new Comprehensive Health and Epidemiological Surveillance System (CHESS).

By monitoring new health threats, tracking population changes through fertility rates, death rates, migration, morbidity and measuring the effect of policy interventions on communities, we provide information that enables policy-makers to make informed decisions and to adapt their programmes to changing conditions.

INDEPTH membership is currently composed of 42 member health research centres that observe through 47 HDSS field sites the life events of over three million, eight hundred people in 18 LMICs in Africa, Asia and Oceania. Since its inception in 1998, the Network has gathered a treasure trove of robust data, and is uniquely positioned both to answer the most pressing questions on health, population dynamics and development, and to provide policy-makers and donors with evidence on the impact of interventions. In a world seeking answers to poverty and underdevelopment, INDEPTH produces the data necessary to study and solve our greatest social problems.

A lack of reliable health and population data has made it difficult for policy-makers in low- and middle income countries (LMICs) to meet the real needs of their people. Births, deaths and causes of death are not registered in the poorest countries, and data from health facilities omit those who do not use health centres. Data on other social questions, meanwhile, are often piecemeal and limited to small samples or brief points in time.