The Network Survival Method for Estimating Adult Mortality: Evidence From a Survey Experiment in Rwanda
Year: 2017
Type: article
Study design: Observational Study
Abstract: Adult death rates are a critical indicator of population health and well-being. Wealthy countries have high-quality vital registration systems, but poor countries lack this infrastructure and must rely on estimates that are often problematic. In this article, we introduce the network survival method, a new approach for estimating adult death rates. We derive the precise conditions under which it produces consistent and unbiased estimates. Further, we develop an analytical framework for sensitivity analysis. To assess the performance of the network survival method in a realistic setting, we conducted a nationally representative survey experiment in Rwanda (n = 4,669). Network survival estimates were similar to estimates from other methods, even though the network survival estimates were made with substantially smaller samples and are based entirely on data from Rwanda, with no need for model life tables or pooling of data from other countries. Our analytic results demonstrate that the network survival method has attractive properties, and our empirical results show that this method can be used in countries where reliable estimates of adult death rates are sorely needed.
Source: Demography
Authors: Dennis M. Feehan, Mary Mahy, Matthew J. Salganik
Language: English
Subfield: Demography
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $2,750
Funders: National Science Foundation, United States Agency for International Development, Microsoft Research, Princeton University, University of Florida, Joint United Nations Programme on HIV/AIDS, Rwanda Biomedical Centre, National Institutes of Health, Centers for Disease Control and Prevention, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: NetSE: Medium: Robust Socio-Technological Networks: An Inter-Disciplinary Approach to Theoretical Foundation and Experimentation, Infrastructure for Population Research, Berkeley Population Center, Improvements to Respondent-Driven Sampling for the Study of Hidden Populations, Improvements to the network scale-up method for studying hard-to-reach population, Infrastructure for the Office of Population Research
OpenAlex ID: W2322183294
