The College of Arts and Sciences’ Department of Sociology is hosting a presentation entitled “New Methods for Mortality Monitoring in Developing Countries in the Era of ‘Big Data’” by Professor Samuel Clark, PhD, associate professor at the University of Washington. Dr. Clark is a faculty candidate for a professor position in the Ohio State Department of Sociology in the area of demography.
When: Friday, Jan. 15, 12:30 – 1:45 p.m.
Where: 248 Townshend Hall
Abstract: This talk will describe two methods development initiatives to improve population and health data describing populations without traditional civil registration and vital statistics:
In regions without complete-coverage civil registration and vital statistics systems there is uncertainty about even the most basic demographic indicators. In such areas the majority of deaths occur outside hospitals and are not recorded. Worldwide, fewer than one-third of deaths are assigned a cause, with the least information available from the most impoverished nations. In populations like this, verbal autopsy (VA) is a commonly used tool to assess cause of death and estimate cause-specific mortality rates and the distribution of deaths by cause. VA uses an interview with caregivers of the decedent to elicit data describing the signs and symptoms leading up to the death. We develop a new statistical tool known as InSilicoVA to classify cause of death using information acquired through VA. InSilicoVA shares uncertainty between cause of death assignments for specific individuals and the distribution of deaths by cause across the population. Using side-by-side comparisons with both observed and simulated data, we demonstrate that InSilicoVA has distinct advantages compared to currently available methods.
Traditionally health statistics are derived from civil and/or vital registration. Civil registration in low- to middle-income countries varies from partial coverage to essentially nothing at all. The state of the art for public health information in these places is efforts to combine or triangulate data from different sources to produce a more complete picture across both time and space.
We propose a new statistical framework for gathering health and population data – HYAK – that leverages the benefits of sampling and longitudinal, prospective surveillance to create a cheap, accurate, sustainable monitoring platform. HYAK organizes two data collection systems to work together: (1) data from HDSS with frequent, intense, linked, prospective follow-up and (2) data from sample surveys conducted in large areas surrounding the HDSS sites using informed sampling to capture as many events as possible.
We conduct a simulation study of the informed sampling component of HYAK based on the Agincourt health and demographic surveillance system site in South Africa. Compared to traditionally cluster sampling, HYAK informed sampling captures more deaths, and when combined with an estimation model that includes spatial smoothing, produces estimates of both mortality counts and mortality rates that have lower variance and small bias.