Research & Surveillance
Rethinking national HIV Surveillance methods and indicators
As HIV control progresses, the methods to track the course of the epidemic need to change, routine data needs to be contextualized, and new sources of data triangulated. This collaborative project involves the Ministry of Health, the Harvard School of Public Health, AFIDEP and Kamuzu University School of Health Sciences, and will both explore indicators of success and new methods of measurement.
Nyanja will provide technical support for reviewing, developing, drafting, and editing updated National HIV Surveillance Strategy, and will assist in the design of surveillance studies to provide more in-depth data to understand specific questions. Nyanja senior epidemiologists will also mentor staff, KUHeS PhD and MSc students on development of study protocols, implementation and analysis. We will also participate in the conceptualisation and design of continuous adaptive household surveys, and assist with coordination of project activities across HIV implementing partners at facility, district and national level.
Population-based severe disease and mortality surveillance
There are not sample-based or specimen-based severe disease and mortality surveillance studies underway yet in Malawi, and is an urgent need for empirical data on all-cause and cause-specific mortality in all ages and populations. An estimated three quarters of all deaths occur at home, most of which are not being captured in routine data collection, and the causes of which are often not confirmed. The changing epidemiology of HIV in a context of high treatment coverage and the increasing non-communicable disease burden in the general population imposes a pressing need for data on causes of mortality and severe disease in both PLHIV and general population.
We propose to implement a phased approach to comprehensive population-based severe disease and mortality surveillance (SDMS) in urban Lilongwe and rural Salima districts, in order to rapidly fill the data gap identified by both the Ministry of Health (MOH) and the National Registration Bureau (NRB). This will include capacity-building in-country to diagnose severe disease using state of the art diagnostics, and to collect, process and interpret postmortem minimally-invasive tissue specimens. While primarily for research purposes, any laboratory findings on admitted patients will be shared immediately with the health care provider to improve clinical care. Once established, we will expand SDMS to a nationally-representative sample of sites and their catchment areas to generate more complete data on the distribution and burden of diseases and causes of death across the nation. Using a population-based approach for SDMS will guarantee our ability to calculate precise all-cause and cause-specific rates, as the frequency and intensity of data collection will ensure we have a data ‘island of completeness’. With accurate denominators to complement the rigorously collected numerators, we will be able to rapidly ascertain accurate estimates of the burden of mild and severe diseases, and cause-specific mortality rates.
Snakebite envenomation incidence and outcomes survey
Snakebite envenomation (SBE) affects the poorest and most vulnerable communities in low- and middle-income countries. It is classified by the World Health Organization (WHO) as a neglected tropical disease (NTD), with an estimated 5.4 million snakebites annually resulting in as many as 400,000 deaths and permanent disabilities. SBE is arguably the most under-funded NTD, and the data that does exist is based on incomplete health facility data. Very few countries have any accurate population-based estimates: Recent surveys in Mozambique and Kenya estimated that snakebite incidence is exponentially higher than believed.
In collaboration with the Global Snakebite Initiative (GSI) and Health and Demographic Surveillance System (HDSS) sites across the continent, we are seeking funding to conduct Africa’s first multi-country SBE incidence and outcomes survey. This approach minimizes cost by making the most of existing research infrastructure, and the data will be invaluable for global modeling and to inform national program planning and resource allocation.