CPHS Seminar: Novel Forms of Data for Understanding LMIC Health Inequalities: The Case of Tanzania

Title: Novel Forms of Data for Understanding LMIC Health Inequalities: The Case of Tanzania.

Abstract: This session reviews the methodological advantages of the Scottish Government annual reports that have monitored socio-economic health inequalities1 across 6505 local datazones since 2007– as manifested across about a dozen routinely collected health outcomes at the national level. Despite some critiques of these annual reports’ selection of health outcomes for analysis, and some of their interpretations2, they are among the best national reports in the world monitoring health inequalities by socioeconomic status (SES). The transferability of this cost-effective approach to Tanzania, to achieving SDG#10 – To Reduce Health Inequalities (HIs) – is then discussed, including the vexing issue of measuring SES in countries with flawed census and income tax data. A novel set of data-sources for this purpose has been proposed in our recent paper in Journal of Global Health3: anonymized, routinely collected administrative data from other (non-health) sectors, including both mobile phone billings and m-banking taxes, to estimate the average SES of each Tanzanian Ward (population 5-20,000). Health inequalities across quantiles of these SES proxies could be analysed using routinely collected health outcomes recorded in local health-care facilities. It is argued that, one put in place, this approach would be not only less expensive, but also less technically complex, and more nationally self-reliant (“kujitegemea” in Swahili) than the current approach. That approach relies on infrequent, costly, and technically complex Demographic and Health Surveys funded by development assistance donors, but insufficiently statistically powerful to track HIs for less common health outcomes, such as maternal mortality, at even the Regional (population 1-3 million), let alone District level.

REFERENCES: 1. Long-Term Monitoring of Health Inequalities: Headline Indicators. Scottish Government Health Analytical Services Division, Edinburgh. 2012. https://www.gov.scot/publications/long-term-monitoring-health-inequalities-headline-indicators-october-2012/ 2. Frank JW, Haw S. Best-Practice Guidelines for Monitoring Socioeconomic Inequalities in Health Status: Lessons from Scotland. The Milbank Quarterly 2011; 89(4):658-693. 3. Frank JW, Pagliari C, Geubbels E, Mtenga S. New forms of data for understanding LMIC health inequalities: the case of Tanzania. Journal of Global Health Dec 2018; 8(2):020302 doi:10.7189/jogh 08.020302.

Speaker: Professor John Frank

Biography: Professor Frank trained in Medicine and Community Medicine at the University of Toronto, in Family Medicine at McMaster University, and in Epidemiology at the London School of Hygiene and Tropical Medicine. He served with VSO/CUSO in Mbeya, Tanzania in 1976-9 as a Medical Officer and teacher of Medical Assistants. His appointments include: Professor (now Emeritus) at the University of Toronto, at the Dalla Lana School of Public Health, since 1983; founding Director of Research at the Institute for Work & Health in Toronto from 1991 to 1997; and inaugural Scientific Director of the Canadian Institutes of Health Research - Institute of Population and Public Health (2000-2008). From 2008 to 2018, he was the founding Director of an Edinburgh-based Unit, funded by the Medical Research Council and the Scottish Chief Scientist Office: the Scottish Collaboration for Public Health Research and Policy. The Collaboration has sought to develop and robustly test novel public health policies and programs to equitably improve health status in Scotland, through the convening and ongoing support of researcher/research-user consortia. Prof Frank holds a Chair in Public Health Research and Policy at the University of Edinburgh, where he was appointed Director of Knowledge Exchange and Research Impact for the Usher Institute in late 2017. In mid-2018, he became an Honorary Public Health Consultant to NHS Scotland, attached to the Scottish Public Health Observatory.

Lunch will be provided