Statistical Modelling for Evidence-Based Public Health (eng)

This book presents a selection of statistical techniques and methods applied to analyze data arising from HIV/AIDS epidemiology, as well as child and maternal health. Evidence-based decision-making in public health interventions requires appropriate techniques applied to solve relevant statistical and epidemiological questions, which would, in turn, bring out relevant outputs for action. The different chapters assembled in this book, address various methodological challenges when analyzing HIV/AIDS, child and maternal health data. These include data issues for handling correlated outcomes and repeated measurements generated through longitudinal or follow-up processes, spatial-temporal correlation, measurement error, missingness, co-morbidity, survival analysis, detection of outlying health outcomes, and joint occurrences of outcomes. Essential approaches that enhance statistical science, are presented, when dealing with variable and model selection.

Each chapter motivates the problem, provides details of the relevant bio-statistical methods used to tackle the problem, applies the methods to the data, and offers some epidemiological or public health recommendations. Readers can replicate the methods to their data, and R command codes are supplied at the end of each chapter.

Dr. Lawrence Kazembe is a Professor of Applied Statistics and Head of Department at the University of Namibia, and has a career that spans 30 years. With a PhD from Kwazulu-Natal University and extensive experience in biostatistics and research, he has held key academic and research roles across Eastern and Southern Africa, including at South African Medical Research Council, and Malawi-Liverpool Wellcome-Trust Clinical Research Programme. His work spans epidemiology, statistical modeling, and public health, with over 130 peer-reviewed publications. He has led multiple international research projects on biostatistics, food security, and health, funded by Wellcome Trust, Open Society, and others. A recognized leader in applied statistics, he contributes to policy and academia through high-impact research and mentorship in statistical sciences. He has supervised over 50 masters and over 15 PhDs.

Tsirizani Mwalimu Kaombe is a Senior Lecturer in Statistics in the Department of Mathematical Sciences within the School of Natural and Applied Sciences at the University of Malawi. He holds a Ph.D. in Biostatistics from the University of Malawi. In the past five years, he has successfully supervised/co-supervised 7 master’s degree theses on biostatistics and is currently supervising 4 more at the University of Malawi. His main area of research is regression diagnostics, particularly for non-linear multivariate data models. He is also interested in bivariate or joint probability distributions’ non-linear models. Tsirizani Kaombe has 10 refereed publications to his name, with more than 15 manuscripts in press.  Dr. Kaombe serves as a journal reviewer for Archives of Public Health, BMC Research Notes, BMC Medical Research Methodology, BMJ Open, BMC Public Health, BMC Pediatrics, Communications Medicine, and BMC Infectious Diseases. He is also serving as regional president for the International Biometric Society (IBS) Malawi chapter.