Statistical Modelling of Complex Correlated and Clustered Data Household Surveys in Africa

$230.00

Ngianga-Bakwin Kandala, PhD (Editor)
Faculty of Engineering and Environment, Northumbria University, UK

Lawrence Kazembe, PhD (Editor)
Head of Department, Statistics and Population Studies, Faculty of Science, University of Namibia

Series: Research Methodology and Data Analysis
BISAC: MAT029000

In order to assist a hospital in managing its resources and patients, modelling the length of stay is highly important. Recent health scholarship and practice has largely remained empirical, dwelling on primary data. This is critically important, first, because health planners generally rely on data to establish trends and patterns of disease burden at national or regional level. Secondly, epidemiologists depend on data to investigate possible risk factors of the disease. Yet the use of routine or secondary data has, in recent years, proved increasingly significant in such endeavours. Various units within the health systems collected such data primarily as part of the process for surveillance, monitoring and evaluation. Such data is sometimes periodically supplemented by population-based sample survey datasets. Thirdly, coupled with statistical tools, public health professionals are able to analyze health data and breathe life into what may turn out to be meaningless data.

The main focus of this book is to present and showcase advanced modelling of routine or secondary survey data. Studies demonstrate that statistical literacy and knowledge are needed to understand health research outputs. The advent of user-friendly statistical packages combined with computing power and widespread availability of public health data resulted in more reported epidemiological studies in literature. However, analysis of secondary data, has some unique challenges. These are most widely reported health literature, so far has failed to recognize resulting in inappropriate analysis, and erroneous conclusions.

This book presents the application of advanced statistical techniques to real examples emanating from routine or secondary survey data. These are essentially datasets in which the two editors have been involved, demonstrating how to tackle these challenges. Some of these challenges are: the complex sampling design of the surveys, the hierarchical nature of the data, the dependence of data at the sampled cluster and missing data among many more challenges. Using data from the Health Management Information System (HMIS), and Demographic and Health Survey (DHS), we provide various approaches and techniques of dealing with data complexity, how to handle correlated or clustered data. Each chapter presents an example code, which can be used to analyze similar data in R, Stata or SPSS. To make the book more concise, we have provided the codes on the book’s website.

The book considers four main topics in the field of health sciences research: (i) structural equation modeling; (ii) spatial and spatio-temporal modeling; (iii) correlated or clustered copula modeling; and (iv) survival analysis.

The book has potential to impact methodologists, including students undertaking Master’s or Doctoral level programmes as well as other researchers seeking some related reference on quantitative analysis in public health or health sciences or other areas where data of similar nature would be applicable. Further the book can be a resource to public health professionals interested in quantitative approaches to answer questions of epidemiological nature. Each chapter starts with a motivating background, review of statistical methods, analysis and results, ending discussion and possible recommendations.
(Imprint: Nova)

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Table of Contents

Table of Contents

Preface

Acknowledgements

Chapter 1. Analysis and Modelling of Complex Secondary Data: An Overview of Methodological Issues and Challenges
(Lawrence N. Kazembe and Ngianga-Bakwin Kandala, Department of Statistics and Population Studies, University of Namibia, Windhoek, Namibia, and others)

Chapter 2. A Mixed Discrete-Time Survival Analysis of Length of Hospitalization: Applications to Malaria Admissions among Peadiatric Children in Malawi
(Lawrence N. Kazembe, Department of Statistics and Population Studies, University of Namibia, Windhoek, Namibia)

Chapter 3. Bivariate Model of Health Seeking Behaviour among Women for Their Under-Five Children with Fever
(Bamidele Oguntoyinbo and Lawrence Kazembe, Mathematical Sciences Department, University of Malawi, Chancellor College, Malawi, and others)

Chapter 4. Mover-Stayer Model on Future Contraceptive Use among Married Women in Malawi
(George T. Mwenye-Phiri and Lawrence N. Kazembe, Malawi College of Health Sciences, Lilongwe, Malawi, and others)

Chapter 5. Investigating Causal and Mediating Risk Factors for Stunting in under Five Children in Malawi Using Structural Equation Modelling Techniques
(Emmanuel Banda, Mavuto Mukaka and Lawrence Kazembe, Mathematical Sciences Department, University of Malawi, Chancellor College, Zomba, Malawi, and others)

Chapter 6. Linking Food Insecurity to Quality of Life Using Structural Equation Models
(T. Shinyemba, N. Nickanor and L. Kazembe, Department of Statistics and Population Studies, University of Namibia, Windhoek, Namibia)

Chapter 7. A Zero-Truncated Negative Binomial Regression Model for Dietary Diversity in Namibian Under-5 Children
(L. Pazvakawambwa, L. and N. Nickanor, Department of Statistics and Population Studies, University of Namibia, Windhoek, Khomas, Namibia)

Chapter 8. A Copula Approach to Sample Selection Modelling of Treatment Adherence and Viral Suppression among HIV Patients on Antiretroviral Therapy (ART) in Namibia
(Jason Nakaluudhe and Lawrence N. Kazembe, Department of Statistics and Population Studies, University of Namibia, Windhoek, Namibia)

Chapter 9. Copula-Linked Generalized Joint Regression Model for Water, Sanitation and Hygiene (WASH) Coverage in Namibia
(Anastasia Johannes and Lawrence Kazembe, Department of Statistics and Population Studies, University of Namibia, Windhoek, Namibia)

Chapter 10. Bivariate Copula-Based Regression to Model Timing and Frequency of Antenatal Care Utilization
(Lawrence N. Kazembe, Department of Statistics and Population Studies, University of Namibia, Windhoek, Namibia)

Chapter 11. Multiscale Spatial Modelling of Diabetes and Hypertension in Namibia
(Tommy R. Harris and Lawrence Kazembe, Division of Population and Housing Census and Demographic Surveys, Namibia Statistics Agency, Windhoek, Namibia, and others)

Chapter 12. Models for Analyzing Spatial Patterns in Risk of Urban Malaria: A Case Study of Blantyre, Malawi
(Don P. Mathanga and Lawrence N. Kazembe, Department of Community Health, College of Medicine, University of Malawi, Blantyre, Malawi, and others)

Chapter 13. Spatio-Temporal Modelling of Malaria Risk in Malawi: An Application to Health Management Information System Data
(Misheck Richard Luhanga and Lawrence Kazembe, Chancellor College, University of Malawi, Zomba, Malawi, and others)

Chapter 14. Modelling Spatial and Spatial-Temporal Patterns of TB and HIV Mortality in Namibia
(Andreas I. Shipanga and Lawrence N. Kazembe, Department of Statistics and Population Studies, University of Namibia, Windhoek, Namibia)

Chapter 15. Attrition of Women Initiating Antiretroviral Therapy (ART) under Option B+: Cox Proportional Hazards, Competing Risks and Multistate Survival Models
(Andrew Mganga and Lawrence N. Kazembe, Mathematical Sciencies Department, University of Malawi, Zomba, Malawi, and others)

Epilogue

About the Contributors

Index

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