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.
General
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