# Epidemiology Sample Solution

Question Summary: –

In the statistical sample solution presented below, the expert has revealed our expertise in answering the following questions: –

• Why there is an increment in z-scores for a corresponding increase in percentage of confidence intervals?
• Explain the concept of epidemiology and the impact that it has on population health.
• Make an interpretation of Biostatistical Methods in epidemiology which pertains to the development, implementation, and evaluation of health services.

Solution: –

As the percentage of confidence interval increases the area becomes wider and hence the Z-score also increases.

The Z-score is given below:

Course Objective:

1. The concept of epidemiology and its impact on population health:

Descriptive Epidemiology: Descriptive epidemiology focuses on describing disease distribution by characteristics relating to time, place, and people. The goal of epidemiology is to establish causal factors for health issues in order to improve the health and safety of entire populations. A population can refer to a town, country, age group, or race. Health issues refer to anything that might impact health in the present or future. For epidemiologists, data on who is most likely to be injured in car crashes can be just as valuable as a topic of inquiry as data on what part of the population is most at risk for developing complications from the flu. In order to accomplish this, epidemiology has two main branches: descriptive and analytical.

Descriptive epidemiology evaluates and catalogs all the circumstances surrounding a person affected by a health event of interest. Analytical epidemiologists use data gathered by descriptive epidemiology experts to look for patterns suggesting causation. The end goal of both branches is to reduce the incidence of health events or diseases by understanding the risk factors for the health events or diseases. Both descriptive and analytical epidemiology often serve public health organizations by providing information that may reduce disease or reduce other kinds of events that impact people’s health.

The primary considerations for descriptive epidemiology are frequency and pattern. Frequency evaluates the rate of occurrence, and pattern helps analytical epidemiologists suggest risk factors. Descriptive epidemiology evaluates frequency and pattern by examining the person, place, and time in relationship to health events.

Descriptive epidemiology examines factors like age, education, socioeconomic status, availability of health services, race, and gender. Evaluations of specific individuals may also include gathering information on behaviors like drug abuse, shift work, eating, and exercise patterns.

Analytical Epidemiology: Epidemiology draws statistical inferences, mostly about causes of disease in populations based on available samples of it.

Epidemiology is the study (or the science of the study) of the patterns, causes, and effects of health and disease conditions in defined populations. It is the cornerstone of public health, and informs policy decisions and evidence-based medicine by identifying risk factors for disease and targets for preventive medicine. Epidemiologists help with study design, collection and statistical analysis of data, and interpretation and dissemination of results (including peer review and occasional systematic review). Epidemiology has helped develop methodology used in clinical research, public health studies and, to some extent, basic research in the biological sciences.

Epidemiologists employ a range of study designs from observational to experimental and generally categorized as descriptive, analytic (aiming to further examine known associations or hypothesized relationships), and experimental (a term often equated with clinical or community trials of treatments and other interventions).

Where descriptive epidemiology describes occurrence of disease (or of its determinants) within a population, the analytical epidemiology aims to gain knowledge on the quality and the amount of influence that determinants have on the occurrence of disease. The usual way to gain this knowledge is by group comparisons. Such a comparison starts from one or more hypotheses about how the determinant may influence occurrence of disease. Analytical epidemiology attempts to determine the cause of an outbreak. Using the case control method, the epidemiologist can look for factors that might have preceded the disease. Often, this entails comparing a group of people who have the disease with a group that is similar in age, sex, socioeconomic status, and other variables, but does not have the disease. In this way, other possible factors, e.g., genetic or environmental, might be identified as factors related to the outbreak.

Epidemiology is often considered the key scientific underpinning of public health practice. This pivotal role of epidemiology was emphasized by the Institute of Medicine in its definition of the substance of public health as organized community efforts aimed at the prevention of disease and promotion of health, with linkages to many disciplines and a scientific core of epidemiology (1, 2).

Lilienfeld and Lilienfeld observed 3 decades ago that “… the discipline of epidemiology has become increasingly divorced from those activities in the real world that result in the improvement of public health” (3, pp. 146–147). The new text from Haveman-Nies et al. addresses the linkage and gaps between epidemiology and public health practice and policy. As noted in the introductory chapter, the book broadens the common definition of epidemiology (i.e., the study of the distribution and determinants of health and diseases in populations). A useful starting point is perhaps the most comprehensive definition and the one most relevant to public health practice (4).

Epidemiology is the study of the health of human populations. Its functions are as follows:The book by Haveman-Nies et al. primarily addresses parts 2–4 of this definition.

1. To discover the agent, host, and environmental factors that affect health to provide the scientific basis for the prevention of disease and injury and the promotion of health;
2. To determine the relative importance of causes of illness, disability, and death to establish priorities for research and action;
3. To identify those sections of the population that have the greatest risk from specific causes of ill health so that the indicated action may be directed appropriately; and
4. To evaluate the effectiveness of health programs and services in improving the health of the population.

In describing the role of epidemiology in public health practice, the authors introduce their 7 epidemiologic steps in the public health cycle. These steps include conducting a needs assessment, setting priorities, formulating objectives, constructing a logic model, developing an evaluation plan, performing quality control, and analyzing processes and outcomes. The heart of the book is the chapter-by-chapter description of each of these steps. Many of these approaches are parallel to frameworks for evidence-based medicine (5) and evidence-based public health practices (6). One could argue that much of what is contained in the 7 core chapters is quite different from the standard literature on epidemiology; yet, in most places, the authors do a nice job of linking epidemiologic contributions to these important public health functions. There are 2 areas in which I would have valued more detail: 1) the important contributions and approaches from economic evaluation and 2) searching the scientific literature when a systematic review is not available (7).

In the later chapters, the authors introduce other disciplines that complement epidemiology (e.g., health services, health promotion, and primary care). In that set of chapters, the section on policy is especially useful and interesting. We know that policy is among the most important determinants of health, and yet the linkages between epidemiologic science and policy-making are often limited and sporadic. Short case studies are scattered throughout the book. These are generally useful, although I would have preferred additional cases from a range of countries beyond the Netherlands. More far-reaching case studies would have broadened the appeal of the text.

This book appears to have been written for practicing public health professionals who do not have extensive formal training in the public health sciences (epidemiology, behavioral science, biostatistics, environmental and occupational health, and health management and policy) and for students in public health and preventive medicine. It will be useful mainly in Western Europe, but many of the concepts are helpful across any part of the world. The text succeeds in addressing its core audiences at the right level.

Overall, the authors are to be applauded for broadening understanding of the role of epidemiology, linking it with other core public health disciplines, and highlighting the core issues of prime importance in the real-world practice of public health.

2.      Interpretation of biostatistical methods in epidemiology relevant for the development, implementation, and evaluation of health services:

Epidemiologists use statistical models in order to track the progress of most infectious diseases. They may also discover the likely outcome of an epidemic or to help manage them by vaccination. Some specific areas that epidemiologists may track are as follows: • transmission, spread and control of infection • persistence of pathogens within hosts • immuno-epidemiology • virulence • strain structure and interactions • evolution and spread of resistance One specific type of mathematical model used for many infectious diseases, such as measles, mumps, and rubella, is the SIR model. This model consists of three variables: S (for susceptible), I (for infectious) and R (for recovered).

Use statistical software to analyze health–related data.

Build a statistical model over real health data.

Estimate and compare efficiency of models.

Describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met.

Perform univariate data analysis for continuous and categorical variables.

Analyze a dataset with categorical variables and explain the theoretical underpinnings that lead to choosing the appropriate analysis methodology.

Interpret inferential findings within Bayesian thinking (e.g. credible intervals, hypothesis testing).

Conduct inference via posterior simulation and simulations tool.

Explain the advantages of a Bayesian data analysis.

Analyze a dataset with censored outcome and explain the theoretical underpinnings that lead to choosing the appropriate analysis methodology.