Analyze current epidemiological data for management, evaluate various determinants and measures of health, and to synthesize this information to make management decisions based on Christian principles.
The case study provides the learner with the opportunity to analyze current epidemiological data for management, evaluate various determinants and measures of health, and to synthesize this information to make management decisions based on Christian principles.
Case studies also provide the future professional with a glimpse into the real-world application of writing briefs or actual case studies for use in management or leadership decision making.
These assignments provide a framework for how niche-specific, social proofs may be used to inform decisions in health care administration. Leaders will utilize these types of case studies to determine how to best serve populations or at-risk demographics when delivering health care.
Case Study Prompts
Case Study: Planning for Mental Health Services Assignment Read/Review: Chapter 11, Case Study 11.2
Respond to the following question sets. Include a description of how you derived the response.
What proportion of treated patients with severe mental disorders was treated by general practitioners only? Explain your response.
What proportion of patients with severe mental disorders was not treated by health services? Explain your answer with help from the data provided/calculated?
Based on the data presented in Table 11.5, what are the resource implications of recommended targets in Queensland for inpatient acute and non-acute beds, ambulatory care clinical staffing, and financial implications of NGO-managed community support services in 2005-2017?
In addition to planning agencies, describe additional direct and indirect methods of healthcare planning.
A brief prompt:
Epidemiological Data Analysis for Management
In epidemiology, timely and accurate data is essential for making informed decisions on interventions to halt the spread of infection. The purpose of this paper is to review the use of epidemiological data in public health management, with a focus on outbreak response. In recent years there have been numerous outbreaks of infectious diseases around the world, such as Ebola, Zika, and pandemic influenza. The ability to rapidly access and analyze epidemiological data has never been more important.
There are a number of different types of data that can be used in epidemiological analysis, including case reports, surveillance data, and clinical data.
Each type of data has its own strengths and weaknesses, and it is important to understand the limitations of each before using them to make decisions.
For example, case reports are useful for identifying potential risk factors for disease, but they are subject to selection bias and may not be representative of the general population. Surveillance data can be used to track the spread of disease and identify hot spots, but it is often delayed and may not be complete. Clinical data can be used to assess the severity of disease and identify potential treatments, but it is often expensive and time-consuming to collect.
The choice of data source will depend on the specific question being asked. For example, if we want to know what percentage of the population is infected with a disease, surveillance data would be more appropriate than case reports.
If we want to identify risk factors for disease, case reports would be more appropriate than surveillance data. And if we want to assess the severity of disease or identify potential treatments, clinical data would be more appropriate than either case reports or surveillance data.
Once the decision has been made on which type of data to use, the next step is to collect and analyze the data. This can be done using a variety of methods, including descriptive epidemiology, statistical analysis, and mathematical modeling.
Each method has its own advantages and disadvantages, and it is important to choose the right method for the question being asked.
For example, descriptive epidemiology is useful for generating hypotheses about risk factors for disease, but it cannot be used to test those hypotheses. Statistical analysis can be used to test hypotheses about risk factors for disease, but it is often complex and time-consuming.
Mathematical modeling can be used to generate predictions about the spread of disease, but it requires a good understanding of the underlying biology and is often difficult to interpret.
The choice of data source and analysis method will depend on the specific question being asked and the resources available. It is important to consult with experts in epidemiology and statistics before making any decisions, as they will be able to advise on the best way to proceed. With the right data and analysis, we can make informed decisions on interventions to halt the spread of disease and save lives.
Centers for Disease Control and Prevention. (2018). Data & Statistics. Retrieved from: CDC website: cdc.gov/data-statistics/.
World Health Organization. (2017). WHO | Epidemiological data collection and analysis in outbreak response. Retrieved from World Health Organization website: who.int/csr/resources/publications/ebola/ebola-epidemiology-20140722/.