Discuss the potential biases that may exist in different public health datasets. Feel free to focus on specific datasets or types of data that you are familiar with, but you can also consider the following types of data:
Infectious disease data from public schools
Fall data from nursing homes
Opioid overdose data from first responder reports
Genetic risk profiles from rural regions in developing countries
Instructions:
Your discussion should include the types of explicit and implicit biases that may be in the data, as well as how both sampling and reporting biases may play a role in the data creation process
Finally, describe the ideal process for creating the data (as unrealistic and infeasible as it may well be), and
Identify steps to creating a feasible dataset on the topic that either reduces biases as much as possible or at least would allow public health experts to better understand the limitations of the data
Full Answer Section
- Implicit biases: Data collectors may be more likely to report falls in nursing homes that serve minority residents or residents with dementia.
- Sampling bias: Data collectors may not sample all nursing homes equally, or they may not sample all residents within a nursing home equally. This could lead to a sample that is not representative of the overall population.
- Reporting bias: Nursing homes may be less likely to report falls to public health authorities, especially if they are concerned about their reputation.
Opioid Overdose Data from First Responder Reports
- Explicit biases: This data may be biased towards areas with higher rates of opioid overdose, as first responders are more likely to be called to these areas.
- Implicit biases: First responders may be more likely to report opioid overdoses in certain racial or socioeconomic groups.
- Sampling bias: First responders may not respond to all opioid overdoses equally, or they may not collect data on all opioid overdoses that they respond to. This could lead to a sample that is not representative of the overall population.
- Reporting bias: First responders may be less likely to report opioid overdoses to public health authorities, especially if they are concerned about the stigma associated with opioid use.
Genetic Risk Profiles from Rural Regions in Developing Countries
- Explicit biases: This data may be biased towards individuals from more affluent families who have access to healthcare and genetic testing.
- Implicit biases: Data collectors may be more likely to collect genetic data from individuals from certain racial or ethnic groups.
- Sampling bias: Data collectors may not sample all individuals from a rural region equally. This could lead to a sample that is not representative of the overall population.
- Reporting bias: Data collectors may be less likely to report genetic data to public health authorities, especially if they are concerned about the privacy of the individuals involved.
Ideal Process for Creating Public Health Datasets
The ideal process for creating public health datasets would be to collect data from all members of the population of interest, using standardized data collection methods and reporting guidelines. However, this is often unrealistic and infeasible.
Steps to Creating a Feasible Dataset
Here are some steps that can be taken to create a feasible dataset that reduces biases as much as possible or at least allows public health experts to better understand the limitations of the data:
- Use a representative sample: When possible, use a sample that is representative of the overall population of interest. This may involve using stratified sampling or random sampling techniques.
- Use standardized data collection methods: Use standardized data collection methods to ensure that data is collected consistently across all participants. This may involve using data collection forms or surveys.
- Use standardized reporting guidelines: Use standardized reporting guidelines to ensure that data is reported consistently across all studies. This may involve using data dictionaries or codebooks.
- Be transparent about the limitations of the data: Be transparent about the limitations of the data, such as the sample size, the sampling method, and the data collection methods. This will allow public health experts to better understand the data and its limitations.
By following these steps, public health experts can create datasets that are more reliable and less biased.
Sample Answer
Potential Biases in Public Health Datasets
Infectious Disease Data from Public Schools
- Explicit biases: This data may be biased towards schools in more affluent areas with better resources, as these schools are more likely to have the resources to collect and report data accurately.
- Implicit biases: Data collectors may be more likely to report infectious diseases in schools that serve minority students or students with disabilities.
- Sampling bias: Data collectors may not sample all schools equally, or they may not sample all students within a school equally. This could lead to a sample that is not representative of the overall population.
- Reporting bias: Schools may be less likely to report infectious diseases to public health authorities, especially if they are concerned about their reputation.
Fall Data from Nursing Homes
- Explicit biases: This data may be biased towards nursing homes with better staffing levels and resources. Nursing homes with fewer resources may be less likely to collect and report data accurately.