Response- DESCRIPTIVE EPIDEMIOLOGY: DATA SOURCES AND DATA COLLECTION
janejayson
ResponseDESCRIPTIVEEPIDEMIOLOGY-DATASOURCESANDDATACOLLECTION.docx
MG
Mar 3 2:42am| Last reply Mar 3 3:04am
Reply from Mobolanle O Greene
Initial Post
The Obesity Epidemic: An Epidemiologic Analysis
Introduction Obesity, a multifaceted and pervasive health issue, affects millions of adults in the United States, with far-reaching consequences for individuals, communities, and the healthcare system. This analysis aims to provide an in-depth examination of the obesity epidemic, focusing on the epidemiologic characteristics of person, place, and time, and exploring potential interventions to address this pressing public health concern.
Epidemiologic Model (Person, Place, and Time)
- Person: Obesity disproportionately affects vulnerable populations, including racial and ethnic minorities, low-income individuals, and those with limited access to healthcare (Hales et al., 2020). The prevalence of obesity is significantly higher among non-Hispanic black adults (49.6%) and Hispanic adults (44.8%) compared to non-Hispanic white adults (42.2%) (Hales et al., 2020). - Place: Geographic disparities in obesity prevalence are striking, with the Southern United States bearing the greatest burden (CDC, 2020). Mississippi, in particular, has the highest prevalence of obesity (39.6%), highlighting the need for targeted interventions in this region (CDC, 2020). - Time: The obesity epidemic has reached alarming proportions, with a significant increase in prevalence from 30.5% in 2000 to 42.4% in 2018 (Hales et al., 2020). This trend underscores the urgent need for effective prevention and treatment strategies.
Sampling Methods for Primary Data Collection
- Random Sampling: Employing random sampling techniques ensures representation of diverse demographics and reduces selection bias, thereby increasing the generalizability of findings (Groves et al., 2009). - Stratified Sampling: Stratifying the sample by age, sex, and ethnicity enables researchers to identify specific subgroups that are disproportionately affected by obesity, informing targeted interventions (Krieger, 2001).
Secondary Data Sources
1. National Health and Nutrition Examination Survey (NHANES): NHANES provides a rich source of data on obesity prevalence, risk factors, and health outcomes, utilizing a complex sampling design and collecting data through interviews, physical examinations, and laboratory tests (CDC, 2020). 2. *Behavioral Risk Factor Surveillance System (BRFSS)*: BRFSS offers valuable insights into obesity prevalence, physical activity, and nutrition habits, leveraging a state-based system and telephone surveys to collect data (CDC, 2020).
Influence on Case Identification and Definition
- NHANES: The use of measured height and weight data in NHANES ensures accurate obesity classification (BMI ≥30), reducing the risk of bias and increasing the reliability of the data (CDC, 2020). - BRFSS: While BRFSS relies on self-reported data, which may underestimate obesity prevalence due to reporting bias, it provides a large sample size and state-level estimates, making it a valuable resource for obesity research (CDC, 2020).
Conclusion The obesity epidemic is a pressing public health concern that requires a comprehensive and multifaceted approach. By understanding the epidemiologic characteristics of obesity and leveraging robust data sources, researchers and policymakers can develop targeted interventions to address this issue and improve health outcomes for affected populations.
References
Centers for Disease Control and Prevention. (2020). Obesity prevalence map. Retrieved from <(link unavailable)>
Hales, C. M., Carroll, M. D., Fryar, C. D., & Ogden, C. L. (2020). Prevalence of obesity and severe obesity among adults: United States, 2017-2018. NCHS Data Brief, (360), 1-8.
Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey methodology (2nd ed.). Wiley.
Krieger, N. (2001). A glossary of epidemiology. International Journal of Epidemiology, 30(5), 1167-1172.
DM
Mar 2 10:18am
Reply from Deonna Murdock
Structural Racism in Healthcare
Structural racism in healthcare continues to shape health outcomes in the United States and remains a major driver of disparities. When examined through the lens of descriptive epidemiology person, place, and time it becomes clear that these inequities are patterned, predictable, and preventable. As an advanced practice nurse, I believe it is critical to move beyond individual-level explanations and examine the systemic forces that influence population health.
Person
From the perspective of person, African American women are disproportionately affected by structural racism in healthcare, particularly in maternal health outcomes. Black women in the United States are significantly more likely to experience pregnancy-related complications and death compared to White women, regardless of income or education level (Taylor, 2019). This indicates that the disparity is not simply socioeconomic it reflects cumulative exposure to bias, differential treatment, and chronic stress associated with racism. Research also demonstrates that implicit bias and unequal treatment within healthcare settings contribute to delayed diagnoses, undertreatment of pain, and dismissal of patient concerns (Yearby et al., 2022). These patterns highlight how race, gender, insurance status, and comorbidities intersect to increase risk.
Place
When examining place, geographic and structural factors further explain disparities. Racial health inequities are more pronounced in historically segregated urban communities and in Southern states where policy decisions have limited healthcare access. Residential segregation, hospital closures in underserved areas, and uneven distribution of specialty care all contribute to poorer outcomes in predominantly Black communities. Facilities that serve minority populations are often under-resourced compared to hospitals in wealthier, predominantly White communities. These structural differences directly influence quality of care, timeliness of intervention, and access to preventive services.
Time
Looking at time, racial disparities in maternal and chronic disease outcomes have persisted for decades. Although medical advancements have improved overall survival rates, the gap between Black and White women has remained largely unchanged (Taylor, 2019). Historical policies such as segregation and redlining continue to influence present-day access to care. More recently, the COVID-19 pandemic further exposed and amplified these inequities, demonstrating how systemic vulnerabilities disproportionately impact marginalized populations. The persistence of these patterns over time reinforces that structural racism is embedded within healthcare systems rather than being an isolated or temporary issue.
Sampling Methods for Primary Data Collection
To collect primary data on this issue, I would use a stratified sampling approach to ensure representation across racial groups, insurance categories, and geographic regions. Oversampling African American women in maternal health clinics or hospital settings would allow for meaningful subgroup analysis and stronger statistical comparisons.
In addition, I would incorporate purposive sampling for qualitative interviews with African American women who have experienced pregnancy-related care. This approach would provide insight into lived experiences of bias, communication barriers, and perceived discrimination. Combining quantitative surveys with qualitative interviews would strengthen the study design by providing both measurable outcome trends and contextual depth.
Secondary Data Sources
Two key secondary data sources that would support this topic include:
1. Centers for Disease Control and Prevention (CDC) WONDER database – This database provides national and state-level mortality and morbidity data stratified by race, ethnicity, and geographic location.
2. Healthcare Cost and Utilization Project (HCUP) – HCUP contains hospital discharge and utilization data that can be used to analyze maternal morbidity, readmissions, payer status, and procedural outcomes across populations.
These sources provide large-scale, population-level data that are essential for identifying trends and disparities.
Influence on Case Identification and Diagnostic Criteria
The selection of data sources significantly influences case identification and case definitions. Secondary databases such as CDC WONDER rely on death certificate data and ICD-10 coding, which may underreport contributing factors or misclassify causes of death. Administrative datasets like HCUP are based on billing codes, which may not capture social determinants of health or experiences of discrimination. As a result, cases related to structural inequities may be underestimated.
Primary data collection improves contextual understanding but may introduce recall bias and limitations in generalizability, depending on sampling strategy. Using standardized definitions such as CDC criteria for severe maternal morbidity helps maintain consistency across datasets.
Overall, integrating both primary and secondary data strengthens surveillance and enhances the completeness of case identification. Broad surveillance data identify patterns, while qualitative data provide insight into mechanisms driving disparities. Together, these approaches support evidence-based policy and system-level change.
References
Taylor, J. (2019). Structural racism and maternal health among Black women. Journal of Law, Medicine & Ethics, 47(1_suppl), 49–52. https://doi.org/10.1177/1073110519920068
Yearby, R., Clark, B., & Figueroa, J. F. (2022). Structural racism in historical and modern U.S. health care policy. Health Affairs, 41(2), 187–194. https://doi.org/10.1377/hlthaff.2021.01466Links to an external site.
