Descriptive Epidemiology: Data Sources and Data Collection
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Descriptive Epidemiology: Data Sources and Data Collection
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Contextual Analysis of Diabetes Associated with Obesity as a Public Health Concern
The health problem selected is diabetes associated with obesity. Diabetes is a chronic disease marked by high blood sugar levels spurred by the body's failure to generate or use insulin. Gallardo-Rincón et al. (2021) state that 32 million persons in Latin America have diabetes. About 463 million adults worldwide have diabetes, with 79% residing in low- and middle-income nations. Type 2 diabetes accounts for 90% of all cases, making it one of the world's leading causes of illness and mortality. It is not unexpected that the prevalence of type 2 diabetes is increasing along with the obesity rates in Latin America and the Caribbean (57% of adults are overweight, and 19% are obese). Obesity has a highly suspicious relationship with the development of type 2 diabetes. In nations like Mexico, the Dominican Republic, Haiti, and Peru, among others, the prevalence has steadily increased. In the USA, there were 28.7 million individuals as of 2019. Among them, 35 of every 100,000 American youths under 20 have diabetes (Wang et al., 2022). According to predictions from the World Diabetes Federation for 2019, 1.6 million additional adults over 20 developed diabetes. The frequency of this disorder was higher among adult men and women living below the federal poverty line. South Americans had the highest rate of diabetes, followed by Central America.
Application of Probability Sampling Techniques in Epidemiological Data Collection
The sampling method for this study's data collection will be probability sampling to assess the frequency and epidemiology of diabetes in a community. The sampling method will be fit because there is a probability that every member of the population, irrespective of age, sex, or line of work, will develop a diabetic condition. According to simple random sampling, the study participants are chosen randomly from the study population, and every member of the population has an equal chance of being included in the study. Also, to minimize the study sampling error, the sample population must be sizable enough to represent the larger population of interest adequately.
Utilization of Secondary Health Data Systems in Epidemiological Research
Important secondary data sources in this study include healthcare data systems like the Centers for Disease Control and Prevention (CDC) and the International Diabetes Federation (IDF) (Friis & Sellers, 2021). Regularly gathered and examined secondary data includes the epidemiologic frequency over time.
Impact of Data Collection Methods on Validity and Completeness of Case Identification
All medical professionals utilize the same case definitions to diagnose diabetes depending on the outcomes of the blood sugar testing. Since not all sources have a comprehensive representation of the American population, the type of secondary data source chosen will impact the data's validity and completeness. As facility-based, data will be gathered and dispersed throughout numerous sites, and secondary data sources like EHR lack thoroughness. Since it is a secondary data source, it is not easy to guarantee data validity. While they examine additional primary and secondary data in the research population, statistics from sources like IDF and CDC give accurate estimations.