Descriptive Epidemiology: Data Sources and Data Collection Essay
Descriptive epidemiology is a systematic approach to organizing and analyzing a given health problem. The approach helps in understanding determinants, variations, and the geographical distribution of disease over time. Descriptive epidemiology examines person, place, and time features in disease that are used to provide important clues concerning the source of disease outbreaks (Friis & Sellers 2021). This essay identifies a population health problem and dataset. Then, it will identify variables in each dataset needed for association of interest, assess the validity of the dataset, and explain challenges a researcher may face when identifying a proper dataset and securing permission to use it. Descriptive Epidemiology: Data Sources and Data Collection Essay
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The identified health issue is malaria, which is a mosquito-borne parasitic disease affecting humans and other animals. The disease is transmitted to humans through bites and is a severe public health issue, especially in third-world countries. Malaria is a leading cause of mortality in the world. According to the CDC (2021), approximately 627,000 people died of malaria in 2020, and a majority of them were young children from sub-Saharan Africa. In addition, almost half of the world’s population is at risk of malaria transmission. The clinical episodes caused by malaria in 2020 were estimated at 241 million. Malaria is a public health concern because of the high costs it imposes on both individuals and governments. Individuals must pay for treatment, preventive measures, and burial expenses if they die. Costs to government include the purchase of infrastructure for health facilities, staff costs, public health interventions, and lost opportunities for joint economic ventures (White, 2022). Direct costs are estimated at $12 billion annually, and the cost of lost economic activity is even higher. Malaria is preventable and treatable, with research-based evidence suggesting that it can be eradicated (Talapko et al., 2019). Descriptive Epidemiology: Data Sources and Data Collection Essay
The chosen datasets are the malaria data set from the World Health Organization (WHO) and the malaria dataset from the Institute of Health Metrics and Evaluation (IHME). The variables in the WHO malaria dataset required to assess the association of interest include country, year, deaths, and malaria incidence. The variables in the IHME data set required to study the correlation of interest include country, year, incidence, and number of cases. Validity is the extent to which an instrument accurately measures what it purports to measure. The three common types of validity include content, construct, and criterion validity. Content validity measures the degree to which items adequately represent the content a researcher wishes to measure, while construct validity measures the extent to which a method truthfully represents a construct. Criteria-related validity refers to the degree to which an instrument’s scores correlate with measurements from different instruments, either in the present or in the future.
The malaria dataset from WHO is a valid and reliable dataset. The data has the content required by the researcher to investigate associations of interest. The dataset has been utilized in previous research and publications. It contains comprehensive information on malaria prevalence and deaths attributed to it per country. The data is up-to-date, well arranged, readily available, and reliable. WHO places no limitation on the malaria dataset’s usage and utilization. Researchers are not required to pay any amount to access it. The dataset from IHME is a valid and reliable dataset. Using content validity, the malaria dataset from IHME contains the information the researcher is interested in and will help in determining correlations between variables of interest. The dataset contains information on malaria prevalence and reported cases per country. The data is up-to-date and easily accessible. For this dataset, a researcher is not required to pay anything to access it, and it has no restrictions on its utilization. The dataset has been utilized in previous research studies and publications. Descriptive Epidemiology: Data Sources and Data Collection Essay
A researcher may encounter several problems in locating a suitable dataset or obtaining permission to use it. Information contained in some datasets may be incomplete and inaccurate. Also, it is very difficult to authenticate the information contained in the dataset. Some datasets are not comprehensive and contain old information that has not been updated to reflect current statistics. A researcher may face challenges accessing some datasets due to the high costs charged. Also, a dataset may be accessible but not be usable by a researcher due to the restrictions placed on its utilization. It may be difficult to gain access to datasets that require a researcher to obtain authorization or permission to access them. Some datasets may have linguistic barriers, making it hard for a researcher to comprehend the data. The financial burden that comes with data acquisition can be an obstacle for research. Despite the difficulties in accessing datasets, the datasets from WHO and IHME are accurate, complete, and current enough to be used in researching the topic of interest. In conclusion, descriptive epidemiology helps improve health outcomes and public health practice. Data for use in epidemiology can be derived from databases and credible libraries. Several obstacles exist in searching for research data, but they can be mitigated to ensure research findings are credible and accurate. Descriptive Epidemiology: Data Sources and Data Collection Essay
References
Centers for Disease Control and Prevention. (2021). Malaria Impact Worldwide.
Curley, A. L. C. (Ed.). (2020). Population-based nursing: Concepts and competencies for advanced practice (3rd ed.). Springer.
Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th ed.). Jones & Bartlett.
Institute of Health Metrics and Evaluation. (2022). Malaria.
Talapko, J., Škrlec, I., Alebić, T., Jukić, M., & Včev, A. (2019). Malaria: the past and the present. Microorganisms, 7(6), 179.
White, N. J. (2022). Severe malaria. Malaria Journal, 21(1), 1-17.
World Health Organization. (2021). WHO Data collections Links to an external site.[Data sets]. https://www.who.int/data/collections Descriptive Epidemiology: Data Sources and Data Collection Essay