NURS 8310 Observational Study Designs Paper

NURS 8310 Observational Study Designs Paper

Epidemiological studies are profound in investigating, exploring, and confirming the suspected associations between risk factors for diseases, populations, and specific health outcomes. According to Munnangi and Boktor (2022), epidemiologic researchers aim to measure or assess the relationship between the disease’s exposure and an outcome. Therefore, the first step of conducting epidemiological research is to define a hypothesis consistent with the research question and decide the study design appropriate for answering the foreground question. Munnangi and Boktor (2022) argue that the study designs are experimental or observational depending on the implemented approach for establishing the associations between exposure and outcomes. Experimental study designs entail assigning patients to intervention and control/comparison groups to isolate post-intervention impacts. On the other hand, observational study designs involve observing the target population in a non-controlled environment without manipulating the course of the study. Although these studies complement each other, they have strengths and weaknesses. Therefore, this paper briefly describes two studies that focus on epidemiological study designs and methods. It describes the strengths and limitations of each study’s designs, identify the population, data sources, and epidemiologic measures, and explores personal insights into the appropriateness of the discussed designs.

Strengths and Limitations of Each Study’s Design

In the article “An ecological study of socioeconomic predictors in the detection of COVID-19 cases across neighborhoods in New York City,” Whittle and Diaz-Artiles (2020) investigate whether the potential socioeconomic factors can explain between-neighborhood variation in the COVID-19 test positivity rate. According to the researchers, socioeconomic factors like race, affluence, poverty, unemployment, insurance, age, and access to healthcare may play a significant role in exacerbating the discrepancies in the reported cases of COVID-19 between neighborhoods in New York City. To realize the study’s objective, Whittle and Diaz-Artiles (2020) used an ecological study design that entailed collecting data from 177 Zip Code Tabulation Areas (ZCTA) in New York City (99.9% of the population).

An ecological study design is common in the context of epidemiologic research since it is applicable in research scenarios where individual data is unavailable. According to Munnangi and Boktor (2022), ecological studies also apply where large-scale comparisons are necessary to explore the population-level effect of exposures on a disease condition. An ecologic study is preferable for epidemiologic research due to the following strength; these studies are cheap if the data already exist. According to Setia (2017), data are easy to collect when datasets regarding global measures and the prevalence of diseases are available. The second strength is the plausibility of generating hypotheses. Setia (2017) contends that ecological studies are useful in generating testable hypotheses. For example, researchers can effectively establish the correlation between per Capita intake of dietary fat and the prevalence of breast cancer in the target population.

Regardless of the strengths associated with ecologic study designs, researchers should familiarize themselves with the possible limitations that would jeopardize the study’s generalizability and reliability. One limitation of an ecologic study is the ecologic fallacy which refers to the logical errors associated with the attribution of group characteristics to an individual (Lokar et al., 2019). It is essential to note that individual characteristics may differ from that of a group despite sharing geographical aspects. The second limitation of an ecologic study is the systematic failure to address confounding variables that contribute to ecological bias and affect dependent variables. These confounders may compromise the study’s validity and generalizability.

In the second study, “E-cigarette use among young adult patients. The opportunity to intervene on risky lifestyle behaviors to reduce cancer risk,” Hillyer et al. (2018) inquired about E-cigarette use and explored demographic and lifestyle factors associated with the use of these products. The study design used to achieve this study objective was the survey design involving core questions regarding the population’s demographics, healthcare access, social determinants of health, cancer screening, and lifestyle behaviors, such as tobacco and E-cigarette use (Hillyer et al., 2018). 804 participants responded to the survey, allowing the researchers to perform univariable analyses to evaluate differences between E-cigarette users and non-users by participant type, smoking status, socio-demographic aspects, and personal history of cancer.

The survey study design applies to epidemiologic research due to its strengths, including cost-effectiveness, reliability, versatility, and generalizability. For instance, researchers can collect data from large samples using surveys, regardless of the prevailing differences in individual characteristics. Also, researchers can effectively incorporate different sampling techniques in survey methods to reduce biases and improve generalizability. However, some limitations compromise the applicability of survey study designs in epidemiologic research. Tan (2018) identifies errors associated with surveys, including administrative, respondent, conceptual, and measurement mistakes. For example, survey designs are susceptible to incidences of wrong data collection, incorrect answers from respondents, and the use of inappropriate measurement interventions. These limitations can affect the quality, accuracy, reliability, and validity of data collected through surveys.

Population, Data Sources, and Epidemiologic Measures of Association used by the Authors

In an ecological study, Whittle and Diaz-Artiles (2020) targeted people with confirmed cases of the COVID-19 pandemic in New York City. Due to a large sample, the researchers collected data from 177 Zip Code Tabulation Areas (ZCTA). The available database included 64,512 (99.3% of total cases. The researchers categorized data into various health, demographic, and economic parameters to establish epidemiologic measures of association. These parameters include age, population density, unemployment, income, poverty, and the percentage of the uninsured population. The primary measure of association used in this ecological study is correlation coefficients that explain the associations between health, demographic, and economic parameters and COVID-19 cases.

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In the survey study, Hillyer et al. (2021) targeted patients in primary and oncology clinics in communities of Washington Heights, the South Bronx, and Central Harlem. The target population was predominantly Hispanic, Black, and underprivileged. The researchers administered a health needs survey via email to 804 patients in adult primary care and oncology clinics. The data analysis instruments involved univariate analyses in evaluating differences between E-cigarette users and non-users, considering differences in smoking status, socio-demographic characteristics, personal history of cancer, and binge drinking behavior (Hillyer et al., 2021). The epidemiological measures of association used in this study were crude relative risk ratios (CRR) and adjusted relative risk ratios.

The Appropriateness of the Design for the Study

Regardless of the identifiable limitations associated with ecologic and survey study designs, these designs are appropriate to the respective studies. In a study aimed to investigate whether potential socioeconomic factors account for the between-neighborhood variations in the COVID-19 test positivity rates in New York City, an ecologic design is the most appropriate due to the need to use a large sample. Munnangi and Boktor (2022) argue that ecological studies are applicable when individual-level data are inadequate or unavailable or when large-scale comparisons are necessary to explain the population-level effect of exposure to disease. For example, the researchers may need to compare the associations between different demographic, economic, and health parameters and variations in COVID-19 test positivity rates. The availability of population-level data makes an ecological study design the most effective in epidemiological research.

Similarly, a survey study design is profound in epidemiological research because it allows researchers to use a large sample to gather broad and relatively shallow sample characteristics. According to Tan (2018), surveys are appropriate for generating quantitative variables like the target population’s educational attainment, income, gender, poverty level, and unemployment. Further, the researchers can correlate these variables with the explored dependent variables. For example, it is possible to correlate E-cigarette use among young adult patients and cancer risk.

Researchers’ Conclusions

Hillyer et al. (2021) conclude that tobacco smoking and binge alcohol consumption are risk factors for cancer. In this sense, inquiries about E-cigarette use among young adults provide ideal opportunities for reducing the risk of cancer. From a personal perspective, this conclusion is justified and informed by rigorous data analysis processes and external scholarly studies. For example, the Centers for Disease Control and Prevention [CDC] (2021) associates tobacco products like cigarettes, cigars, and pipes with at least 70 chemicals that can cause different types of cancer, including esophageal, liver, pancreatic, stomach, colorectal, mouth, and throat cancer. This evidence source justifies the conclusion by Hillyer et al. (2021).

In the study aimed at exploring the associations between socioeconomic factors and neighborhood variations in the COVID-19 test positivity rates, Whittle and Diaz-Artiles (2020) conclude that neighborhoods with a large percentage of the black population, low-income individuals, and a low percentage of the white population had high COVID-19 test positivity rates. The significant associations between these population factors and COVID-19 test positivity rates emanate from the role of social determinants of health (SDOH) and COVID-19 prevalence. According to Morante-Garcia et al. (2022), people in underprivileged areas, including the low-income population grapple with limited access to timely care, health illiteracy, hygiene and sanitation problems, housing issues, and inadequate living conditions that increase their susceptibility to the COVID-19 pandemic. Therefore, the study’s conclusion is justified by data analysis instruments and the current scholarly literature that explores the association between social determinants of health and COVID-19 prevalence and severity.

References

Centers for Disease Control and Prevalence. (2021). Tobacco and cancer. https://www.cdc.gov/cancer/tobacco/index.htm

Hillyer, G. C., Nazareth, M., Lima, S., Schmitt, K. M., Reyes, A., Fleck, E., Schwartz, G. K., & Terry, M. B. (2021). E-cigarette use among young adult patients: The opportunity to intervene on risky lifestyle behaviors to reduce cancer risk. Journal of Community Health, 47(1), 94–100. https://doi.org/10.1007/s10900-021-01027-7

Lokar, K., Zagar, T., & Zadnik, V. (2019). Estimation of the ecological fallacy in the geographical analysis of the association of socio-economic deprivation and cancer incidence. International Journal of Environmental Research and Public Health, 16(3), 296. https://doi.org/10.3390/ijerph16030296

Morante-García, W., Zapata-Boluda, R. M., García-González, J., Campuzano-Cuadrado, P., Calvillo, C., & Alarcón-Rodríguez, R. (2022). Influence of social determinants of health on COVID-19 infection in socially vulnerable groups. International Journal of Environmental Research and Public Health, 19(3), 1294. https://doi.org/10.3390/ijerph19031294

Munnangi, S., & Boktor, S. W. (2022). Epidemiology of study design. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK470342/

Setia, M. (2017). Methodology series module 7: Ecologic studies and natural experiments. Indian Journal of Dermatology, 62(1), 25. https://doi.org/10.4103/0019-5154.198048

Tan, W. (2018). Research methods: A practical guide for students and researchers. World Scientific.

Whittle, R. S., & Diaz-Artiles, A. (2020). An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York City. BMC Medicine, 18(1). https://doi.org/10.1186/s12916-020-01731-6

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WEEK THREE: DISCUSSION INSTRUCTIONS
OBSERVATIONAL STUDY DESIGNS

A clinical pediatric nurse has noticed a rise in childhood asthma diagnoses among the Hispanic population served by the local clinic. The nurse is concerned about this increase in asthma incidence in the patient population and turns to the literature to explore current research on this topic. The nurse finds, through the reading, that there appears to be an association between parental smoking and childhood asthma and wonders if this could be the cause of the rise in cases.
This type of suspected association between a risk factor (exposure) and a particular outcome (childhood asthma) can be evaluated using an observational study design. A relevant case-control study would match a group of controls (no asthma) with the case group (asthma diagnosis). Both groups would then be assessed on certain historical exposures like (a) family history; (b) early childhood respiratory infections; (c) secondhand smoke exposure; (d) urban residence (ozone); and (e) obesity. Measures might include interviews, surveys, and medical records. If results show the case group has a higher rate of exposure to a given risk factor, the researcher may conclude that exposure results in greater odds of asthma.
In any epidemiological study, the design and methodology used should be appropriate for that study and for the research question. It is important for researchers to understand the strengths and limitations of each of the study designs and methods. This gives them a better chance of correctly interpreting results and synthesizing them for use in developing and implementing evidence-based population health programs. For this Discussion, you will explore the strengths and limitations of various types of observational study designs and critique their appropriateness for specific studies.
This type of suspected association between a risk factor (exposure) and a particular outcome (childhood asthma) can be evaluated using an observational study design. A relevant case-control study would match a group of controls (no asthma) with the case group (asthma diagnosis). Both groups would then be assessed on certain historical exposures like (a) family history; (b) early childhood respiratory infections; (c) secondhand smoke exposure; (d) urban residence (ozone); and (e) obesity. Measures might include interviews, surveys, and medical records. If results show the case group has a higher rate of exposure to a given risk factor, the researcher may conclude that exposure results in greater odds of asthma.
In any epidemiological study, the design and methodology used should be appropriate for that study and for the research question. It is important for researchers to understand the strengths and limitations of each of the study designs and methods. This gives them a better chance of correctly interpreting results and synthesizing them for use in developing and implementing evidence-based population health programs. For this Discussion, you will explore the strengths and limitations of various types of observational study designs and critique their appropriateness for specific studies.

LEARNING RESOURCES TO GUIDE WITH ASSIGNMENT:
 Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th ed.). Jones & Bartlett.
o Chapter 6, “Study Designs: Ecologic, Cross-Sectional, Case Control”
o Chapter 7, “Study Designs: Cohort Studies”

 Bahr, R., Clarsen, B., Derman, W., Dvorak, J., Emery, C. A., Finch, C. F., Hägglund, M., Junge, A., Kemp, S., Khan, K. M., Marshall, S. W., Meeuwisse, W., Mountjoy, M., Orchard, J. W., Pluim, B., Quarrie, K. L., Reider, B., Schwellnus, M., Soligard, T., Stokes, K. A., … Chamari, K. (2020). International Olympic Committee consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport 2020 (including STROBE Extension for Sport Injury and Illness Surveillance (STROBE-SIIS))Links to an external site.. British Journal of Sports Medicine, 54(7), 372–389. https://doi.org/10.1136/bjsports-2019-101969
 Community Preventative Services Task Force. (n.d.). The community guideLinks to an external site.. https://www.thecommunityguide.org/
 Framingham Heart Study. (n.d.). Epidemiological background and design: The Framingham heart studyLinks to an external site.. https://www.framinghamheartstudy.org/fhs-about/history/epidemiological-background/
 STROBE. (2021). HomeLinks to an external site.. https://www.strobe-statement.org/
The STROBE Checklist is considered the gold-standard in assessing the quality of observational research studies.
 U.S. Department of Health and Human Services. (n.d.). Healthy People 2030Links to an external site.. https://health.gov/healthypeople
Assignment: Discussion
Post a brief description of the two studies with a particular focus on the study design and methods. Then:
• Describe at least one strength and one limitation of each study’s design.
• Identify the population, data sources, and epidemiologic measures of association that the authors used.
• Finally, share your insights about the appropriateness of the design for the study. Do you agree with the researchers’ choice of design?
• Do you agree with the researchers’ conclusions? Justify your reasoning.

Article for Discussion:

1. Hillyer, G. C., Nazareth, M., Lima, S., Schmitt, K. M., Reyes, A., Fleck, E., Schwartz, G. K., & Terry, M. B. (2021). E-cigarette use among young adult patients: The opportunity to intervene on risky lifestyle behaviors to reduce cancer riskLinks to an external site.. Journal of Community Health. Advance online publication. https://doi.org/10.1007/s10900-021-01027-7
2. Whittle, R. S., Diaz-Artiles, A. (2020). An ecological study of socioeconomic predictors in detection of COVID-19 cases across neighborhoods in New York CityLinks to an external site.. BMC Medicine, 18(1),Article 271. https://doi.org/10.1186/s12916-020-01731-6