Defining the Problem and Research Methods Research Paper

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Designing epidemiological research requires substantial preparation and preliminary analysis. At the research planning stage, the issues of importance include considering psychosocial factors’ role in predicting chronic conditions, assessing epidemiological problems’ magnitude, applying descriptive epidemiology tools to search for person-, place-, and time-related patterns, and finalizing research design selection. This submission presents the first two sections of the major assessment paper developed with reference to the epidemic of type II diabetes (T2D) in Hispanic American adults.

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The Research Problem

Outline of the Selected Environment

The hypothetical epidemiological study would explore the long-term effects of culturally adopted education for Hispanic adults with prediabetes versus generalized recommendations on preventing T2D development in the research sample. The bulk of the research activities, including recruiting, teaching sessions, and diagnostic assessments, would be conducted in a diabetes clinic, making the latter the key research environment. Home environments and patients’ health-promotion behaviors at home will be partially considered by making follow-up phone calls after the intervention and prior to the final diabetes-focused health examination.

The Population Health Problem and Key Descriptive Epidemiological Characteristics

Place

Partially due to its low-income neighborhoods, the U.S. has become one of the planet’s leaders in terms of T2D rates per capita. As the world’s third country in diabetes prevalence after China and India, the U.S. has experienced an increase in T2D cases among citizens, with around 13% of all adults having one of the diabetes-related conditions (Aguayo-Mazzucato et al., 2018). As the CDC reports, in 2016, depending on the geographic location, diabetes prevalence in U.S. citizens older than 20 varied between 12% and 33%, marking the starting point of the epidemic (Centers for Disease Control and Prevention, 2020). Two factors make the U.S. a place of interest when exploring diabetes epidemics. Firstly, the country’s fastest-growing ethnic minority group, Hispanic and Latino Americans, faces increased T2D risks, so immigration does not go unnoticed for the country’s diabetes situation (Aguayo-Mazzucato et al., 2018). Secondly, due to peculiar nutritional patterns, sedentary lifestyle habits, everyday stress, and underlying health conditions’ prevalence, the country remains one of the global leaders in obesity (Aguayo-Mazzucato et al., 2018; Nianogo & Arah, 2018). As a prominent risk factor for T2D, the condition contributes to the epidemic.

Person

Regarding person-related characteristics, T2D in the U.S. population has clear ethnicity-related predictors, including being of Latin American and Hispanic descent. An adult U.S. citizen from this subgroup is 80% more likely to develop T2D compared to a non-Hispanic white peer, and the risks are further exacerbated by high BMI levels, no post-secondary education, and low incomes common in U.S. Hispanics (Aguayo-Mazzucato et al., 2018). Diabetes risks are partially genetically predetermined, making descendants of Mexicans and Puerto Ricans particularly prone to T2D (Smith-Miller et al., 2017). Thanks to the genome-wide association and whole exome sequencing research, a few genetic predictors of T2D development in U.S. Hispanics, including the SLC16A11 locus, HNF1A variants, and peculiar glycemic traits, have been identified (Mercader & Florez, 2017). Among the relevant person-related non-genetic characteristics increasing Hispanic Americans’ T2D risks are high-carb diets, levels 2 and 3 on the acculturation scale, long duration of residence or being born in the U.S., and adherence to the traditions of simpatia and regular family meals (Aguayo-Mazzucato et al., 2018). Therefore, the T2D epidemic in Hispanic Americans is linked with diverse genetic and lifestyle variables.

Time

Diabetes prevalence in the U.S. has been increasing since the 2000s. As per the data from the U.S. Diabetes Surveillance System, the maximal county-level prevalence of diagnosed diabetes among those aged 20+ has grown from 6.9% to 33% between 2004 and 2016, with changes in T2D incidence being consistent across diverse ethnic groups (CDC, 2020). Disease awareness and access to timely healthcare in the racial minority population have historically been insufficient, serving as contributors to the epidemic. Between 2005 and 2016, the total U.S. population’s prediabetes awareness has changed from 6.5% to 13.6% (CDC, 2020). Notably, for U.S. Hispanics, the awareness level did not even reach 12%, even though up to 38% of adult Hispanic Americans get the prediabetes diagnosis (CDC, 2020). Based on these time-related trends, studying the quality of T2D prevention education for Hispanic Americans might also be crucial.

The Problem’s Magnitude: Current Statistics

Nowadays, T2D in Hispanic Americans is often referred to as an epidemic that increases the economic burden on the U.S. healthcare system. In 2018, the prevalence of diagnosed diabetes in American Hispanics exceeded 12.5%, making this population the second most affected racial subgroup after American Indians (CDC, 2020). According to the Hispanic Community Health Study in the U.S. that considers undiagnosed cases and sex-based differences, diabetes prevalence in Hispanics varies between 18.7% for males of Mexican descent and 10.2% for South American women and men (Aguayo-Mazzucato et al., 2018). In comparison, disease prevalence for non-Hispanic white citizens is around 7.5% (CDC, 2020). Therefore, T2D in U.S. Hispanics is among the issues of great magnitude.

The Research Question

In overweight adult U.S. Hispanics with prediabetes, how does a culturally-tailored nutrition guide versus general prediabetes diet guidelines influence T2D incidence within a 1-year timeframe?

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Selecting Research Methods

The Epidemiological Study Design Suitable for the Problem

The research question above emphasizes the disease prevention aspect of T2D epidemiological research through comparisons between standard and culturally tailored nutrition advice for individuals with prediabetes, which makes an RCT design the optimal choice. RCT studies in epidemiology have various benefits over descriptive, analytical, and observational research, including greater internal validity levels and randomization, making RCTs a universally accepted standard of high-quality evidence (Gupta et al., 2017). In the hypothetical case, addressing the previously formulated research question using the RCT method would produce generalizable evidence shedding light on cause-effect relationships between the nutrition education mode and T2D outcomes. The research procedure would follow the basic RCT structure and include the following: approaching the population of interest (adult Hispanic Americans with BMI levels above 25 and 5.7%–6.4% blood sugar), sample selection, assigning individuals to groups randomly, and intervention implementation. Follow-up communication, final T2D-focused health examinations, and statistical analysis would be the next steps. Despite being a resource-consuming option, this design would produce knowledge to inform further T2D prevention programs aimed at Hispanics.

Assessment Strategies

The hypothetical research process would involve stratified randomization techniques for group formation and an ANOVA test to compare outcomes. Randomization involving stratification is used when crucial variables aside from the intervention/control condition create the risk of forming groups with unequal distribution of a variable of interest (Broglio, 2018). In T2D prevention research in Hispanics, male participants have historically been underrepresented, so having enough female and male participants in both intervention and control groups is important in terms of ensuring results’ generalizability (McCurley et al., 2017). Therefore, simple randomization with stratification, which could be performed in Microsoft Excel, would be suitable for addressing the problem. Next, an ANOVA test could be conducted by using SPSS software or manually in Microsoft Excel to analyze the change in blood sugar and BMI levels in both groups comparatively. A significant difference between the two groups’ mean values would be indicative of the compared T2D prevention interventions’ effectiveness.

The Considerations of Convenience

In research planning, convenience is essential to justify tool selection. Despite requiring sufficient practical skills, SPSS use would simplify calculations and bring time efficiency by handling large datasets automatically and enabling the creation of graphics to represent the results. Microsoft Excel would be convenient for basic statistical operations, such as randomization, by reducing the risks of group size imbalance associated with manual approaches, for instance, the coin flip randomization method.

Hypothetical Data Collection Activities: A Summary

In the hypothetical RCT, patient data collection would represent a three-stage process. Firstly, the researchers would collect data directly from the participants, including individual surveys with questions regarding age, gender, medical history, nutritional habits, and contact information. After answering survey questions, each participant would be assessed in a clinical setting, and the research team would collect and document data on BMI levels (weight and height) and blood sugar levels. Secondly, there would be post-intervention data collection activities; three, six, and nine months after the teaching intervention, the research team would make follow-up phone calls to collect data on each participant’s progress in observing the provided guidelines. Thirdly, twelve months after the intervention, each participant will undergo an objective diabetes-focused health assessment. It will include height, weight, and blood sugar level measurements, as well as the use of T2D screening tools to conclude on the person’s diabetic status. Therefore, diverse data collection modes, including in-person communication, phone calls, and health examinations, would be applicable to address the posed research question.

References

Aguayo-Mazzucato, C., Diaque, P., Hernandez, S., Rosas, S., Kostic, A., & Caballero, A. E. (2018). Diabetes/Metabolism Research and Reviews, 35(2), 1-35.

Broglio, K. (2018). . JAMA, 319(21), 2223-2224.

Centers for Disease Control and Prevention. (2020).

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Gupta, V., Walia, G. K., & Sachdeva, M. P. (2017).. Public Health, 145, 113-119.

McCurley, J. L., Gutierrez, A. P., & Gallo, L. C. (2017). American Journal of Preventive Medicine, 52(4), 519-529.

Mercader, J. M., & Florez, J. C. (2017). . Frontiers in Public Health, 5, 1-7.

Nianogo, R. A., & Arah, O. A. (2018). American Journal of Preventive Medicine, 55(6), 795-802.

Smith-Miller, C. A., Berry, D. C., & Miller, C. T. (2017). Research in Nursing & Health, 40(6), 541-554.

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