The impact of social determinants of health on the diagnosis of type 2 diabetes mellitus among Asian Indians in New Jersey
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Joseph, Maya E..
The impact of social determinants of health on the diagnosis of type 2 diabetes mellitus among Asian Indians in New Jersey. Retrieved from
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TitleThe impact of social determinants of health on the diagnosis of type 2 diabetes mellitus among Asian Indians in New Jersey
Date Created2022
Other Date2022-05 (degree)
Extent151 pages
DescriptionPurpose: The purpose of this study was to examine the relationship between social determinants of health (SDH) and the diagnosis of type two diabetes mellitus (T2DM), and prediabetes (PDM) among Asian Indians (AI) in New Jersey (NJ).Rationale: The global AI diaspora is experiencing disproportionately high rates of T2DM. Multiple studies in the US have indicated that AIs have a high prevalence of T2DM when compared to the other races after adjusting for confounding factors such as age and body mass index (BMI). Paradoxically, the prevalence of T2DM among AIs is not limited to the traditional risk factors of high BMI and waist circumferences.
Methods: The theoretical underpinning of this project is the Conceptual Framework of SDH by the Commission on Social Determinants of Health (CSDH) by the World Health Organization (WHO). This was a quantitative study with a cross-sectional study design. This study was a secondary data analysis using the NJ data from the Behavioral Risk Factor Surveillance System (BRFSS) of the Centers for Disease Control and Prevention (CDC) from 2013 to 2017. Non-institutionalized adults of 18 years and above participated in the study. Participants who were self-identified as AIs were included in the analyses. The independent variables of the study were income, education, employment, home ownership, internet use, BMI, exercise, fruit and vegetable intake, tobacco and alcohol consumption, and access to care factors such as health plan, medical check-ups, medical cost, and personal doctor. The dependent variables of this study were T2DM, PDM, and DS (diabetic status). Participants who were positive for either T2DM or PDM were categorized as positive for diabetic status (DS). Statistical analyses included descriptive statistics, chi-square analyses, logistic regression analyses, and mediation analyses.
Results: The results indicated that the odds of being diagnosed with T2DM were 68% lower with using the internet in comparison to not using the internet (OR = 0.32, 95% CI: 0.11-0.99) when adjusted for age, sex, BMI, and home ownership, and were 5 times higher with having a personal doctor than with not having a personal doctor (OR =5.34, 95% CI: 1.84-15.50). The logistic regression analysis did not identify statistically significant structural social determinants for the diagnosis of PDM. The odds of being diagnosed with PDM were 11 times higher among AIs who reported having at least one medical check-up in the last two years than those who reported having no medical check-ups in the last two years (OR =10.92, 95% CI: 1.27-94).
The odds of having a positive DS were 66% lower with the use of the internet (OR =0.34, 95% CI: 0.14-0.84) when adjusted for age, sex, BMI, and homeownership compared to not using the internet. The odds of having a positive DS were 4 times higher for AIs who reported having medical checkups in the last two years (OR = 4.40, 95% CI: 1.05-18.48) than those who did not have medical check-ups in the last two years and 4 times higher for those who have a personal doctor (OR = 4.03, 95% CI: 2.03-8.00) than those who did not have a personal doctor.
Moreover, the odds of being diagnosed with T2DM were 4 times higher among AIs greater than 45 years of age in comparison to AIs less than 45 years (OR = 3.89, 95% CI: 1.78-8.52), and the odds of having a positive DS were 4 times higher among AIs older than 45 years (OR = 3.89, 95% CI: 1.78-8.52). There was no statistically significant relationship between behavioral factors and T2DM, PDM, or DS in this study. Mediation analysis showed that 14% of the variation in the relationship between internet use and diagnosis of T2DM was explained by having a personal doctor and 8% of the variation in the relationship between internet use and diagnosis of DS was explained by having a personal doctor. One percent of the variance in the relationship between age and diagnosis of PDM was explained by the mediator medical check-up. As additional findings, there was a high proportion of high BMI (69.2%) among AIs in this study. The internet use was higher among participants of younger age and higher income.
Conclusions: There is substantial evidence in the literature about the relationship of Socioeconomic Position(SEP) and behavioral factors with the diagnosis of T2DM. However, there is a lack of consistency in the relationships and dearth of studies on this topic among AIs in NJ. This study indicates a significant relationship between internet use, having personal doctor, and the diagnosis of DS among AIs in NJ. While healthier behaviors and BMI are associated with a lower diagnosis of DS in the general population, this study among AIs did not show any significant association between healthier behaviors and BMI and the diagnosis of DS. The nature of the relationships established in this study should be explored further using studies with a larger sample size, survey tools specifically developed for AIs, and using longitudinal study designs. The results of this study have implications on public health, clinical, and research aspects of health care.
NotePh.D.
NoteIncludes bibliographical references
Genretheses
LanguageEnglish
CollectionGraduate School - Newark Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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