Journal of Obesity & Weight Loss Therapy
Open Access

Amidst strong empirical evidence on the disproportionately higher prevalence of Type 2 Diabetes Mellitus (T2DM) among Asian Indians (AIs) at an early age and at lower Body Mass Index (BMI) compared to the other races, the matter is seldom addressed in the mainstream health care discussions in the US. Multiple studies have noted that the diabetes prevalence rates are the highest among AIs in the US compared to the other ethnicities after adjusting for age [1,2]. However, only limited studies have explored the factors associated with T2DM among AIs in the US, and there is a lack of consistency in findings in the current studies. Additionally, available national surveys do not allow public reporting on AI-specific data to ensure participant identity protection. This in turn reduces the accessibility of providers to the AI-specific data. Moreover, being represented under the umbrella of Asians and the significant variation in the diabetes prevalence among different Asian subgroups grossly under report the actual diabetes-related statistics of Asian Indians. In addition to this, the nonuse of ethnic-specific BMI criteria in national survey reporting prevents accurate reporting of the T2DM-related data on AIs. This secondary analysis of BRFSS survey results examines the variables related to T2DM in the AI-specific dataset.

The Commission of Social Determinants of Health (CSDH) framework of the WHO was the theoretical framework for the study [3]. There is a dearth of studies among AIs in the US. Studies conducted among AIs inside and outside the US provide insight into the associations between many social determinants of health and the prevalence of T2DM. While some studies attributed high SEP to the increased prevalence of T2DM [4,5], studies in the US indicated either no relationship [6,7] between the two or a curvilinear relationship [8]. One study even noted an inverse relationship between high SEP and the prevalence of T2DM [9]. Thus, there is conflicting information in the literature about SEP and the diagnosis of T2DM among AIs.

Behavioral factors such as physical inactivity, high carbohydrate intake, and metabolic syndrome were linked to the increased prevalence of T2DM among AIs in numerous studies that addressed the relationship between behavioral factors and the prevalence of T2DM among AIs [10,5]. However, an inverse relationship between carbohydrate intake and the prevalence of T2DM was also noted in a major study in the US [7]. Demographic factors, age, and BMI were positively linked to the prevalence of T2DM among AIs in multiple studies [11,12].

Regarding access to care factors, current data indicate the number of doctor visits among Asian Americans (AA), is second last compared to other races in the US [13]. Wellness visits are also lesser among AAs than among Black and White populations according to these statistics. AI-specific data is not available on doctor visits or wellness visits. In conclusion, the direction of relationships differed in the studies that analyzed the relationship between socioeconomic, and behavioral factors, and the prevalence of T2DM among AIs. The literature lacks information on the access to care factors among AIs in the US.

This secondary data analysis of BRFSS survey results over five years (2013-2017) included 1,132 adult AIs more than 18 years of age living in New Jersey, who self-identified as AIs. The study ensured an adequate sample size by conducting a power analysis and adopted specific measures to reduce the chances of including type 1 diabetes or gestational diabetes in the data. The SDOH factors such as demographic, socioeconomic, behavioral, and access to care factors were the independent variables of the study. Dependent variables were the diagnosis of T2DM and Pre-diabetes (PDM). Diabetes Status (DS) is when the participant is positive for either T2DM or PDM.

This study executed robust statistical analysis methods using SAS statistical software (version 9.4) to analyze the data. Proper weighing of the data ensured that the study results were representative of AIs in NJ. Characteristics of the study sample are summarized. Using a hierarchical multivariate approach, logistic regression analyses built predictive models for the study outcomes in three blocks: Socio demographic factors, behavioral factors, and access to care factors. A brief analysis of the study results is documented below.

Socioeconomic factors and diabetes status

Despite higher the socioeconomic position of AIs, the prevalence of T2DM and PDM (10% and 6% respectively) are much higher than the Non-Hispanic White counterparts (5.8%) in the US. This reflects that higher SEP of AIs is not protective of the diagnosis of DS. SEP factors such as income, occupation, homeownership, and education were analyzed in the study. However, internet use was the only socioeconomic factor that had a statistically significant association with DS. The odds of being diagnosed with DS were 66% lower with internet use after adjusting for age, sex, BMI, income, and house ownership (OR=0.34, 95% CI: 0.14-0.84). The results of this study were consistent with some previous studies about the positive impact of internet use on health by reducing the diagnosis of DS [14]. The mechanism of the relationship between internet use and the diagnosis of DS requires further exploration.

Behavioral factors and diabetes status

Interestingly enough, healthier behaviors were not linked to the diagnosis of DS in the regression analysis of this study, and this could be attributed to the small cell sizes on the variables included in the analysis. The behavioral factors were mostly in the optional module set of BRFSS. The optional modules were not included in the survey every year leading to missing data in these variables. The rate of overweight/obesity (69%) in this study is much higher than the highest obesity level among non-Hispanic Black adults (49.9%) in the US national data [15]. Having a positive DS was significantly higher among those with an obese/overweight BMI (X2=8.72, p=0.003) in the bivariate analysis of the study. However, this study did not find a statistically significant relationship between BMI and the diagnosis of DS in regression analysis. This strengthens the current knowledge that AIs are prone to developing DS regardless of their BMI levels.

This study categorized the BMI levels based on the Asia-Pacific BMI criteria. It is important to note that the national surveys and standard care protocols in the US do not use ethnic-specific criteria for BMI categorization leading to a significant underrepresentation of overweight and obesity among AIs. This can also lead to reduced screening and underdiagnosis of obesity-related chronic diseases including T2DM among AIs. Having a positive DS was significantly higher among older AIs (OR=3.89, 95% CI: 1.78-8.52).

Access to care factors and diabetes status

Among the access to health care variables, people who had a personal doctor had four times increased odds of DS compared to the people who did not have a personal doctor (OR=4.03, 95% CI: 2.03-8.00). The increased odds of diagnosis of DS with having a personal doctor could be attributed to the early detection of DS by the personal doctor or to the reverse causation of having a personal doctor after getting diagnosed with DS. Similarly, having more than one medical check-up in the last two years was significantly related to higher odds of DS (OR=4.40, 95% CI, 1.05-18.48). This could be understood in the context of a previous study that demonstrated individuals having a personal doctor or medical check-ups are getting diagnosed during regular check-ups [16]. The odds of being diagnosed with PDM were 11 times higher among AIs who reported having at least one medical checkup in the last two years than those who reported having no medical check-ups in the last 2 years (OR=10.92, 95% CI: 1.27-94). This is a critical finding since the diagnosis and management of PDM can prevent or delay the onset of T2DM and its complications. Those who do not have regular check-ups may remain undiagnosed even if they have positive DS. This is extremely important since AIs typically develop T2DM a decade earlier than other ethnicities, leading to more complications and early death in this ethnic group [17-20].

It is well acknowledged that AIs have a unique susceptibility to T2DM. This retrospective secondary analysis of BRFSS data over five years reflected some novel relationships and reinforced the importance of immediate steps for early screening, proper weight management, and physician-led preventive care among AIs in the US. Analysis of AI-specific national data is imperative as newer survey results emerge. AIs being the second largest foreign-born immigrant population and the fastest growing racial group in the US, their preventive care is vital to reducing the substantial physical and financial burden on society.

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