Abstract
Background
Among US adults with prediabetes, only half engage in recommended prevention behaviors and little has been done to examine the role of multiple social risk factors.
Methods
Data from 4,310 adults with prediabetes (weighted 34,442,989) in the National Health Interview Survey 2016–2017 was used. Predictor variables included six social risk factors. Outcome variables included (1) receiving counseling for lifestyle change and (2) engaging in lifestyle change behaviors. Multiple logistic regression (glm function with poisson family and log link) models were run to estimate prevalence ratios, adjusting for relevant covariates.
Results
In the fully adjusted models, inadequate access to care was associated with lower prevalence of receiving all forms of counseling for physical activity (PR = 0.63; CI 0.51; 0.78), fat/calories (PR = 0.70; CI 0.56; 0.86) and weight loss program (PR = 0.33; CI 0.19; 0.59). Lack of community was associated with lower prevalence of increasing physical activity (PR = 0.94; CI 0.88; 1.00). Educational deficit was associated with lower prevalence of increasing physical activity (PR = 0.88; CI 0.82; 0.94), reducing fat/calories (PR = 0.89; CI 0.83; 0.95) and being in a weight loss program (PR = 0.66; CI 0.51; 0.87). Food insecurity was associated with lower prevalence of reducing fat/calories (PR = 0.91; CI 0.84; 0.98). Inadequate access to care was associated with lower prevalence of being in a weight loss program (PR = 0.43; CI 0.23; 0.81).
Conclusion
This study underscores the role of social risk factors in limiting diabetes prevention at a national level. To effectively address the barriers to diabetes prevention adoption in the US, social risk factors need to be accounted for as a part of a holistic care plan.
Keywords: Pre-diabetes, Social risk factors, Diabetes prevention, Population health
Introduction
According to the CDC, 97.6 million people, or 38% of the adult population in the United States have prediabetes [1]. Prediabetes is a health condition where blood glucose levels are elevated, but below criteria for a diagnosis of type 2 diabetes [1]. It is estimated that 5–10% of people with prediabetes will develop type 2 diabetes within a year, and the majority of those with prediabetes will progress to type 2 diabetes within 5 years [1, 2]. However, over 80% of those with prediabetes are not aware of the condition [1].
Prediabetes is a reversible condition with moderate lifestyle modifications, in particular weight loss and physical activity [3–8]. A first line approach for treatment includes lifestyle modification and losing 7% of current body weight and performing 150 min of physical activity per week to slow progression of type 2 diabetes and reverse prediabetes [3, 5]. Lifestyle and behavior change to delay the progression of prediabetes is recommended to occur at the individual level or through joining a diabetes prevention program, in person, via distance learning, or hybrid [9]. To increase engagement in diabetes prevention programs, expansion of coverage by Medicare and Medicaid, as well as third party payers is being emphasized as a key priority [4, 9].
Population based studies show that among US adults with prediabetes, only half engage in recommended prevention behaviors, including lifestyle modification and joining diabetes prevention programs [10]. Evidence suggests that social risk factors, the adverse social conditions associated with poor health [11], may serve as barriers to diabetes prevention programs and lifestyle modifications [12–15]. While the existing literature has identified barriers to diabetes prevention among adults with prediabetes, data is largely limited to qualitative approaches [12, 16, 17]. For example, a recent meta-analysis of qualitative studies underscores multilevel barriers present for lifestyle and behavior change among people with prediabetes, occurring across the individual, community, and healthcare settings [16]. These include stress, competing demands and priorities, family and cultural norms around diet and exercise, and the cost of healthy foods as well as access to healthy food options and indoor workout facilities [16]. These data provide critical context from the lived experiences of those living with pre-diabetes and highlights the need to understand from a population based approach the role of social risk factors in engaging in diabetes prevention behaviors.
Identifying social risk factors as they occur at the national level will inform diabetes prevention efforts across public and private sectors. In addition, evidence shows that social risk factors do not occur in isolation, suggesting the need to identify multiple or co-occurring social risk factors that will support a root cause approach to preventing type 2 diabetes. Currently, little has been done to examine the role of multiple social risk factors as possible barriers to diabetes prevention among adults with prediabetes. Therefore, the objective of this study is to address this gap in knowledge by examining the relationship between key social risk factors, prevention recommendations, and prevention behavior in a national sample of US adults with prediabetes.
Methods
Data source
This study used data from the National Health Interview Survey (NHIS) years 2016–2017. The NHIS survey contains data from United States residents and includes information on chronic conditions, health care utilization, and other health related items [18]. The data files used in this study included the sample adult files along with the person and family files.
The sample for this study was adults with prediabetes, created as a subpopulation to allow weighting of analyses per NHIS guidelines. Adults were defined as individuals aged 18 and older based on self-report age. Prediabetes was defined as individuals who self-reported having prediabetes, reported as yes or no in NHIS. Though self-report of chronic disease has been validated, individuals who indicated both yes to prediabetes and yes to diabetes diagnosis were considered to have diabetes and not included in the subpopulation. Individuals were only excluded from analyses if they did not answer questions that allowed creation of social risk factors and outcomes (detailed below). Individuals responding to at least one outcome were not excluded from the sample but were only represented in analyses for the outcomes where they had data. The final sample included 4,310 adults with prediabetes (when weighted translates to 34,442,989 US adults with prediabetes).
Social risk factors
Social risk was defined in this study to allow investigation into the burden of multiple social risk factors rather than detailed investigation into any single factor. Six social risk factors representing five Healthy People domains of social determinants of health (SDOH) were included as primary independent variables. Multiple social risk measures were used to create each domain within the NHIS dataset as defined by Wray 2022 [19]. Each social risk was coded to be binary (having the risk factor as 1, not having the risk factor as 0). Each domain was then created as a binary variable (having any risk factor with the domain as 1, having no risk factors in the domain as 0) [20–22]. Individuals answering at least one question in a domain were scored using the structure outlined. The items used to define each social risk category are outlined below:
Economic Instability (SDOH domain of Economic Stability):
Welfare assistance.
Income from state/county welfare.
Unemployed.
Ever applied for Social Security Disability Insurance.
Subsidized rent.
Worry about maintaining current standard of living.
Worry about enough money for retirement.
Worry about paying normal monthly bills.
Worry about inability to pay rent, mortgage, or housing costs.
Worry about making minimum payment on credit cards.
Lack of Community (SDOH domain of Social/Community Context):
People in your neighborhood do not help each other out.
There are no people you can count on in your neighborhood.
People in your neighborhood cannot be trusted.
Do not live in a close-knit neighborhood.
Educational Deficit (SDOH domain of Education Access and Quality):
No college or graduate degree.
English not well spoken.
Food Insecurity (SDOH domain of Economic Stability):
Lose weight because not enough money for food.
Cut size of meals or skip meals in the past month.
Eat less than you should because not enough money for food.
Ever hungry but did not eat because no money for food.
Ever receive food stamps/SNAP in past year.
Worried that food would run out.
Food did not last until you could buy more.
Did not eat balanced meals due to costs.
Received benefits or food subsidies from WIC program.
Social Isolation (SDOH domain of Neighborhood Built Environment/Social/Community Context):
Lives alone.
Difficult to participate in social activities.
Difficult to go to events.
Delayed getting medical care due to lack of transportation.
Inadequate Access to Care (SDOH domain of Healthcare Access and Quality):
Lacks regular place to go to when sick or need health advice.
Outcomes
The outcome variables included three items used to capture receiving counseling for lifestyle change and three items used to capture engaging in lifestyle change behaviors. All responses were self-reported. All six outcomes were coded as binary: ‘yes’ as 1 and ‘no’ as 0.
The three counseling for lifestyle change items were created from the question: “During the past 12 months, have you been told by a doctor or health professional to do any of the following: 1) increase physical activity or exercise?, 2) reduce the amount of fat or calories in diet?, and 3) participate in a weight loss program? Response options were yes or no. Based on these questions, three counseling for lifestyle outcomes were created: 1) told to increase physical activity, 2) told to reduce fat or calories, 3) told to participate in weight loss program.
In addition, three lifestyle change items were created from the questions: “Are you now doing any of the following: 1) increasing your physical activity or exercise? 2) reducing the amount of fat or calories in your diet?, 3) participating in a weight loss program? Response options were yes or no. Based on these questions, the three lifestyle Change behavior outcomes were created: 1) currently increasing physical activity, 2) currently reducing fat or calories in diet, and 3) currently in a weight loss program.
Covariates
Covariates were all self-report and included age (18–39, 40–49, 50–64, 65–74, 75 and older), sex (male, female), race/ethnicity (Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Other Race, Hispanic), insurance status (insured, uninsured), obesity (yes, no), mental health: depression/anxiety problem (yes, no), and comorbidities (hypertension, coronary heart disease, heart attack, stroke, asthma, ulcer, cancer, emphysema/COPD, kidney disease, liver disease, arthritis, migraine, and chronic pain) each added as its own variable.
Statistical analyses
All analyses were weighted using the svydesign function in R according to NHIS documentation to allow generalization to the US population. First, the sample demographics, social risk factors, and SDOH domains were each summarized with percentages. Unadjusted logistic regression models using the glm function with poisson family and log link were run for each of the six outcomes and each individual SDOH domain to estimate the prevalence ratios. Next, for each of the six outcomes, the six SDOH domains were added concurrently to investigate independent association using the glm function with poisson family and log link to estimate prevalence ratios. Finally, all models were adjusted by adding age, sex, race/ethnicity, insurance status, obesity, mental health, and comorbidities to provide prevalence ratios adjusted for demographic and comorbidity factors. In all analyses, each outcome was run as a separate model and p < 0.05 indicated statistical significance. R v 4.0.3 was used for all analyses.
Results
Table 1 shows the sample characteristics of adults with prediabetes in 2016–2017. The majority of participants, 64.6%, were age 50 and older. By race/ethnicity, 63.1% were non-Hispanic White, 16.2% were Hispanic, 12.7% were non-Hispanic Black, and 8.0% were non-Hispanic Other race. Most of the sample, 94%, had health insurance. The majority of participants had at least 1 or more comorbidity. Looking at prevalence by the primary outcomes, 57.6% reported being told to increase physical activity in the past 12 months; 61.5% reported currently increasing physical activity; 52.1% reported being told to reduce fat or calories in their diet in the past 12 months; 61.8% reported currently reducing fat or calories in their diet; 18% were told to join a weight loss program in the past 12 months; and 11.4% reported currently being in a weight loss program.
Table 1.
Characteristics of adults with prediabetes, NHIS, 2016–2017
| Total Sample (N = 34,442,989) (n = 4,310) |
|
|---|---|
| Age | |
| 18–39 | 19.2% |
| 40–49 | 16.2% |
| 50–64 | 35.5% |
| 65–74 | 18.6% |
| 75+ | 10.5% |
| Sex | |
| Male | 44.4% |
| Female | 55.6% |
| Race/Ethnicity | |
| Non-hispanic white | 63.1% |
| Non-hispanic black | 12.7% |
| Non-hispanic other | 8.0% |
| Hispanic | 16.2% |
| Health insurance | 94.3% |
| Obese | 47.4% |
| Mental health issue | 4.5% |
| Comorbidities | |
| Hypertension | 53.6% |
| Coronary heart disease | 6.9% |
| Heart attack | 5.1% |
| Stroke | 4.2% |
| Asthma | 19.0% |
| Ulcer | 10.3% |
| Cancer | 14.1% |
| Emphysema/COPD | 6.1% |
| Kidney disease | 3.1% |
| Liver disease | 3.6% |
| Arthritis | 39.3% |
| Migraine | 18.9% |
| Chronic pain | 49.8% |
| Comorbidity count | |
| 0 | 14.9% |
| 1–2 | 43.0% |
| 3–4 | 30.9% |
| 5+ | 11.1% |
| Primary outcomes | |
| Told to increase physical activity in past 12 m | 57.6% |
| Currently increasing physical activity | 61.5% |
| Told to reduce fat/calories in diet in past 12 m | 52.1% |
| Currently reducing fat/calories in diet | 61.8% |
| Told to join weight loss program in past 12 m | 18.0% |
| Currently in weight loss program | 11.4% |
Table 2 shows the SDOH domain prevalence across the sample population. The majority of participants, 83.5% indicated economic instability, 46.0% indicated a lack of community, 39.0% indicated an educational deficit, 27.1% indicated food insecurity, 32.6% indicated social isolation, and 5.3% indicated inadequate access to care.
Table 2.
Characteristics of social risk factors in adults with prediabetes, NHIS, 2016–2017
| Total Sample (N = 34,442,989) (n = 4,310) |
|
|---|---|
| Economic instability domain | 83.5% |
| Welfare assistance | 1.6% |
| Income from state/county welfare | 1.1% |
| Unemployed | 46.1% |
| Ever applied for Social Security Disability Insurance (SSDI) | 12.0% |
| Subsidized rent | 3.9% |
| Worry about maintaining current standard of living | 44.5% |
| Worry about enough money for retirement | 52.0% |
| Worry about paying normal monthly bills | 33.7% |
| Worry about inability to pay rent, mortgage, or housing costs | 26.3% |
| Worry about making minimum payment on credit cards | 14.9% |
| Lack of community domain | 46.0% |
| People in your neighborhood do not help each other out | 19.9% |
| There are no people you can count on in your neighborhood | 19.4% |
| People in your neighborhood cannot be trusted | 16.5% |
| Do not live in a close-knit neighborhood | 40.1% |
| Educational deficit domain | 39.0% |
| No college or graduate degree | 37.6% |
| English not well spoken | 5.6% |
| Food insecurity domain | 27.1% |
| Lose weight because not enough money for food | 2.6% |
| Cut size of meals or skip meals in the past month | 7.2% |
| Eat less than you should because not enough money for food | 7.8% |
| Ever hungry but did not eat because no money for food | 4.7% |
| Ever receive food stamps/SNAP in past year | 13.9% |
| Worried that food would run out | 15.8% |
| Food did not last until you could buy more | 14.0% |
| Did not eat balanced meals due to costs | 13.1% |
| Received benefits or food subsidies from WIC program | 3.9% |
| Social Isolation Domain | 32.6% |
| Lives alone | 18.7% |
| Difficult to participate in social activities | 12.0% |
| Difficult to going to events | 15.0% |
| Delayed getting medical care due to lack of transportation | 3.1% |
| Inadequate access to care domain | 5.3% |
| Lacks regular place to go to when sick or need health advice | 5.3% |
Table 3 shows the unadjusted logistic regression models for counseling for lifestyle and engagement in lifestyle change by social risk domain. Educational deficit was associated with significantly higher prevalence of receiving counseling for reducing fat/calories in diet (PR = 1.12; CI 1.04; 1.21). Only inadequate access to care was significantly associated with lower prevalence of counseling for physical activity (PR = 0.61; CI 0.49; 0.76); reducing fat or calories in diet (PR = 0.72; CI 0.58; 0.90); and joining a weight loss program (PR = 0.32; CI 0.18; 0.56). Economic instability (PR = 0.91; CI 0.84; 0.99); lack of community (PR = 0.93; CI 0.87; 0.99); and social isolation (PR = 0.91; CI 0.85; 0.97) were all associated with significantly lower prevalence of counseling for physical activity. Educational deficit was associated with significantly lower prevalence of counseling for physical activity (PR = 0.86; CI 0.80; 0.92); currently reducing fat/calories in diet (PR = 0.91; CI 0.85; 0.96); and currently being in a weight loss program (PR = 0.65; CI 0.50; 0.83). Food insecurity was significantly associated with lower prevelance of being in a weight loss program (PR = 0.75; CI 0.57; 0.98). Inadequate access to care was significantly associated with lower prevalence of being in a weight loss program (PR = 0.45; CI 0.25; 0.79).
Table 3.
Unadjusted logistic regression models for counseling for lifestyle Change and lifestyle Change behaviors by social determinants of health domains in adults with prediabetes, NHIS, 2016–2017
| Counseling for lifestyle change | |||
| Told to Increase Physical Activity | Told to Reduce Fat or Calories in Diet | Told to Join Weight Loss Program | |
| Prevalence Ratio (CI) | Prevalence Ratio (CI) | Prevalence Ratio (CI) | |
| Economic instability domain | 1.04 (0.95, 1.15) | 0.98 (0.88, 1.08) | 0.97 (0.78, 1.21) |
| Lack of community domain | 1.00 (0.93, 1.07) | 1.03 (0.95, 1.12) | 1.04 (0.87, 1.24) |
| Educational deficit domain | 1.01 (0.94, 1.08) | 1.12 (1.04, 1.21)** | 0.93 (0.78, 1.12) |
| Food insecurity domain | 1.03 (0.95, 1.11) | 1.06 (0.97, 1.15) | 1.02 (0.84, 1.23) |
| Social isolation domain | 1.00 (0.93, 1.07) | 0.95 (0.89, 1.02) | 1.07 (0.90, 1.28) |
| Inadequate access to care domain | 0.61 (0.49, 0.76)*** | 0.72 (0.58, 0.90)** | 0.32 (0.18, 0.56)*** |
| Lifestyle change behaviors | |||
| Prevalence Ratio (CI) | Prevalence Ratio (CI) | Prevalence Ratio (CI) | |
| Currently Increasing Physical Activity | Currently Reducing Fat/Calories in Diet | Currently in Weight Loss Program | |
| Economic instability domain | 0.91 (0.84, 0.99)* | 1.01 (0.93, 1.10) | 0.86 (0.64, 1.14) |
| Lack of community domain | 0.93 (0.87, 0.99)* | 1.00 (0.94, 1.06) | 1.05 (0.83, 1.34) |
| Educational deficit domain | 0.86 (0.80, 0.92)*** | 0.91 (0.85, 0.96)** | 0.65 (0.50, 0.83)*** |
| Food insecurity domain | 0.94 (0.87, 1.01) | 0.93 (0.87, 1.00) | 0.75 (0.57, 0.98)* |
| Social isolation domain | 0.91 (0.85, 0.97)** | 0.97 (0.91, 1.03) | 0.96 (0.76, 1.21) |
| Inadequate access to care domain | 0.86 (0.73, 1.01) | 0.95 (0.83, 1.09) | 0.45 (0.25, 0.79)** |
CI Confidence Interval
*p-value < 0.05
**p-value < 0.01
***p-value < 0.001
Table 4 shows the fully adjusted logistic regression model for counseling for lifestyle by social risk domain. After adjustement, inadequate access to care remained significantly associated with lower prevalence of counseling for physical activity (PR = 0.63; CI 0.51; 0.78), counseling to reduce fat or calories in diet (PR = 0.70; CI 0.56; 0.86), and counseling for joining a weight loss program (PR = 0.33; CI 0.19; 0.59).
Table 4.
Fully adjusted logistic regression models for counseling for lifestyle Change by social determinants of health domains in adults with prediabetes, NHIS, 2016–2017
| Told to Increase Physical Activity | Told to Reduce Fat or Calories in Diet | Told to Join Weight Loss Program | |
|---|---|---|---|
| Prevalence Ratio (CI) | Prevalence Ratio (CI) | Prevalence Ratio (CI) | |
| Economic instability domain | 1.03 (0.94, 1.13) | 0.99 (0.90, 1.10) | 1.03 (0.83, 1.28) |
| Lack of community domain | 0.97 (0.91, 1.04) | 0.99 (0.92, 1.07) | 1.00 (0.84, 1.18) |
| Educational deficit domain | 0.98 (0.92, 1.05) | 1.06 (0.98, 1.14) | 0.91 (0.76, 1.08) |
| Food insecurity domain | 0.94 (0.86, 1.03) | 0.93 (0.85, 1.01) | 0.93 (0.76, 1.14) |
| Social isolation domain | 0.95 (0.89, 1.03) | 0.95 (0.88, 1.02) | 1.03 (0.86, 1.23) |
| Inadequate access to care domain | 0.63*** (0.51, 0.78) | 0.70*** (0.56, 0.86) | 0.33*** (0.19, 0.59) |
| Age | |||
| 18–39 (ref) | - | - | - |
| 40–49 | 1.06 (0.95, 1.19) | 1.02 (0.90, 1.16) | 1.10 (0.83, 1.47) |
| 50-64 | 1.06 (0.96, 1.17) | 1.08 (0.97, 1.20) | 1.34* (1.04, 1.73) |
| 65–74 | 0.98 (0.87, 1.10) | 0.91 (0.80, 1.03) | 1.02 (0.76, 1.37) |
| 75+ | 0.87 (0.74, 1.01) | 0.78** (0.66, 0.92) | 0.62* (0.42, 0.92) |
| Sex (Female) | 1.07* (1.00, 1.14) | 0.97 (0.90, 1.04) | 0.81* (0.68, 0.96) |
| Race/Ethnicity | |||
| NHW (ref) | - | - | - |
| NHB | 1.05 (0.95, 1.16) | 1.13* (1.02, 1.25) | 1.07 (0.84, 1.37) |
| NHO | 1.30*** (1.16, 1.47) | 1.35*** (1.16, 1.58) | 1.25 (0.85, 1.82) |
| Hispanic | 1.15** (1.04, 1.28) | 1.39*** (1.25, 1.54) | 1.02 (0.79, 1.31) |
| Health insurance (Uninsured) | 0.97 (0.83, 1.13) | 0.97 (0.81, 1.16) | 0.82 (0.50, 1.32) |
| Obese | 1.55*** (1.44, 1.66) | 1.84*** (1.69, 1.99) | 3.51*** (2.81, 4.37) |
| Mental health issue | 1.15 (0.99, 1.32) | 1.02 (0.85, 1.23) | 1.10 (0.78, 1.54) |
| Hypertension | 1.18*** (1.10, 1.27) | 1.19*** (1.10, 1.30) | 1.24* (1.03, 1.48) |
| Coronary heart disease | 1.12 (0.99, 1.26) | 1.14 (0.99, 1.31) | 1.26 (0.94, 1.69) |
| Heart attack | 0.92 (0.79, 1.08) | 0.97 (0.81, 1.15) | 0.79 (0.52, 1.20) |
| Stroke | 0.91 (0.78, 1.07) | 0.87 (0.72, 1.05) | 0.97 (0.63, 1.48) |
| Asthma | 1.00 (0.92, 1.08) | 0.99 (0.90, 1.09) | 1.19 (0.99, 1.44) |
| Ulcer | 1.06 (0.97, 1.16) | 0.95 (0.86, 1.06) | 0.78 (0.59, 1.03) |
| Cancer | 1.03 (0.94, 1.13) | 0.98 (0.87, 1.09) | 0.87 (0.68, 1.13) |
| Emphysema/COPD | 1.07 (0.94, 1.21) | 1.01 (0.87, 1.18) | 0.85 (0.61, 1.19) |
| Kidney disease | 1.16* (1.01, 1.33) | 0.93 (0.75, 1.14) | 0.98 (0.63, 1.52) |
| Liver disease | 1.03 (0.88, 1.19) | 1.15 (0.99, 1.34) | 1.50** (1.14, 1.98) |
| Arthritis | 1.05 (0.97, 1.13) | 1.06 (0.97, 1.16) | 0.99 (0.81, 1.21) |
| Migraine | 1.02 (0.93, 1.11) | 0.97 (0.89, 1.07) | 1.05 (0.84, 1.31) |
| Chronic pain | 1.03 (0.96, 1.11) | 1.05 (0.97, 1.14) | 1.06 (0.88, 1.28) |
CI Confidence Interval
*p-value < 0.05
**p-value < 0.01
***p-value < 0.001
Table 5 shows the fully adjusted logistic regression model for engaging in lifestyle change behaviors by social risk domain. After adjustment, lack of community remained significantly associated with lower prevalence of behaviors to increase physical activity (PR = 0.94; CI 0.88; 1.00). Educational deficit remained significantly associated with lower prevalence of behaviors to increase physical activity (PR = 0.88; CI 0.82; 0.94), lower prevalence of behaviors to reduce fat/calories in diet (PR = 0.89; CI 0.83; 0.95) and lower prevalence of being in a weight loss program (PR = 0.66; CI 0.51; 0.87). Food insecurity remained significantly associated with lower prevalence of reducing fat/calories in diet (PR = 0.91; CI 0.84; 0.98). Inadequate access to care remained significantly associated with lower prevalence of being in a weight loss program (PR = 0.43; CI 0.23; 0.81).
Table 5.
Fully adjusted logistic regression model for lifestyle Change behaviors by social determinants of health domains in adults with prediabetes, NHIS, 2016–2017
| Currently Increasing Physical Activity | Currently Reducing Fat/Calories in Diet | Currently in Weight Loss Program | |
|---|---|---|---|
| Prevalence Ratio (CI) | Prevalence Ratio (CI) | Prevalence Ratio (CI) | |
| Economic instability domain | 0.98 (0.91, 1.06) | 1.06 (0.97, 1.15) | 0.96 (0.73, 1.27) |
| Lack of community domain | 0.94* (0.88, 1.00) | 0.99 (0.93, 1.05) | 1.06 (0.82, 1.35) |
| Educational deficit domain | 0.88*** (0.82, 0.94) | 0.89*** (0.83, 0.95) | 0.66** (0.51, 0.87) |
| Food insecurity domain | 0.96 (0.88, 1.05) | 0.91* (0.84, 0.98) | 0.73 (0.53, 1.01) |
| Social isolation domain | 0.96 (0.89, 1.03) | 0.96 (0.90, 1.03) | 1.00 (0.77, 1.28) |
| Inadequate access to care domain | 0.90 (0.75, 1.08) | 1.04 (0.90, 1.20) | 0.43** (0.23, 0.81) |
| Age | |||
| 18–39 (ref) | - | - | - |
| 40–49 | 1.07 (0.96, 1.19) | 1.11 (0.98, 1.26) | 1.06 (0.70, 1.60) |
| 50–64 | 1.05 (0.94, 1.17) | 1.17** (1.05, 1.30) | 1.15 (0.80, 1.65) |
| 65–74 | 0.91 (0.80, 1.03) | 1.05 (0.93, 1.17) | 0.87 (0.57, 1.33) |
| 75+ | 0.81** (0.69, 0.95) | 0.88 (0.76, 1.02) | 0.52* (0.30, 0.91) |
| Sex (Female) | 1.07* (1.00, 1.14) | 1.16*** (1.09, 1.24) | 1.09 (0.86, 1.39) |
| Race/Ethnicity | |||
| NHW (ref) | - | - | - |
| NHB | 1.11* (1.01, 1.21) | 1.09 (1.00, 1.20) | 0.79 (0.56, 1.11) |
| NHO | 1.10 (0.98, 1.23) | 1.07 (0.96, 1.20) | 1.33 (0.84, 2.11) |
| Hispanic | 1.06 (0.95, 1.18) | 1.18*** (1.07, 1.29) | 1.11 (0.77, 1.59) |
| Health insurance (Uninsured) | 0.86 (0.72, 1.02) | 0.90 (0.76, 1.06) | 1.42 (0.83, 2.42) |
| Obese | 1.02 (0.95, 1.09) | 1.21*** (1.14, 1.28) | 1.62*** (1.29, 2.04) |
| Mental health issue | 0.94 (0.79, 1.13) | 1.09 (0.93, 1.28) | 1.13 (0.68, 1.88) |
| Hypertension | 1.06 (0.99, 1.13) | 1.13*** (1.06, 1.20) | 1.13 (0.88, 1.46) |
| Coronary heart disease | 1.08 (0.95, 1.23) | 1.15* (1.01, 1.30) | 1.34 (0.85, 2.11) |
| Heart attack | 0.92 (0.78, 1.09) | 0.93 (0.80, 1.09) | 0.98 (0.54, 1.78) |
| Stroke | 1.12 (0.99, 1.26) | 1.01 (0.88, 1.15) | 1.25 (0.77, 2.01) |
| Asthma | 1.03 (0.95, 1.12) | 1.01 (0.93, 1.09) | 1.37* (1.02, 1.83) |
| Ulcer | 1.05 (0.95, 1.15) | 1.01 (0.91, 1.11) | 1.01 (0.66, 1.53) |
| Cancer | 1.05 (0.96, 1.14) | 0.95 (0.87, 1.04) | 0.92 (0.67, 1.26) |
| Emphysema/COPD | 0.92 (0.80, 1.06) | 0.86* (0.75, 1.00) | 0.50** (0.30, 0.82) |
| Kidney disease | 0.99 (0.84, 1.18) | 0.97 (0.81, 1.15) | 0.92 (0.49, 1.71) |
| Liver disease | 0.91 (0.76, 1.10) | 1.09 (0.96, 1.25) | 1.00 (0.58, 1.73) |
| Arthritis | 1.00 (0.92, 1.08) | 1.05 (0.98, 1.12) | 1.06 (0.80, 1.42) |
| Migraine | 0.95 (0.87, 1.04) | 0.84*** (0.77, 0.93) | 1.02 (0.75, 1.38) |
| Chronic pain | 1.01 (0.94, 1.08) | 1.02 (0.95, 1.09) | 0.97 (0.75, 1.26) |
CI Confidence Interval
*p-value < 0.05
**p-value < 0.01
***p-value < 0.001
Discussion
Overall, in this national sample of US adults with prediabetes, prevalence of social risk was high. Specifically, 83.5% reported experiencing economic instability, 46.0% reported having a lack of community, 39.0% reported an educational deficit, 27.1% reported food insecurity, and 32.6% reported social isolation. However, only 5.3% of the population reported inadequate access to care, consistent with the high national rates of health care coverage based on having health insurance. The relationship between social risk factors and receiving counseling for lifestyle Changes was significant only for the health care access domain, with those reporting inadequate access to healthcare associated with a 37% lower prevalence of receiving counseling for physical activity, a 30% lower prevalence of being told to reduce calories/fat from diet, and a 67% lower prevalence of being advised to join a weight loss program, controlling for other social risk domains and covariates. This is not a surprising finding considering the lifestyle counseling was conducted by a physician or health care provider, suggesting those who lacked insurance were less likely to have such encounters. When evaluating the relationship between social risk factors and engaging in diabetes prevention behavior, there was a difference in which social risk factors was associated with the behavior of interest. Reporting a lack of community and reporting an educational deficit were significantly associated with 6% and 12% lower prevalence, respectively, of increasing physical activity. Reporting an educational deficit, and reporting food insecurity were significantly associated with 11% and 9% lower prevalence, respectively, of reducing calories/fat from diet. Finally, reporting an educational deficit and inadequate access to care were associated with 34% and 57% lower prevalence, respectively, of being in a weight loss program.
This is among the first studies to examine the relationship between multiple social risk factors and diabetes prevention counseling and lifestyle behavior Change among the US adult population with prediabetes. Existing work has emphasized the role of social risk in prevalence of diabetes as well as risk of developing diabetes however has not formally examined multiple social risk factors and their association. Lack of community for example, as measured by social cohesion is associated with higher prevalence of type 2 diabetes [23]. Specifically, patients living in low social cohesion neighborhood have a 22% higher prevalence of type 2 diabetes [23]. Other social risk factors examined in the literature include social isolation and food insecurity as risk factors for developing type 2 diabetes [24, 25]. In their cohort study comparing social isolation in the UK and China, Song et al. found that loneliness and social isolation were independently and jointly associated with a higher risk of developing type 2 diabetes, regardless of other risk factors [24]. In a UK GWAS analysis, regular participation in social interactions such as sports clubs, religious activities, or adult education classes were all associated with a reduced risk of type 2 diabetes [24]. When looking at risk of diabetes among adults with food insecurity, Levi et al. provide three paths through which food insecurity can cause or exacerbate diabetes: nutritional, behavioral, and mental health. In the nutritional pathway, a limited budget for groceries leads to preferential selection of energy-dense, nutritionally poor, and highly processed food items leading to increased risk of insulin resistance and elevated blood glucose levels [25]. The behavioral pathway shows that food insecure households have difficulty allocating resources to all necessities, resulting in cost-related medication underuse and skipped or delayed healthcare visits, leading to overall poorer disease control [25]. The mental health pathway shows food insecurity is linked with higher rates of depression, anxiety, and stress [25].
The current study noted that food insecurity was associated with a lower prevalence of reducing fat/calories from their diet. In a study of national data, individuals with prediabetes were 39% more likely to be food insecure [26]. Barriers to healthy diets for food insecure individuals include a lack of understanding of nutritional terms and labels, an inability to afford balanced meals, barriers to preparing food such as access to a stove, and inadequacies of healthy food at food pantries [27–30]. As a result, individuals who are food insecure may approach behavior Change related to food very differently than those who are not food insecure. A focus on reducing fat or reducing calories may not be the most relevant or realistic focus of diet Change given other pressures experienced in eating a healthy diet. To effectively prevent and delay type 2 diabetes among adults with prediabetes experiencing food insecurity, tailored approaches are needed that account the complexity of food access, choice, and healthy lifestyle opportunities.
These findings add to the current body of literature by underscoring how key social risk factors limit diabetes prevention practices. By identifying key social risk factors that are associated with lower engagement in lifestyle change, diabetes prevention programs, diabetes self-management specialists, as well as providers may tailor evidence-based approaches to account for key social risk factors that may serve as a barrier to behavior change [31] for diabetes prevention. Specifically, as the field moves toward integration of social and medical care, accounting for social risk in diabetes prevention may be facilitated through cross-collaboration across the health systems and communities in which the systems operate [32]. This can be achieved in a number of ways. First, a return to equity models such as the chronic care model that illustrate the strength of partnerships between the community and health systems should be emphasized to effectively leverage resources that maximize population health. These resources include grass roots efforts that support community health and wellness, activation of existing community organizations to serve as diabetes prevention program sites, and integration of social needs within medical care. Second, from a public health standpoint, strategies placed on primary and secondary prevention need to be at the forefront of clinical care which cannot be done in the absence of identifying facilitators and barriers to health as they occur in the individual patients social setting. Third, the healthcare system alongside the community need to work in collaboration to identify and prioritize strategies to address policies for improve health at the population level that brings together medical and social expertise accounting for the individual lived experience. Finally, to better understand how to intervene on social risk factors that serve as barriers to diabetes prevention, research to understand the mechanisms, pathways, and implementation strategies at the population level accounting for cost and workforce development to support need resolution are greatly needed [33, 34]. One such model that can be drawn from is the LINK study designed to improve diabetes care and outcomes by focusing on social needs [35]. This study screens for health-related social needs and works to provide resolution while also supporting skill development for improving diabetes management. Findings of this study have demonstrated improved glycemic control as well as improved self-efficacy and diet quality among those who are experiencing food insecurity [36]. While focused on a type 2 diabetes population, models such as these can be adapted for pre-diabetes.
Limitations
While this study is strengthened by use of a large population-based dataset, there are limitations that should be considered. First, this study used a cross-sectional design and therefore, causality cannot be determined. Second, as responses are based on self-report measures there may be some potential for recall bias. However, validation reports demonstrate that recall bias is low when reporting on chronic disease conditions as well as self-reporting high stress experiences such as social risk exposure [37–41]. Third, while the six categories of social risk are robust, there may be existing social risks that have not been accounted for such as environmental exposures, structural barriers, neighborhood safety and the role of chronic stress in disease development. Future work should incorporate domains of influence identified in the evidence that account for the lived experience such as chronic stress and competing demands, as well as treatment preference of patients living with prediabetes. Additionally, this analysis used a definition of social risk that allowed capture of the burden of multiple risk factors. In doing so, detail about any single risk factors was limited. Additional work is needed to understand nuances of specific social risk factors. Finally, longitudinal examination to explore how changes in social conditions might affect engagement in diabetes prevention as well as pathways and mechanisms and differences by race/ethnicity and sex need to be examined further.
Conclusions
In conclusion, this study identified key social risk factors associated with both counseling for lifestyle Change and engagement in lifestyle Changes among a nationally representative sample of adults with prediabetes. Inadequate access to care was associated with lower prevalence of receiving counseling for diet and exercise in the last 12 months. A lack of community, having an educational deficit, being food insecure, and having inadequate access to care were all significantly associated with lower prevalence of engaging in lifestyle behavior change. To effectively address the barriers to diabetes prevention adoption in the US, social risk factors need to be accounted for as a part of a holistic care plan. Key steps can be taken to achieve this that include establishing support for screening and resolution of social needs within routine clinical care, integration of comprehensive barrier navigation in national diabetes prevention programs and designing implementation strategies at the national level that account for the unique and diverse needs populations face when navigating diabetes prevention. Future work is needed to better understand the pathways and mechanisms of social risk factors and diabetes prevention at the population level.
Acknowledgements
Not applicable.
Authors’ contributions
LEE designed the study and interpreted results. XW, AT, and LEE organized the dataset and conducted analyses. CE, JAC, XW, AT, RJW, and LEE drafted the manuscript. All authors critically revised and approved the final manuscript.
Funding
Effort for this study was partially supported by the National Institute of Diabetes and Digestive Kidney Disease (R01DK118038, R01DK120861, PI: Egede; K01DK131319, PI: Campbell), the National Institute for Minority Health and Health Disparities (R01MD013826, PI: Egede/Walker; R01MD017574, PI: Egede/Linde; R01MD018012, PI: Egede/Linde).
Data availability
The dataset generated and analyzed during the current study is publicly available and can be accessed on the National Health Interview Survey (NHIS) website. Available at: https://archive.cdc.gov/www_cdc_gov/nchs/nhis/nhis_2016_data_release.htm and https://archive.cdc.gov/www_cdc_gov/nchs/nhis/1997-2018.htm.
Declarations
Ethics approval and consent to participate
IRB review and ethics approval was not required as data was publicly available and non-identifiable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The dataset generated and analyzed during the current study is publicly available and can be accessed on the National Health Interview Survey (NHIS) website. Available at: https://archive.cdc.gov/www_cdc_gov/nchs/nhis/nhis_2016_data_release.htm and https://archive.cdc.gov/www_cdc_gov/nchs/nhis/1997-2018.htm.