Introduction

Developmental disabilities encompass a group of conditions that affect the developing nervous system, causing motor, cognitive, language, behavior, and/or sensory functioning impairments. Most developmental disabilities are caused by a complex combination of genetic factors, prenatal health and behaviors during pregnancy, and birth complications. These impairments often create barriers to full social participation, significantly impacting the affected individuals’ quality of life. While the global prevalence of developmental disabilities varies widely across studies, the median prevalence of developmental disabilities is 7.2% in children and adolescents1. Approximately 1.2% of the global population had developmental intellectual disabilities in 20212,3.

The health status of individuals with developmental disabilities has been a major concern in public health research and policy4,5. Despite advances in healthcare, there are still significant gaps in understanding the specific health challenges faced by this population. One critical area of concern is the higher prevalence of cardiovascular disease (CVD) risk factors among individuals with developmental disabilities6,7, which is the leading cause of morbidity and mortality worldwide, accounting for one-third of all deaths in 20213. Individuals with intellectual disability, in particular, face heightened risks of early-onset cardiovascular conditions, particularly stroke, heart failure, and cerebrovascular disease6. Research has highlighted disparities in key cardiovascular prevention indicators within this population, including higher rates of obesity, abnormal cholesterol levels, and lower levels of physical activity6,8,9,10.

Individuals with developmental disabilities are disproportionately affected by high rates of overweight, obese, and severely obese. A higher proportion of overweight, obese, and severely obese children with disabilities have abnormal SBP, fasting lipid and glucose levels, and metabolic syndrome8. Individuals with developmental disabilities also demonstrate alarmingly low levels of physical activity9. Furthermore, smoking and alcohol consumption pose significant risks to cardiovascular health in this population10. Although studies have reported varied rates of tobacco and alcohol use, these behaviors can exacerbate existing health conditions and increase the risk of substance abuse10,11.

In South Korea, 272,524 individuals were registered with developmental disabilities in a national registration system, representing around 0.53% of the population, as of 2023. This figure includes about 229,780 people (0.45%) with intellectual disabilities and 42,744 people (0.08%) with autism spectrum disorder (ASD)12. The South Korean National Health Insurance system, in which almost all citizens enrolled, provides regular, comprehensive health screenings targeting cardiovascular and cerebrovascular risk factors. These screenings are available for free or at a minimal cost to reduce the impact of financial barriers13, which offer a valuable dataset for assessing CVD risk and related health disparities between individuals with and without developmental disabilities.

While previous studies have addressed some aspects of health disparities among individuals with developmental disabilities, they often relied on survey data and underrepresented these population on a national scale8,14. In addition, these studies frequently assessed obesity using self-reported body mass index (BMI)15,16,17, which may be inaccurate for individuals with disabilities and could underestimate actual obesity rates4. Furthermore, much of the existing literature focuses primarily on children and adolescents8,10,18, overlooking the health challenges faced by adults with developmental disabilities, who often live in more disadvantaged circumstances, as public policies tend to prioritize developmental disabilities during childhood19. Although many studies have accounted for socio-economic factors, sex, and age as covariates, these variables strongly predict health outcomes and may introduce confounding bias if not appropriately handled.

In light of these gaps, this study leverages Korea’s National Health Insurance and disability registration database to provide a more comprehensive analysis of cardiovascular health disparities among individuals with developmental disabilities. Through a propensity score matching approach, this study aims to eliminate confounding effects of socio-demographic factors and offers accurate insights into the relationship between developmental disabilities and CVD risk factors. Specifically, this study focuses on key cardiovascular health indicators such as obesity, hypertension, diabetes, and dyslipidemia, as well as lifestyle factors like physical activity, smoking, and drinking behaviors.

Results

Among 5,641,005 individuals, 17,012 had disabilities and 5,623,993 were non-disabled. After exact age, sex, and 1:1 PS matching, each group included 13,863 individuals. Table 1 presents the baseline characteristics of all individuals before and after matching. Before matching, all variables exhibited different distributions between the two groups. After matching, all variables were well-balanced between the two groups (Table 1). Both groups contained 35.9% females, and the average ages were 38.9 and 38.8 for those with and without disabilities, respectively, showing no significant age difference. The proportion of individuals enrolled in employment-based health insurance was 46.7% for both groups. In contrast, the proportion of those enrolled in other NHI programs was 11.6% for those with disabilities and 11.7% for those without. The distribution of income groups and residence areas was similar between the two groups.

Table 1 Descriptive characteristics.
Full size table

Before matching, individuals with disabilities exhibited a significantly higher risk of obesity compared to those without disability (p-value < 0.001). Specifically, 45.3% of individuals with intellectual disabilities and 51.7% of those with ASD were obese, compared to 38.7% of individuals without disability (p-value < 0.001 for both ASD and intellectual disabilities). After matching and adjusting for covariates, those with disabilities still had a higher risk of obesity compared to those without, although the difference decreased (p-value < 0.001 for both groups) (Fig. 1a).

Fig. 1
figure 1

Comparison of risk of obesity by disability status before and after propensity score matching. Note: *** p < 0.001, ** p < 0.01, * p < 0.05, and + p < 0.1.

Full size image

The difference in abdominal obesity between individuals with and without disabilities was consistent with the pattern observed for overall obesity. When broken down by disability type, 33.4% of individuals with intellectual disabilities and 40.5% of those with ASD had a higher risk of abdominal obesity compared to 25.1% of non-disabled individuals before matching (p-value < 0.001 for both). After matching and adjusting for covariates, 33.8% of individuals with intellectual disabilities and 42.5% of those with ASD still had a higher risk of abdominal obesity compared to 27.7% of individuals without disability (p-value < 0.001 for both) (Fig. 1b).

Individuals with disabilities had a significantly lower risk of high blood pressure than individuals without disabilities before matching (p-value < 0.001 for both ASD and intellectual disabilities). After matching and adjusting for covariates, however, there was no significant difference between individuals with and without disabilities (p-value = 0.42 for ASD and 0.73 for intellectual disabilities) (Fig. 2a). We also examined differences in systolic and diastolic blood pressure, separately. Before matching, individuals with disabilities showed a significantly lower prevalence of high systolic blood pressure (p-value < 0.001 for both ASD and intellectual disabilities) while a lower prevalence of high diastolic blood pressure was observed only in those with ASD (p-value < 0.001). After matching and adjusting for covariates, the risks of both high systolic and diastolic blood pressure were not statistically different between individuals with intellectual disabilities/ASD and individuals without disability (p > 0.1 for both outcomes and disability types) (Fig. 2b, c).

Fig. 2
figure 2

Comparison of risk of high blood pressure and abnormal fasting blood sugar levels by disability status before and after propensity score matching. Note: *** p < 0.001, ** p < 0.01, * p <0.05, and + p < 0.1.

Full size image

Before matching, the risk of abnormal fasting blood sugar levels was lower in individuals with disabilities compared to non-disabled individuals (p-value < 0.001 for both). After matching and adjusting for covariates, individuals with disabilities did not have a lower risk of abnormal fasting blood sugar levels compared to those without disabilities (p-value = 0.07 for intellectual disabilities and 0.9 for ASD) (Fig. 2d).

For abnormal levels of total cholesterol, low-density lipoprotein (LDL), and triglyceride, individuals with disabilities exhibited significantly lower risks compared to individuals without disabilities, both before and after matching and adjusting for covariates (p-value < 0.01 for all). When analyzed by disability type, similar patterns were seen for individuals with intellectual disabilities and ASD, except for abnormal LDL cholesterol levels in individuals with ASD after matching and adjusting for covariates. Conversely, individuals with disabilities had significantly higher risks of abnormal high-density lipoprotein (HDL) cholesterol levels, both before and after matching and adjusting for covariates (p-value < 0.001 for both). When disaggregated by disability type, individuals with intellectual disabilities demonstrated higher risks of abnormal HDL cholesterol levels both before and after matching and adjusting for covariates, whereas those with ASD showed higher risks only before matching (Fig. 3a, b, c, d).

Fig. 3
figure 3

Comparison of abnormal levels of cholesterol risk and triglyceride by disability status before and after propensity score matching. Note: *** means p < 0.001, ** p < 0.01, * p < 0.05, and + p < 0.1.

Full size image

Individuals with disabilities exhibited a significantly higher risk of not engaging in moderate or vigorous physical activities compared to those without disabilities, both before and after matching and adjusting for covariates (p-value < 0.001 for both). When disaggregated by disability type, individuals with intellectual disabilities had consistently higher risks of not engaging in moderate or vigorous physical activities, both before and after matching and adjusting for covariates (p-value < 0.001 for all). In contrast, individuals with ASD were significantly less likely to engage in vigorous physical activities, but not moderate physical activities, after matching and adjusting for covariates, compared to those without disabilities (p-value < 0.001) (Fig. 4a, b).

Individuals with disabilities showed significantly lower risks for drinking and smoking, both before and after matching and adjusting for covariates (p-value < 0.001 for both comparisons). When disaggregated by disability type, similar patterns were observed for individuals with intellectual disabilities and ASD (Fig. 4c, d).

Fig. 4
figure 4

Comparison of abnormal levels of physical activities, drinking and smoking by disability status before and after propensity score matching. Note: *** p < 0.001, ** p < 0.01, * p < 0.05, and + p < 0.1.

Full size image

Discussion

This study examined the health disparities faced by individuals with developmental disabilities, particularly those with ASD and intellectual disabilities. We focused on cardiovascular risk factors, such as obesity, blood pressure, fasting blood sugar levels, physical activity, smoking, and drinking habits, using Korean national health screening data. Our findings indicated that matching and adjusting for risk factors minimized the differences between individuals with and without disabilities. Notably, individuals with developmental disabilities showed higher risks of obesity and lower levels of physical activity compared to individuals without these conditions, even after propensity score matching. These patterns were particularly pronounced in those with intellectual disabilities compared to those with ASD. In addition, individuals with these disabilities exhibit lower risks of smoking and drinking, highlighting distinct health behavior patterns in this population.

Obesity and physical inactivity by disability status

There was a higher prevalence of overall and abdominal obesity in individuals with disabilities both before and after matching, although this difference was partially attributed to socio-demographic factors, which led to reduced disparities after matching. Research indicates a high prevalence of overweight and obesity in adults with ASD in the U.S20., and adults with intellectual disabilities have markedly higher obesity rates than those without disabilities in Britain and Ireland21,22. Individuals with intellectual disabilities tend to be less physically active, lead more sedentary lifestyles, and have lower fitness levels, making them more prone to being overweight or obese compared to the general population23,24. Indeed, our study highlights that individuals with intellectual disabilities had significantly higher risks of not engaging in moderate or vigorous physical activities, both before and after matching.

Hypertension and diabetes by disability status

In contrast, unmatched samples showed that individuals with disabilities had a lower risk of high blood pressure and elevated fasting blood glucose levels than those without disability; however, no significant differences were found after matching and adjustment. Previous studies reported similar or slightly lower prevalence rates of hypertension among individuals with intellectual disabilities25and significantly lower fasting blood sugar levels compared to the general population26. These findings may be related to age differences between the two groups, as our study revealed that a higher proportion of individuals with disabilities were in their 20s and 30s, potentially contributing to the lower prevalence of hypertension and diabetes. Aging exerts a marked effect on the cardiovascular system and increases the incidence of type 2 diabetes27,28, and merely adjusting for age as a covariate may not sufficiently address these systematic differences.

Lifestyle factors and health outcomes by disability status

In addition, our findings indicated that individuals with intellectual disabilities or ASD were less likely to participate in physical activity but did not engage in heavy smoking or drinking. The combined effects of lifestyle factors, higher obesity rates, and lower physical activity levels, alongside lower exposure to smoking and alcohol consumption, may explain the health outcomes related to hypertension and diabetes in this population.

The lower risks of smoking and drinking behaviors among individuals with developmental disabilities, both before and after matching, align with findings from a previous research. A similar study reported that the rates of smoking and regular alcohol consumption were relatively low in this population compared to the general population in Ireland29. However, an exploratory analysis revealed that risky drinking behaviors were more prevalent among a subpopulation of individuals with ASD, such as those with co-occurring attention-deficit/hyperactivity disorder (ADHD) and/or learning disorders, than among those without such comorbidities30.

Abnormal levels of cholesterol risk and triglyceride by disability status

Effective cholesterol management through healthy lifestyle choices may help prevent heart disease, as high total cholesterol, elevated LDL, and low HDL levels can increase cardiovascular risk31. This study demonstrated that individuals with intellectual disabilities or ASD had lower risks of abnormal total cholesterol and triglyceride levels, with individuals having intellectual disabilities exhibiting lower abnormal LDL and higher abnormal HDL cholesterol levels compared to those without disabilities after matching. Similarly, a study involving French-Canadian individuals with ASD found that hypocholesterolemia (abnormally low cholesterol levels) was significantly more prevalent in those with ASD than in the general population32. The Australian Autism Biobank study also identified that ASD-related lipidome differences were primarily influenced by dietary habits and sleep disturbances33.

The cholesterol patterns observed in this and previous studies may be linked to lifestyle factors in individuals with developmental disabilities. For example, physical inactivity and obesity, which were more prevalent in our study’s population, can increase LDL levels and lower HDL levels. Alcohol consumption raises total cholesterol levels, while smoking elevates LDL and decreases HDL levels34. Understanding how these combined lifestyle factors influence various cholesterol types can facilitate personalized treatment approaches for individuals with intellectual disabilities or ASD35.

Limitations of study

This study has several limitations. First, although it aims to explore health disparities among individuals with developmental disabilities, intellectual disabilities and ASD present distinct challenges and health issues. While our additional analysis provided separate patterns for each group, we matched persons with and without developmental disabilities as a whole. Future studies should involve a more detailed analysis by matching the groups separately. Second, since ASD is more prevalent in children, and our analysis was limited to adults aged 20 years and older due to the availability of health examination data, the study may not fully capture the cardiovascular risk factors in persons with ASD. Third, dietary patterns are crucial factors influencing obesity and cardiovascular risk. However, since our database lacked dietary habit information, we could not examine potential differences in dietary patterns between individuals with and without disabilities. Fourth, although we analyzed individuals participating in a regular health screening program, individuals with developmental disabilities and severe health conditions are less likely to undergo screening services than other individuals. Our work may underestimate disparities in CVD risk factors between individuals with and without developmental disabilities. Lastly, this study is limited by its cross-sectional design using 2019 and 2020 GHSP data, preventing long-term follow-up to assess actual CVD incidence. While previous studies indicate that individuals with disabilities face a higher CVD risk7, we could not directly evaluate long-term outcomes such as atherosclerotic cardiovascular disease (ASCVD) risk. Future research should employ longitudinal designs to track CVD progression and investigate targeted interventions for individuals with disabilities.

Conclusions

This study assessed the status of cardiovascular risk factors among persons with developmental disabilities and found that they tend to be overweight but have generally healthier lifestyles. This study reveals that individuals with disabilities, particularly those with ASD and intellectual disabilities, have higher risks of overall and abdominal obesity and lower physical activity levels compared to individuals without disability, even after matching for age, sex, and other factors. They had lower total cholesterol, LDL and triglyceride levels but higher abnormal HDL cholesterol levels. Conversely, they are less likely to smoke or drink. We did not note significant differences in high blood pressure and fasting blood sugar levels after matching. This comprehensive analysis underscores significant disparities in obesity and physical activity levels among individuals with disabilities, emphasizing the need for targeted health interventions that recognize their lower risks of certain unhealthy behaviors.

Methods

Data sources

We derived the study population from the National Health Insurance Service (NHIS) database. The NHIS provides mandatory health care coverage for nearly all Koreans (approximately 50 million people), including NHI enrollees (97%) and Medical Aid beneficiaries (3%), including emergency, inpatient, and outpatient care. The NHIS database contains socio-demographic data such as sex, age, residence, health coverage type and NHI contributions and clinical information regarding medical claims. In addition, the database includes disability information from a national disability registration system, which recognizes 15 types of disabilities graded from 1 (most severe) to 6 (least severe) or as moderate (grades 4 to 6) to severe (grades 1 to 3).

NHIS also delivers the National General Health Screening Program (GHSP). Employees and self-employed are eligible every two years for the program (employees involved in manual labor every year) regardless of age, as are dependents aged 20 years or older and Medical Aid beneficiaries aged 20 to 64 years36. The GHSP includes anthropometric data, blood pressure (BP), laboratory examinations including fasting glucose and total cholesterol, and a standardized self-reporting questionnaire regarding medical history and health-related lifestyle factors, including smoking, alcohol consumption, and physical exercise.

This study sampled 10,413,089 participants, 20% of the 2012 population, considering sex, age, and regional distribution from the NHIS database. Enrollee information and GHSP data were collected through 2020 (data number: NHIS-2022-1-629).

Ethical considerations

All study process and methods were performed in accordance with the relevant guidelines and regulations of NHIS and Korea Institute for Health and Social Affairs. Since this study used de-identified data provided by the NHIS after anonymization according to strict confidentiality guidelines, the Institutional Review Board (IRB) of Korea Institute for Health and Social Affairs (IRB number: 2022-004) exempted an ethics review. The IRB recognizes that informed consent is not applicable for the analysis of de-identified data.

Study design and population

We collected 2019 and 2020 GHSP information for individuals aged 20 years or older with and without developmental and intellectual disabilities. Among 17,171,277 individuals (8,554,053 in 2019 and 8,617,224 in 2020), 5,641,005 (5,623,993 without and 17,012 with disabilities) participated in GHSP. We matched participants with developmental or intellectual disabilities controls by age (± 1-year band) and sex. We then matched participants in a 1:1 ratio using propensity score.

Definition of the study population

In this study, we defined the target population based on the Welfare Law for Persons with Disabilities as those certificated as having developmental disabilities (intellectual disability, ASD) in the registration system37. Intellectual disability denotes impaired intellectual development without ASD, typically defined by an intelligence quotient (IQ) of 70 or lower, presenting clear limitations in intellectual and cognitive abilities. Autism, often referred to as ASD, is a developmental disorder characterized by challenges in interpersonal communication38.

Outcomes

We measured overweight based on anthropometric data and high blood pressure, abnormal fasting blood glucose, and high blood cholesterol based on laboratory examination. We categorized an individual as overweight if they had a body mass index over 25.0 kg/m2and a waist circumference more than 85 cm for females and 90 cm for males based on the Korean Society for the Study of Obesity39. High blood pressure was defined as a diastolic BP ≥ 90 mm Hg or a systolic BP ≥ 140 mm Hg. We defined diabetes as fasting blood glucose levels of ≥ 126 mg/dl. Abnormal cholesterol levels were defined as ≥ 240 mg/dl total cholesterol, a HDL level of < 40 mg/dl, and ≥ 160 mg/dl LDL levels.

Next, we assessed physical activity, smoking, and drinking habits based on the self-reported questionnaires. We verified if individuals performed vigorous and moderate physical activity at least once a week. Vigorous physical activities included running, aerobic exercise, and high-speed bicycling, while moderate activities included power walking, doubles tennis, and normal-speed bicycling. Vigorous and moderate physical activity were defined as binary variables, with a value of 1 if individuals engaged in the respective activity at least once a week and 0 otherwise. We also measured current smoking and drinking status over the past year. Current smoking status was defined as a binary variable, with a value of 1 for individuals who were current smokers and 0 for non-smokers over the past year. Drinking status was defined as a binary variable, with a value of 1 for individuals who consumed alcohol in the past year and 0 for non-drinkers.

Potential confounders

We included the following potential confounding variables as covariates: sex, age, health coverage type, residence (metropolitan, city, and rural), income quintile, and examination year (2019, 2020). We employed age as a continuous variable with age square. Healthcare coverage included employment-based NHI enrollees, other NHI enrollees, and Medical Aid, which is a subsidy program for individuals with a low income. We categorized income level into five groups using contribution quintiles: medical Aid and first contribution quintile, second contribution quintile, third contribution quintile, fourth contribution quintile, and fifth contribution quintile.

Statistical analyses

To minimize any effects from measured confounders and obtain comparability between groups, we matched (1:1 ratio) the cohort based on the propensity score (PS). We estimated PS using a multivariable logistic regression model within the sex-age exact-matched sample with all predefined covariates. Baseline characteristics for both unmatched and matched samples were compared using chi-square tests for categorical variables and t-statistics for continuous variables. We first examined differences in each outcome between those with and without disabilities using both unmatched and matched samples, employing t-statistics for both. Then, in the age and sex exact- and PS-matched samples, we carried out a multivariable logistic regression using disability groups and other covariates for each outcome measure to estimate the incidence and 95% confidence intervals (CI) across groups. We performed all statistical analyses using SAS Enterprise Guide 7.1 for Windows (SAS Institute, Cary, USA).