Abstract
The global prevalence of diabetes is increasing rapidly, with particular concern for undiagnosed, uncontrolled, and untreated diabetes. This study used data from the Thai National Health Examination Surveys in 2004, 2009, 2014, and 2020 to estimate the overall prevalence and trends of diabetes, diagnosed, treated, and controlled diabetes. We also used multivariable logistic regression models to examine the factors related to the prevalence of diabetes, as well as diagnosed, treated, and controlled diabetes, in the 2020 survey cycle. Following the global trends, age- and sex-standardized prevalence of diabetes in Thailand increased from 7.5% (95%CI: 6.5, 8.5) in 2004 to 10.1% (95%CI: 9.0, 11.1) in 2020; only 62.8% (95%CI: 59.9, 65.8) were aware of their diagnosis, 42.9% (95%CI: 39.0, 46.9) were treated, and only 20.5% (95%CI: 17.8, 23.1) had controlled blood glucose levels. Individuals in younger age groups and lower socioeconomic status were more likely to be independently associated with unawareness, untreated, and uncontrolled diabetes. These findings underscore the importance of addressing the awareness, treatment, and control gaps in diabetes management in Thai adults. Strengthening diabetes prevention and management requires a comprehensive approach for individuals, communities, and the health system, including improved screening strategies, enhanced lifestyle modification, treatment practices, multidisciplinary collaboration, and resource allocation.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-025-17619-5.
Keywords: Diabetes, National health examination survey, Prevalence, Thailand
Subject terms: Risk factors, Diabetes, Public health
Introduction
Diabetes is one of the most common chronic diseases, associated with complications that disable individuals, affecting their quality of life and life expectancy1. Globally, diabetes affects an estimated 537 million people, or 10.5% of the world population in 2021, and its prevalence has dramatically increased2. As a result, healthcare costs for treating diabetes are predicted to exceed US$966 billion, and by 2045, they are expected to surpass $1054 billion3. According to a recent study, between 2021 and 2045, middle-income countries would experience the greatest relative rise (21%), followed by high-income (12.2%) and low-income countries (11.9%)2. In addition, it is estimated that about 1.31 billion individuals would develop diabetes by 20503.
One important issue with diabetes is the high rate of undiagnosed cases. In 2021, 44.7% (or 239.7 million) of adults aged 20–79 worldwide were unaware of their diabetes diagnosis. Africa has the highest undiagnosed diabetes rate (53.6%), followed by the Western Pacific (52.8%) and South-East Asia (51.3%)4. In addition, treatment coverage for diabetes worldwide was unmet. Data from 55 low- and middle-income countries revealed that the prevalence of diabetes was 9.0%, with 43.9% of people having a prior diagnosis; however, only 4.6% of people with diabetes reported receiving recommended treatments, and 50.5% were covered by glucose-lowering medication5. According to a recent study, undiagnosed diabetes has a greater risk of all-cause mortality than people without diabetes6. Thus, the 80-80-80 targets for diabetes were approved by the World Health Organization in 2022. These aims state that 80% of people with diabetes have been diagnosed, 80% have adequate glycemic control, and 80% have good blood pressure control7. These targets were cost-effective and reduced DALYs lost from diabetes complications8.
In Thailand, a middle-income country, the highest burden of disability-adjusted life years (DALYs) was attributed to diabetes, with over 111,000 deaths reported in 20199. The age-standardized prevalence of diabetes in the Thai population increased from 7.7% in 2004 to 7.8% in 2009 and 9.9% in 2014, with 34.7% remaining undiagnosed in 2009, rising to 46.2% in 201410. The sixth Thailand National Health Examination Survey (NHES VI) was conducted in 2020 to provide updated national estimates and evaluate trend estimates of diabetes, awareness, and control between 2004 and 2020. This information is critical for guiding public health efforts to prevent non-communicable diseases (NCDs). This study aims to determine the prevalence, awareness, treatment, and control of diabetes in Thai adults between 2004 and 2020 and to examine the factors that influence the prevalence of diabetes, awareness, treatment, and glycemic control.
Methods
Data source
This study used data from the NHESs III, IV, V, and VI conducted during the following periods: Jan to April 2004, July, 2008 to March,2009, October 2013 to December 2014, and August 2019 to October 2020, respectively. The NHES is Thailand’s only nationwide survey that combines information gathered through face-to-face interviews with biophysical measurements conducted by trained field staff. The NHES is a cross-sectional survey using multistage, stratified sampling of the Thai population. A detailed description of the NHES series has been published elsewhere10–12. Briefly, the random sampling process involved several stages. In the first stage, five provinces were selected from each of the four regions of Thailand. In the second stage, 3 to 5 districts were selected based on population size. The third stage involves the selection of villages in rural areas and enumeration units in urban areas. The final stage included selecting individuals from five age groups (< 15, 15–59, 60–69, 70–79, >=80 years). The present study included participants aged 20 years and older. Exclusion criteria included pregnant women, individuals who did not participate in the interview or health examination, and those unable to communicate verbally in Thai, either due to language barriers or health conditions that limited direct communication. The study was approved by the Ramathibodi Ethical Review Board (COA. MURA2022/546).
Blood pressure was measured using a standard automatic blood pressure monitor (Omron model HEM-7117, Omron HealthCare Co. Ltd., Kyoto, Japan). Each participant was seated for at least 5 min before the first of three serial blood pressure measurements at 1-minute intervals. Classification of high blood pressure was based on the Seventh Report of the Joint National Committee (JNC7) on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure13 and was based on the average of the two serial blood pressure measurements with the lowest variability in pulse pressure. Hypertension was defined as a systolic blood pressure (SBP) 140 mmHg or a diastolic blood pressure (DBP) 90 mmHg or treatment with blood pressure-lowering drugs during the previous 2 weeks. Height and weight were measured using standard measurements for the calculation of body mass index (BMI).
Fasting plasma glucose (FPG) was measured by enzymatic hexokinase method with regular quality control. Serum cholesterol (LDL-C) was measured by enzymatic methods at the central laboratory of Ramathibodi Hospital. The laboratory was standardized according to the criteria of the Lipid Standardization Program of the Centers for Disease Control and Prevention and the National Heart, Lung, and Blood Institute14.
Definitions of outcomes
In this study diabetes refers to diabetes mellitus, defined as an individual who had been previously diagnosed with diabetes by health professionals, was taking glucose-lowering medication, or had fasting plasma glucose (FPG) ≥ 126 mg/dL15 Diagnosed diabetes (or awareness) was defined as self-reporting as being diagnosed with diabetes by health professionals or reported using antidiabetic medication. Treated diabetes was defined as self-reporting the use of anti-glycemic medication (both insulin and non-insulin) in the past two weeks. Controlled diabetes was defined as individuals on glucose-lowering medication with FPG < 130 mg/dL. Controlled blood pressure was defined as individuals with SBP < 130 mmHg and DBP < 80 mmHg.
Covariates
Covariates included in this study were socioeconomic characteristics (sex, age groups, area of residence, education, and household wealth index), behavioral risk factors (body mass index (BMI), smoking status, alcohol drinking, and physical inactivity), and underlying diseases (history of cardiovascular disease, hypertension, hypertriglyceridemia, and hypercholesterolemia). Age groups were categorized into 20–39 years, 40–59 years, and 60 years and older. The area of residence was rural or urban. Educational level was categorized into primary and secondary. The household wealth index was calculated by assessing participants’ household assets and categorized as quantiles 1–5 (lower quintiles indicate less wealth)16. BMI was categorized as underweight (< 23 kg/m2), normal (≥ 23 and < 25 kg/m2), overweight (≥ 25 and < 30 kg/m2) and obese (≥ 30 kg/m2). Smoking status was categorized as never, former, and current smokers. Alcohol drinking was defined as drinking at least one drink of alcohol in the past 30 days, and physical inactivity was defined as the absence of moderate-to-vigorous physical activity. History of cardiovascular disease was defined as self-reporting as being diagnosed with cardiovascular disease by physicians. Hypertension was defined as individuals who had been previously diagnosed with hypertension by health professionals, were taking anti-hypertensive medication, or had SBP ≥ 140 mmHg or DBP ≥ 90 mmHg. Hypertriglyceridemia and hypercholesterolemia were defined if participants had a triglyceride level (TG) ≥ 150 mg/dL and a total cholesterol level (Chol) ≥ 200 mg/dL, respectively.
Statistical analysis
Descriptive statistics of participants, including socioeconomic characteristics, behavioral risk factors, and illnesses, were presented as numbers of subjects and percentages for categorical variables, and as means with standard deviation for continuous variables. Sample weighted were calculated for each participant based on the probability of selection of each stage of sampling, (geographic region, enumeration area, age group, and sex) and the overall probability of selecting an individual was computed as a product of selection probability at each stage. The probability weight for each individual was defined as the inverse of the probability of selection17,18. We accounted for the survey weights and complex survey design using the svy command in Stata 17.0 to estimate the overall prevalence of diabetes, diagnosed, treated, and controlled diabetes based on the socioeconomic characteristics of participants for 2004, 2009, 2014, and 2020. Age-standardized prevalence was calculated using the Thai population in 2020 as a standard population. Confidence intervals (CI) for prevalence estimates were calculated using the Taylor series linearization method, accounting for the complex survey design, including sampling weights, clustering, and stratification, using the svy commands in Stata. We tested for the trend of prevalence per cycle from 2004 to 2020, using 2004 as a reference. Additionally, we compared the prevalence between 2014 and 2020. The trends were analyzed using logistic regression and reported as odds ratio and 95% CI.
We used the lincom commands to estimate changes in diabetes prevalence, awareness, treatment, and control. We applied multivariable logistic regression models to examine the factors related to the prevalence of diabetes, as well as diagnosed, treated, and controlled diabetes, in the 2020 survey cycle. Statistical significance was determined with p < 0.05.
Results
A total of 92,470 participants aged 20 and older were included in the study. The mean age was 54.9 years (SD = 16.2), and 54.9% of the participants were women, 53.5% lived in urban areas, 30.1% completed secondary education, and 18.8% lived in the poorest quintile of the wealth index. Regarding behavioral risk factors, 27.9% of participants were classified as overweight, 9.5% were obese, 20.0% were current smokers, 36.1% were current alcohol drinkers, and 27.0% were physically inactive. In addition, 2.4% of participants had a history of cardiovascular disease, 37.6% had hypertension, 35.7% had high triglyceride levels, and 56.7% had high total cholesterol. Characteristics of participants by survey year are shown in Table 1.
Table 1.
Characteristics of study populations (unweighted).
| Characteristics | All n = 92,470 |
Yr 2004 n = 35,846 |
Yr 2009 n = 18,553 |
Yr 2014 n = 17,890 |
Yr 2020 n = 20,181 |
|---|---|---|---|---|---|
| % (n) | % (n) | % (n) | % (n) | % (n) | |
| Response rate (%) | 92.8 | 93.3 | 93.0 | 92.5 | 92.2 |
| Socio-demographic factors | |||||
| Sex | |||||
| Men | 45.1 (41,730) | 47.5 (17,026) | 47.4 (8,801) | 42.1 (7,536) | 41.5 (8,367) |
| Women | 54.9 (50,740) | 52.5 (18,820) | 52.6 (9,752) | 57.9 (10,354) | 58.5 (11,814) |
| Age: Mean (SD) | 54.9 (16.2) | 55.7 (16.2) | 55.1 (16.5) | 54.3 (15.3) | 53.9 (16.5) |
| 20–39 years | 20.6 (19,083) | 20.2 (7,253) | 20.8 (3,867) | 18.1 (3,246) | 20.6 (19,083) |
| 40–59 years | 32.4 (29,935) | 28.7 (10,273) | 31.3 (5,805) | 40.9 (7,309) | 32.4 (29,935) |
| >=60 years | 47.0 (43,452) | 51.1 (18,320) | 47.9 (8,881) | 41.0 (7,335) | 47.0 (43,452) |
| Area of residence | |||||
| Rural | 46.5 (42,988) | 47.8 (17,131) | 46.0 (8,542) | 47.1 (8,420) | 44.1 (8,895) |
| Urban | 53.5 (49,482) | 52.2 (18,715) | 54.0 (10,011) | 52.9 (9,470) | 55.9 (11,286) |
| Education | |||||
| Primary | 69.9 (64,324) | 78.6 (28,150) | 71.0 (13,063) | 65.9 (11,738) | 56.8 (11,373) |
| Secondary | 30.1 (27,731) | 21.4 (7,677) | 29.0 (5,329) | 34.1 (17,811) | 43.2 (8,652) |
| Wealth index | |||||
| Q1 | 18.8 (9,789) | - | 17.4 (3,221) | 20.3 (2,926) | 19.0 (3,642) |
| Q2 | 17.3 (9,011) | - | 16.6 (3,085) | 15.6 (2,256) | 19.2 (3,670) |
| Q3 | 19.8 (10,308) | - | 21.3 (3,945) | 18.7 (2,710) | 19.1 (3,653) |
| Q4 | 20.6 (10,745) | - | 19.5 (3,610) | 21.2 (3,059) | 21.3 (4,076) |
| Q5 | 23.6 (12,287) | - | 25.3 (4,692) | 24.1 (3,485) | 21.5 (4,110) |
| Behavioral risk factors | |||||
| BMI (kg/m2) | |||||
| < 23 | 44.2 (40,556) | 48.5 (17, 243) | 46.1 (8,476) | 39.9 (7,079) | 38.8 (7,758) |
| 23- <25 | 28.4 (16,864) | 18.4 (6,551) | 18.2 (3,346) | 18.6 (3,301) | 18.4 (3,666) |
| 25-<30 | 27.9 (25,613) | 25.7 (9,152) | 27.0 (4,964) | 30.9 (5,483) | 30.1 (6,014) |
| >=30 | 9.5 (8,695) | 7.4 (2,643) | 8.8 (1,621) | 10.7 (1,892) | 12.7 (2,539) |
| Smoking status | |||||
| Never | 65.6 (60,617) | 64.9 (23,245) | 63.4 (11,767) | 66.9 (11,965) | 67.6 (13,640) |
| Former | 14.5 (13,357) | 12.1 (4,340) | 15.0 (2,783) | 15.8 (2,831) | 16.9 (3,403) |
| Current | 20.0 (18,487) | 23.0 (8,252) | 21.6 (4,003) | 17.3 (3,094) | 15.6 (3,138) |
| Current alcohol drinking | |||||
| Yes | 36.1 (9,323) | - | - | 27.9 (1,604) | 25.8 (5,187) |
| No | 63.9 (16,519) | - | - | 72.1 (4,136) | 74.2 (14,915) |
| Physical inactivity | |||||
| Yes | 27.0 (24,456) | 26.4 (9,095) | 24.8 (4,500) | 20.7 (3,698) | 35.5 (7,163) |
| No | 73.1 (66,274) | 73.6 (25,407) | 75.2 (13,657) | 79.3 (14,192) | 64.5 (13,018) |
| Chronic diseases | |||||
| CVD history | |||||
| Yes | 2.4 (916) | - | - | 2.6 (456) | 2.3 (460) |
| No | 97.6 (36,912) | - | - | 97.4 (17,262) | 97.7 (19,650) |
| Diabetes | |||||
| Yes | 12.1 (11,228) | 11.9 (4,263) | 11.2 (2,069) | 13.0 (2,320) | 12.8 (2,576) |
| No | 87.9 (81,242) | 88.1 (3,1583) | 88.9 (16,484) | 87.0 (15,570) | 87.2 (17,605) |
| Diagnosed diabetes | |||||
| Yes | 65.9 (7,397) | 55.4 (2,361) | 72.6 (1,502) | 67.3 (1,561) | 76.6 (1,973) |
| No | 34.1 (3,831) | 44.6 (1,902) | 27.4 (567) | 32.7 (759) | 23.4 (603) |
| Treated diabetes | |||||
| Yes | 61.7 (6,928) | 52.8 (2,252) | 69.7 (1,442) | 64.5 (1,496) | 67.5 (1,738) |
| No | 38.3 (4,300) | 47.2 (2,011) | 30.3 (627) | 35.5 (824) | 32.5 (838) |
| Controlled diabetes | |||||
| Yes | 26.9 (3,022) | 20.0 (854) | 35.2 (729) | 26.9 (623) | 31.7 (816) |
| No | 73.1 (8,206) | 80.0 (3,409) | 64.8 (1,340) | 73.2 (1,697) | 68.3 (1,760) |
| Hypertension | |||||
| Yes | 37.6 (34,212) | 39.7 (14,197) | 34.8 (6,455) | 37.5 (6,685) | 36.2 (6,875) |
| No | 62.4 (56,880) | 60.3 (21,536) | 65.2 (12,080) | 62.5 (11,148) | 63.8 (12,116) |
| High Triglyceride (TG ≥ 150 mg/dL) | |||||
| Yes | 35.7 (19,335) | - | 37.3 (6,272) | 34.9 (5,972) | 35.2 (7,091) |
| No | 64.3 (34,772) | - | 62.7 (10,557) | 65.1 (11,151) | 64.8 (13,064) |
| High Total Cholesterol (TC ≥ 200 mg/dL) | |||||
| Yes | 56.7 (31,572) | - | 57.8 (10,657) | 50.8 (8,698) | 60.6 (12,217) |
| No | 43.3 (24,126) | - | 42.2 (7,777) | 49.2 (8,411) | 39.4 (7,938) |
Notes: Wealth index, current alcohol drinking, CVD history, blood triglyceride, and cholesterol were not available in the NHES 3 survey. Current alcohol drinking and CVD history were not available in the NHES 4 survey.
Abbreviations: bmi, body mass index; cvd, cardiovascular disease; tc, cholesterol; n, number; q, quintile; sd, standard deviation; tg, triglyceride; yr, year.
The overall prevalence of diabetes increased from 7.5% (95%CI: 6.5, 8.5) in 2004 to 10.1% (95%CI: 9.0, 11.1) in 2020, with notable disparities across gender, age, education, wealth, and urban-rural areas (Table 2). Women consistently exhibited a higher prevalence of diabetes compared to men across all years. In males, the prevalence increased from 6.7% (95%CI: 5.5, 7.8) to 9.3% (95%CI: 7.9, 10.7), while in women, it rose from 8.3% (95%CI: 7.3, 9.3) to 10.7% (95%CI: 9.6, 11.8) between 2004 and 2020. The prevalence increased with age, reaching the highest in the 60 + age group, with a significant increase from 14.3% (95%CI: 12.9, 15.7) in 2004 to 20.4% (95%CI: 19.0, 21.8) in 2020. While the prevalence of diabetes increased significantly between 2004 and 2020 across all groups, including rural and urban areas, and across different educational levels, individuals living in rural areas and with lower educational attainment consistently exhibited a higher prevalence of diabetes compared to their urban and higher-educated counterparts.
Table 2.
Age-standardized prevalence and trends of all diabetes by socio-demographic characteristics, 2004–2020.
| Characteristics | 2004 % (95%CI) |
2009 % (95%CI) |
2014 % (95%CI) |
2020 % (95%CI) |
Change 2020-2004 |
Change 2020-2014 |
||
|---|---|---|---|---|---|---|---|---|
| OR (95%CI) | P-value | OR (95%CI) | P-value | |||||
|
Estimated number of adults with diabetes (million) |
2.7 | 3.2 | 4.7 | 5.0 | ||||
| All | 7.5 (6.5,8.5) | 7.9 (7.0,8.8) | 9.9 (8.7,11.1) | 10.1 (9.0,11.1) |
1.03 (1.01, 1.04) |
< 0.001 |
1.00 (0.98, 1.02) |
0.858 |
| Sex | ||||||||
| Men | 6.7 (5.5,7.8) | 6.9 (5.6,8.2) | 8.9 (7.5,10.3) | 9.3 (7.9,10.7) |
1.03 (1.01, 1.04) |
0.002 |
1.00 (0.98, 1.03) |
0.682 |
| Women | 8.3 (7.3,9.3) | 8.8 (7.7,10.0) | 10.8 (9.4,12.1) | 10.7 (9.6,11.8) |
1.02 (1.01, 1.04) |
0.001 |
1.00 (0.98, 1.02) |
0.93 |
| Age group (yrs) | ||||||||
| 20–39 | 2.9 (2.1,3.7) | 1.9 (1.3,2.5) | 3.2 (1.7,4.8) | 3.2 (2.1,4.4) |
1.00 (0.99, 1.01) |
0.564 |
1.00 (0.98, 1.02) |
0.978 |
| 40–59 | 10.0 (8.7,11.2) | 8.6 (7.7,9.5) | 11.1 (10.1,12.1) | 10.4 (9.1,11.6) |
1.00 (0.99, 1.02) |
0.608 |
0.99 (0.98, 1.01) |
0.386 |
| >=60 | 14.3 (12.9,15.7) | 15.9 (14.0,17.8) | 18.1 (15.6,20.5) | 20.3 (19.0,21.8) |
1.06 (1.04, 1.08) |
< 0.001 |
1.02 (0.99, 1.05) |
0.120 |
| Area of residence | ||||||||
| Urban | 9.0 (8.2,9.9) | 9.9 (9.3,10.5) | 9.9 (8.8,10.9) | 10.8 (9.7,12.0) |
1.02 (1.01, 1.03) |
0.002 |
1.01 (0.99, 1.03) |
0.278 |
| Rural | 7.0 (5.8,8.2) | 7.0 (5.9,8.1) | 9.9 (8.2,11.7) | 9.6 (8.4,10.8) |
1.03 (1.01, 1.04) |
< 0.001 |
1.00 (0.97, 1.02) |
0.771 |
| Education | ||||||||
| Primary | 8.3 (7.1,9.5) | 8.8 (7.6,10.1) | 12.2 (11.1,13.4) | 12.3 (11.4,13.3) |
1.04 (1.03, 1.06) |
< 0.001 |
1.00 (0.99, 1.02) |
0.886 |
| Secondary | 6.8 (5.9,7.6) | 6.7 (6.0,7.5) | 7.1 (5.6,8.6) | 8.2 (7.0,9.3) |
1.01 (1.01, 1.03) |
0.033 |
1.01 (0.99, 1.03) |
0.324 |
| Wealth index | ||||||||
| Q1 | - | 6.3 (4.9,7.7) | 9.6 (8.0,11.2) | 8.9 (7.6,10.2) | - | - |
0.99 (0.97, 1.01) |
0.504 |
| Q2 | - | 7.4 (5.5,9.4) | 9.8 (7.6,12.0) | 9.3 (7.6,11.0) | - | - |
1.00 (0.97, 1.03) |
0.751 |
| Q3 | - | 7.6 (6.6,8.5) | 10.7 (8.7,12.6) | 10.3 (7.7,12.9) | - | - |
1.00 (0.96, 1.03) |
0.836 |
| Q4 | - | 8.9 (7.2,10.6) | 10.9 (9.0,12.7) | 10.8 (8.8,12.8) | - | - |
1.00 (0.97, 1.03) |
0.953 |
| Q5 | - | 9.8 (9.1,10.4) | 9.9 (8.0,11.8) | 10.7 (9.1,12.3) | - | - |
1.00 (0.98, 1.03) |
0.501 |
ci, confidence interval; or, odds ratio; q, quintile.
Diabetes awareness increased significantly (p < 0.001) from 42.3% (95%CI: 37.0, 47.6) in 2004 to 62.8% (95%CI: 59.9, 65.8) in 2020. Between 2004 and 2020, the awareness also showed an increasing trend in all the subgroups (Table S1). For example, in males, it rose from 34.7% (95%CI: 28.9, 40.5) to 58.3% (95%CI: 52.4, 64.3), while in women, it rose from 49.5% (95%CI: 42.9, 56.0) to 67.1% (95%CI: 61.4, 72.7). Among older people (≥ 60 years), the awareness rose from 55.9% (95%CI: 52.4, 59.4) to 84.1% (95%CI: 80.8, 87.4). Diabetes awareness among people who live in rural areas also increased from 40.4% (95%CI: 33.8, 47.0) to 63.0% (95%CI: 58.3, 67.6). However, between 2014 and 2020, diabetes awareness remained relatively stable in all the subgroups except for the elderly population (≥ 60 years), where the awareness increased significantly (p < 0.001).
Regarding treatment, the overall treatment rate remained unchanged (p = 0.353) between 2004 (40.2%; 95%CI 35.1, 45.3) and 2020 (42.9%; 95%CI 39.0, 46.9). However, the treatment rate significantly increased (p < 0.001) among the elderly population (≥ 60 years) from 53.6% (95%CI 50.2, 57.0) in 2004 to 83.2% (95%CI: 79.8, 86.6) in 2020. Treatment rates were also significantly higher among the elderly than younger individuals (Table S2). In contrast, treatment rates among people aged 45–59 decreased significantly (p = 0.016) from 45% (95%CI: 39.0, 51.0) in 2004 to 35.2% (95%CI: 30.1, 40.2) in 2020.
Among people with diabetes, the proportion of people with adequate diabetes control increased significantly (p < 0.001) from 12.6% (95%CI: 10.0, 15.1) in 2004 to 20.5% (95%CI: 17.8, 23.1) in 2020 (Table S3). Control rates also increased significantly in all the subgroups except women (p = 0.352) and people aged 45–59 (p = 0.223). The proportions of adequate diabetes control were somewhat higher in the elderly than in other age groups. However, between 2014 and 2020, the proportion of people with adequate diabetes control remained relatively stable in all the subgroups.
Factors associated with prevalence, awareness, treatment, and control of diabetes
Factors associated with greater odds of diabetes were older age (age ≥ 60: OR 5.3, 95%CI 3.6, 7.7; age 45–59: OR 3.1, 95%CI 2.1, 4.6), higher BMI (BMI ≥ 30: OR 2.7, 95%CI 2.1, 3.6; BMI 25-<30: OR 1.8, 95%CI 1.5, 2.2), physical inactivity (OR 1.2, 95%CI 1.1, 1.4), hypertension (OR 1.7, 95%CI 1.4, 2.1), and elevated triglycerides (OR 1.8, 95%CI 1.6, 2.1). Conversely, those with high total cholesterol (OR 0.6, 95%CI 0.5, 0.7) and those in the lowest wealth quintile (OR 0.7, 95%CI 0.6, 0.9) were less likely to have diabetes (Table 3).
Table 3.
Factors associated with awareness, treatment, and control in 2020 (adjusted for all variables in the table).
| Diabetes | Awareness | Treatment | Control | |
|---|---|---|---|---|
| Odds ratio (95%CI) | Odds ratio (95%CI) | Odds ratio (95%CI) | Odds ratio (95%CI) | |
| Sex | ||||
| Men | Ref | Ref | Ref | Ref |
| Women | 1.123 (0.912–1.384) | 1.301 (0.692–2.445) | 1.216 (0.641–2.308) | 0.912 (0.456–1.827) |
| Age group (yrs) | ||||
| 20–39 | Ref | |||
| 45–59 | 3.119*** (2.099–4.636) | 1.972*** (1.429–2.721) | 1.014 (0.598–1.720) | 0.712 (0.302–1.678) |
| >=60 | 5.262*** (3.610–7.672) | 3.726*** (2.390–5.809) | 7.685*** (4.248–13.90) | 1.732 (0.798–3.757) |
| Area of residence | ||||
| Urban | 0.97 (0.804–1.170) | 0.897 (0.672–1.197) | 0.790 (0.606–1.031) | 1.064 (0.739–1.532) |
| Rural | Ref | Ref | Ref | Ref |
| Education | ||||
| Primary | 1.183 (0.995–1.406) | 1.316 (0.770–2.249) | 1.102 (0.640–1.899) | 1.155 (0.787–1.695) |
| Secondary | Ref | Ref | Ref | Ref |
| Wealth index | ||||
| Q1 | 0.736** (0.552–0.980) | 0.446** (0.239–0.832) | 0.514** (0.306–0.864) | 0.798 (0.556–1.145) |
| Q2 | 0.795 (0.540–1.170) | 0.517** (0.268–0.998) | 0.514** (0.281–0.941) | 0.640** (0.435–0.942) |
| Q3 | 0.832 (0.552–1.255) | 0.577 (0.306–1.090) | 0.752 (0.471–1.202) | 0.965 (0.614–1.517) |
| Q4 | 0.912 (0.652–1.274) | 1.096 (0.637–1.883) | 1.355 (0.669–2.746) | 1.232 (0.898–1.690) |
| Q5 | Ref | Ref | Ref | Ref |
| BMI (kg/m2) | ||||
| < 23 | Ref | Ref | Ref | Ref |
| 23- <25 | 1.22 (0.944–1.578) | 0.805 (0.406–1.593) | 0.782 (0.354–1.724) | 0.779 (0.408–1.488) |
| 25-<30 | 1.823*** (1.522–2.184) | 0.705 (0.366–1.357) | 0.882 (0.467–1.667) | 0.622 (0.382–1.013) |
| >=30 | 2.735*** (2.084–3.588) | 0.778 (0.416–1.458) | 0.82 (0.465–1.448) | 0.693 (0.425–1.129) |
| Smoking status | ||||
| Never | Ref | Ref | Ref | Ref |
| Former | 1.169 (0.934–1.464) | 1.034 (0.574–1.860) | 0.711 (0.381–1.329) | 0.888 (0.394–2.003) |
| Current | 0.943 (0.685–1.297) | 0.798 (0.423–1.505) | 0.584 (0.326–1.048) | 0.832 (0.389–1.781) |
| Current alcohol drinking | ||||
| Yes | 0.938 (0.801–1.098) | 0.511*** (0.371–0.703) | 0.633 (0.378–1.059) | 1.068 (0.676–1.686) |
| No | Ref | Ref | Ref | Ref |
| Physical inactivity | ||||
| Yes | 1.241*** (1.078–1.430) | 0.767 (0.550–1.071) | 0.746** (0.560–0.995) | 0.873 (0.699–1.092) |
| No | Ref | Ref | Ref | Ref |
| CVD history | ||||
| Yes | 1.537 (0.878–2.692) | 1.11 (0.491–2.511) | 1.281 (0.605–2.710) | 1.088 (0.556–2.127) |
| No | Ref | Ref | Ref | Ref |
| Hypertension | ||||
| Yes | 1.675*** (1.363–2.057) | 1.724*** (1.249–2.381) | 1.551 (0.939–2.563) | 1.771*** (1.255–2.501) |
| No | Ref | Ref | Ref | Ref |
| High Triglyceride (TG ≥ 150 mg/dL) | ||||
| Yes | 1.823*** (1.566–2.121) | 0.954 (0.631–1.442) | 0.99 (0.744–1.318) | 0.672*** (0.505–0.895) |
| No | Ref | Ref | Ref | Ref |
| High Total Cholesterol TC ≥ 200 mg/dL) | ||||
| Yes | 0.581*** (0.501–0.674) | 0.355*** (0.263–0.480) | 0.424*** (0.296–0.607) | 0.602** (0.397–0.915) |
| No | Ref | Ref | Ref | Ref |
| Observations | 17,897 | 2,203 | 2,203 | 2,203 |
bmi, body mass index; ci, confidence interval; cvd, cardiovascular disease; q, quintile; ref, reference; tc, total cholesterol; tg, triglyceride.
Regarding diabetes awareness, older people were more likely to be aware of diabetes than younger people (age ≥ 60: OR 3.7, 95%CI 2.4, 5.8; age 45–59: OR 2.0, 95%CI 1.4, 2.7). People with hypertension were also more likely to be aware of diabetes (OR 1.7, 95%CI 1.4, 2.1). In contrast, factors associated with lower odds of diabetes awareness were people in quantiles 1–2 of the wealth index (Q1: OR 0.4, 95%CI 0.2, 0.8; Q2: OR 0.5, 95%CI 0.3, 0.9), current alcohol drinkers (OR 0.5, 95%CI 0.4, 0.7), and people with elevated total cholesterol (OR 0.4, 95%CI 0.3, 0.5).
Older people (age ≥ 60) were also more likely to receive diabetes treatment (OR 7.7, 95%CI 4.2, 13.9) than younger individuals. However, people in the lower quintile of the wealth index (Q1: OR 0.5, 95%CI 0.3, 0.9; Q2: OR 0.5, 95%CI 0.3, 0.9), physically inactive individuals (OR 0.7, 95%CI 0.6, 0.9), and people with elevated total cholesterol (OR 0.4, 95%CI 0.3, 0.6) were less likely to receive diabetes treatment.
People who had hypertension were more likely to have adequate control of diabetes (OR 1.8, 95%CI 1.3, 2.5). In contrast, people with high triglyceride levels (OR 0.7, 95%CI 0.5, 0.9) and high total cholesterol levels (OR 0.6, 95%CI 0.4, 0.9) were more likely to have poor control of diabetes.
Discussion
This study focused on type 2 diabetes mellitus (T2DM), which accounts for the vast majority of diabetes cases among adults in Thailand. While the diagnostic criteria used in the survey did not distinguish between diabetes types, the observed prevalence and trends are highly likely to reflect T2DM. Type 1 diabetes, which is relatively rare and typically presents in childhood or adolescence, was not the focus of this study. The prevalence of diabetes in the Thai population increased from 2004 to 2014 and was relatively stable between 2014 and 2020. In 2020, only 62.8% were aware of their diagnosis, 42.9% were treated, and only 20.5% had controlled blood glucose levels. These rates are in line with the global prevalence of diabetes awareness (60%), treatment (45%), and control (22%)19. However, they are far from the WHO’s global target for diabetes7.
The increasing trend of diabetes in Thailand could be attributed to several factors, including enhancing screening coverage, lifestyle changes, and the ageing population. Between 2009 and 2011, the Thai Ministry of Public Health launched a special project, “The Project of the King’s Initiative on People’s Health for Prevention and Control Diabetes Mellitus and Hypertension,” to screen people aged 35 and older for diabetes and hypertension20. Over 20 million Thai people were screened, and 1.7 million (8.0%) were found to be at high risk of developing diabetes21.
Since 2011, Thailand has launched the Thailand Healthy Lifestyle Strategic Plan for 2011–2020, the first national strategic plan to fight the growing NCD epidemic22. Diabetes was one of five important NCDs covered in the plan, and the strategy aimed to screen 90% of persons aged 35 and older for diabetes22. The national strategic plan for NCD has been revised periodically, and the most recent plan was set for 2023–2027 to achieve the nine global targets for NCD23. However, other than strengthening the screening diabetes program, other strategies to combat diabetic-related indicators seemed ineffective as the prevalence of obesity and sedentary behaviors increased23.
Thus, the growing prevalence of diabetes might be attributed to increased obesity and sedentary lifestyle as a result of significant economic and health transitions. Thai lifestyles, including diet and activities, have evolved. The nutritional pattern shifted from traditional high-carbohydrate meals based primarily on grains and vegetables to fat and sugar-rich diets24. Thai people’s food consumption shifted from home-cooked meals to pre-made, ready-to-eat meals, both in rural and urban areas25. During the past decade, trends in total sales volumes of ultra-processed foods in Thailand grew significantly; for example, functional & flavored water, soft drink concentrates, and baked goods grew over 122%, 88%, and 86%, respectively26. These foods contained excessive total sugars26 and could affect the prevalence of diabetes. In addition, the impact of the COVID-19 pandemic aggravated fast-food consumption, one of the diabetic risk factors27as online food retail in Thailand increased by 655% between 2013 and 201928. It would be noted that individuals in the lowest wealth quintile were less likely to have diabetes compared to the highest quintile. This may be partly explained by differences in risk factor profiles. Individuals in the lowest wealth quintile tend to have higher levels of physical activity, likely due to more physically demanding occupations (e.g., high physical activity: 50% vs. 41% in the highest quintile), and also have lower average BMI (24.0 vs. 24.8). These factors may contribute to the lower prevalence of diabetes observed in this group.
In addition to an unhealthy lifestyle, Thailand’s ageing population could be attributed to higher rates of diabetes. Thailand is rapidly becoming the second-oldest society in ASEAN, behind Singapore; more than 20% of Thai people are above 60 years old29. Like many low- and middle-income countries, the rate of population aging in Thailand is growing faster than the development of supporting systems, resulting in an increasing health and social care burden30. Another factor is the improved survival rate of diabetes patients. With advancements in medical treatments and better management strategies, individuals with diabetes are living longer. This has led to a larger population of people living with diabetes, contributing to the overall increase in prevalence. However, we observed a decline in treatment rates among individuals aged 45–59. This may be due to the latter half of the 2019 survey coincided with the COVID-19 outbreak and associated lockdown measures, which may have contributed to reduced access to health care services. Additionally, individuals in this working-age group may be less likely to perceive themselves as being at high risk for diabetes and may face challenges in attending clinic appointments due to work obligations. These factors could have contributed to the observed decline in treatment uptake. Further investigation is needed to better understand the underlying causes of this trend.
Overall, the increasing trend of awareness of diagnosed diabetes in the Thai population can be attributed to improved healthcare infrastructure, public health campaigns, collaboration between healthcare professionals and community organizations, and the growing prevalence of diabetes worldwide. Continued efforts to raise awareness, early detection, and preventive measures will be essential in effectively addressing the challenges of diabetes in Thailand.
There may be challenges related to healthcare accessibility and availability of resources. Despite the increasing awareness of diabetes, individuals may face barriers to accessing appropriate treatment and medications. Limited access to healthcare facilities, particularly in rural areas, and financial constraints can hinder individuals from receiving timely and comprehensive diabetes management. Secondly, there may be gaps in healthcare delivery and continuity of care. Even when individuals with diabetes receive initial treatment, follow-up care and monitoring may be lacking. This can lead to suboptimal glycemic control and difficulties in achieving treatment goals31.
Our study demonstrates that individuals with hypercholesterolemia or hypertriglyceridemia were negatively associated with their awareness, treatment compliance, and glycemic control. This may be attributed to the fact that dyslipidemia is a common comorbidity of diabetes. Previous studies have reported associations between hypertriglyceridemia, hypercholesterolemia, and diabetes32,33. Furthermore, individuals who are unaware of their diabetes, fail to adhere to treatment, or lack glycemic control may also exhibit poorer dyslipidemia management. Previous research has also highlighted challenges in access to care and limitations in the effectiveness of follow-up after screening, which may contribute to the persistently low levels of awareness34.
The implication of findings regarding the awareness of diabetes prevalence, treatment, and control for blood pressure and lipids have significant implications for diabetes prevention and management. Firstly, the high prevalence of undiagnosed diabetes highlights the need for improved screening and diagnostic strategies. Enhancing awareness campaigns, promoting regular health check-ups, and implementing a diabetic prevention policy can aid in identifying individuals with undiagnosed diabetes, initiating timely treatment, and promoting healthy lifestyles. Secondly, the stagnant trend in treatment and control for blood pressure and lipids among individuals with diabetes emphasizes the importance of targeted interventions to enhance management practices. Healthcare systems should prioritize the provision of comprehensive care, including regular monitoring of blood pressure and lipid levels, along with appropriate medication and lifestyle interventions. Thirdly, there is a need to improve accessibility to awareness and quality of care for those with disadvantaged socioeconomic status. Implementing evidence-based guidelines and ensuring access to affordable and effective treatments can improve outcomes. Furthermore, the research findings emphasize the need for a multidisciplinary approach to diabetes management. Collaborative efforts involving healthcare providers, educators, policymakers, and the community are crucial for developing comprehensive diabetes prevention and management programs. These programs should promote healthy lifestyle behaviors, enhance treatment adherence, and address psychosocial aspects of living with diabetes.
Limitations
This study has several limitations that should be considered when interpreting the results. Firstly, relying on single-occasion lab measurements for diabetes diagnosis may introduce inaccuracies, as it does not confirm the diagnosis. Secondly, the study utilized fasting plasma glucose as the primary measurement for diabetes assessment rather than incorporating additional tests such as HbA1c or glucose tolerance tests, which might underestimate the prevalence and controlled rate of diabetes. In addition, although this study focused on diabetes indicators, the presence of comorbid conditions such as hypertension and dyslipidemia, which are common among individuals with diabetes, is clinically relevant and should be addressed in future analyses to guide integrated management approaches. However, the study has several strengths that contribute to the robustness of the findings. Firstly, the study utilized data from a nationally representative population survey, providing a comprehensive picture of the prevalence, treatment, and control of diabetes in the Thai population. This increases the generalizability of the findings to the larger population. Moreover, incorporating data from multiple survey cycles enables valuable insights into the changes in diabetes awareness, treatment, and control patterns across surveys. Lastly, the study accounted for various demographic and socioeconomic factors, such as age, sex, educational level, and area of residence, in the analysis. This consideration allows for a more comprehensive understanding of the factors associated with diabetes prevalence, awareness, treatment, and control.
Conclusions
The findings underscore addressing the awareness, treatment, and control gaps in diabetes management. Strengthening diabetes prevention and management requires a comprehensive approach for the individual population level through the health system, including improved screening strategies, enhanced healthy lifestyles, treatment practices, multidisciplinary collaboration, and resource allocation. By implementing these measures, healthcare systems can work towards reducing the burden of diabetes and improving health outcomes.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The NHES was supported by the Ministry of Public Health, Thai Health Promotion Foundation, National Health Security Office, Health System Research Institute, National Research Council of Thailand, and Faculty of Medicine Ramathibodi Hospital, Mahidol University.
Author contributions
WA developed the idea for the study, analyzed the data, and wrote the manuscript. RP analyzed the data and wrote the manuscript. SB, SC, SA, ST, and NN contributed to acquiring data and reviewing the manuscript. BO and WN reviewed and edited the manuscript. All authors read and approved the final manuscript. RP is responsible for the overall content as guarantor. The guarantor accepts full responsibility for the finished work and/or the conduct of the study, had access to the data, and controlled the decision to publish.
Data availability
All data used to prepare this paper are available from the cited sources. NHES data are available upon reasonable request from the corresponding author.
Competing interests
The authors declare no competing interests.
Ethics approval
The study was approved by the Institutional Review Board of the Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand (approval number: COA. MURA2022/546).
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Heald, A. H. et al. Estimating life years lost to diabetes: outcomes from analysis of National diabetes audit and office of National statistics data. Cardiovasc. Endocrinol. Metab.9, 183–185. 10.1097/xce.0000000000000210 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Sun, H. et al. IDF diabetes atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res. Clin. Pract.183, 109119. 10.1016/j.diabres.2021.109119 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Global & national burden of diabetes. From 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the global burden of disease study 2021. Lancet402, 203–234. 10.1016/s0140-6736(23)01301-6 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Ogurtsova, K. et al. IDF diabetes atlas: global estimates of undiagnosed diabetes in adults for 2021. Diabetes Res. Clin. Pract.183, 109118. 10.1016/j.diabres.2021.109118 (2022). [DOI] [PubMed] [Google Scholar]
- 5.Flood, D. et al. The state of diabetes treatment coverage in 55 low-income and middle-income countries: a cross-sectional study of nationally representative, individual-level data in 680 102 adults. Lancet Healthy Longev.2, e340–e351. 10.1016/s2666-7568(21)00089-1 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Young Choi, J., Ali, M. K. & Choi, D. Determinants of health and mortality in undiagnosed diabetes: a nationally representative US adult, 2011–2020. Diabetes Res. Clin. Pract.210, 111634. 10.1016/j.diabres.2024.111634 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.World Health Organization. First-ever global coverage targets for diabetes adopted at the 75th World Health Assembly (2022). https://www.who.int/news-room/feature-stories/detail/first-ever-global-coverage-targets-for-diabetes-adopted-at-the-75-th-world-health-assembly.
- 8.Basu, S. et al. Estimated effect of increased diagnosis, treatment, and control of diabetes and its associated cardiovascular risk factors among low-income and middle-income countries: a microsimulation model. Lancet Glob Health. 9, e1539–e1552. 10.1016/s2214-109x(21)00340-5 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Burden of Disease Thailand. Burden of disease attributable to risk factors in Thailand 2019 (2023). https://bodthai.net/download//.
- 10.Aekplakorn, W. et al. Prevalence of diabetes and relationship with socioeconomic status in the Thai population: National health examination survey, 2004–2014. J. Diabetes Res.2018, 1654530. 10.1155/2018/1654530 (2018). [DOI] [PMC free article] [PubMed]
- 11.Aekplakorn, W. et al. Prevalence and management of diabetes and associated risk factors by regions of thailand: third National health examination survey 2004. Diabetes Care. 30 (8), 2007–2012. 10.2337/dc06-2319 (2007). [DOI] [PubMed] [Google Scholar]
- 12.Aekplakorn, W. et al. Trends in hypertension prevalence, awareness, treatment, and control in the Thai population, 2004 to 2020. BMC Public. Health. 24 (1), 3149. 10.1186/s12889-024-20643-1 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Chobanian, A. V. et al. Seventh report of the joint National committee on prevention, detection, evaluation, and treatment of high blood pressure. Hypertension42 (6), 1206–1252 (2003). [DOI] [PubMed] [Google Scholar]
- 14.Myers, G. L., Cooper, G. R., Winn, C. L. & Smith, S. J. The centers for disease Control-National heart, lung and blood Institute lipid standardization program. An approach to accurate and precise lipid measurements. Clin. Lab. Med.9 (1), 105–135 (1989). [PubMed] [Google Scholar]
- 15.American Diabetes Association Professional Practice Committee. Diagnosis and classification of diabetes: standards of care in Diabetes—2024. Diabetes Care. 47 (Suppl 1), S20–S42 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Srithanaviboonchai, K. et al. Characteristics and determinants of thailand’s declining birth rate in women age 35 to 59 years old: data from the fourth National health examination survey. J. Med. Assoc. Thai. 97, 225–231 (2014). [PubMed] [Google Scholar]
- 17.Heeringa, S. G., West, B. T. & Berglund, P. A. Applied Survey Data Analysis 2nd edn (CRC, 2017).
- 18.Aekplakorn, W., Puckcharern, H. & Satheannoppakao, W. The 6th Thai National Health Examination Survey 2019–2020 (2021). https://www.rama.mahidol.ac.th/commed/th/news/announcement/11062021-1038-th.
- 19.Shahrestanaki, E. et al. The worldwide trend in diabetes awareness, treatment, and control from 1985 to 2022: a systematic review and meta-analysis of 233 population-representative studies. Front. Public. Health. 12, 1305304. 10.3389/fpubh.2024.1305304 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Laorsri, J., Supphachai, K. & Numkhang, C. Evaluation of the project of the king’s initiative on people’s health for prevention and control diabetes mellitus and hypertension in kamphaengphet Province. Disease Control J.39, 22–29. 10.14456/dcj.2013.22 (2013). [Google Scholar]
- 21.Department of Medical Services. M. o. P. H. Following in the footsteps of His Majesty’s wishes 52–53 (2017).
- 22.Deerochanawong, C. & Ferrario, A. Diabetes management in thailand: a literature review of the burden, costs, and outcomes. Globalization Health. 9, 11. 10.1186/1744-8603-9-11 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Department of Disease Control, M. o. P. H. (ed Department of Disease Control). Thailand (2023).
- 24.Jitnarin, N. et al. Risk factors for overweight and obesity among Thai adults: results of the National Thai food consumption survey. Nutrients2, 60–74. 10.3390/nu20100060 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Kosulwat, V. The nutrition and health transition in Thailand. Public Health. Nutr.5, 183–189. 10.1079/PHN2001292 (2002). [DOI] [PubMed] [Google Scholar]
- 26.Phulkerd, S. et al. Profiling ultra-processed foods in thailand: sales trend, consumer expenditure and nutritional quality. Globalization Health. 19, 64. 10.1186/s12992-023-00966-1 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Ntarladima, A. M. et al. Associations between the fast-food environment and diabetes prevalence in the netherlands: a cross-sectional study. Lancet Planet. Health. 6, e29–e39. 10.1016/S2542-5196(21)00298-9 (2022). [DOI] [PubMed] [Google Scholar]
- 28.Scapin, T., Dean, S., Sacks, G., Wood, B. & Cameron, A. Mapping Analysis of the Food Retail Market in Thailand (Deakin University, 2024).
- 29.United Nations Population Fund. Comprehensive Policy Framework: A Life-Cycle Approached to Ageing in Thailand. Bangkok (2021).
- 30.Tan, M. P. Healthcare for older people in lower and middle income countries. Age Ageing. 51, 895. 10.1093/ageing/afac016 (2022). [DOI] [PubMed]
- 31.Yahaya, J. J., Doya, I. F., Morgan, E. D., Ngaiza, A. I. & Bintabara, D. Poor glycemic control and associated factors among patients with type 2 diabetes mellitus: a cross-sectional study. Sci. Rep.13 (1), 9673. 10.1038/s41598-023-36675-3 (2023). [DOI] [PMC free article] [PubMed]
- 32.Cui, J. et al. The ability of baseline triglycerides and total cholesterol concentrations to predict incidence of type 2 diabetes mellitus in Chinese men and women: a longitudinal study in qingdao, China. Biomed. Environ. Sci.32, 905–913. 10.3967/bes2019.113 (2019). [DOI] [PubMed] [Google Scholar]
- 33.Zhao, J. et al. Triglyceride is an independent predictor of type 2 diabetes among middle-aged and older adults: a prospective study with 8-year follow-ups in two cohorts. J. Translational Med.17, 403. 10.1186/s12967-019-02156-3 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Yan, L. D. et al. Universal coverage but unmet need: National and regional estimates of attrition across the diabetes care continuum in Thailand. PLoS One. 15 (1), e0226286 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data used to prepare this paper are available from the cited sources. NHES data are available upon reasonable request from the corresponding author.