Prevalence of Diabetes and its Associated Factors among Adults in Malaysia: Findings from the National Health and Morbidity Survey 2019

doi:10.21203/rs.3.rs-3955646/v1

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Prevalence of Diabetes and its Associated Factors among Adults in Malaysia: Findings from the National Health and Morbidity Survey 2019

https://doi.org/10.21203/rs.3.rs-3955646/v1

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Background

Diabetes is a major non-communicable health problem in both developed and developing countries, including Malaysia. People with diabetes are living longer and are vulnerable to microvascular and macrovascular complications, thus representing a significant public health problem with high morbidity, mortality, and substantial economic repercussions. We aim to determine the national prevalence of diabetes and identify associated factors among the adult population in Malaysia.

Methods

A nationwide cross-sectional study was conducted in 2019 involving 10,464 respondents in Malaysia. A two-stage stratified sampling design was used to select a representative sample of Malaysian adults. Structured validated questionnaires with face-to-face interviews were used to obtain data from selected respondents aged ≥ 18. Finger-pricked fasting blood glucose tests using the Accutrend GC machine were performed on respondents who claimed they did not have diabetes. Chi-square tests and logistic regression were used to determine associations between diabetes and associated factors at the significance level of = 0.05.

Results

The overall prevalence of diabetes among adult respondents was 18.3% (n = 2629). The highest prevalence of diabetes was among females (18.4%), those aged 60 & above (41.5%), Indian (31.4%), those without formal education (28.3%), widow/widower/divorce (33.2%), not working (24.9%), inactive (22.5%), obese (27.1%), hypertension (36.0%) and high cholesterol level (30.4%). Logistic regression analyses revealed that age group, ethnicity, education level, marital status, physical activity level, obesity, blood pressure status and cholesterol status were associated with diabetes among adults.

Conclusion

This study showed that the prevalence of diabetes among adult populations in Malaysia is high, with about one in five adults found to have diabetes. Awareness programs and interventions on diabetes need to be improved, especially related to behavioural charge as a strategy for the prevention and control of diabetes and its complications.

Prevalence

Diabetes

Adult

factors

Malaysia

Diabetes is a prioritised non-communicable (NCD) disease due to its significant public health problem all over the world, including in Malaysia. The World Health Organization (WHO) states that 442 million people worldwide have diabetes, especially in low or middle-income countries. A total of 1.6 million deaths are attributed to diabetes each year, and the number of deaths and prevalence of diabetes has continued to increase over the past few decades.¹ The World Health Organization's 2019 Global Health Estimates state that seven of the top ten causes of death worldwide are non-communicable diseases.²

Trends in diabetes have shown a rise in the disease in youth and adolescents.³ The increase in overweight and obesity may be one of the factors contributing to the rise in diabetes.⁴ In Malaysia, the National Health and Morbidity Survey (NHMS) in 2006⁵ showed that the prevalence of diabetes was 11.6%. This figure increased to 15.2% and 17.5% in NHMS 2011⁶ and NHMS 20015⁷ respectively.

The increasing prevalence of diabetes from year to year around the world has raised concerns of many parties, especially those involved in the field of healthcare. According to International Diabetes Federation (IDF) in 2021 estimated approximately 537 million adults (20–79 years) are living with diabetes worldwide, and the number is expected to rise to 643 million by 2030 and 783 million by 2045. More worryingly, data show nearly 1 in 2 (240 million) adults with diabetes are undiagnosed.⁸. Untreated diabetes has a significant relationship with morbidity and mortality and can lead to microvascular (retinopathy, neuropathy, and nephropathy) and macrovascular (heart attack, stroke and peripheral vascular) complications.⁹

The increase in cases of diabetes is usually due to lifestyle changes and changes in food intake patterns, primarily related to sugar and fat. For those with strong genetic factors, environmental factors such as the tendency to eat more food, especially sugar, less exercise and obesity are precipitating agents. Other studies also showed certain socio-demographics to be associated with diabetes, such as gender, age, higher BMI, and co-morbidities, such as hypertension.^10,11 Hence, the current study is to determine the national prevalence of diabetes and identify associated factors among the adult population in Malaysia.

Data collection. The National Health and Morbidity Survey (NHMS) 2019 was a cross-sectional nationwide survey conducted by the Institute for Public Health, Ministry of Health Malaysia. NHMS 2019 implemented a multi-stage stratified sampling design proportionate to the population size throughout all states in Malaysia, covering both urban and rural areas. The target population was residents of all non-institutional living quarters (LQs) in Malaysia for at least two weeks before data collection.

This survey used structured questionnaires administered by trained interviewers. The data collection team conducted face-to-face interviews using mobile devices with a system developed specially for the data collection. Completed questionnaires were sent to the data management team through the NHMS server in the Institute of Public Health whenever there was an internet connection. Quality control checks were performed (especially on the respondent ID, outliers or incorrect data).

Dependent variable. The fasting blood glucose measurement was performed on finger-prick blood samples using the CardioChek® PA Analyzer portable blood test system. Only respondents who claimed to be non-diabetic were tested for glucose levels. For this study, a respondent was classified as having diabetes" when the respondent self-reported or was told to have diabetes by a doctor or assistant medical officer or had a fasting capillary blood glucose (FBG) of ≥ 7mmol/L.

Independent variables. This survey involved structured questionnaires that covered general household, socio-demographic, and specific health problems. The categorical variables that were considered were "sex" (male, female), "age group" (18–39, 40–59, 60&above), "residence" (urban, rural), "ethnic" (Malay, Chinese, Indian, other indigenous, others), "education level" (no formal education, primary school education, secondary school education, tertiary education, unclassified), "marital status" (single, married, widow/widower/divorcee) and "occupational status" (working, not working). Meanwhile, in the specific health problem, we've included "physical activity" (active, inactive, "obesity" (non-obese, obese), "blood pressure status" (normotensive, hypertensive) and "cholesterol status" (normal cholesterol, high cholesterol).

Trained research assistants did the clinical anthropometric measurements while qualified nurses took the finger-prick biochemistry tests. Informed consent was obtained from all the respondents before the questionnaire was administered. All follow the protocol for blood glucose intake and blood pressure measurements.

This survey used respondents' blood pressure assessment using Omron Japan Model HEM-907. Three readings were taken, and an average of the second and third readings was computed for the findings. Respondents were considered hypertensive if their average systolic blood pressure was ≥ 140 mmHg and /or diastolic blood pressure ≥ 90 mmHg or were self-reported hypertensive.

The respondent cholesterol level will be finger-pricked for total cholesterol measurement using the CardioChek® portable blood test system. A validated and calibrated HemoCue® Machine Hb 201 + was used to measure haemoglobin level, and respondents were considered high cholesterol if they had total cholesterol of 5.2mmol/L or more.

The weight of respondents was measured in kilograms using a digital weighing machine, Tanita Personal Scale HD 319 and height was measured in centimeters using SECA Stadiometer 213. Both tools had also been validated and calibrated before the survey. Body Mass Index (BMI) was calculated as weight divided by height squared. Respondents with a BMI of 30.0 kg/m2 or more were classified as obese.¹²

The respondents' physical activity level was assessed using the short form of the International Physical Activity Questionnaire (IPAQ). Respondents were classified as physically active if they achieved a minimum combination of vigorous-intensity, moderate-intensity and walking activities of 600 metabolic equivalent of task (MET)-minute/week.¹³

Statistical Analysis

Data were entered, cleaned and analysed using SPSS (Statistical Package for Social Science) Version 20. (SPSS, Chicago, IL, USA). Descriptive statistics were calculated to describe data as frequencies and percentages. Crude and adjusted odds ratios were used with 95% confidence interval (CI) and standard deviations (SD). Pearson's chi-square test was used to determine the association between categorical variables.

The multiple logistic regression analysis method was performed to determine diabetes-associated variables when the effects of possible confounders were controlled. The variable selection was based on the significant changes in the − 2 log-likelihood. The response variable was diabetes, and the predictor variables included in the analysis were age group, ethnic group, education level, marital status, occupational status, physical activity level, obesity, blood pressure status and cholesterol status. Adjusted odds ratios (AORs) and 95% confidence intervals (CIs) were estimated. Statistical significance was set at P value < 0.05.

Socio-demographic characteristics of the study respondents

The total number of respondents to the diabetes questionnaire was 10,472, with a response rate of 91.1%. The proportion of female and male respondents was similar to that of fewer women (54.3%). Based on age, respondents aged 18–39 made up 41.3%, and the majority of the respondents (60.9%) were from urban population. Respondents who were Malays are the majority ethnic group with 64.8%, followed by China (12.7%) and other indigenous groups (10.3%). More than 60.0% of respondents received either primary or secondary education. Nearly 70.0% of respondents were married, and more than 50.0% of respondents were still employed (Table 1)

Table 1

Socio-demographic characteristics of the respondents (N = 10,472)
Socio-demographic characteristic	N
Gender
Male	4785	45.7
Female	5687	54.3
Age group (mean, sd) : (45.6, 16.9)
18–39	4322	41.3
40–59	3697	35.3
60 & above	2453	23.4
Residence
Urban	6380	60.9
Rural	4092	39.1
Ethnic group
Malay	6787	64.8
Chinese	1327	12.7
Indian	662	6.3
Other indigenous	1078	10.3
Others	618	5.9
Education level
No formal education	644	6.2
Primary school education	2379	22.8
Secondary school education	4969	47.6
Tertiary education	2425	23.2
Unclassified	31	0.3
Marital status
Single	2186	20.9
Married	7154	68.3
Widow/widower/divorcee	1132	10.8
Occupation status
Working	5944	56.8
Not working	4520	43.2

Prevalence of diabetes by socio-demographic characteristic

The prevalence of diabetes in this study was 18.3% (95% CI: 17.08, 19.58). There were significant differences in age group, ethnic group, education status, marital status, occupational status, physical activity, obesity, hypertension status and cholesterol status. However, gender and home were not important factors in this study.

Table 2

Prevalence of diabetes by socio-demographic characteristics
Demographic Characteristics	Diabetes
	count	Prevalence	% (95 CI)
	count	(%)	Lower	Upper
National	2629	18.3	17.08	19.58
Gender
Male	1191	18.2	16.63	19.90
Female	1438	18.4	16.99	19.89
Age group
18–39	438	8.4	7.16	9.75
40–59	1129	25.3	23.27	27.47
60 & above	1062	41.5	38.54	44.55
Residence
Urban	1519	18.3	16.8	19.8
Rural	1110	18.3	16.48	20.4
Ethnic group
Malay	1868	21.6	20.02	23.17
Chinese	276	15.1	25.85	37.53
Indian	250	31.4	25.85	37.53
Other indigenous	160	11.6	9.15	14.62
Others	75	8.8	6.21	12.21
Education level
No formal education	231	28.7	23.10	34.94
Primary school education	795	23.9	21.14	26.98
Secondary school education	1196	18.1	16.59	19.77
Tertiary education	399	12.7	10.76	14.85
Unclassified	3	4.8	1.21	17.18
Marital status
Single	195	7.7	6.27	9.53
Married	1975	21.3	19.74	22.94
Widow/widower/divorcee	459	33.2	29.27	37.30
Occupation status
Working	1166	14.5	13.06	16.01
Not working	1461	24.9	23.23	26.65
Physical activity
Active	1799	16.8	15.49	18.23
Inactive	796	22.5	20.26	24.81
Obesity
Non Obese	1702	16.0	14.76	17.38
Obese	747	27.1	24.31	30.05
Blood pressure status
Normotensive	916	10.7	9.54	12.04
Hypertensive	1713	36.0	33.84	38.16
Cholesterol status
Normal cholesterol	839	10.9	9.62	12.22
High cholesterol	1790	30.4	28.48	32.42

Factors associated with diabetes.

The risk factors identified using the logistic regression model based on sociodemographic and health characteristics were like those identified using the fully adjusted model. There was a positive association between older age and the risk of diabetes. The risk was significantly higher in individuals aged 40–59 years [aOR 1.95(95% CI: 1.68,2.27)] and 60 years old and above [aOR2.68 (95% CI: 2.23,3.20)]. In addition, the risk of diabetes was significantly associated with ethnic groups [Malays: aOR1.77 (95% CI: 1.50,2.09) and Indians: aOR 3.02 (95% 2.38, 3.83)], educational level [no formal education: aOR 1.37 (95% CI: 1.07, 1.76) and secondary education: aOR1.20 (95 CI: 1.04, 1.38)], marital status (married: aOR 1.79 (95% CI: 1.49, 2.16)] and widow/widower/divorced: aOR1.83 (95 CI: 1.45, 2.31)], physically inactive: aOR 1.21 (95% CI: 1.07,1.36)], obese: aOR1.63 (95% CI: 1.45,1.83)], hypertensive: aOR 2.19 (95% CI: 1.95, 2.45) and high cholesterol: aOR 1.93 (95% CI: 1.73,2.15)]. No significant associations were found for gender, residence, and occupational status.

Table 3

Factors associated with diabetes (using multiple logistic regression)
Risk Factors	Simple Logistic Regression		Multiple Logistic Regression
Risk Factors	Crude OR (95% CI)	p-value	Adjusted OR* (95% CI)	p-value
Gender
Male	1.00	-	-	-
Female	1.02 (0.94, 1.12)	0.629	-	-
Age group
18–39	1.00		-
40–59	3.99 (3.45, 4.40)	< 0.001	1.95 (1.68, 2.27)	< 0.001
60 & above	6.76 (5.96, 7.68)	< 0.001	2.68 (2.23, 3.20)	< 0.001
Residence
Rural	1.19 (1.09,1.31)	< 0.001	-	-
Urban	1.00		-	-
Ethnic group
Chinese	1.00		-	-
Malays	1.45 (1.25,1.67)	< 0.001	1.77 (1.50, 2.09)	< 0.001
Indians	2.31 (1.88,2.84)	< 0.001	3.02 (2.38, 3.83)	< 0.001
Other Bumiputra	0.66 (0.54, 0.82)	< 0.001	0.82 (0.64, 1.04)	0.103
Others	0.53 (0.40, 0.70)	< 0.001	0.92 (0.68, 1.26)	0.616
Education level
No formal education	2.84 (2.34, 3.45)	< 0.001	1.37(1.07,1.76)	0.012
Primary education	2.55 (2.22, 2.93)	< 0.001	1.18 (0.99,1.40)	0.061
Secondary education	1.61 (1.42, 1.83)	< 0.001	1.20 (1.04, 1.38)	0.012
Tertiary education	1.00		-	-
Marital status
Single	1.00	-	-	-
Married	3.89 (3.33, 4.54)	< 0.001	1.79 (1.49, 2.16)	< 0.001
Widow/widower/divorced	6.95 (5.75, 8.39)	< 0.001	1.83 (1.45, 2.31)	< 0.001
Occupation status
Working	1.00	-	-	-
Not working	1.96 (1.79, 2.14)	< 0.001	-
Physical activity level
Active	1.00	-	-	-
Inactive	1.39 (1.26, 1.54)	< 0.001	1.21 (1.07, 1.36)	0.002
Obesity
Non Obese	1.00		-	-
Obese	1.86 (1.68, 2.07)	< 0.001	1.63 (1.45, 1.83)	< 0.001
Blood pressure status
Hypertensive	4.38 (3.99, 4.81)	< 0.001	2.19 (1.95, 2.45)	< 0.001
Normotensive	1.00		-
Cholesterol status
Normal cholesterol	1.00		-
-High cholesterol	3.40 (3.10,3.74)	< 0.001	1.93 (1.73, 2.15)	< 0.001

*Backward likelihood ratio multiple logistic regression as an applied Multicollinearity and interaction were chock and not found-Hosmer lame show test p = 0.074, classification table coverall correctly classified percentage 76.9% and receiver operating characteristic (ROC) curve = 77.0% were accepted to chock model fitness.

In our cross-sectional survey, we provided information about the associated factors of diabetes among adults in Malaysia. The prevalence of diabetes in the current study was found to be high at 18.3% (95% CI: 17.08, 19.58), which means that 1 in 5 Malaysians have diabetes. The prevalence from our survey is highest compared to different studies in other countries, including China (13%), Indonesia (10.8%), Thailand (11.6%), Singapura (14.7%), Nepal (8.5%) and Brazil (7.5%).^14,15,16

However, compared to countries in the Arab World, our prevalence studies are lower, such as the Kingdom of Arabia at 31.6%, Oman at 29.0%k, Kuwait at 25.4% and Bahrain at 25.0%.¹⁷ The International Diabetes Federation's (IDF) 7th edition of the Diabetes Atlas provided relatively sweeping statistics, which specified that 415 million people worldwide have diabetes, which should rise to 642 million by 2040. More worryingly, the IDF estimates that 193 million people are still unaware that they have diabetes.¹⁸

The high prevalence of diabetes among Malaysian adults could be partly due to a lack of awareness of the necessity of health screening at an earlier age and of early diabetes symptoms. Another issue that prevents Malaysians from getting screened for diabetes is the high patient load in public primary healthcare facilities.¹⁹ Population increase, population aging, growing urbanization, rising obesity and physical inactivity rates, particularly in developing and developed nations, could all be contributing factors.

Based on Table 2, The prevalence of diabetes rose with age and was substantially more significant (41.5%) in people 60 years and older. Furthermore, based on Table 3, our findings reveal age was the one that showed the highest effect on the risk of diabetes. This finding is similar to findings in other studies, where the disease becomes more common among individuals at an elderly age,^20,21,22 which showed that further age-related glucose intolerance is related to decreased insulin sensitivity and decreased β- cell function.²³ This is most likely brought on by an ageing population's increased propensity to gain weight, lose muscle mass, and engage in less physical activity. It is known that cells in the body are more resistant to insulin when there is a greater abundance of fatty tissue.²⁴

This study shows a high prevalence of diabetes among Malaysian Indians, followed by Malays and lastly, Chinese. In both the NHMS 2015 and 2011, as well as the most recent NHMS 2019, respondents of Indian heritage had the highest prevalence of diabetes. Table 3 shows that, in comparison to other ethnic groups, Indians had a 1.37-fold higher risk of diabetes. In the Malaysian context, this could be explained by variations in the three main races' food consumption patterns, dietary habits, lifestyles, and genetic susceptibilities.

Table 2's data indicate that married and widow/widower/ divorced individuals have a greater frequency of diabetes than single individuals. The result in Table 3 shows that married individuals are 1.79 times more likely to have diabetes; meanwhile, widow/widower/ divorced individuals are 1.83 times more likely to have diabetes compared to single. According to particular research, married couples who struggle to balance their work and families may not have as many opportunities or knowledge of preventative measures.²⁵

Regarding educational attainment, based on Table 2, higher prevalence was observed in those without formal education, followed by those with primary education. There was a statistically significant association between education status and diabetes. Less awareness and fewer opportunities for prevention and control may be linked to lower education levels but not higher education. However, the current study is unlike earlier studies that found no connection.^26,27 Further research is needed to confirm the association between diabetes and education level.

According to the study's findings, obese people had a higher risk of diabetes. The results showed that the risk of diabetes was 1.63 times higher in obese individuals. Research conducted in China ²⁸ and Vietnam²⁹ also revealed a strong positive association between diabetes and obesity or a high body mass index (BMI). Abdominal fat accumulation may impact insulin metabolism by releasing free fatty acids (FFA). Furthermore, interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), two signalling molecules implicated in the development of insulin resistance, are secreted by fat cells.³⁰

The present study found that the risk of diabetes was 1.21 times higher among inactive individuals. However, we found no association between physical activity and diabetes in another study.³¹ Physical inactivity has been identified by several studies as a significant modifiable risk factor for diabetes; engaging in moderate physical activity can lower the risk of the disease. ^32,33 Physical activity is crucial in controlling blood sugar levels, metabolising proteins and fats, enhancing insulin function, preventing diabetes-related complications, enhancing muscle strength and flexibility, improving cardiovascular health, and extending patients' lives. ³⁴ Some individuals needed to change their lifestyle after they were diagnosed as diabetics, which could have influenced the result.

According to this study also, respondents with hypertension had a 2.19-fold increased risk of having diabetes mellitus. Similar findings were reported in a study by China²⁸, Pakistan³⁵ and Ethiopia³⁶. Physiological features may have some role in this connection, meaning that the consequences of one disease enhance the chance of having the other. Furthermore, the two are more likely to co-occur due to their significant common risk factors. Concerningly, these relationships significantly raise the risk of cardiovascular problems as a result of insulin resistance. ^37,38 In older people, high blood pressure doubles the risk of developing diabetes ³⁹. This suggests that people with high blood pressure in Malaysia should also be the focus of routine diabetes screening.

The results also showed respondents with high cholesterol had a 1.19-fold increased risk of having diabetes. This is in line with findings from studies conducted elsewhere, including in Bangladesh³⁷, Ethiopia,⁴⁰ and China.⁴¹ Diabetes with dyslipidaemia or high cholesterol can significantly raise the risk of cardiovascular disease and death.³⁴ The risk of cardiovascular and cerebrovascular events must therefore be managed and prevented.

This study has some limitations. Although the NHMS 2019 was a national study that provided representative data, there were only a limited number of variables on the possible risk factors for undiagnosed Diabetes that could be included in the analysis.

In Malaysia, the number of people with diabetes increased significantly in 2019. The study's findings indicate that age, ethnicity, educational attainment, marital status, degree of physical activity, obesity, blood pressure, and cholesterol are significantly associated with diabetes. Awareness programs and interventions on diabetes need to be improved, especially related to behavioural charge as a strategy for the prevention and control of diabetes and its complications. The public must be made more aware of the significance of early diabetic screening, particularly among individuals 30 years of age and older and among high-risk populations, to prevent more severe complications and lessen the burden of this disease.

Acknowledgements

The authors express their gratitude to the Director-General of Health Malaysia for his kind support and permission to publish this paper.

-Ethics approval and consent to participate

This study obtained ethical approval from the Medical Research and Ethics Committee of the Ministry of Health Malaysia and was registered in the National Medical Research Registry, bearing registration number NMRR-18- 3085-44207. Informed consent was obtained from all the respondents before the questionnaire was administered.

-Consent for publication

Not applicable

-Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

-Competing interests

The authors declare no competing interests.

-Funding

The author(s) received no financial support for this article's research and authorship.

-Authors' contributions

HI oversaw all aspect of the study and was also involved in the writing of the manuscript. TAS was involved in supervising nationwide data collection and contributed significantly to the writing of the manuscript. TGRL was involved in data collection and writing. WSRH contributed to the data analysis and writing, NLAM were involved in data collection and writing of the manuscript, MFMY was involved in cleaning the raw data and data analysis and contributed to manuscript writing.

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Prevalence of Diabetes and its Associated Factors among Adults in Malaysia: Findings from the National Health and Morbidity Survey 2019

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Introduction

Materials and Methods

Statistical Analysis

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Socio-demographic characteristics of the study respondents

Prevalence of diabetes by socio-demographic characteristic

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