Dietary Patterns of Persons with Chronic Conditions in a Multi-Ethnic Asian Population

doi:10.21203/rs.3.rs-1016513/v2

Background: Chronic conditions are a leading cause of death and disability worldwide and respective data on dietary patterns remain scant. The present study aimed to investigate dietary patterns and identify sociodemographic factors associated with Dietary Approach to Stop Hypertension (DASH) scores within a multi-ethnic population with various chronic conditions.

Method: The present study utilised data from the Knowledge, Attitudes, and Practices on diabetes study in Singapore – a nationwide survey conducted to track the knowledge, attitudes, and practices pertaining to diabetes. The study analysed data collected from a sample of 2,895 Singapore residents, with information from the sociodemographic section, DASH diet screener, and the modified version of the World Mental Health Composite International Diagnostic Interview (CIDI) version 3.0 checklist of chronic medical conditions.

Results: Respondents with no chronic condition had a mean DASH score of 18.5 (±4.6), those with one chronic condition had a mean DASH score of 19.2 (±4.8), and those with two or more chronic conditions had a mean DASH score of 19.8 (±5.2). Overall, the older age groups [35– 49 years (B = 1.78, 95% CI: 1.23 – 2.33, p <0.001), 50–64 years (B = 2.86, 95% CI: 22.24 – 3.47, p <0.001) and 65 years and above (B = 3.45, 95% CI: 2.73 – 4.17, p <0.001)], Indians (B = 2.54, 95% CI: 2.09 – 2.98, p <0.001) reported better diet quality, while males (B = -1.50, 95% CI: -1.87 – -1.14, p <0.001) reported poorer diet quality versus females.

Conclusion: Overall, respondents with two or more chronic conditions reported better quality of diet while the sociodemographic factors of age, gender and ethnicity demonstrated a consistent pattern in correlating with diet quality, consistent with the extant literature. Results provide further insights for policymakers to refine ongoing efforts in relation to healthy dietary practices for Singapore.

Occupational Medicine

Environmental Policy

Chronic conditions

Dietary patterns

DASH

Singapore

According to the US Department of Health and Human Services, chronic conditions are defined as “conditions lasting a year or more that require ongoing medical attention and/or limit activities of daily living” (1). Chronic conditions are an ongoing cause of substantial ill health, disability, and premature death, making them an important global, national, and individual health concern (2). Based on the latest statistics from the World Health Organization, it is estimated that the contribution of major chronic conditions toward death and global burden of disease is approximately 71% as of 2021 (3).

Globally, one in three adults live with multiple chronic conditions (MCC) (4), a figure that is expected to rise significantly with studies suggesting that the proportion of patients with four or more diseases is expected to double by 2035 in countries such as the United Kingdom (5). MCC has long been associated with adverse health outcomes, with individuals experiencing early mortality (6), poorer physical and mental health (7) and reduced quality of life (8). Additionally, given the complexities of clinical treatment and patient management, MCC is consequently associated with higher resource utilisation and increased medical costs (9–10). Specifically, average per capita health care spending increases exponentially with the number of chronic conditions – from $1,081 for people with no chronic conditions to $5,074 for those with two chronic conditions and $14,768 for people with five or more chronic conditions (11).

The etiology of chronic conditions is complex and multifactorial. Risk factors include age, family history, genetic predisposition, current and lifetime weight and physical activity, smoking, alcohol, and diet (12–13). Of these risk factors, dietary choices may be the most amenable to modification and with the greatest public health impact (14). Of note, Sofi et al. (2010) highlighted that individuals reporting a greater degree of adherence to healthier dietary intake such as the Mediterranean diet showed a significant protection against the development of chronic conditions; with approximately 6-13% reduction in deaths and/or incidence of neurodegenerative disease, cardiovascular disease and cancer. Evidently, such a dietary pattern is recognised as a major contributing factor towards the prevention, development and treatment of chronic conditions (15). Correspondingly, a growing and evolving body of scientific inquiry on dietary intake with health and chronic conditions has led to recommendations emphasizing a variety of plant-based foods (e.g., vegetables, fruits, legumes, whole grains, nuts, and seeds) and deemphasizing processed food consumption with added sugar or a diet that is rich in meat content (14, 16).

Studies in Asian populations have identified various dietary patterns labelled “traditional,” “meat,” “Western,” and “prudent”. Dietary patterns observed in China characterised by a high intake of meat and dairy products have been associated with obesity (17–18). In Thailand, a more traditional carbohydrate-rich pattern was associated with metabolic syndrome (19). In other populations such as Japan and Korea, a more traditional dietary pattern was inversely associated with risk factors such as high blood pressure (20–21). In Pakistan where 33% of the adult population suffers from hypertension, the high intake of fish, prawns and yoghurt was found to be inversely associated with hypertension (22). Within the Singapore Chinese population, a “fruit-vegetable-soy” pattern was inversely associated, and a “meat-dim-sum” pattern was directly associated with cardiovascular disease mortality (23). Evidently, the association between various dietary patterns and chronic conditions has been established within singular ethnic populations across the considerable corpus of research in this area. Yet, much less is known regarding the association between healthy dietary patterns and chronic conditions across a multi-ethnic population.

Singapore is a multi-ethnic city-state situated in Southeast Asia with a population of approximately 5.6 million of which 4.1 million are Singapore residents (Singapore citizens or permanent residents) (24). The population is largely comprised of inhabitants from 3 major Asian ethnic groups: Chinese (76.0%), Malay (15.0%) and Indian (7.5%) (25). Based on the results from the Singapore Mental Health Study, it was found that a total of 25.4% individuals reported having one chronic condition, and 16.3% had MCC (26). Given the relatively substantial figures of individuals living with chronic conditions, it underscores the need to address the paucity of dietary data in this population. Additionally, a study in this setting provides a unique opportunity to elucidate the dietary patterns of a multi-ethnic population, the results of which can be extrapolated to other countries with a similar ethnic composition.

Given the diverse ethnic composition in Singapore, it follows that a culturally relevant diet screener is required for use in this multi-ethnic population. Whitton et al. (2018) developed a reliable and validated short diet screener designed to assess the intake of selected food groups that is representative of the overall dietary patterns across a multi-ethnic Asian population. Specifically, the short 37-item diet screener assesses the intake of selected food groups representative of a multi-ethnic Asian population via a priori dietary quality indices such as the Dietary Approaches to Stop Hypertension (DASH). In the extant literature, DASH has been demonstrated to be the most sensitive diet score to examine associations between diet and various health-related outcomes (28). Adopting DASH dietary patterns has several benefits including but not limited to lowered mortality from cardiovascular diseases and diabetes (28–29), lowered blood pressure (30), decreased body weight and waist circumference in obesity related weight management (31).

Taken together, the aims of the present study were to 1) characterise and compare the dietary patterns of a multi-ethnic population between persons with no chronic condition, one chronic condition and MCC through scoring their dietary intake according to the DASH score, and 2) identify socio-demographic correlates of DASH scores amongst persons with no chronic condition, one chronic condition and MCC.

Participants and procedures

The data for this research comes from a population based, cross-sectional study aimed at evaluating the Knowledge, Practice and Attitudes towards Diabetes Mellitus (DM) amongst residents of Singapore aged 18 years and above. A more detailed methodology of the study can be found in an earlier paper (32). The sample was randomly selected via a disproportionate stratified sampling design according to ethnicity (Chinese, Malay, Indian, Others) and age groups (18-34, 35-49, 50-64, 65 and above) from a national population registry database of all citizens and permanent residents within Singapore. The study oversampled certain minority populations, such as Malay and Indian ethnicity, as well as those above 65 years of age, in order to ensure sufficient sample size and to improve the reliability of the parameter estimates for these subgroups.

Citizens and permanent residents who were randomly selected were sent notification letters followed by home visits by a trained interviewer from a survey research company to obtain their informed consent to participate in the study. Face-to-face interviews with those who were agreeable to participate were conducted in their preferred language (English, Mandarin, Malay, or Tamil). Responses were captured using computer assisted personal interviewing. Individuals who were unable to be contacted due to incomplete or incorrect addresses, were living outside of the country, or were incapable of attending the interview due to severe physical or mental conditions, language barriers, or were institutionalized or hospitalized at the time of the survey were excluded from the study. Written informed consent was obtained from all respondents prior to the survey, and for those aged 18 to 20 years, parental consent was sought as the official age of majority in Singapore is 21 years and above.

Measures

Socio-demographic information and body mass index

Socio-demographic data on age (18-34, 35-49, 50-64 and 65 and above), gender (Female, Male), ethnicity (Chinese, Malay, Indian and Others), education (Primary and below, Secondary, Pre-U/Junior College, Vocational Institute/ITE, Diploma, Degree, professional certifications and above), marital status (Single, Married/Cohabiting, Divorced/Separated/Widowed), employment (Employed, Economically inactive and Unemployed), and monthly personal income (Below $2,000, and no income, $2,000-$3,999, $4,000-$5,999, $6000-$9,999 and $10,000 and above) was collected. Further, Body Mass Index (BMI) scores were categorised into four groups based on World Health Organization guidelines: ‘underweight (< 18.5 kg/m²), ‘normal range’ (≥ 18.5 kg/m² and < 25 kg/m²), ‘overweight’ (≥ 25 kg/m² and < 30 kg/m²), and ‘obese’ (>30 kg/m²) (33).

Diet screener

The diet screener comprises a list of 30 food/beverage items, that respondents’ rate on a 10-point scale ranging from ‘never/rarely’ to ‘6 or more times per day’, the frequency at which they consumed a particular food/beverage within the last one year (Whitton et al. 2018). The diet screener was interviewer-administered. Standard serving sizes were indicated for each food/beverage item to facilitate this process. Intake frequencies were standardised to a number of servings per day for each food/beverage item. DASH scores were calculated to account for seven intake components: fruit, vegetables, nuts/legumes, whole grains, red and processed meat, low fat dairy, and sweetened beverages. For each of these seven components, participants received a score between 1 and 5 corresponding to the quintile of the intake they fall in, with reverse scoring utilized for meat and sweetened beverages, and these seven quintile scores were summed to form the overall DASH score.

Chronic physical conditions

A modified version of the World Mental Health Composite International Diagnostic Interview (CIDI) version 3.0 (34) checklist of chronic medical conditions was used and the respondents were asked to report any of the conditions listed in the checklist. The question was read as, “I am going to read to you a list of health problems some people have. Has a doctor ever told you that you have any of the following chronic medical conditions?” This was followed by a list of 18 chronic physical conditions (such as asthma, high blood sugar, hypertension, arthritis, cancer, neurological condition, Parkinson’s disease, stroke, congestive heart failure, heart disease, back problems, stomach ulcer, chronic inflamed bowel, thyroid disease, kidney failure, migraine headaches, chronic lung disease, and hyperlipidaemia) which were considered to be prevalent among Singapore’s population. If the participant gave a positive response for any of the conditions listed, they were then asked, “How old were you when you were diagnosed with the medical condition?” and, “Did you receive any treatment for it at any time during the past 12 months?”.

Statistical analysis

Analyses in the present study were conducted with Stata version 15. In order to ensure representativeness of the data to the general population, survey weights were used to account for complex survey design. Means and standard deviations are provided for continuous variables, while frequencies and percentages are presented for categorical variables. In order to examine the variables associated with the total DASH score, four linear regressions were conducted (within the total sample, and the three subgroups of no chronic condition, one chronic condition and multimorbidity) with the following predictor variables: age, gender, ethnicity, education, marital status, employment status, personal income, BMI and chronic conditions. Statistical significance was set at the conventional alpha level of p < 0.05, using two-tailed tests.

Socio-demographics distribution of the sample

Table 1 summarised the socio-demographic characteristics for the sample of 2,895 respondents. 46.2% had no chronic medical condition, 26.3% had one chronic medical condition, and 27.2% had two or more chronic medical conditions. Chinese respondents made up 75.8% of the sample distribution, Malays 12.7%, Indians 8.6%, and others 2.9%. 51.6% of the respondents were female, and BMI scores indicated that 53.4% of the respondents were in the normal range based on WHO BMI classification.

DASH components score distribution and comparisons between chronic medical condition groups

The means and standard deviations of each of the seven DASH components (fruit, vegetables, nuts/legumes, low fat dairy, whole grains, red and processed meat, and sweetened beverages) and the overall DASH scores of the total sample, those with no chronic medical condition, one chronic medical condition and two or more chronic medical conditions are displayed in Table 2.

Socio-demographic correlates of DASH score

Table 3 shows the socio-demographic correlates associated with DASH scores within the full sample and across the three chronic medical condition groups. Within the full sample, the older age group [35– 49 years (B = 1.78, 95% CI: 1.23 – 2.33, p <0.001), 50–64 years (B = 2.86, 95% CI: 2.24 – 3.47, p <0.001) and 65 years and above (B = 3.45, 95% CI: 2.73 – 4.17, p <0.001)] had significantly higher DASH scores as compared to those aged 18-34. Males reported significantly lower DASH scores (B = -1.50, 95% CI: -1.87 – -1.14, p <0.001) than females. Indians (B = 2.54, 95% CI: 2.09 – 2.98, p <0.001) had significantly higher DASH scores than Chinese. Respondents who were less educated [primary and below (B = -1.99, 95% CI: -2.69 – -1.29, p <0.001), secondary (B = -1.72, 95% CI: -2.31 – -1.12, p <0.001), vocational institute/ITE (B = -1.42, 95% CI: -2.15 – -0.69, p <0.001), and diploma (B = -0.84, 95% CI: -1.40 – -0.27, p =0.004)] had significantly lower DASH scores than those with degree, and above qualifications.

Across all three groups, the older age groups reported [no chronic medical condition group: 35– 49 years (B = 1.77, 95% CI: 1.02 – 2.52, p <0.001), 50–64 years (B = 2.38, 95% CI: 1.47 – 3.28, p <0.001) and 65 years and above (B = 3.41, 95% CI: 2.20 – 4.63, p <0.001), one chronic medical condition group: 35– 49 years (B = 1.54, 95% CI: 0.45 – 2.63, p =0.01), 50–64 years (B = 2.87, 95% CI: 1.67 – 4.07, p <0.001) and 65 years and above (B = 4.19, 95% CI: 2.76 – 5.63, p <0.001) and two or more chronic medical condition group: 35– 49 years (B = 2.90, 95% CI: 1.52 – 4.27, p =0.001), 50–64 years (B = 4.10, 95% CI: 2.78 – 5.41, p <0.001) and 65 years and above (B = 3.87, 95% CI: 2.45 – 5.29, p <0.001)] significantly higher DASH scores as compared to those aged 18-34.

Males reported [no chronic medical condition group: (B = -1.89, 95% CI: -2.42 – -1.35, p <0.001), one chronic medical condition group: (B = -0.97, 95% CI: -1.71 – -0.22, p =0.01) and two or more chronic medical condition (B = -1.48, 95% CI: -2.19 – -0.77, p <0.001)] significantly lower DASH scores than females.

Respondents of Indian ethnicity reported [no chronic medical condition group: (B = 2.65, 95% CI: 1.99 – 3.31, p <0.001), one chronic medical condition group: (B = 2.19, 95% CI: 1.30 – 3.09, p <0.001) and two or more chronic medical condition group: (B = 2.61, 95% CI: 1.78 – 3.44, p <0.001)] significantly higher DASH scores than Chinese.

In the no chronic medical condition group, respondents who were less educated [primary and below (B = -2.06, 95% CI: -3.23 – -0.88, p =0.001), secondary (B = -1.42, 95% CI: -2.28 – -0.57, p =0.001), vocational institute/ITE (B = -1.75, 95% CI: -2.77 – 0.73, p =0.001)] had significantly lower DASH scores than those with degree, professional certification and above. Similarly, respondents with lower education in the one chronic medical condition group [primary and below (B = -3.04, 95% CI: -4.39 – -1.69, p <0.001), secondary (B = -2.35, 95% CI: -3.54 – -1.16, p <0.001), diploma (B = -1.51, 95% CI: -2.61 – -0.40, p =0.01)] had significantly lower DASH scores in contrast to their counterparts with degree, professional certification and above. In the two or more chronic medical condition group, respondents of “Others” ethnicities (B = 1.49, 95% CI: 0.01 – 2.97, p =0.04) demonstrated significantly higher DASH scores than Chinese ethnicity. Respondents who were classified as obese (B = -1.06, 95% CI: -1.94 – 0.18, p =0.02) demonstrated significantly lower DASH scores than those classified in the normal range.

The current study found that 42.9% of the population had no chronic medical/physical condition, 26.3% had one chronic medical/physical condition, and 30.5% had MCC. Overall, people with MCC had demonstrated better dietary practices in terms of number of servings taken per day for the DASH components. In the context of Singapore, this could be attributed to various initiatives and support received from primary care providers. Firstly, the focus upon nudging and facilitating healthier dietary choices is seen across the multitude of nationwide health promotion campaigns. One such example relates to the “Healthier Dining Programme” launched in 2014 which provides incentives to restaurants offering 500-calorie meals. Essentially, consumers are “nudged” towards choosing such healthier meal options that are identified with a “Healthier Choice Symbol” on menus in these restaurants (35). More importantly, better dietary practices amongst persons with MCC can also be attributed to the role of primary care providers as outlined in several guidelines and regulations. Of relevance, the “Chronic Disease Management Programme” introduced earlier in 2006 represents one avenue aiding in the management of chronic physical conditions in Singapore (37). Briefly, the programme involves structured disease management aimed at reducing out-of-pocket payments for outpatient treatments required in the management of an individual’s chronic diseases (37). In terms of dietary habits, healthcare professionals (e.g., physicians and dieticians) provide individuals with practical dietary guidelines aimed at making adjustments to current dietary choices (38). Taken together, the combination of both healthier dietary practices campaigns and prevention efforts in the clinical setting provide plausible explanations to the present finding of better dietary patterns observed in persons with MCC.

Findings from the present study lends further support to several well-established risk factors associated with diet quality in the extant literature. Within the study population, females and those of older age reported better diet quality based on their respective DASH scores. In a study determining the demographic profile of fast-food consumers amongst a Singapore population, Whitton et al. (2013) reported that older adults consumed lesser fast food in comparison to their younger counterparts. As highlighted by Allman-Farinelli et al. (2016), this is consistent with the notion that younger individuals demonstrate certain dietary habits that reduce overall diet quality. Food and beverages with high saturated fat, sugar and sodium contents such as those purchased at quick service restaurants feature prominently in the younger population’s diet across many countries such as USA, UK and Australia (39).

Gender differences in terms of dietary patterns was also demonstrated to be consistent with prior studies; where women tended to report better diet quality in comparison to men. For example, in Montreal, women’s diets were closer to recommendations for vegetables, fruits, and sodium intake as compared to men (41). As demonstrated in prior literature, women generally reported being more invested in relation to food-related matters and having better knowledge in terms of food and nutrition (42–43). Additionally, women reported consuming higher intakes of fruits and vegetables, dietary fibre, and lower intakes of fat and salt (42, 44). Taken together, it follows that greater importance attributed by women to their diet correspondingly translates into better dietary practices.

Across the sample population, ethnicity was also identified to be significantly associated with diet quality. Among the major ethnic groups in Singapore, Indians reported having healthier diet based on respective DASH scores. This is consistent with the healthy dietary pattern as outlined in the National Nutrition Survey conducted in 2010 (45), with Indians consuming the most bread and breakfast cereals, vegetable dishes, fruit, milk and dairy products and fewer eggs, poultry and meat dishes.

Diet and nutrition represent important factors in both promotion and maintenance of good health throughout the entire life course. We identified several important factors associated with diet quality among persons with no chronic conditions and one chronic condition. Within these subgroups, education level was significantly associated with diet quality. Specifically, those with less than a degree reported having poorer diet quality. Similar findings regarding educational level have been reported elsewhere; adults with a college diploma in USA demonstrated having a better overall diet quality as compared to all other education levels (46). Therein, it has been posited that education might be associated not only with increased nutritional knowledge, but could also be an indicator of ability to translate such nutritional knowledge into better dietary practices throughout the person’s lifetime (46). Lastly, it was interesting to observe that BMI was not a factor associated with diet quality in the present study. In current literature, it has been evidenced that individuals with chronic conditions generally reported higher-than-normal BMI (47). Accordingly, multiple studies have also demonstrated that healthier dietary patterns are associated with lower BMI (48–49). Nonetheless, it should be noted that the use of self-reported chronic conditions may plausibly have resulted in reporting bias – underestimating the true prevalence of chronic conditions and therein, an underestimation of the strength of the association between BMI and diet quality.

The present study is a nationwide survey conducted in four different languages (English, Chinese, Malay and Tamil) to address potential language barriers for participation in a multi-ethnic population. Additionally, the use of a large sample size, randomised design and survey weighted analysis improves the overall reliability of the present results. Nonetheless, several limitations of the present study warrant comment and conclusions drawn should be considered in light of these limitations. Firstly, given that the present study adopts a cross-sectional design, we are not able to establish any causal relationship between dietary pattern and chronic conditions. Secondly, the diet screener utilised is fundamentally based on the past-year self-reported diet recall of the respondent, with no correlation to any blood or urinary parameters. In that regard, we are not able to rule out the likelihood of recall bias in this self-reported format.

Chronic conditions remain the leading cause of death and disability worldwide. Despite this, it should be noted that chronic conditions are largely preventable, and dietary patterns play a key role in such prevention activities. Given its significance, it reasons that establishing population norms in relation to dietary habits represent an important aspect of research in this area. Given the limited research conducted regarding dietary patterns of a multi-ethnic population, the present study furthers the understanding on the dietary patterns of a multi-ethnic population like Singapore. Notably, while dietary patterns of individuals with chronic conditions are positive, the present study highlights areas of improvements for better dietary practices amongst individuals without chronic conditions. Given that individuals who are younger and healthy at present tended to have lower DASH scores, it is critical that efforts are directed towards such groups before the less-than-ideal dietary practices escalates into the development of chronic conditions. Therein, the present study provides policymakers with up-to-date information on the dietary patterns of a multi-ethnic population and thus aids in the development of government initiatives targeted at encouraging healthier dietary habits amongst persons with varied number of chronic conditions.

DASH: Dietary Approach to Stop Hypertension

CIDI: World Mental Health Composite International Diagnostic Interview

MCC: Multiple Chronic Conditions

DM: Diabetes Mellitus

WHO: World Health Organization

BMI: Body Mass Index

Pre-U: Pre-University

ITE: Institute of Technical Education

SGD: Singapore Dollars

Ethics Approval and consent to participate

Ethics approval for the study was obtained from the relevant ethics committee (Domain Specific Review Board, National Healthcare Group, Singapore), and all respondents provided written informed consent before participating in the study.

Consent for Publication

N.A.

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 that they have no competing interests.

Funding

This research funding was provided by National Medical Research Council of Singapore (NMRC/HSRG/0085/2018). The funding bodies did not have any role in the design of the study and collection, analysis, and interpretation of data or in writing the manuscript.

Authors’ Contribution

MS, AN, LES, and SCF conceptualised the design of the study. MS, AN, FD, KR, WP, LES, and SCF were involved in questionnaire design. EA provided the statistical design and sampling strategy, while JHL analysed and interpreted the data. LYY, CW, SS, SC, AJ and CBY have substantively revised the work. TYWB wrote the first draft of the manuscript. All the authors provided intellectual input in the development of the article. All authors have read and approved the manuscript.

Acknowledgements

The authors would like to thank all participants and staff involved in the study.

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Table 1

Socio-demographic distribution of the sample (n = 2895)

Socio-demographic characteristics	Total sample (n = 2895)			No chronic physical condition (n = 1243) (Unweighted = 42.92%, Weighted = 46.21%)
	n	Unweighted %	Weighted %	n	Unweighted %	Weighted %
Age groups
18-34	823	28.43	29.89	537	43.20	42.63
35-49	719	24.84	28.22	369	29.69	31.45
50-64	774	26.74	26.75	239	19.23	18.27
65 and above	579	20.00	15.13	98	7.88	7.65
Gender
Female	1,474	50.92	51.55	605	48.67	51.64
Male	1,421	49.08	48.45	638	51.33	48.36
Ethnicity
Chinese	796	27.50	75.81	360	28.96	75.15
Malay	974	33.64	12.73	410	32.98	12.97
Indian	918	31.71	8.59	375	30.17	8.64
Others	207	7.15	2.86	98	7.88	3.24
Education
Primary and below	637	22.00	20.36	160	12.87	12.30
Secondary	684	45.63	20.28	272	21.88	20.14
Pre-U^a/Junior College	126	49.98	4.78	60	4.83	5.51
Vocational Institute/ITE^b	267	59.21	6.62	132	10.62	7.17
Diploma	479	75.75	18.46	267	21.48	21.85
Degree, professional certifications and above	702	24.25	29.50	352	28.32	33.04
Marital Status
Single	731	25.25	29.20	443	35.64	39.10
Married/Cohabiting	1,860	64.25	61.69	721	58.00	54.49
Divorced/Separated/Widowed	303	10.47	9.10	78	6.28	6.40
Refused	1	0.03	0.01	1	0.08	0.01
Employment
Employed	1,933	66.77	70.49	922	74.18	77.16
Economically inactive	829	28.64	25.37	263	21.16	19.05
Unemployed	133	4.59	4.14	58	4.67	3.79
Personal Income (Monthly)^c
Below 2,000/No income	1,455	42.69	45.31	545	43.85	40.26
2,000-3,999	698	24.11	23.94	334	26.87	26.59
4,000-5,999	318	10.98	12.77	162	13.03	13.74
6,000-9,999	183	6.32	7.82	92	7.40	8.89
10,000 and above	117	4.04	5.68	45	3.62	5.25
Don’t know/Refused	124	4.28	4.48	65	5.23	5.28
BMI^d
Normal range ≥ 18.5 & <25	1263	43.63	53.39	617	49.64	55.80
Underweight < 18.5	151	5.22	6.95	90	7.24	10.04
Overweight ≥ 25 & <30.0	858	29.64	26.57	338	27.19	23.80
Obese ≥ 30.0	420	14.51	8.97	150	12.07	7.63
Refused	203	7.01	4.13	48	3.86	2.72
Classifications
No chronic physical condition	1,243	42.94	46.21	-	-	-
One chronic physical condition	760	26.25	26.28	-	-	-
Two or more chronic physical conditions	884	30.54	27.18	-	-	-

^aPre-U: Pre-University

^bITE: Institute of Technical Education

^cDenoted in Singapore Dollars (SGD). USD1 ≈ SGD1.4

^dBMI: Body Mass Index

Table 1 (cont)

Socio-demographic distribution of the sample (n = 2895)

Socio-demographic characteristics	One chronic physical condition (n = 760) (Unweighted = 26.31%, Weighted = 26.32%)			Two or more chronic physical conditions (n = 884) (Unweighted = 30.51%, Weighted = 27.21%)
	n	Unweighted %	Weighted %	n	Unweighted %	Weighted %
Age groups
18-34	207	27.24	29.06	78	8.82	9.34
35-49	215	28.29	30.50	135	15.27	20.86
50-64	203	26.71	27.48	328	37.10	40.01
65 and above	135	17.76	12.96	343	38.80	29.79
Gender
Female	416	54.74	54.96	448	50.68	47.63
Male	344	45.26	45.04	436	49.32	52.37
Ethnicity
Chinese	206	27.11	75.36	227	25.68	77.28
Malay	251	33.03	12.47	310	35.07	12.60
Indian	243	31.97	8.86	298	33.71	8.31
Others	60	7.89	3.31	49	5.54	1.81
Education
Primary and below	163	21.45	18.91	309	34.95	35.00
Secondary	162	21.32	18.41	248	28.05	22.17
Pre-U/Junior College	34	4.47	4.56	32	3.62	3.80
Vocational Institute/ITE	76	10.00	7.91	58	6.56	4.46
Diploma	124	16.32	19.10	88	9.95	12.31
Degree, professional certifications and above	201	26.45	31.13	149	16.86	22.26
Marital Status
Single	182	23.95	27.50	105	11.88	14.30
Married/Cohabiting	501	65.92	66.10	633	71.61	69.97
Divorced/Separated/Widowed	77	10.13	6.40	146	16.52	15.72
Refused	0	0.00	0.00	0	0.00	0.00
Employment
Employed	525	69.08	72.22	482	54.52	57.56
Economically inactive	204	26.84	24.39	358	40.50	36.94
Unemployed	31	4.08	3.39	44	4.98	5.50
Monthly Personal Income
Below 2,000/No income	366	48.16	44.62	537	60.75	54.27
2,000-3,999	190	25.00	23.19	173	19.57	20.09
4,000-5,999	90	11.84	14.21	66	7.47	9.90
6,000-9,999	47	6.18	6.79	44	4.98	7.09
10,000 and above	36	4.74	6.88	36	4.07	5.31
Don’t know/Refused	31	4.08	4.31	28	3.17	3.34
BMI
Normal range ≥ 18.5 & <25	346	45.53	59.31	298	33.71	43.78
Underweight < 18.5	40	5.26	6.16	20	2.26	2.17
Overweight ≥ 25 & <30.0	222	29.21	25.22	296	33.48	32.55
Obese ≥ 30.0	100	13.16	6.58	168	19.00	13.62
Refused	52	6.84	2.73	102	11.54	7.89
Classifications
No chronic physical condition	-	-	-	-	-	-
One chronic physical condition	-	-	-	-	-	-
Two or more chronic physical conditions	-	-	-	-	-	-

^aPre-U: Pre-University

^bITE: Institute of Technical Education

^cDenoted in Singapore Dollars (SGD). USD1 ≈ SGD1.4

^dBMI: Body Mass Index

Table 2

DASH components and overall DASH scores of no chronic physical condition, one physical chronic condition and two or more chronic physical conditions

		Total sample (n = 2895)	No chronic physical condition (n = 1243) (Unweighted = 42.92%, Weighted = 46.21%)	One chronic physical condition (n = 760) (Unweighted = 26.31%, Weighted = 26.32%)	Two or more chronic physical conditions (n = 884) (Unweighted = 30.51%, Weighted = 27.21%)
DASH^a components (servings/d)		Mean (S.D.)
	Fruits	0.96	0.89	1.02	1.03
		(0.91)	(0.84)	(0.96)	(0.95)
	Vegetables	1.57	1.52	1.62	1.60
		(1.14)	(1.12)	(1.14)	(1.19)
	Nuts/Legumes	0.67	0.61	0.77	0.65
		(0.82)	(0.75)	(0.91)	(0.85)
	Low fat dairy	0.29	0.29	0.29	0.31
		(0.47)	(0.46)	(0.45)	(0.49)
	Whole grains	1.63	1.51	1.73	1.75
		(1.87)	(1.72)	(2.10)	(1.88)
	Red and processed meat	0.57	0.59	0.58	0.51
		(0.66)	(0.67)	(0.68)	(0.63)
	Sweetened beverages	0.34	0.36	0.37	0.27
		(0.52)	(0.52)	(0.56)	(0.47)
DASH score*		19.05	18.54	19.19	19.75
DASH score*		(4.84)	(4.57)	(4.83)	(5.17)

^aDietary Approach to Stop Hypertension

*DASH scores are calculated based on quintile score from each of the seven components

Table 3

Results of the linear regression analyses examining the correlates of the DASH scores amongst the total sample, no chronic physical condition, one physical chronic condition and two or more chronic physical conditions

	DASH score
	Total sample (n = 2571)			No chronic physical condition (n = 1133)
	B	95% CI	p	B	95% CI	p
Age groups
18-34	ref			ref
35-49	1.78	1.23 – 2.33	<0.001	1.77	1.02 – 2.52	<0.001
50-64	2.86	2.24 – 3.47	<0.001	2.38	1.47 – 3.28	<0.001
65 and above	3.45	2.73 – 4.17	<0.001	3.41	2.20 – 4.63	<0.001
Gender
Female	ref			ref
Male	-1.50	-1.87 – -1.14	<0.001	-1.89	-2.42 – -1.35	<0.001
Ethnicity
Chinese	ref			ref
Malay	0.26	-0.20 – 0.72	0.27	0.45	-0.23 – 1.13	0.19
Indian	2.54	2.09 – 2.98	<0.001	2.65	1.99 – 3.31	<0.001
Others	0.67	-0.05 – 1.39	0.07	0.96	-0.07 – 1.98	0.07
Education
Degree, professional certifications and above	ref			ref
Primary and below	-1.99	-2.69 – -1.29	<0.001	-2.06	-3.23 – -0.88	0.001
Secondary	-1.72	-2.31 – -1.12	<0.001	-1.42	-2.28 – -0.57	0.001
Pre-U/Junior College	-0.39	-1.28 – 0.51	0.40	0.82	-0.48 – 2.11	0.22
Vocational Institute/ITE	-1.42	-2.15 – -0.69	<0.001	-1.75	-2.77 – 0.73	0.001
Diploma	-0.84	-1.40 – -0.27	0.004	-0.64	-1.42 – 0.13	0.11
Marital Status
Married/Cohabiting	ref			ref
Single	-0.28	-0.79 – 0.24	0.30	-0.17	-0.89 – 0.55	0.64
Divorced/Separated/Widowed	-0.55	-1.16 – -0.07	0.08	-0.29	-1.41– 0.83	0.61
Employment
Employed	ref			ref
Economically inactive	0.45	-0.03 – 0.93	0.07	0.60	-0.16 – 1.37	0.12
Unemployed	0.39	-0.44 – 1.23	0.35	0.35	-0.89 – 1.59	0.58
Personal Income (Monthly)
Below 2,000/No income	ref			ref
2,000-3,999	-0.30	-0.79 – 0.19	0.23	-0.10	-0.82 – 0.62	0.78
4,000-5,999	-0.27	-0.92 – 0.38	0.42	-0.17	-1.11 – 0.77	0.73
6,000-9,999	-0.03	-0.85 – 0.79	0.95	0.43	-0.76 – 1.61	0.48
10,000 and above	0.13	-0.86 – 1.12	0.80	0.15	-1.41 – 1.71	0.85
BMI
Normal range ≥ 18.5 & <25	ref			ref
Underweight < 18.5	-0.14	-0.90 – 0.62	0.81	-0.41	-1.38 – 0.56	0.41
Overweight ≥ 25 & <30.0	0.22	-0.18 – 0.63	0.27	0.65	0.04 – 1.25	0.05
Obese ≥ 30.0	-0.17	-0.68 – 0.34	0.52	-0.16	-0.97 – 0.65	0.70
Classifications
No chronic physical condition	ref			-	-	-
One chronic physical condition	0.04	-0.38 – 0.46	0.84	-	-	-
Two or more chronic physical conditions	0.14	-0.31 – 0.59	0.54	-	-	-

B – represents unstandardized coefficient; 95% CI: 95% confidence interval of β

Bold print denotes statistically significant B value

Numbers included within analyses may not tally with sample size due to missing data on variables

Table 3 (cont)

Results of the linear regression analyses examining the correlates of the DASH scores amongst the total sample, no chronic physical condition, one physical chronic condition and two or more chronic physical conditions

	DASH score
	One chronic physical condition (n = 681)			Two or more chronic physical conditions (n = 757)
	B	95% CI	p	B	95% CI	p
Age groups
18-34	ref			ref
35-49	1.54	0.45 – 2.63	0.01	2.90	1.52 – 4.27	<0.001
50-64	2.87	1.67 – 4.07	<0.001	4.10	2.78 – 5.41	<0.001
65 and above	4.19	2.76 – 5.63	<0.001	3.87	2.45 – 5.29	<0.001
Gender
Female	ref			ref
Male	-0.97	-1.71 – -0.22	0.01	-1.48	-2.19 – -0.77	<0.001
Ethnicity
Chinese	ref			ref
Malay	-0.47	-1.40 – 0.46	0.32	0.61	-0.27 – 1.49	0.18
Indian	2.19	1.30 – 3.09	<0.001	2.61	1.78 – 3.44	<0.001
Others	-0.35	-1.75 – 1.04	0.62	1.49	0.01 – 2.97	0.04
Education
Degree, professional certifications and above	ref			ref
Primary and below	-3.04	-4.39 – -1.69	<0.001	-1.04	-2.33 – 0.26	0.12
Secondary	-2.35	-3.54 – -1.16	<0.001	-1.19	-2.41 – 0.03	0.06
Pre-U/Junior College	-1.09	-2.85 – 0.68	0.23	-1.44	-3.25 – 0.38	0.12
Vocational Institute/ITE	-1.22	-2.61 – 0.17	0.08	-0.76	-2.38 – 0.86	0.36
Diploma	-1.51	-2.61 – -0.40	0.01	-0.22	-1.56 – 1.11	0.74
Marital Status
Married/Cohabiting	ref			ref
Single	-0.53	-1.60 – 0.54	0.33	-0.18	-1.28 – 0.91	0.74
Divorced/Separated/Widowed	-0.66	-1.94 – 0.62	0.31	-0.68	-1.60 – 0.25	0.15
Employment
Employed	ref			ref
Economically inactive	0.26	-0.69 – 1.22	0.59	0.34	-0.55 – 1.23	0.45
Unemployed	0.97	-0.81 – 2.74	0.29	-0.07	-1.56 – 1.43	0.93
Personal Income (Monthly)
Below 2,000/No income	ref			ref
2,000-3,999	-0.32	-1.27 – 0.63	0.51	-0.54	-1.49 – 0.41	0.26
4,000-5,999	0.41	-0.85 – 1.67	0.52	-1.41	-2.79 – -0.04	0.04
6,000-9,999	-0.12	-1.74 – 1.50	0.89	-0.59	-2.26 – 1.09	0.49
10,000 and above	-0.66	-2.50 – 1.17	0.48	1.01	-0.82 – 2.85	0.28
BMI
Normal range ≥ 18.5 & <25	ref			ref
Underweight < 18.5	-0.12	-1.65 – 1.40	0.87	0.68	-1.37 – 2.73	0.52
Overweight ≥ 25 & <30.0	0.21	-0.57 – 1.00	0.60	-0.44	-1.19 – 0.31	0.25
Obese ≥ 30.0	0.85	-0.20 – 1.89	0.11	-1.06	-1.94 – 0.18	0.20
Classifications
No chronic physical condition	-	-	-	-	-	-
One chronic physical condition	-	-	-	-	-	-
Two or more chronic physical conditions	-	-	-	-	-	-

B – represents unstandardized coefficient; 95% CI: 95% confidence interval of β

Bold print denotes statistically significant B value

Numbers included within analyses may not tally with sample size due to missing data on variables

Dietary Patterns of Persons with Chronic Conditions in a Multi-Ethnic Asian Population

Status:

Version 2

Abstract

Background

Method

Participants and procedures

Measures

Socio-demographic information and body mass index

Diet screener

Chronic physical conditions

Statistical analysis

Results

Socio-demographics distribution of the sample

DASH components score distribution and comparisons between chronic medical condition groups

Socio-demographic correlates of DASH score

Discussion

Conclusion

Abbreviations

Declarations

References

Tables

Status:

Version 2