Study design and population

A cross sectional study was conducted at a university primary care clinic in Selangor, Malaysia. A total of 381 patients aged 18 to 80 years old who fulfilled the eligibility criteria were recruited. The inclusion and exclusion criteria for this study were similar to those used in our previous study involving patients with MetS [13].

The inclusion criteria were patients who: (a) attended the primary care clinic for at least 6 months; (b) had blood investigations (fasting serum lipid [FSL], fasting plasma glucose [FPG] and haemoglobin A1c [HbA1c]) done in the past 6 months; (c) could read and understand the Malay language; (d) fulfilled at least 3 out of 5 Joint Interim Statement (JIS) 2009 diagnostic criteria for MetS [14], i.e. systolic blood pressure (BP) ≥130 mmHg and/or diastolic BP ≥85 mmHg or on treatment for hypertension (HPT); FPG ≥5.6 mmol/L or on treatment for elevated glucose; triglycerides (TG) ≥1.7 mmol/L or on treatment for dyslipidaemia; high-density lipoprotein cholesterol (HDL-C): male < 1.0 mmol/L, female < 1.3 mmol/L or on treatment for dyslipidaemia; waist circumference (WC) South Asian cut-points: male ≥90 cm, female ≥80 cm; and (e) were willing to participate in this study.

The following patients were excluded: (a) presented with severe HPT (systolic BP > 180 mmHg and/or diastolic BP > 110 mmHg); (b) had underlying secondary HPT; (c) diagnosed with circulatory disorders requiring secondary care over the past 1 year (e.g. unstable angina, heart attack, stroke, transient ischemic attacks, peripheral vascular disease); (d) on renal dialysis; (e) received shared care at secondary care centres; (f) enrolled in another intervention study; (g) pregnant; (h) diagnosed with malignancy; (i) had any form of mental disorders or cognitive impairments that would affect the ability to answer the questionnaire; and (j) unable to give informed consent. Figure 1 shows the flow chart of the study.

Fig. 1
figure1

Flow chart of the conduct of the study

Study tools

Traditional and complementary medicine (TCM) proforma

A proforma was used to gather information about TCM utilisation which comprised of a) sociodemographic characteristics; b) clinical characteristics including anthropometric measurements, blood investigation results and medical background; c) patterns of TCM use including reasons for use, source of information regarding TCM, source of TCM, types of TCM, cost spent on TCM in a year and reason for non-disclosure to medical practitioner about TCM use.

PACIC-M questionnaire

The Malay version of the PACIC questionnaire (PACIC-M) was used in this study to measure patients’ experience on chronic disease conventional care that they received [15]. It is valid and reliable with Cronbach’s α of 0.94 and the intra-class correlation coefficient of 0.93 [15]. This questionnaire consists of 19 items, framed within three components: a) goal setting/tailoring and problem solving/contextual; b) follow-up/coordination and c) patient activation and delivery system design/decision support [15]. Participants were required to response to at least the last 9 items of PACIC-M (item 11–19) which represent the problem solving/contextual and follow-up/coordination scales in order to be included in the analysis [11]. Each item is scored by 5-point Likert Scales from 1 (almost never) to 5 (almost always) [15]. The mean score of items from each component and the overall score across all 19 items were measured in this study [15]. A higher PACIC-M mean score represents better patient’s experience in receiving chronic disease care that aligns with the CCM [11]. Table 1 shows the 3 components of PACIC-M and the corresponding items for each component.

Table 1 Components of Patient Assessment of Chronic Illness Care-Malay version (PACIC-M) and items for each component

Definition of study variables

The dependent variable (DV) of this study was TCM utilisation which was divided into ‘TCM user’ and ‘TCM non-user’. TCM in this study was defined as one or more practices or modalities being used by the participants other than treatment provided by the current treating health care providers (HCP) [1]. The reasons for use included prevention and treatment of physical or mental illness or as part of cultural practices or religious belief [1]. The types of TCM included in this study were as categorised by the Ministry of Health (MOH), Malaysia in the National Health and Morbidity Survey (NHMS) 2015 report. There were six main TCM practices [16]. The subtypes for each main practice are shown in Table 2 [16].

Table 2 Types of Traditional and Complementary Medicine in Malaysia

‘Current TCM user’ was defined as a person who was using one or more types of TCM among the six main practices within the past 1 year prior to data collection [16]. ‘Past TCM user’ was defined as a person who used one or more types of TCM among the six main practices for at least once in his/her lifetime, but was no longer using within the last 1 year prior to data collection [16]. ‘Non-TCM user’ was defined as a person who never use any type of TCM in his/her lifetime [16]. In this study, the DV for ‘TCM user’ was defined as ‘current TCM user’. Meanwhile, ‘TCM non-user’ was defined as the combination of ‘non-TCM user’ and ‘past TCM user’. This is shown in Table 3.

Table 3 Definition of Traditional and Complementary Medicine use

The independent variables (IV) of this study were sociodemographic characteristics, clinical characteristics and PACIC-M mean score. Sociodemographic data included age, gender, marital status, ethnicity, education level, occupational status and household income group. Ethnicity was categorized based on the main ethnic groups in Malaysia which are Malay, Chinese and Indian. Education level was categorized based on the Malaysian education system which comprised of no formal education, primary education (standard 1 to 6), secondary education (Form 1 to 5) and higher tertiary education (pre-university course, diploma, degree, masters and PhD level). With regards to the household income, it was grouped based on the monthly household income categorized by the Department of Statistics, Malaysia (DOSM) in 2016 [17]. The top 20% (T20) were those who earned more than or equal to Ringgit Malaysia (RM) 9620 per month, the middle 40% (M40) were those who earned RM 4360 to RM 9619 per month and the bottom 40% (B40) were those who earned less than RM 4360 per month [17].

The clinical characteristic data included smoking status, BMI, WC, BP, FPG, TG, HDL and HbA1C levels. The BMI is categorized based on the recommended cut-off points for the Asian population which are underweight (BMI < 18.5 kg/m2), normal (18.5–22.9 kg/m2), overweight (23.0–27.4 kg/m2) and obese (≥27.5 kg/m2) [18]. The cut-off points for other clinical factors were defined according to the JIS criteria [19]. The PACIC-M score was regarded as a continuous variable where the overall mean score and the mean score for each component were calculated.

Sample size determination

Sample size was calculated using the OpenEpi Version 3 opensource calculator for ‘Sample size for a proportion of descriptive study’ available from https://www.openepi.com/SampleSize/SSPropor.htm

The equation for the calculation was:

$$boldsymbol{n}=left[mathbf{DEFF}ast mathbf{Np} left(mathbf{1}hbox{-} mathbf{p}right)right]/left[right({mathbf{d}}^{mathbf{2}}/{{mathbf{Z}}^{mathbf{2}}}_{mathbf{1}hbox{-} boldsymbol{upalpha} /mathbf{2}}ast left(mathbf{N}hbox{-} mathbf{1}right)+mathbf{p}ast left(mathbf{1}hbox{-} mathbf{p}right)Big]$$

n = sample size

DEFF = design effect (for cluster surveys)

N = population size (for finite population correction factor or fpc)

p = hypothesized % frequency of outcome factor in the population

d = desired precision

Z2 1 − α/2 = confidence interval

The population size was determined by the total number of patients registered in the electronic medical record (EMR) in the primary care clinic i.e. 10,000 in a year. The prevalence of TCM utilisation was estimated as 31.7% based on a previous community-based cross-sectional survey among adults with cardiovascular risk factors in Pahang, Malaysia [3]. By taking confidence level of 95% with the desired precision of 5%, the minimum sample required for this study was 323 participants. After considering 20% of non-eligibility and non-responder rate, we targeted to approach a total of 388 participants.

Sampling method and participant recruitment

Participant recruitment and data collection were conducted over 14-week duration from September to December 2019. Adults aged ≥18 years old who attended the primary care clinic were approached and invited to participate in this study. Patient information leaflet was given. Those who verbally agreed to participate were screened for eligibility and written informed consent was obtained.

Sources of data

The data for this study were obtained from several sources i.e. i) anthropometry measurements, ii) EMR for medical background and blood investigation results, iii) face-to-face interview for the sociodemographic data and TCM utilization. All of these data were transferred into the TCM Proforma. Data on patient’s experience on chronic disease care were obtained through self-administration of the PACIC-M questionnaire.

Data collection procedure

Anthropometry measurement

The anthropometry measurements i.e. BP, WC and Body Mass Index (BMI) were performed by the trained nurses in the pre-treatment room. BP was measured using an automatic digital blood pressure monitor (Omron HBP-1100). The participant was advised not to smoke, exercise, or consume caffeinated beverages in the last 30 min prior to the BP measurement [20]. The participant was allowed to rest for at least 5 min before the measurements were taken [20]. The participant was seated upright with the back laid and supported [20]. The right arm was placed on the table with the upper arm at the heart level [20]. The appropriate BP cuff was placed covering two third of the right upper arm [20]. BP was measured twice with 2 min interval [20]. The average of these two BP readings was taken as the BP value for each participant [20].

WC was measured to the nearest 0.1 cm using a non-stretchable measuring tape. The measurement was taken at the midpoint between the lower rib margin and the iliac crest, while the participants standing with both arms at the side [21]. WC was measured at the end of exhalation [21].

Weight in kilogram (kg) and height in metre (m) were measured using the adult weighing scale and stadiometer (Charder MS4900). The weight was measured to the nearest 0.1 kg when the participant was standing on the scale with light clothing and without foot wear. The height was measured to the nearest 0.01 m when the participant was standing on the same scale facing forward with the back, buttock and heels against the scale. Two readings for each weight and height were measured and the average measurement was recorded. The Body Mass Index (BMI) was calculated using the formula, BMI = weight (kg)/ [height (m)]2.

Retrieval of data from electronic medical record

The participant’s medical background and the latest biochemistry investigation results were retrieved from the EMR. The investigation results included FPG, FSL (TG and HDL-C) and HbA1c taken within the last 6 months.

Face-to-face interview

Data on sociodemographic and TCM utilization were obtained through face-to-face interview by two research assistants. To minimize interview bias, the interviewers were trained prior to data collection to ensure consistency and standardization.

Questionnaire administration

The PACIC-M questionnaire was given to the participants for self-administration after the interview. A clear verbal instruction on the questionnaire answering technique was given. Participants were required to complete the questionnaire on their own without referring to any notes or their companion. The questionnaire was returned to the research assistants once completed and it was checked for completeness.

Statistical analysis

Descriptive analyses were used to describe the socio-demographic characteristics, clinical characteristics, proportion of TCM use, TCM pattern, overall PACIC-M score and score of its components. Normality of the data was assessed using histogram, Kolmogorov-Smirnov and Shapiro-Wilk tests for continuous variables. If the data were normally distributed, they were described using mean and standard deviation (SD). If the data were not normally distributed, they were described using median and interquartile range (IQR). Categorical variables were described in numbers and percentages.

For comparison of PACIC-M score between ‘TCM users’ and TCM non-users’, the normality of data distribution and equality of variance was examined accordingly. Data for the overall PACIC-M score and the score for each of the component were normally distributed. Therefore, independent t-test was used to compare the difference in overall mean PACIC-M score and the mean score for each component between ‘TCM users’ and ‘TCM non-users’.

Simple Logistic Regression (SLogR) analysis was used to screen the association between the independent variables (sociodemographic data, clinical characteristics, overall PACIC-M score and score of each component) with TCM utilisation. Variables with a P-value of < 0.25 from the SLogR analysis were then included in the Multiple Logistic Regression (MLogR) analysis [22]. Stepwise forward and backward binary logistic regressions were performed. Confounders were adjusted in the MLogR using stepwise selection procedure. The underlying assumption for this approach is that all potential confounders would be selected and included into the regression model [23]. Model fitness was checked using the Hosmer-Lemeshow goodness-of-fit test. Interactions, multicollinearity, and assumptions were also checked. The best fit regression model was chosen as the final model for this study. Statistical significant was taken at a P-value of < 0.05 [22]. The data were analysed using the IBM SPSS Statistics software version 23.

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