Skip to main content
NCBI home page
PLOS One logoLink to PLOS One
. 2022 May 5;17(5):e0267897. doi: 10.1371/journal.pone.0267897

Health insurance status and its determinants among patients with type 2 diabetes mellitus in a tertiary teaching hospital in Malaysia

Essam Ali Al-Sanaani 1,2, Aniza Ismail 1,*, Mohd Rizal Abdul Manaf 1, Leny Suzana Suddin 3, Norlaila Mustafa 4, Norlela Sukor 4, Alabed Ali A Alabed 5, Ahmed Abdelmajed Alkhodary 1,6, Syed Mohamed Aljunid 6,7
Editor: Mohammed Abdelfatah Mosa Alhoot8
PMCID: PMC9070921  PMID: 35511889

Abstract

Introduction

Even in a country with a tax-based healthcare financing system, health insurance can play an important role, especially in the management of chronic diseases with high disease and economic burden such as Type 2 Diabetes Mellitus (T2DM). The insurance coverage among T2DM patients in Malaysia is currently unclear. The aim of this study was to determine the insurance status of T2DM patients in public and private healthcare facilities in Malaysia, and the association between this status and patients’ sociodemographic and economic factors.

Methods

A cross-sectional study among T2DM patients seeking inpatient or outpatient treatment at a public tertiary hospital (Hospital Canselor Tuanku Muhriz) and a private tertiary hospital (Universiti Kebangsaan Malaysia Specialist Centre) in Kuala Lumpur between August 2019 and March 2020. Patients were identified via convenience sampling using a self-administered questionnaire. Data collection focused on identifying insurance status as the dependent factor while the independent factors were the patients’ sociodemographic characteristics and economic factors.

Results

Of 400 T2DM patients, 313 responded (response rate, 78.3%) and 76.0% were uninsured. About 69.6% of the respondents had low monthly incomes of <RM5000. Two-thirds of participants (59.1%) spent RM100–500 for outpatient visits whilst 58.5% spent <RM100 on medicines per month (RM1 = USD0.244). Patients who visited a private facility had five times more likely to have insurance than patients who visited a public facility. Participants aged 18–49 years with higher education levels were 4.8 times more likely to be insured than participants aged ≥50 years with low education levels (2 times).

Conclusions

The majority of T2DM patients were uninsured. The main factors determining health insurance status were public facilities, age of ≥ 50 years, low education level, unemployment, and monthly expenditure on medicines.

Introduction

In Malaysia, the healthcare system has a two-tier structure. The first tier is a heavily funded public healthcare system that requires minimal out-of-pocket (OOP) payments, while the second tier is private healthcare services that are funded by patients through OOP expenditure, private health insurance, employer-funded health insurance or other employer-funded healthcare schemes [1,2].

As the Malaysian health system is mainly tax-based, people who do not have private insurance can still access healthcare in government facilities that charge with minimal fees [3,4]. However, due to the high workload at these facilities, it can be difficult for patients to receive treatment, e.g. long waiting times for consultation or surgery [5]. These shortcomings in the public healthcare system can have a very detrimental effect on the health of people with chronic diseases such as diabetes mellitus, heart disease or cancer [6,7]. It is possible that delays in treatment can lead to poorer health of patients [8]. It is therefore not surprising that patients with chronic diseases in Malaysia purchase private insurance schemes for the treatment of their illnesses [9].

Although the Malaysian health system is mainly tax-based, fees in public and private hospitals are generally higher for foreigners than those for locals. Foreigners seeking treatment at health facilities may incur high costs [10,11]. To financially protect migrant workers from the costs of inpatient treatment or surgery at Ministry of Health hospitals, the government has made membership in a health insurance scheme for foreign workers, the SPIKPA (Foreign Worker Hospitalisation and Insurance Scheme), mandatory since 2011 [12,13].

Therefore, health insurance is generally one of several healthcare financing mechanisms that may be able to prevent health-related financial disasters among those who contribute to the schemes [2]. The role of health insurance is important even in a country that has a tax-based system of healthcare financing.

Diabetes and health insurance

Diabetes mellitus (DM) is a metabolic disease that is considered one of the most common chronic diseases worldwide [14–17]. The World Health Organisation (WHO) has classified DM as a non-communicable epidemic disease [18]. In 2015, there were 415 million diabetics aged 20–79 years worldwide, and this number is expected to increase to 642 million by 2040 [15]. Diabetes is expected to account for more than 16% of global healthcare expenditure in middle-income countries by 2040 [15].

T2DM is currently considered a global health and financial concern [19–21]. T2DM is associated with several morbidities: Cardiovascular disease is a major cause of mortality in about one-third (32.2%) of T2DM patients [22]. The disease is associated with an increased risk of hospitalisation [23].

T2DM is a costly disease to manage because of the high rate of diabetes-related complications. These complications of T2DM affect patients’ quality of life [24,25] and increase the economic burden on patients [25–27]. Several cost-related issues have been identified in the diabetes management literature, including medication costs, physician consultations, and laboratory investigations [28].

T2DM patients who have to pay OOP for health care services and medicine themselves may have high health expenditures. This contributes to financial problems that can push them into poverty [29]. Poverty, in turn, may lead diabetics to spend less money on other basic needs and forgo healthcare that would enable them to survive their illnesses [30,31].

The T2DM and its complications also result in reduced individual productivity and income due to early retirement, which increases the financial burden on both the individual and the government through the loss of labour productivity and increased expenditure on disease-related healthcare [32].

In Malaysia, T2DM is one of the most prevalent chronic diseases [33]. In the last decade, it has even been classified as an epidemic as the prevalence of T2DM has increased significantly in the country [34,35]. According to the 2019 National Health and Morbidity Survey (NHMS) report, the prevalence of DM in Malaysia has increased from 11.2% in 2011 to 13.4% in 2015 and 18.3% in 2019 [36], meaning that almost one in five Malaysian adults has diabetes. By 2025, seven million Malaysian adults are likely to have diabetes—an alarming trend that will lead to a diabetes prevalence of 31.3% among adults aged ≥18 years [37]. This has serious implications for the healthcare and social system and the national economy. Therefore, Malaysian health policymakers consider T2DM as a major public health concern [33].

In Malaysia, the government provides highly subsidised healthcare services to Malaysian patients in public hospitals. For example, Malaysian patients at Hospital Canselor Tuanku Muhriz (HCTM) pay only Malaysian Ringgit (RM)30 per visit for outpatient services and RM50 per day for inpatient services [38]. Although these treatment costs are considered low, some patients prefer to either pay themselves (OOP) or purchase insurance due to the aforementioned long waiting times and overcrowding at public healthcare facilities. In addition, there is a perception that private facilities provide high quality services.

Many studies have been conducted on the characteristics of people who purchase health insurance policies. Local studies have shown that health insurance status is positively associated with occupation. For example, people with higher incomes were willing to pay for premium health insurance [39,40]. In contrast, people with lower incomes found it difficult to pay for premium health insurance [40,41]. Mustafa et al. pointed out that this affected the demand for health insurance by 37.0% [42]. In additions, individuals with higher household incomes were more likely to have private insurances, which increases their awareness of the importance of insurance coverage as they protect their dependents against financial health disasters [43–45], compared to individuals with lower incomes [45,46].

Several studies among T2DM have shown that sociodemographic factors (age, gender, marital status, ethnicity, and education) and socioeconomic factors (household income and occupation) are associated with insurance status [39,41,42,47,48].

Since the main objective of policymakers in healthcare in any country is to reduce OOP expenditure on healthcare in general and chronic diseases such as DM in particular so that patients can access healthcare services without economic hardship [31], there is a need for a study that captures the characteristics of patients who purchase health insurance in Malaysia. This knowledge is important for the various stakeholders in the country’s healthcare system as it can help policymakers to review and design appropriate mechanisms to finance healthcare for people with chronic diseases. However, to date, there is a lack of findings on health insurance status among T2DM patients in Malaysia. Therefore, the aim of this study was to determine the health insurance status and its association with the sociodemographic and economic factors of T2DM patients in Malaysia.

Materials and methods

Data source

To ensure representativeness of diabetes patients in terms of those seeking treatment at public and private healthcare facilities, this study was conducted at Hospital Canselor Tuanku Muhriz (HCTM), a public tertiary hospital under the Ministry of Higher Education, Malaysia, and its private arm, the Universiti Kebangsaan Malaysia Specialist Centre (UKMSC). Both facilities are located in Kuala Lumpur. At these facilities, participants for this study were recruited from both the outpatient (endocrine clinics) and inpatient (wards) facilities.

Sample size and sampling

The sample size was calculated using Pocock’s sample size [49]. The minimum sample size, calculated for a 95% confidence level (95% CI), was 400 patients. The sample population included all T2DM patients who had consented to participate in the study, patients who had visited the endocrine clinic, and patients who had been hospitalised at the two hospitals during the data collection period.

The inclusion criteria were T2DM diagnosis, under treatment, or follow-up at HCTM and UKMSC. All participants were Malaysians, aged ≥18 years, and able to understand and communicate in English or Malay language. Exclusion criteria were refusal to participate in the study and disability (visual, speech, or hearing disability). Data were collected using a convenience sampling approach.

Measurements and questionnaires

The study tool was a validated questionnaire adapted from a previous study [50]. The questionnaire used in this current study was in English and Malay and was translated from English to Malay and vice versa, underpinned by face validity and content validity. Content validity was determined by two local public health and health economics specialists, while face validity was determined by diabetic patients who attended another health center. The questionnaire consists of two sections. The first section of the questionnaire collected data on sociodemographic characteristics such as age, gender, ethnicity, marital status, and education level. The second section collected data on economic factors, namely occupation, household income, household expenditure and health insurance status (i.e., insured and uninsured), healthcare expenditure per visit, and monthly expenditure on medicines.

In the data collection tool, the patient’s health insurance status was the dependent factor, while the independent factors were the patient’s sociodemographic characteristics and economic factors.

Definition of variables

Health insurance status in this study can be either “insured” or “uninsured”. Health insurance status refers to the current status of respondents, i.e. whether they have active private health insurance (insured) or whether they do not have active private health insurance and instead paid their medical bills through OOP or government subsidies (uninsured).

Patients were defined as fully insured if they did not make any payments for medical services, as insurance companies covered all their medical expenses. Patients were defined as partially insured if they had partial health insurance, where the insurance companies only covered part of the patient’s medical costs and patients had to share the cost of their medical treatments with the health insurance company through co-payment. The currency used in the study was the Malaysian Ringgit, with an exchange rate of RM1 = USD 0.244 in 2020.

Data collection

Data collection was conducted by the researchers and trained research assistants from August 2019 until March 2020. Each patient was fully informed about the aim of the study and its procedures before answering the questions. Written informed consent was obtained from each patient before the questionnaire was administered to the consenting patient. Data collection took place on days when the endocrine clinic was in operation and on weekdays in the hospital wards.

Ethical approval and consent to participate

The study was approved by the UKM Medical Research Ethics Committee (reference: UKMPP1/111/8/JEP.2019.186), and by the HCTM and UKMSC management. Written informed consent was obtained from the participants and the consent form was approved by the ethics committees. All study-related methods were performed in accordance with the applicable guidelines and regulations in Malaysia.

Statistical analysis

Data were analysed using the Statistical Package for the Social Sciences (SPSS, version 25). The sociodemographic and economic variables were identified using descriptive statistics and frequency tables. Pearson’s Chi-square test was performed for the bivariate analysis and multiple logistic regressions were performed for the multivariable analysis. The significance level of p < 0.05 was established for the entire analysis.

Results

Characteristics of the Participants’

A total 313 out of 400 T2DM patients responded to the questionnaire, representing a response rate of 78.3%. Of these, 235 respondents were from HCTM while 78 were from UKMSC. Eighty-seven patients were classified as non-respondents as they belonged to the group of patients who had not completed the questionnaires and had not returned the questionnaires.

A total of 76.0% of the patients (n = 238/313) were uninsured. Most of the patients treated at HCTM (n = 203/235, 86.4%) were uninsured. In contrast, about half of the UKMSC-treated patients (n = 43/78, 55.1%) were insured. Fig 1 illustrates the insurance status of the patients with HCTM and UKMSC. Of the total sample, 64.9%-HCTM and 11.2%-UKMSC patients were uninsured, while 10.2%-HCTM and 13.7%-UKMSC patients were insured.

Fig 1. Insurance status of T2DM patients at HCTM and UKMSC.

Fig 1

Open in a new tab

Table 1 shows the distribution of respondents by sociodemographic characteristics. Most (n = 235/313; 75.1%) were treated for HCTM. About half (52.1%) of the respondents were from outpatient clinics. Up to 53.0% of the sample consisted of female patients. Most respondents were ≥50 years old (76.4% of the total sample) and most were married (89.1%). Malay patients accounted for 64.2% of the total sample, while Chinese and Indian patients accounted for 17.0% and 18.8%, respectively. Of the total sample, 64.5% of patients had a low level of education.

Table 1. Distribution of respondents by sociodemographic characteristics.

Variable n (%)
Service provider
    HCTM 235 (75.1)
    UKMSC 78 (24.9)
Source of patient
    Inpatient 150 (47.9)
    Outpatient 163 (52.1)
Gender
    Male 147 (47.0)
    Female 166 (53.0)
Age Group
    18-49years 74 (23.6)
    50 years & above 239 (76.4)
Marital Status
    Married 279 (89.1)
    Unmarried 34 (10.9)
Ethnicity
    Malay 201 (64.2)
    Chinese 53 (17.0)
    Indian 59 (18.8)
Education Level
    Low education 202 (64.5)
    High education 111 (35.5)
Open in a new tab

Table 2 shows the distribution of respondents by socioeconomic characteristics. Most respondents were uninsured, accounting for 76.0% of the total sample. Data on the occupational status of respondents show that the highest percentage (33.5%) were pensioners. Respondents with a household income of <RM5000 accounted for 69.6% of the total sample. Up to 80.5% of the respondents had monthly family expenditure of <RM5000 and 59.1% of the respondents spent between RM100 and RM500 per visit. In addition, 58.5% of the respondents spent <RM100 per month on medicines.

Table 2. Distribution of respondents by socioeconomic characteristics.

Variable n (%)
Health Insurance Status
Insured 75 (24.0)
Uninsured 238 (76.0)
Occupation
Private 68 (21.7)
Government 40 (12.8)
Unemployed 100 (32.0)
Pensioner 105 (33.5)
Household Income
<RM5000 218 (69.6)
≥ RM5000 95 (30.4)
Family Expenditure
< RM5000 252 (80.5)
≥ RM5000 61 (19.5)
Treatment Spending/ Visit
< RM100 70 (22.4)
RM100—RM500 185 (59.1)
> RM 500 58 (18.5)
Medicine Spending/ month
< RM100 183 (58.5)
RM100—RM500 119 (38.0)
> RM500 11(3.5)
Open in a new tab

Bivariate analysis results

A Chi-square test for independence was performed to assess the relationship between participants’ health insurance status and sociodemographic characteristics. The results are summarised in Table 3. In terms of study sites (HCTM versus UKMSC), the results showed that most HCTM-treated participants were uninsured (n = 203/235; 86.4%) compared with UKMSC-treated patients (n = 35/78; 44.9%). Chi-square test showed that health insurance status was significantly associated with study site [χ2 (1, N = 313) = 55.386, p < 0.001].

Table 3. Association between health insurance status and sociodemographic characteristics.
Variable n Health insurance status P-value
Insured, n (%) Uninsured, n (%)
Service Provider
    HCTM 235 32 (13.6) 203 (86.4) <0.001*
    UKMSC 78 43 (55.1) 35 (44.9)
Source of patients
    Inpatient 150 21 (14.0) 129 (86.0) <0.001*
    Outpatient 163 54 (33.1) 109 (66.9)
Gender
    Male 147 38 (25.9) 109 (74.1) 0.461
    Female 166 37 (22.3) 129 (77.7)
Age Groups
    18–49 years 74 37 (50.0) 37 (50.0) <0.001*
    50 & above 239 38 (15.9) 201 (84.1)
Marital status
    Unmarried 34 10 (70.6) 24 (29.4) 0.276
    Married 279 65 (23.3) 214 (76.7)
Ethnicity
    Malay 201 59 (29.4) 142 (70.6) 0.006*
    Chinese 53 10 (18.9) 43 (81.1)
    Indian 59 6 (10.2) 53 (89.8)
Educational level
    1Low 202 29 (14.4) 173 (85.6) <0.001*
    2High 111 46 (41.4) 65 (58.6)
Open in a new tab

*Significant P value<0.05

1Low = none, primary school, secondary school

2High = professional higher education or university; HCTM–public facility, UKMSC–private facility.

Similarly, in terms of patient source (outpatient versus inpatient facility), 66.9% (n = 109/163) of respondents treated in outpatient clinics were uninsured compared with 86% (n = 129/150) of respondents treated in inpatient wards. The chi-square test showed that health insurance status was significantly associated with patient source [χ2 (1, N = 313) = 15.688, p < 0.001].

Regarding age, the analysis showed that 84.1% (n = 201/239) of respondents aged ≥50 years were uninsured compared to only 50.0% (n = 37/74) of those aged 18–49 years. The chi-square test showed that health insurance status was significantly associated with age group [χ2 (1, N = 313) = 36.062, p < 0.001].

Ethnicity is also a significant factor associated with health insurance status. A total of 89.8% (n = 53/59) of Indian respondents were uninsured compared to 70.6% (n = 142/201) of Malays and 81.1% (n = 43/53) of Chines. Again, the chi-square test showed that health insurance status was significantly associated with ethnicity [χ 2 (2, N = 313) = 10.121, p = 0.006]. In addition, 85.6% (n = 173/202) of respondents with low education level were uninsured compared to 58.6% (n = 65/111) with high education level; health insurance status was significantly associated with education level [χ 2 (1, N = 313) = 28.843, p<0.001].

Fig 2 shows the distribution of occupations among participants and the association between occupation and health insurance status. Pensioners accounted for the largest portion of respondents, representing 33.5% of the total sample. In addition, 88.6% of pensioners were not covered by health insurance. Participants who worked in the private sector had higher health insurance coverage, 54.4% of all respondents. The Chi-square test showed that health insurance status was significantly associated with occupation status [χ 2 (3, N = 313) = 67.357, p<0.001].

Fig 2. Distribution of participants’ occupation and association with health insurance status among T2DM patients at UKM teaching hospital.

Fig 2

Open in a new tab

Table 4 explains the association between the respondents’ health insurance status with other economic factors. As many as 85.8% of respondents with a monthly household income of <RM5000 did not have health insurance, as did 82.5% of respondents with monthly family expenses of <RM5000. Chi-square test showed that health insurance status was significantly associated with household income and family expenditure [χ 2 (1, N = 313) = 37.4, p<0.001; χ 2 (1, N = 313) = 29.997, p<0.001, respectively]. Regarding monthly expenditure on medicines, 82.0% of the respondents who spent <RM100 per month on medicines were uninsured. Chi-square test showed that health insurance status was significantly associated with monthly expenditure on medicines [χ 2 (1, N = 313) = 14.974, p = 0.001].

Table 4. Association between health insurance status, income and health spending.
Variables N Health insurance status P-value
Insured, n (%) Uninsured, n (%)
Household Income/Month
< RM 5000 218 31 (14.2) 187 (85.8) <0.001*
≥ RM 5000 95 44 (46.3) 51 (53.7)
Family Expenditures/Month
< RM5000 252 44 (17.5) 208 (82.5) <0.001*
≥ RM5000 61 31(50.8) 30 (49.2)
Treatment Spend Per Visit
    Less than RM100 70 10 (14.3) 60 (85.7) 0.086
    RM 100—RM500 185 51(27.6) 134 (72.4)
    Above RM500 58 14 (24.1) 44 (75.9)
Medicine Spending/Month
    Less than RM100 183 33 (18.0) 150 (82.0) <0.001*
    RM100—RM500 119 35 (29.4) 84 (70.6)
    Above RM500 11 7 (63.6) 4 (36.4)
Open in a new tab

*Significant P value < 0.05.

Multivariate analysis

Table 5 shows that the probability of being insured was five times higher among UKMSC participants compared to HCTM participants [95% CI: 2.4–10.7]. Respondents aged 18–49 years were 4.8 times more likely to be insured compared to respondents aged ≥50 years [95%CI: 2.4–9.6]. Respondents with a high level of education were twice as likely to be insured as respondents with a low level of education [95% CI: 1.02–3.74].

Table 5. Multivariate analysis of health insurance status for sociodemographic and economic factors.

Reference group B Adjusted OR 95%CI P-value
lower upper
Service provider 
UKMSC (Private facility) HCTM (Public facility) 1.61 5.01 2.4 10.7 <0.001*
Age groups 
18–49 years 50 years &above 1.56 4.77 2.38 9.55 <0.001*
Education level
High education Low education 0.67 1.96 1.02 3.74 0.042*
Occupational status <0.001*
Government Private - 0.311 0.733 0.319 1.685 0.464
Unemployed -2.267 0.104 0.042 0.257 <0.001*
Pension -1.805 0.165 0.073 0.373 <0.001*
Medicine spending/month 0.041*
RM100-RM500 < RM100 0.526 1.692 0.896 3.195 0.105
> RM500 1.714 5.552 1.221 25.235 0.026*
Open in a new tab

*Significant P value < 0.05.

Unemployed respondents and pensioners were 10.4% and 16.5% less likely to be insured than respondents who worked in the private sector [95% CI: 0.042–0.257; 95% CI: 0.073–0.373, respectively]. Respondents who spent more than RM500 per month on medicines were 5.6 times more likely to be insured than those who spent less than RM100 per month on medicines [95% CI: 1.22–25.2].

Discussion

Our study shows that the majority of T2DM patients treated at a public facility (HCTM) were uninsured. This could be due to the Malaysian government’s efforts to minimise the financial burden of healthcare expenditure on citizens by subsidizing healthcare services in public facilities. This explains why most Malaysian citizens prefer to visit public healthcare facilities rather than private ones, as the Malaysian government bears their health expenditure with a minimal fee [51–55].

Our findings also show that there is a significant association between the insurance status, healthcare provider, source of patients, age group, ethnicity, and education level, which is consistent with the Balqis-Ali et al. [41]. In addition, it was found that the low insurance rate among Malaysians is due to the fact that the government heavily subsidises the cost of health care through universal health coverage, enrolment in health insurance is voluntary and most Malaysians are not willing to pay for their healthcare services [56]. These findings are consistent with those of previous studies in Malaysia, which found that 26.8% of Malaysians were privately insured [41,57].

Here, we found a significant association between health insurance status and age group, with the majority of participants with insurance coverage being in the younger age group [41]. It is worth noting that older people may need health insurance coverage to ensure easy access to medical care without financial burden [58,59]. According to Arsenijevic et al, (2016) and Chiew, (2017), older people are highly vulnerable to financial shocks as they often have low incomes. Our data show that older people very rarely purchase insurance. This could be due to the fact that low-income elderly people are not aware of the importance of health insurance [41,60,61]. Another reason for this result could be the highly subsidised public health services provided by the government [53,62]. It is worth noting that 77.8% of the elderly participants had a household income of less than RM5000 compared to 33.2% of the younger participants. This decreases the likelihood of having health insurance [41,61], while higher income leads to a better access to health insurance [39,46,63–65].

Our data are also consistent with previous studies that have shown that low-income people are less likely to purchase health insurance. For example, in 2015, Nelson and colleagues [66] found that uninsured people were more likely to be in the low-income group and were often poorer, unemployed and with low levels of education [67].

We also found out that there is a significant association between enrollment in health insurance and the level of education: Respondents with a high level of education had a higher enrolment rate in health insurance than respondents with a low level of education [43,68,69]. Moreover, our results are consistent with those of Mustafa et al, (2019), who reported that a higher level of education increases enrolment in the health insurance system [42]. It is well known that there is a positive association between health insurance enrolment patterns and awareness of the importance of health insurance in protecting against health-related financial disasters [70–74]. In addition, patients with high income and education levels prefer to seek health services in private facilities, which may be due to the fact that quality healthcare services are easier to obtain in private facilities than in public facilities [46]. Our findings are consistent with previous studies that have shown that patients with higher levels of education and income are more likely to be able to pay their health care expenses than others [41,46,75]. Greater awareness of the importance of protecting family members from health-related financial disasters leads to greater willingness to enroll in health insurance programmes [43,44,72,76].

Our study showed that there is a significant association between health insurance status and occupation [41,77]. Here, the pensioners and unemployed participants had a higher probability of being uninsured. However, participants who work in the private sector had a higher probability of having health insurance than participants in other occupations [41]. The pensioners are likely to be in the low-income category; this may be their barrier to paying health insurance premiums [78]. In addition, financial disasters may delay patients’ treatments and lead to diabetes-related complications [79] and diabetes-related hospitalisations, further burdening their financial status [8,80].

Our results show that 24% of the participants had health insurance overall. This can be interpreted to mean that these participants prefer to use private healthcare services, which are easier to obtain [41]. For example, private healthcare services are faster and have shorter waiting times in outpatient and routine surgeries than public facilities [75,81,82].

Limitations of the study

To date, this is the first study on the health insurance status of T2DM patients in Malaysia. However, there are some limitations in this study: Firstly, the data only reflect the experiences of two sites (public and private departments) UKM Teaching Hospital. Therefore, our findings may not be generalisable to other hospitals at other sites. It is worth noting that UKM Teaching Hospital is considered one of the largest referral hospitals in Malaysia and treats a large number of diabetic patients [83]. Secondly, convenience sampling may not be the best way to comprehensively represent our findings; we opted for this approach after facing a low response rate from patients. Originally, all HCTM and UKMSC patients with T2DM were selected using a systematic random sampling approach. However, due to the low response rate and lack of patients willing to participate in this study, we changed the sampling method to convenience sampling. The sample of the present study sample was the largest that could be reached during the study period. Nevertheless, we believe that it accurately reflects the health insurance status of HCTM and UKMSC patients. In addition, we had a problem with patient recruitment as some of the HCTM and UKMSC patients declined to participate in the study. Therefore, we recommend future studies in other Malaysian healthcare facilities T2DM patients to extend our findings. It was expected that the data obtained through the survey would have recall bias. We attempted to overcome this problem by asking the respondents the questions in laymen’s terms and by giving them some hints so that they had enough time to think before answering the questions.

Conclusions

Based on the discussion of this study, it can be concluded that most T2DM patients were uninsured. They were more likely to attend public facilities, were of Indian ethnicity, and belonged to the age group ≥50-year. They had lower levels of education, were mostly unemployed, had low household incomes and family expenses, and spent <RM100 per month on medicines. On the other hand, T2DM patients who had insurance were more likely to be treated in a private facility. They were of Malay ethnicity and belonged to the 18–49 age group. They generally had higher levels of education, worked in the private sector, had high household incomes and family expenses, and spent >RM500 per month on medicines. If the services in the private facility are indeed better than those in the public facility, then according to the findings of this study, there is an inequity in the treatment of T2DM as only those who can afford the private services (including the insured) have access to those services. It is therefore recommended that further studies be carried out to determine perceptions of service and treatment quality and to inform future strategies for allocating financial resources in healthcare, including raising awareness among the population about the benefits of various health insurance schemes. Finally, our findings should also be relevant for policymakers and provide useful information for the future improvement of the healthcare system.

Acknowledgments

The authors would like to thank the patients and staff of the UKMSC and HCTM for their support during the study period.

Abbreviations

UKM

Universiti Kebangsaan Malaysia (National University of Malaysia)

HCTM

Hospital Canselor Tuanku Muhriz

DM

diabetes mellitus

T2DM

Type2 Diabetes Mellitus

OOP

Out-Of-Pocket

OECD

Organisation for Economic Co-operation and Development

RM

Malaysian ringgit

NHMS

National Health and Morbidity Survey

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

References

  • 1.Chee HL. Ownership, control, and contention: Challenges for the future of healthcare in Malaysia. Soc Sci Med [Internet]. 2008. [cited 2021 May 29];66(10):2145–56. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0277953608000610. doi: 10.1016/j.socscimed.2008.01.036 [DOI] [PubMed] [Google Scholar]
  • 2.OECD. S-G of the. OECD Economic Surveys: Malaysia 2019. OECD Economic Surveys: Malaysia: 2019. 2019. [Google Scholar]
  • 3.Chua HT, Cheah JCH. Financing universal coverage in Malaysia: A case study. BMC Public Health [Internet]. 2012;12(SUPPL. 1):S7. Available from: http://www.biomedcentral.com/1471-2458/12/S1/S7. doi: 10.1186/1471-2458-12-S1-S7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Bakar AA. The Effect Of Health Insurance On Health Care Utilization: Evidence From Malaysia. In 2016. p. 362–8. Available from: http://www.futureacademy.org.uk/files/images/upload/ISSC201650.pdf.
  • 5.Institute for Public Health, Ministry of Health M. National Health &Morbidity Survey 2015. Vol. III. Kuaa]la Lumpur; 2015.
  • 6.Safurah J, Kamaliah MN, Khairiyah AM, Nour Hanah O, Healy J, Kalsom M, et al. Malaysia Health System Review. Health Syst Transit. 2013;3. [Google Scholar]
  • 7.Institute for Public Health (IPH) NI of H. National Health and Morbidity Survey (NHMS) 2019:Technical Report, Healthcare Demand [Internet]. Vol. II. 2020. Available from: http://www.ihsr.moh.gov.my/publication.
  • 8.Li SQ, Guthridge S, Lawton P, Burgess P. Does delay in planned diabetes care influence outcomes for aboriginal Australians? A study of quality in health care. BMC Health Serv Res. 2019;19(1):1–7. doi: 10.1186/s12913-018-3827-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Samsudin S, Abu-Bakar A, Regupathi A, Aljunid SM. Does health insurance ownership determine access and frequency of hospitalization? Evidence from the two-part poisson model. Int J Econ Financ Issues. 2016;6(7Special Issue):21–6. [Google Scholar]
  • 10.Chan CK. Healthcare policy in Malaysia: Universalism, targeting and privatization. ET Gomez. 2012. [Google Scholar]
  • 11.Onn LP. What Lies Ahead for Malaysian Healthcare? Vol. No. 2015–4, ISEAS–Yusof Ishak Institute. 2015. [Google Scholar]
  • 12.Ministry of Health Malaysia. Circular of the Financial Division No.1/2011: Procedures for the administration of hospital admissions and medical claims for foreign workers under the Foreign Workers Health Insurance Scheme (SPIKPA) at Ministry of Health Malaysia hospitals. Malaysia; 2011.
  • 13.Loganathan T, Chan ZX, Pocock NS. Healthcare financing and social protection policies for migrant workers in Malaysia. PLoS One [Internet]. 2020;15(12 December):1–20. Available from: doi: 10.1371/journal.pone.0243629 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.World Health Organization. Noncommunicable diseases country profiles 2018 [Internet]. Geneva PP—Geneva: World Health Organization; 2018. Available from: https://apps.who.int/iris/handle/10665/274512. [Google Scholar]
  • 15.Ogurtsova K, da Rocha Fernandes JD, Huang Y, Linnenkamp U, Guariguata L, Cho NH, et al. IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Pract [Internet]. 2017;128:40–50. Available from: doi: 10.1016/j.diabres.2017.03.024 [DOI] [PubMed] [Google Scholar]
  • 16.Liu J, Ren Z-H, Qiang H, Wu J, Shen M, Zhang L, et al. Trends in the incidence of diabetes mellitus: results from the Global Burden of Disease Study 2017 and implications for diabetes mellitus prevention. BMC Public Health [Internet]. 2020;20(1):1415. Available from: doi: 10.1186/s12889-020-09502-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Organization WH. Global Report on Diabetes, 2016 [Internet]. World Health Organization; Switzerland; 2018. Available from: https://apps.who.int/iris/bitstream/handle/10665/204871/9789241565257_eng.pdf;jsessionid=8C5C729BF53F549F5E46357AC5FE5EEF?sequence=1. [Google Scholar]
  • 18.World Health Organization. Global Status Report On Noncommunicable Diseases 2014. 2014. [Google Scholar]
  • 19.Federation ID. IDF Diabetes Atlas Ninth. Dunia IDF. 2019.
  • 20.Malaysian Healthcare Performance Unit M of HM. Malaysia Diabetes Care Performance Report 2016 [Internet]. Kuala Lumpur; 2017. Available from: file:///Users/apple/Desktop/refrence/Diabetes_Care_Performance_Report_2016new (1).pdf.
  • 21.Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87(1):4–14. doi: 10.1016/j.diabres.2009.10.007 [DOI] [PubMed] [Google Scholar]
  • 22.Einarson TR, Acs A, Ludwig C, Panton UH. Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007–2017. Cardiovasc Diabetol [Internet]. 2018;17(1):83. Available from: doi: 10.1186/s12933-018-0728-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Seringa J, Marques AP, Moita B, Gaspar C, Raposo JF, Santana R. The impact of diabetes on multiple avoidable admissions: A cross-sectional study. BMC Health Serv Res. 2019;19(1):1–8. doi: 10.1186/s12913-018-3827-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Afroz A, Alramadan MJ, Hossain MN, Romero L, Alam K, Magliano DJ, et al. Cost-of-illness of type 2 diabetes mellitus in low and lower-middle income countries: A systematic review. BMC Health Serv Res. 2018;18(1):1–10. doi: 10.1186/s12913-017-2770-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Salman RA, Alsayyad AS, Ludwig C. Type 2 diabetes and healthcare resource utilisation in the Kingdom of Bahrain. BMC Health Serv Res. 2019;19(1):1–10. doi: 10.1186/s12913-018-3827-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Ng CS, Toh MPHS, Ko Y, Lee JYC. Direct medical cost of type 2 diabetes in Singapore. PLoS One. 2015;10(3):1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Liu Y, Yang J, Tao L, Lv H, Jiang X, Zhang M, et al. Risk factors of diabetic retinopathy and sight-threatening diabetic retinopathy: a cross-sectional study of 13 473 patients with type 2 diabetes mellitus in mainland China. BMJ Open [Internet]. 2017. Sep 1;7(9):e016280. Available from: doi: 10.1136/bmjopen-2017-016280 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Yasmin F, Ali L, Banu B, Rasul FB, Sauerborn R, Souares A. Understanding patients’ experience living with diabetes type 2 and effective disease management: A qualitative study following a mobile health intervention in Bangladesh. BMC Health Serv Res. 2020;20(1):1–13. doi: 10.1186/s12913-019-4811-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Sangar S, Dutt V, Thakur R. Comparative assessment of economic burden of disease in relation to out of pocket expenditure. Front Public Heal. 2019;7(JAN):1–5. doi: 10.3389/fpubh.2019.00009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.World Health Organization. Arguing for Universal Health Coverage. 2013;39. Available from: www.who.int. [Google Scholar]
  • 31.World Health Organization. Health systems financing the path to universal coverage. World Health Organization; 2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Schofield D, Cunich MM, Shrestha RN, Passey ME, Veerman L, Callander EJ, et al. The economic impact of diabetes through lost labour force participation on individuals and government: Evidence from a microsimulation model. BMC Public Health. 2014;14(1):1–8. doi: 10.1186/1471-2458-14-220 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Hussein Z, Taher SW, Gilcharan Singh HK, Chee Siew Swee W. Diabetes Care in Malaysia: Problems, New Models, and Solutions [Internet]. Vol. 81, Annals of Global Health. Elsevier Inc; 2015. p. 851–62. Available from: doi: 10.1016/j.aogh.2015.12.016 [DOI] [PubMed] [Google Scholar]
  • 34.Mustaffa BE. Diabetes epidemic in Malaysia. Med J Malaysia. 2004;59(3):295–6. [PubMed] [Google Scholar]
  • 35.Mohamed M. DiabCare Malaysia: Journey to improving diabetes care in Malaysia. Diabetes Res Clin Pract [Internet]. 2016. Oct;120:S2–3. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0168822716308786. [Google Scholar]
  • 36.Institute for Public Health (IPH) Ministry of Health Malaysia NI of H. National Health and Morbidity Survey (NHMS) 2019: Vol. I: NCDs–Non-Communicable Diseases: Risk Factors and Other Health Problems [Internet]. Vol. 1, Ministry of Health Malaysia. 2020. Available from: http://www.iku.gov.my/nhms-2019. [Google Scholar]
  • 37.Akhtar S, Nasir JA, Ali A, Asghar M, Majeed R, Sarwar A. Prevalence of type-2 diabetes and prediabetes in Malaysia: A systematic review and meta-analysis. PLoS One [Internet]. 2022;17(1 January):1–14. Available from: doi: 10.1371/journal.pone.0263139 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Aljunid SM, Aung YN, Ismail A, Rashid SAZA, Nur AM, Cheah J, et al. Economic burden of hypoglycemia for type II diabetes mellitus patients in Malaysia. PLoS One. 2019;14(10):1–10. doi: 10.1371/journal.pone.0211248 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Abu Bakar A, Regupathi A, Aljunid SM, Omar MA. Factors affecting demand for individual health insurance in Malaysia. BMC Public Health. 2012;12(S2):2458. [Google Scholar]
  • 40.Mohd Taipor S. Risk Preference in Health Insurance: Is There Evidence of Advantageous Selection? Doctor of Business Administration July 2018. 2018. [Google Scholar]
  • 41.Balqis-Ali NZ, Anis-Syakira J, Fun WH, Sararaks S. Private Health Insurance in Malaysia: Who Is Left Behind? Asia-Pacific J Public Heal. 2021;33(8):861–9. doi: 10.1177/10105395211000913 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Mustafa N, Salleh F, Afthanorhan A, Daud WNW, Yazid AS, Rashid N. A Systematic Literature Review and Meta-Analysis of Medical Health Insurance and Takaful: Evidence from Malaysia. Int J Acad Res Bus Soc Sci. 2019;9(1):1266–82. [Google Scholar]
  • 43.Dror DM, Shahed Hossain SA, Majumdar A, Koehlmoos TLP, John D, Panda PK. What factors affect voluntary uptake of community-based health insurance schemes in low- and middle-income countries? A systematic review and meta-analysis. PLoS One. 2016;11(8):1–31. doi: 10.1371/journal.pone.0160479 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Ogundeji YK, Akomolafe B, Ohiri K, Butawa NN. Factors influencing willingness and ability to pay for social health insurance in Nigeria. PLoS One. 2019;14(8):1–10. doi: 10.1371/journal.pone.0220558 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Fang K, Shia B-C, Ma S. Health insurance coverage, medical expenditure and coping strategy: evidence from Taiwan. BMC Health Serv Res [Internet]. 2012. Dec 3;12(1):442. Available from: http://bmchealthservres.biomedcentral.com/articles/10.1186/1472-6963-12-442. doi: 10.1186/1472-6963-12-442 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Jayaraman K, Alesa N, Azeema N. Factors influencing the purchase intention of health insurance policy-an empirical study in Malaysia. Int J Econ Res. 2017;14(16):1–13. [Google Scholar]
  • 47.Abbasi YF, See OG, Ping NY, Balasubramanian GP, Hoon YC, Paruchuri S. Diabetes knowledge, attitude, and practice among type 2 diabetes mellitus patients in Kuala Muda District, Malaysia–A cross-sectional study. Diabetes Metab Syndr Clin Res Rev [Internet]. 2018;12(6):1057–63. Available from: 10.1016/j.dsx.2018.06.025. [DOI] [PubMed] [Google Scholar]
  • 48.Myint CY, Pavlova M, Groot W. Health insurance in Myanmar: Knowledge, perceptions, and preferences of Social Security Scheme members and general adult population. Int J Health Plann Manage. 2019;34(1):346–69. doi: 10.1002/hpm.2643 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Pocock SJ. Clinical trials: a practical approach. John Wiley & Sons; 2013. [Google Scholar]
  • 50.Suddin LS. Economic Burden of Diabetes Milieus Type2 and The Cost Effectiveness of Self-Mentoring Blood Glucose (SMBG) in A Teaching Hospital. University Kebangssan Malaysia (UKM); 2015. [Google Scholar]
  • 51.Neoh CF, Long CM, Lim SM, Ramasamy K, Shahar S, Majeed ABA. Medication use and adherence among multi-ethnic community-dwelling older adults in Malaysia. Geriatr Gerontol Int. 2017;17(8):1214–20. doi: 10.1111/ggi.12849 [DOI] [PubMed] [Google Scholar]
  • 52.Poh SCJ. The government subsidies 98% of the cost of medical services. Sing Chew Jit Poh Malaysia. 2005;23. [Google Scholar]
  • 53.Siti Nurul Akma A, Sharifa Ezat WP, Azimatun NA. Willingness to pay for drugs in future national health financing scheme among Malaysian population. World Appl Sci J. 2014;30(3):271–8. [Google Scholar]
  • 54.Thomas S, Beh L, Nordin R Bin. Health care delivery in Malaysia: changes, challenges and champions. J Public Health Africa. 2011;2(2). doi: 10.4081/jphia.2011.e23 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Kananatu K. Healthcare financing in Malaysia. Asia Pacific J Public Heal. 2002;14(1):23–8. doi: 10.1177/101053950201400106 [DOI] [PubMed] [Google Scholar]
  • 56.Puteh SEW, Ahmad SNA, Aizuddin AN, Zainal R, Ismail R. Patients’ willingness to pay for their drugs in primary care clinics in an urbanized setting in Malaysia: a guide on drug charges implementation. Asia Pac Fam Med. 2017;16(1):1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Ministry of Health Malaysia. Malaysia National Health Accounts: Health Expenditure Report 1997–2017 [Internet]. 2019. Available from: https://www.moh.gov.my/moh/resources/Penerbitan/PenerbitanUtama/MNHA/Laporan_MNHA_Health_Expenditure_Report_1997–2017_03122019.pdf.
  • 58.Bakar AA. Profiles of health insurance policyholders and health care users: evidence from Malaysia. In: future Academy. Malaysia; 2016. p. 327–32. [Google Scholar]
  • 59.Zeng Y, Luo J, Ou L, Yuan M, Zhou Z, Han Y, et al. The impact of medical insurance on medical expenses for older Chinese: Evidence from the national baseline survey of CLHLS. Med (United States). 2019;98(39). doi: 10.1097/MD.0000000000017302 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Arsenijevic J, Pavlova M, Rechel B, Groot W. Catastrophic health care expenditure among older people with chronic diseases in 15 European countries. PLoS One [Internet]. 2016;11(7):1–18. Available from: doi: 10.1371/journal.pone.0157765 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Chiew HL. Expanding insurance and Takaful solutions for the underserved segment. Cent Bank Malaysia. 2017;1–2. [Google Scholar]
  • 62.Aliza N. The Importance of Health Insurance Policy on Purchase Intention / Continuance–An Empirical Study in Malaysia. Universiti Sains Malaysia; 2012. [Google Scholar]
  • 63.Finkelstein A, Hendren N, Shepard M. Subsidizing health insurance for low-income adults: Evidence from Massachusetts. Am Econ Rev. 2019;109(4):1530–67. [PubMed] [Google Scholar]
  • 64.Nikolova S, Stearns S. The impact of CHIP premium increases on insurance outcomes among CHIP eligible children. BMC Health Serv Res. 2014;14:2–7. doi: 10.1186/1472-6963-14-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Pinilla J, López-Valcárcel BG. Income and wealth as determinants of voluntary private health insurance: Empirical evidence in Spain, 2008–2014. BMC Public Health. 2020;20(1):1–10. doi: 10.1186/s12889-019-7969-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Nelson KM, Chapko MK, Reiber G, Boyko EJ. The association between health insurance coverage and diabetes care; data from the 2000 Behavioral Risk Factor Surveillance System. Vol. 40, Health Services Research. 2005. p. 361–72. doi: 10.1111/j.1475-6773.2005.00361.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Perlman F, Balabanova D, McKee M. An analysis of trends and determinants of health insurance and healthcare utilisation in the Russian population between 2000 and 2004: The “inverse care law” in action. BMC Health Serv Res. 2009;9:1–11. doi: 10.1186/1472-6963-9-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Hopkins S, Kidd MP. The determinants of the demand for private health insurance under Medicare. Appl Econ. 1996;28(12):1623–32. [Google Scholar]
  • 69.Jin Y, Hou Z, Zhang D. Determinants of Health Insurance Coverage among People Aged 45 and over in China: Who Buys Public, Private and Multiple Insurance. 2016; Available from: http://charls.ccer.edu.cn/. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Jain A, Swetha S, Johar Z, Raghavan R. Acceptability of, and willingness to pay for, community health insurance in rural India. J Epidemiol Glob Health. 2014;4(3):159–67. doi: 10.1016/j.jegh.2013.12.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Wang W, Temsah G, Mallick L. The impact of health insurance on maternal health care utilization: evidence from Ghana, Indonesia and Rwanda. Health Policy Plan [Internet]. 2017. Apr 1;32(3):366–75. Available from: https://academic.oup.com/heapol/article-lookup/doi/10.1093/heapol/czw135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Alkhamis A, Cosgrove P, Mohamed G, Hassan A. The personal and workplace characteristics of uninsured expatriate males in Saudi Arabia. BMC Health Serv Res [Internet]. 2017. Dec 19;17(1):1–12. Available from: http://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-017-1985-x. doi: 10.1186/s12913-016-1943-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Nageso D, Tefera K, Gutema K. Enrollment in community based health insurance program and the associated factors among households in Boricha district, Sidama Zone, Southern Ethiopia; a cross-sectional study. PLoS One [Internet]. 2020;15(6):1–14. Available from: 10.1371/journal.pone.0234028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Id DN, Tefera K, Gutema K. Enrollment in community based health insurance program and the associated factors among households in Boricha district, Sidama Zone, Southern Ethiopia; a cross-sectional. 2020;81:1–14. Available from: 10.1371/journal.pone.0234028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Atake EH. Does the type of health insurance enrollment affect provider choice, utilization and health care expenditures? BMC Health Serv Res. 2020;20(1):1–14. doi: 10.1186/s12913-020-05862-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Meskarpour Amiri M, Teymourzadeh E, Ravangard R, Bahadori M. Health informal payments and their main determinants: The case of Iran. Proc Singapore Healthc. 2019. [Google Scholar]
  • 77.Eberlin KR, Hartzell TL, Kuo P, Winograd J, Day C. Patients transferred for emergency upper extremity evaluation: does insurance status matter? Plast Reconstr Surg [Internet]. 2013;131(3):593–600. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23446571. doi: 10.1097/PRS.0b013e31827c6e82 [DOI] [PubMed] [Google Scholar]
  • 78.Jusoh Z, Tohid H, Omar K, Muhammad NA, Ahmad S. Clinical and sociodemographic predictors of the quality of life among patients with type 2 diabetes mellitus on the east coast of Peninsular Malaysia. Malaysian J Med Sci. 2018;25(1):84–95. doi: 10.21315/mjms2018.25.1.10 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Tino S, Wekesa C, Kamacooko O, Makhoba A, Mwebaze R, Bengo S, et al. Predictors of loss to follow up among patients with type 2 diabetes mellitus attending a private not for profit urban diabetes clinic in Uganda—A descriptive retrospective study. BMC Health Serv Res. 2019;19(1):1–9. doi: 10.1186/s12913-018-3827-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Pérez-Panero AJ, Ruiz-Muñoz M, Cuesta-Vargas AI, Gónzalez-Sánchez M. Prevention, assessment, diagnosis and management of diabetic foot based on clinical practice guidelines: A systematic review. Medicine (Baltimore). 2019;98(35):e16877. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Bradley CJ, Dahman B, Bear HD. Insurance and inpatient care: Differences in length of stay and costs between surgically treated cancer patients. Cancer. 2012;118(20):5084–91. doi: 10.1002/cncr.27508 [DOI] [PubMed] [Google Scholar]
  • 82.Hooten KG, Neal D, Lovaton Espadin RE, Gil JN, Azari H, Rahman M. Insurance status influences the rates of reportable quality metrics in brain tumor patients: a nationwide inpatient sample study. Neurosurgery [Internet]. 2015;76(3):238–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25603104. doi: 10.1227/NEU.0000000000000594 [DOI] [PubMed] [Google Scholar]
  • 83.Ismail A, Suddin LS, Sulong S, Ahmed Z, Kamaruddin NA, Sukor N. Economic burden of managing Type 2 diabetes mellitus: Analysis from a Teaching Hospital in Malaysia. Indian J Public Heal [Internet]. 2017;61(4):243–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29219128. doi: 10.4103/ijph.IJPH_24_16 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

Articles from PLoS ONE are provided here courtesy of PLOS

RESOURCES