Graphical abstract

Introduction

Coronary heart disease (CHD) remains the leading cause of death globally and shows a concerning rise in developing countries [1,2]. Despite advancements in treating heart attacks and strokes, these advancements particularly benefit those with milder symptoms, which poses a significant challenge if they delay seeking treatment [3].

Treatment delays encompass 3 phases: the patient decision time, the transportation time, and the in-hospital time [4]. The longest delays occur in the patient decision time, defined as the period extending from the onset of symptoms to the decision to seek treatment. Significant improvements can be realized during this phase if patients promptly recognize their symptoms [3,4]. Recognizing symptoms promptly holds immense potential for reducing this delay and ultimately saving lives.

Delays in seeking timely intervention can adversely impact patient outcomes. A prolonged delay in the patient decision-making process is linked to a higher incidence of severe heart conditions, such as cardiac arrest and sudden death. Optimal results are achieved when treatment is initiated within 90 minutes of symptom onset. It is estimated that for each minute of treatment delay beyond this crucial 90-minute window, there is a proportional increase in mortality of 7.5% within a year [5].

Delays in seeking treatment remains a significant issue in controlling CHD, particularly during the decision-making phase, which often becomes the lengthiest stage in this process [4]. Furthermore, numerous factors including demographics, individual behaviors, and medical history are reported to influence CHD treatment delays. However, studies on specific factors that influence this decision-making phase, such as comorbidities, are inconsistent.

This study focused on the crucial decision-making phase of delay in CHD treatment, by investigating the potential influence of individual comorbidities (diabetes, stroke, and psychological distress) on this delay. The primary objective was to assess the association between the number of comorbidities and the delay in seeking treatment among individuals with CHD.

Materials and Methods

Study Design

This was a longitudinal study that utilized secondary data from the Non-Communicable Disease Risk Factor (NCDRF) cohort study, which was conducted in 5 subdistricts of Central Bogor District, Bogor City, Indonesia (i.e., Kebon Kalapa, Babakan Pasar, Babakan, Ciwaringin, and Panaragan). We initiated a baseline study that encompassed 3 distinct stages: in the first stage we recruited respondents in 2011, in the second stage we recruited respondents in 2012, and in the third stage we recruited respondents in 2015, and routine monitoring was carried out 3 times per year.

Participants

The population included individuals aged ≥25 years (at baseline) who were permanent residents of the 5 selected subdistricts and who participated in the NCDRF cohort study. The data sets of individuals who received a diagnosis of CHD during the NCDRF cohort study over 6 years (2017 for first-stage respondents and 2018 for second-stage respondents) were included. Respondents who had not yet undergone monitoring through the 6th year (2017/2018) were excluded.

Prior to starting the study, we calculated the minimum sample size using the application for sample size determination in health studies by Lwanga and Lemeshow [6]. We conducted hypothesis testing for 2 populations: the proportion of individuals with CHD and no comorbidities who delayed seeking treatment (p₁=45%) [7] and the proportion of individuals with CHD and comorbidities who delayed seeking treatment (p₂=30%) [8]. With a 95% confidence interval (CI) and the power of a study (1-β) of 90%, we obtained a minimum sample size of 177 participants. To increase the precision of the study, we used data from all eligible participants, analyzing the data of 812 participants.

To minimize the potential for selection bias related to the presence of other types of heart disease, the sample was chosen based on a confirmed diagnosis of CHD, established by examining the electrocardiogram (ECG) and Minnesota codes (at rest) from the NCDRF cohort study. The diagnosis was further confirmed through consultations with qualified cardiologists.

To minimize the risk of information bias, data on treatment behavior, considered a dependent variable, were obtained from a questionnaire specifically for respondents who had been diagnosed with CHD in the NCDRF cohort study.

Data Collection

The data in this paper were obtained from the Health Development Policy Agency with the provisions and procedures that apply through www.badankebijakan.kemkes.go.id.

Variables

For this analysis we used data from a 6-year monitoring period. The dependent variable (i.e., delay in seeking treatment) was obtained from the 2017 data of respondents recruited in 2011 and the 2018 data of respondents recruited in 2012. Data on treatment seeking behaviors were obtained from a special questionnaire for respondents who had been diagnosed with CHD during monitoring for the NCDRF cohort study. This special questionnaire was first administered in 2017. In our analysis, the dependent variable was a delay in seeking treatment, which was obtained from the 6th-year evaluation. Individuals who had not sought CHD treatment by the 6th year of monitoring, were classified as having a delay in seeking treatment. Subjects who had sought CHD treatment by the 6th year of monitoring, were classified as no delay in seeking treatment. The dependent variable “delay in seeking treatment” was grouped into 2 categories, yes or no.

In this study, the main independent variable was a comorbidity other than CHD, including chronic diseases and psychological distress. In the NCDRF cohort study, apart from CHD, only diabetes mellitus (DM) and stroke data were collected during the examinations conducted every 2 years. Therefore, we analyzed the data for DM and stroke from the baseline, 2nd, 4th, and 6th year of monitoring.

Data on DM diagnoses were obtained from blood glucose level examinations, which included a fasting blood glucose (FBG) and 2-hour postprandial blood glucose (2HPPBG). A diagnosis of DM was established if the FBG was ≥126 mg/dL or the 2HPPBG was ≥200 mg/dL [9] at the baseline, 2-year, 4-year, or 6-year blood glucose examination. Data on stroke diagnoses were obtained from neurological examinations performed by neurologists. Stroke status was assigned if the participant had a stroke diagnosis at the baseline, 2-year, 4-year, or 6-year examinations.

Psychological distress was measured annually using the 20-question self-reporting questionnaire (SRQ-20) to assess symptoms experienced by the respondent in the past month. There was a minimum cutoff of 6 “yes” answers [10,11] to determine psychological distress. Data on psychological distress were obtained from the 6-year evaluation because it could change over time, and because what participants felt in the past 30 days could influence their decision to seek treatment. The reliability of the SRQ-20 in this study was confirmed by a Cronbach alpha of 0.780. This value (0.780) was higher than the “r” table value (0.070), indicating that all questionnaire items related to psychological distress were reliable.

The main independent variable (a comorbidity other than CHD) included chronic diseases (DM and stroke) and psychological distress. We grouped this variable into 3 categories based on the number of comorbidities: (1) no comorbidity; (2) 1 comorbidity (psychological distress alone, DM alone, or stroke alone); and (3) ≥2 comorbidities (psychological distress and DM; psychological distress and stroke; DM and stroke; or psychological distress, DM, and stroke). We included psychological distress in the comorbidity variable because it has a bidirectional relationship with chronic diseases like CHD, diabetes, and stroke. Psychological distress can be a risk factor for chronic diseases, and the long-term nature of chronic diseases can trigger psychological distress [12,13].

The covariate variables were age, sex, education level, occupation, marital status, and health insurance ownership. Because these sociodemographic variables largely remain unchanged, we used data from the 6th year of monitoring. Age was calculated as the difference between the year of the interview and the year of birth. The main independent variable, comorbidity, is usually found in older people [14]; therefore, we classified age as <60 years and ≥60 years. Sex was categorized as male or female. Education was classified into 2 categories: low (did not graduate from high school) or high (graduated from high school). Occupational status was categorized into 2 groups, yes (working) or no (not working). Marital status was classified into 2 categories, not married/divorced or married. Health insurance status was categorized as insured or uninsured [15].

Statistical Analysis

We analyzed the data using IBM SPSS ver. 25.0 (IBM Corp.). The proportion of participants with CHD who delayed seeking treatment for each characteristic category was assessed using crosstabulation with the chi-square test. Descriptive analysis and the proportion of delays in seeking treatment for coronary heart disease according to participant characteristics is shown in Table 1. Descriptive analysis of comorbidities in participants with coronary heart disease according to the characteristics of study participants is shown in Table 2.

Simple logistic regression analysis was used to assess the association between each characteristic and a delay in seeking treatment in the participants with CHD. Statistical significance was set at p<0.05, with a crude OR and 95% CI. Multivariable logistic regression analysis was used to assess the association between comorbidities and the delay in seeking treatment for CHD, adjusted for age, sex, education level, and health insurance status. Statistical significance was set at p<0.05. The crude OR and adjusted ORs for delays in seeking treatment for CHD are shown in Table 3.

Ethics Statement

This study was conducted according to the Research Ethics Commission’s research protocol. Ethical approval was provided by the Health Research Ethics Commission, Health Research and Development Agency (KEPK-BPPK) (No. LB.02.01/2/KE. 108/2017 dated March 27, 2017 and No. LB.02.01/2/KE.076/2018 dated March 1, 2018). The questionnaire distributed to the respondents included an explanation of the research purpose and an online consent form that respondents could read.

Results

In the baseline study, 5,690 respondents participated in the NCDRF cohort study. Of these, 5,312 had complete data (interview, measurement, and examination data). Of the 5,312 participants with complete data, 1,204 had been diagnosed with CHD based on heart examination results using an ECG with Minnesota codes. Although 1,143 participants with CHD participated in monitoring until 2017/2018 (stage 1 and 2 respondents), 331 participants who did not attend the 6th year of monitoring were excluded. We analyzed data from the remaining 812 respondents (Figure 1).

Of the 812 participants with CHD (Figure 2), 702 (86.5%) delayed seeking treatment and only 110 (13.5%) did not delay seeking treatment. The percentage of participants who delayed seeking treatment decreased as the number of comorbidities increased. The highest proportion of participants who delayed seeking treatment was in the group without comorbidities, followed by the group with 1 comorbidity. Unfortunately, there were no data regarding the reasons why respondents did not seek treatment.

Based on the respondent characteristics (Table 1), a higher proportion of participants were aged <60 years old; female; had a low education level; were unemployed; married; had no health insurance; no DM; no stroke; and no psychological distress.

Based on crosstabulation (Table 1), the percentage of people who delayed seeking treatment was higher and statistically different for the variables of age (p=0.001), sex (p=0.006), education level (p=0.001), DM (p=0.022), and stroke (p<0.001). For the variables of currently working (p=0.937), marital status (p=0.279), health insurance status (p=0.056), and psychological distress (p=0.159), there were no statistically significant differences between the groups.

Initially, chronic disease and psychological distress were combined into a single variable (comorbidity) and divided into 8 categories. This was done to identify the risk of each category for delay in seeking treatment. These categories were (1) no comorbidity; (2) psychological distress alone; (3) DM alone; (4) stroke alone; (5) psychological distress and DM; (6) psychological distress and stroke; (7) DM and stroke; and (8) psychological distress, DM, and stroke. However, because of the small number of samples in several categories, the CIs were very wide and reduced the precision of the population estimate. Therefore, we reclassified the single comorbidity variable into 3 categories based on the number of comorbidities, (1) no comorbidity, (2) 1 comorbidity (psychological distress alone or DM alone or stroke alone), and (3) ≥2 comorbidities (psychological distress and DM, psychological distress and stroke, DM and stroke, or psychological distress, DM, and stroke).

The number of comorbidities according to participant characteristics is shown in Table 2. People without comorbidities were more likely to delay seeking treatment than those with comorbidities. People <60 years old who had an occupation, were married, and were uninsured were more likely to delay seeking treatment than people with comorbidities. The percentage of people without comorbidities was significantly different among these 4 groups (p<0.05), except for the uninsured group.

The percentage of people with 1 comorbidity was significantly higher than the participant groups with no comorbidity and ≥2 comorbidities among those who had no occupation and were single or divorced (p<0.05). Meanwhile, there were no significant differences among females, those with a high level of education, and those who were insured.

The percentage of people with ≥2 comorbidities was significantly higher than the participant groups with no comorbidity and 1 comorbidity among those who did not delay seeking treatment and were ≥60 years old (p<0.05). Meanwhile, there were no significant differences among males and those with a low level of education.

When evaluating covariate variables, we discovered that age was a confounding factor in the relationship between comorbidity and delay in seeking treatment, with a change in OR of 18.6%. Therefore, we added the age variable back to the model. Based on previous studies, we also included several factors that can influence the delay in seeking treatment in the final model: sex, education level, and health insurance status.

Bivariate analysis (Table 3) showed that the risk of delay in seeking treatment was significantly higher in the participant groups with no comorbidities (OR, 3.98; 95% CI, 2.026–7.831; p<0.001) and 1 comorbidity (OR, 2.61; 95% CI, 1.288–5.272; p=0.008) than those with ≥2 comorbidities. The risk of delay in seeking treatment was also significantly higher in the participant groups aged <60 years (OR, 1.98; 95% CI, 1.311–2.975; p=0.001), female (OR, 1.93; 95% CI, 1.225–3.044; p=0.005), with a low education level (OR, 3.23; 95% CI, 1.616–6.441; p=0.001), and uninsured (OR, 2.59; 95% CI, 1.023–6.543; p=0.045). Employment status and marriage status were not statistically significant (OR<1, p>0.05).

Multivariate analysis revealed that participants with CHD were at risk of delay in seeking treatment, with an OR of 3.5 (95% CI, 1.735–7.036; p<0.001) for those with no comorbidities and an OR of 2.6 (95% CI, 1.259–5.418; p=0.010) for those with 1 comorbidity when compared to those with ≥2 comorbidities (adjusted for age, sex, education level, and insurance status).

Discussion

In our analysis, the data on treatment seeking for the early monitoring periods were limited and our results only reflect the delays in seeking CHD treatment for the 6th year of monitoring. In addition, the reasons for the delays in seeking treatment remain unclear due to the absence of relevant data. However, we tried to elevate the internal validity of this study by minimizing the possibility of selection and information bias and by controlling for confounders.

Of the 812 participants with CHD in the study sample, 702 (86.5%) either did not seek treatment or delayed it. This was higher than in the study by Venkatesan et al. [7], which reported that 44.08% of patients with acute myocardial infarction (AMI) delayed seeking treatment. This discrepancy could be due to the fact that the study by Venkatesan et al. [7] was conducted in a hospital setting and patients who delayed treatment may not have reported it (i.e., underreporting). The data used in our study was collected from the community, resulting in a higher proportion of reported delays in seeking treatment than in the hospital study.

It is important to note that, in our study, participants without the 3 specified comorbidities were not necessarily free of other diseases. All participants in our sample were diagnosed with CHD during the cohort study examination and, in our research, individuals classified as having no comorbidities were considered to be solely affected by CHD.

Our analysis revealed that among participants with CHD, there was a 3.5-fold higher risk (95% CI, 1.735–7.036; p<0.001) of delay in seeking treatment for those with no comorbidities and a 2.6-fold higher risk (95% CI, 1.259–5.418; p=0.010) for those with 1 comorbidity than those with ≥2 comorbidities. This relationship remains statistically significant even after adjusting for age, sex, education level, and insurance status. Interestingly, our community-based research aligns with the hospital-based research of Stafford et al. [16], which showed that, over a 2-year study period, 3 times as many people with multiple comorbidities were admitted to the hospital than people with a single comorbidity. In other words, Stafford et al. [16] showed that people with a single comorbidity had a greater tendency to delay seeking treatment than those with multiple comorbidities.

A study exploring the relationship between the number of comorbidities, autonomic modulation, and quality of life in patients with coronary artery disease found that a higher level of pain was associated with an increased number of comorbidities [17]. Interestingly, pain serves as a motivating symptom, and in cases of acute coronary syndrome (ACS) and acute stroke, severe pain is linked to reducing the delay in seeking treatment [3].

Our study indicated that a lower comorbidity level was associated with a greater likelihood of delay in seeking treatment and was consistent with other research revealing that individuals experiencing mild chest pain were 10 times more likely to delay the decision to seek treatment (OR, 10.05; 95% CI, 6.50–15.54) [18]. The nature of symptom presentation plays a crucial role in prehospital delay. Continuous or high levels of symptom intensity predict a shorter prehospital delay, while intermittent or low-intensity symptoms tend to predict a longer delay [19]. Reduced pain levels, absence of chest pain, or experiencing chest pain alone are factors associated with delayed treatment seeking in people with AMI [7].

Patients with limited awareness of their symptoms may underestimate the severity of their condition, leading to delays in seeking treatment [20]. A study conducted in Tanzania suggested that many individuals delayed seeking treatment due to a lack of understanding about their diagnosis or treatment, a lack of awareness regarding the severity of their symptoms, or concerns about the side effects of medication [21].

Our study found an inverse relationship between the number of comorbidities and the risk of delaying treatment. In other words, patients with a greater number of comorbidities were less likely to delay seeking treatment. As mentioned previously, our findings concur with the study by Stafford et al. [16], which indicated that people with multiple conditions are less likely to delay seeking treatment. However, our results diverge from the study by Banharak et al. [22], which reported that various conditions and symptoms, including diabetes, hypertension, heart failure, stroke, impaired gait, angina pectoris, current smoking, and a history of hospitalization, were associated with prolonged treatment delays.

Our study identified that participants with CHD who had a single comorbidity (diabetes alone, stroke alone, or psychological distress alone) were 2.6 times more likely to delay seeking treatment than those who have ≥2 comorbidities. This finding is statistically significant and aligns with Beza et al. [18] who reported a longer prehospital delay among ACS patients with diabetes. The study by Mohan et al. [23] also showed a longer prehospital delay among myocardial infarction patients who had diabetes (OR, 1.3), although this result was not statistically significant (p=0.317).

Another study investigated the role of personality in AMI. It found that women with AMI and a type D personality (TDP) who have a tendency to experience negative emotions and to inhibit the expression of emotions, were generally less likely to delay seeking treatment compared to those without TDP (OR, 0.28; 95% CI, 0.08–0.98) [24]. However, depression may increase the delay in seeking treatment among participants with CHD [25]. Generalized anxiety disorders or panic disorders are typically associated with higher levels of care-seeking behaviors, while social anxiety disorder or specific phobias (e.g., blood-injection-injury phobia) may lead to decreased, delayed, or inconsistent healthcare utilization [26]. This delay is due to their fear of depending on others, rather than due to inadequate knowledge or fear of their disease [27]. Unfortunately, in our study, we did not analyze specific comorbidities, such as stroke, diabetes, or psychological distress.

Regarding age, we found that individuals <60 years old were 1.75 times more likely to delay seeking treatment than those aged ≥60 years. This finding is consistent with the study by Okunrintemi et al. [28], which showed that younger patients may be inclined to deny their medical condition. Older patients with atherosclerotic cardiovascular disease are more likely to have accepted their diagnoses and, therefore, be more willing to comply with suggested medical therapies. Patierno et al. [29] reported that participants with CHD who denied their illness had a longer delay in seeking treatment than those who acknowledged their illness. However, our findings were inconsistent with tthe study by Mohan et al. [23], which showed that participants with CHD and aged >60 years, tended have a prehospital delay 1.6-fold higher (95% CI, 1.048–2.487; p=0.030) than those aged ≤60 years. Several studies also concurred with Mohan et al., that older people tend to delay seeking treatment [22,30,31]. This difference may be because most participants in our study were <60 years old and underwent the ECG examination as part of routine screening in the cohort study, rather than in response to a disturbance in their health. Therefore, they might tend to ignore or deny the diagnosis of the ECG examination.

Our study showed that women were more likely to delay seeking treatment than men. This result is consistent with a qualitative study in Iran of 39 women with first-time ACS that found women tended to delay seeking help when their symptoms were mild and developed gradually. Women often underestimated the symptoms and attributed them to noncardiac causes, making them more likely to delay seeking treatment for CHD symptoms [32,33]. Our findings were also in line with a study of Pakistani society, which showed that, because women are overburdened with their family and household responsibilities, many are unaware of or fail to pay attention to symptoms. Women may not prioritize their health, resulting in delays in seeking treatment [34]. However, our findings differ from those of Walsh and Joynt [35], who found that women with stroke were 0.66 times as likely to experience treatment delays as males (p=0.04). The difference is likely due to the fact that stroke symptoms can be more severe and sudden, making them harder to ignore than CHD, which is often without symptoms.

Our findings on education level align with Banharak et al. [22], suggesting that individuals with lower education levels may be more likely to delay treatment. Conversely, Mosleh et al. [36] found that participants with higher education were at risk of delaying treatment. This difference may be due to several factors. Although those with low levels of education might have limited knowledge of CHD symptoms and a decreased awareness of their health circumstances, participants with higher education might exhibit a tendency to self-diagnose or delay seeking professional help for various other reasons.

Regarding possession of health insurance, we found that uninsured participants were 2.25 times more likely to delay seeking treatment, although this association was not statistically significant. Other studies found that, among uninsured participants with CHD, there is a tendency to delay treatment due to the cost [22,36] and that uninsured patients are at risk for longer delays in presenting to the hospital (adjusted OR, 1.38; 95% CI, 1.17–1.63; p<0.001) [15]. Patients with cardiovascular disease face a long-term disease course, often without symptoms, as well as the presence of comorbidities, all of which require lifelong treatment with frequent medications [37]. While previous studies highlight the financial barriers associated with being uninsured, our findings may reflect the impact of Indonesia’s governmental health insurance program (Badan Penyelenggara Jaminan Sosial Kesehatan, BPJS). The health insurance program provided by the Indonesian government through BPJS specifically caters to the needs of disadvantaged individuals, as indicated by data from the Indonesian Social Service. Therefore, participants in this study could still get health services even though they were not obligated to pay themselves, thereby mitigating the financial barrier to seeking treatment.

Our study found that participants with CHD but without additional comorbidities were more likely to delay seeking treatment, with age as a confounding factor (i.e., most participants aged <60 years do not have other comorbidities). Widayanti et al. [38] found that sociocultural context plays a vital role in shaping the individual’s concept of health and disease. In Indonesia, individuals often perceive themselves as healthy if they can carry out their daily activities without significant disruption, leading to a delay in seeking treatment until the disease worsens [38]. This delay is due to health beliefs and perceptions, including adaptive emotions that lead the individual to respond to illness by considering that the symptoms experienced are usual and will disappear soon [39]. Among patients with AMI, denial of their illness is common, particularly during the first few hours and even the first day after chest pain first occurs. Although this unconscious physiological response allows patients to cope with and overcome their anxiety and fear, it often leads to delays in seeking treatment, as patients attribute their symptoms to causes other than cardiac problems [40]. Our study showed that younger persons aged <60 years who did not experience disturbing symptoms and tended to be less aware of their health status, were more likely to deny their medical diagnosis and consequently delay seeking treatment.

Conclusion

A high proportion of participants with CHD delayed seeking treatment. Our findings indicate that the level of comorbidity played a significant role in this delay. Specifically, low levels of comorbidity were associated with a higher likelihood of delaying seeking treatment.

Implications

This study suggests that healthcare providers should be aware of the increased risk of delayed treatment among patients with CHD and ≤1 comorbidity. These patients may require additional support and education to encourage them to seek treatment promptly when experiencing symptoms of CHD.

Recommendation

These findings highlight the importance of health workers in Puskesmas and cadres in Posbindu, providing increased attention to people diagnosed with CHD, including those without other comorbidities and those who do not feel any symptoms. To effectively manage the prevalence and mortality rates associated with CHD, particularly among persons aged <60 years, it is crucial to raise public awareness by educating them about the following critical aspects: (1) the diverse nature and levels of intensity of CHD symptom presentation; (2) the significance of seeking medical intervention even if they do not perceive substantial disruptions to their overall health, to prevent the progression of their condition; and (3) the potentially severe consequences that may arise from neglecting CHD symptoms [19].

HIGHLIGHTS

• Delays in seeking treatment remain a problem in controlling coronary heart disease (CHD).

• Subjects with CHD and minimal comorbidities have a greater tendency to delay seeking treatment.

• Priority should be given to individuals with CHD who do not have other comorbidities, especially those <60 years old, who require a heightened awareness of their health status. Education should focus on recognizing the diverse symptoms of CHD and the potential consequences of neglecting these symptoms, emphasizing the importance of seeking treatment even when the symptoms are not severe. Educational initiatives can be effectively conducted at local health care facilities.

Article information

Ethics Approval

This study was conducted according to the Research Ethics Commission’s research protocol. Ethical approval was provided by the Health Research Ethics Commission, Health Research and Development Agency (KEPK-BPPK) (No. LB.02.01/2/KE.108/2017 dated March 27, 2017 and No. LB.02.01/2/KE.076/2018 dated March 1, 2018). The questionnaire distributed to the respondents included an explanation of the research purpose and an online consent form that respondents could read.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Funding

None.

Availability of Data

This published article and its supplementary files include all data generated or analyzed during this study. The data supporting this study’s findings are available from the Data Management Laboratory of the Health Development Policy Agency, Ministry of Health of Indonesia. Data can be made available after approval of a written request to the Data Management Laboratory at: datin.bkpk@kemkes.go.id

Authors’ Contributions

Conceptualization: ST, LI, WR, SI, II; Data curation: all authors; Formal analysis: ST, LI, SI, TW, WR, NS; Investigation: ST, LI, WR, TW, AML, NS, ASP; Methodology: ST, LI, WR, TW, SI, II; Supervision: all authors; Validation: ST, LI, WR, TW; Visualization: ST, LI, TW; Writing–original draft: all authors; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Acknowledgements

The authors would like to thank all parties who helped in the process of creating this scientific paper, the technical staff from the Data Management Laboratory of the Center for Data and Information, namely the Ministry of Health of the Republic of Indonesia as the data provider, and other individuals who facilitated the realization of this research.

Figure 1.

Flow chart of sample selection in a study of treatment delays for participants with CHD.

NCDRF, Non-Communicable Disease Risk Factor cohort study; CHD, coronary heart disease.

Figure 2.

Proportion of participants with coronary heart disease (CHD) who delayed seeking treatment based on their number of comorbidities.

Delay-seeking treatment (red line); Not delay-seeking treatment (blue line).

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