Abstract
Objective
To identify clinical and polysomnographic factors predicting poor long‐term positive airway pressure (PAP) compliance in patients with obstructive sleep apnea (OSA) in a Southeast Asian population and to develop a simple risk model applicable in routine practice.
Study Design
Retrospective cohort study.
Setting
Songklanagarind Hospital, Thailand, from January 2012 to December 2022.
Methods
Adult OSA patients aged 18 to 65 years prescribed PAP therapy were included. Adherence was objectively recorded from device downloads. Good adherence was defined as ≥4 hours per night on ≥70% of nights at 12 months. Logistic regression identified predictors of poor long‐term adherence.
Results
A total of 343 patients were enrolled; 253 had follow‐up data at 12 months. Good adherence was observed in 47.8% of patients. In multivariate analysis, age < 50 years (odds ratio [OR] 1.92; 95% CI 1.01‐3.67; P = .046) and poor short‐term adherence (OR 4.47; 95% CI 2.33‐8.72; P < .001) independently predicted poor long‐term adherence. The resulting “AP” model (Age and Poor early adherence) achieved an area under the curve of 0.73 (95% CI 0.66‐0.79), with a high specificity of 94%.
Conclusion
Although predictors of PAP adherence have been described in Western populations, this study provides the first large data set from Thailand. The AP model, while simple, is pragmatic and easily applied in resource‐limited settings. Prospective, multicenter validation across Southeast Asia is warranted to enhance its generalizability and incorporate modifiable predictors.
Keywords: adherence, obstructive sleep apnea, positive airway pressure, predictors
Obstructive sleep apnea (OSA) is a common sleep disorder characterized by repetitive partial or complete obstruction of the upper airway during sleep, resulting in intermittent hypoxemia, hypercapnia, and frequent arousals. 1 , 2 The overall prevalence of OSA is rising owing to the increasing rates of obesity and aging populations, with estimates ranging from 9% to 38%. 3 , 4 Untreated OSA has been linked to various adverse health outcomes, including cardiovascular diseases, type 2 diabetes, neurocognitive impairments, and reduced quality of life. 5 , 6 , 7 , 8 , 9 , 10 , 11
Positive airway pressure (PAP) therapy is widely recognized as the most effective treatment for OSA because it maintains airway patency, eliminates apneic events, improves associated symptoms, and decreases the risk of cardiovascular complications. 12 , 13 , 14 , 15 , 16 Despite its proven efficacy, adherence rates in Western cohorts remain disappointingly low, with 29% to 83% of patients discontinuing PAP within the first year. 17 , 18 In contrast, data from Southeast Asia are limited. Previous studies have identified factors linked to PAP use; however, the findings remain inconsistent. Patient demographics, socioeconomic status, disease severity, psychological factors, physical discomfort related to mask fit, pressure settings, and pressure‐related side effects have been shown to influence compliance. 19 , 20 , 21 , 22 , 23 Importantly, patients in Southeast Asia may differ from Western populations in terms of upper airway anatomy, fat distribution, cultural factors, and PAP‐related challenges. This study aimed to identify the predictors of long‐term adherence in Thai patients and develop a simple clinical model. Recognizing these factors can help clinicians provide personalized interventions and optimize PAP adherence.
Methods
Study Population
This retrospective cohort study included adult patients aged 18 to 65 years diagnosed with OSA and prescribed continuous positive airway pressure (CPAP) between January 2012 and December 2022 at the Songklanagarind Hospital, Songkhla, Thailand. Patients were excluded if they had any of the following: incomplete data including polysomnography (PSG) or PAP compliance records. The history of upper airway surgery was not an exclusion criterion. This study was approved by the Ethics Committee of the Faculty of Medicine, Prince of Songkla University (REC 66‐124‐13‐1).
Data Collection and Measurement
Diagnosis of OSA was based on the clinical history and examination, confirmed using standard in‐laboratory PSG (Compumedics) and scored manually using standard criteria. 24 Apnea was defined by a decrease in the thermistor signal ≥90% of the pre‐event baseline for ≥10 seconds. The hypopnea was a 30% decrease in nasal pressure signals for ≥10 seconds associated with ≥3% desaturation or an arousal. 24 The decision to prescribe PAP therapy depended on the patient's symptoms and OSA severity. Typically, CPAP is used to treat moderate‐to‐severe OSA. The primary outcome was factors that predicted long‐term CPAP compliance at 12 months. Data regarding the baseline demographic characteristics, physical findings, and PSG were collected. Data from follow‐up visits within 3 and 12 months were collected, including PAP usage, PAP‐related side effects, Epworth sleepiness scale (ESS) 25 , 26 changes, body mass index (BMI) changes, and apnea‐hypopnea index (AHI) changes. PAP compliance was objectively monitored using a built‐in compliance meter and downloaded by the staff of the sleep clinic. Short‐ and long‐term adherence was measured within 3 and 12 months after CPAP initiation. Good adherence was defined as usage ≥4 hours per night on ≥70% of nights.
Statistical Analysis
The baseline data were reported as numbers (percentages), means (standard deviations), or medians (interquartile ranges). Comparisons between groups were performed using the chi‐square test for nominal variables, the Mann‐Whitney U test for ordinal or continuous variables with non‐normal distribution, and the t test for continuous variables with normal distribution. Statistical significance was set at P < .05. Logistic regression and multivariate model were used to analyze the significant factors related to poor CPAP adherence, and the results are presented as odds ratios (ORs) and 95% confidence intervals (CIs). All statistical analyses were performed using R software version 4.5.1.
Results
Patient Characteristics
In total, 343 patients diagnosed with OSA who were initiated on CPAP treatment were included in the study (Table 1). The mean age was 51.1 ± 13.2 years, and 68.3% of the patients were male. The average BMI was 29.9 ± 6.0 kg/m2, and the mean AHI was 44.3 ± 28.6 events/h. Of these, 253 out of 343 patients (73.8%) were CPAP acceptance at 1 year (Figure 1). At 12 months, good long‐term CPAP adherence was observed in 164 (47.8%) patients, and poor adherence was observed in 89 (26.0%) patients.
Table 1.
Baseline Characteristics and Comparisons of Clinical Data Between the Good and Poor Adherence Groups at 12 Months
| Factors | Baseline data (n = 343) | Long‐term CPAP compliance (at 12 mo) (n = 253) | ||
|---|---|---|---|---|
| Good adherence (n = 164) | Poor adherence (n = 89) | P‐value | ||
| Age, y, mean (SD) | 51.1 (13.2) | 54.0 (13.5) | 48.9 (12.3) | .003* |
| Age group, n (%) | .025* | |||
| <50 y | 116 (33.8) | 55 (33.5) | 43 (48.9) | |
| ≥50 y | 227 (66.2) | 109 (66.5) | 45 (51.1) | |
| Sex, n (%) | ||||
| Male | 236 (68.8) | 115 (70.6) | 57 (64.0) | |
| Female | 107 (31.2) | 48 (29.4) | 32 (36.0) | |
| BMI, kg/m2, mean (SD) | 29.9 (6.0) | 29.4 (6.1) | 30.4 (6.1) | .209 |
| NC, cm, mean (SD) | 39.2 (3.9) | 39.4 (4.0) | 38.6 (3.7) | .124 |
| WC, cm, mean (SD) | 101.3 (15.1) | 101.0 (16.2) | 101.3 (13.1) | .903 |
| Marital status, n (%) | .012* | |||
| Married | 252 (73.5) | 131 (80.4) | 58 (65.2) | |
| Single | 83 (24.2) | 32 (19.6) | 31 (34.8) | |
| Divorced | 8 (2.3) | 0 | 0 | |
| Smoking, n (%) | 26 (7.6) | 7 (4.3) | 4 (4.5) | 1.000 |
| Alcohol consumption, n (%) | 15 (4.4) | 15 (9.1) | 7 (7.9) | .911 |
| Underlying disease, n (%) | ||||
| Hypertension | 139 (40.5) | 68 (47.2) | 40 (50.6) | .728 |
| Type II diabetes | 53 (15.5) | 26 (18.1) | 14 (17.7) | 1.000 |
| Dyslipidemia | 109 (31.8) | 49 (34.0) | 31 (39.2) | .544 |
| Myocardial infarction | 23 (6.7) | 16 (11.1) | 6 (7.6) | 1.000 |
| Atrial fibrillation | 18 (5.2) | 10 (6.9) | 5 (6.3) | 1.000 |
| Congestive heart failure | 3 (0.9) | 1 (0.7) | 1 (1.3) | 1.000 |
| Depression | 8 (2.3) | 3 (2.1) | 3 (3.8) | .668 |
| ESS scores, median (IQR) | 10 (7, 14.8) | 10 (6, 13.8) | 10 (7, 13.5) | .447 |
| STOP‐Bang,a median (IQR) | 4 (3, 5) | 4 (3, 5) | 4 (3, 5) | .571 |
| Tonsillar grading, n (%) | .009* | |||
| Grades 1‐2 | 296 (84.8) | 257 (96.9) | 78 (87.6) | |
| Grades 3‐4 | 53 (15.2) | 5 (3.1) | 11 (12.4) | |
| FTP grading, n (%) | .580 | |||
| Grades I‐II | 167 (49.1) | 78 (52.3) | 39 (49.4) | |
| Grades III‐IV | 173 (50.9) | 71 (47.7) | 40 (10.6) | |
Abbreviations: BMI, body mass index; CPAP, continuous positive airway pressure; ESS, Epworth sleepiness scale; FTP, Friedman tongue position; IQR, interquartile range; NC, neck circumference; SD, standard deviation; WC, waist circumference.
The STOP‐Bang acronym stands for snoring history, tired during the day, observed stop of breathing while sleeping, high blood pressure, BMI > 35 kg/m2 (or 30 kg/m2), age >50 years, neck circumference > 40 cm, and male gender.
Significant difference (P < .05).
Figure 1.
Flowchart of enrolled participants. OSA, obstructive sleep apnea; PAP, positive airway pressure.
The average CPAP compliance rates were 71.5% and 72.5% at 3 and 12 months, respectively. The mean nightly usage was 5.5 ± 2.0 h at 3 months and 5.5 ± 1.9 h at 12 months. At 3 months, the good adherence group had a compliance rate of 84.0% and mean usage of 6.4 ± 1.8 hours, whereas the poor adherence group had a compliance rate of 45.9% and mean usage of 4.1 ± 1.5 hours. At 12 months, compliance and usage were 89.6% and 5.5 ± 1.9 hours, respectively, in the good adherence group versus 40.9% and 3.8 ± 1.8 hours, respectively, in the poor adherence group (Figure 2).
Figure 2.
Percentages of initial continuous positive airway pressure acceptance and compliance rate at 3 and 12 months. CPAP, continuous positive airway pressure.
Comparison of Clinical, Polysomnographic, and CPAP Setting Characteristics
The clinical characteristics stratified by long‐term CPAP adherence are summarized in Table 1. Patients with poor adherence were significantly younger (48.9 ± 12.3 years vs 54.0 ± 13.5 years; P = .003) and more likely to be single (34.8% vs 19.6%; P = .012) than those with good adherence. Tonsillar hypertrophy (grades 3‐4) was more prevalent in the poor adherence group than in the good adherence group (12.4% vs 3.1%, P = .009). No significant differences in sex, BMI, neck circumference, comorbidities, or baseline polysomnographic parameters, including AHI, oxygen desaturation index (ODI), and lowest SpO2, were observed between the groups. No significant differences in the CPAP modes were observed between the groups. Most patients (97.2%) used a nasal mask, and only 2.8% used a full‐face mask. The average pressure was 9.2 ± 2.6 cm H2O in the good adherence group and 8.9 ± 2.3 cm H2O in the poor adherence group (P > .05). The use of expiratory relief mode, humidifiers, or mask leakage was not associated with long‐term adherence (Tables 2 and 3).
Table 2.
Comparisons of Polysomnographic Characteristics Between the Good and Poor Adherence Groups at 12 Months
| Long‐term CPAP compliance (12 mo) | |||
|---|---|---|---|
| Factors | Good adherence (n = 164) | Poor adherence (n = 89) | P‐value |
| Sleep efficiency, %, mean (SD) | 75.5 (15.0) | 75.9 (14.0) | .816 |
| Sleep latency, min, mean (SD) | 13.5 (13.4) | 14.4 (17.2) | .665 |
| AHI, events/h, mean (SD) | 46.2 (28.3) | 43.4 (29.8) | .464 |
| AI, events/h, mean (SD) | 24.1 (28.2) | 18.5 (26.4) | .129 |
| HI, events/h, mean (SD) | 22.4 (14.6) | 24.9 (16.3) | .217 |
| REM AHI, events/h, mean (SD) | 40.8 (26.7) | 42.7 (29.9) | .617 |
| Lowest SpO2, %, mean (SD) | 77.3 (12.5) | 78.7 (11.8) | .328 |
| ODI, events/h, mean (SD) | 29.7 (29.9) | 27.7 (31.6) | .633 |
| Mean heart rate, bpm, mean (SD) | 65.5 (9.8) | 66.6 (9.9) | .397 |
Abbreviations: AHI, apnea‐hypopnea index; AI, apnea index; CPAP, continuous positive airway pressure; HI, hypopnea index; ODI, oxygen desaturation index; REM AHI, apnea‐hypopnea index during rapid eye movement; SD, standard deviation.
Table 3.
Comparisons of Continuous Positive Airway Pressure Parameters Between the Good and Poor Adherence Groups at 12 Months
| Long‐term CPAP compliance (12 mo) | |||
|---|---|---|---|
| Factors | Good adherence (n = 164) | Poor adherence (n = 89) | P‐value |
| PAP mode, n (%) | 1.000 | ||
| CPAP mode | 89 (54.3) | 49 (55.1) | |
| APAP mode | 75 (45.7) | 40 (44.9) | |
| Nasal mask, n (%) | 159 (97.0) | 87 (97.8) | 1.000 |
| EPR mode on, n (%) | 118 (91.5) | 70 (93.3) | .836 |
| Humidifier on, n (%) | 20 (12.3) | 14 (15.7) | .518 |
| Mean pressure, cm H2O, mean (SD) | 9.2 (2.6) | 8.9 (2.3) | .303 |
| Short‐term PAP compliance within 3 mo, n (%) | <.001* | ||
| Good compliance | 128 (82.6) | 40 (47.1) | |
| Poor compliance | 27 (17.4) | 45 (52.9) | |
| Mask leakage at 3 mo, L/min, mean (SD) | 20.8 (20.2) | 20.1 (21.1) | .824 |
Abbreviations: APAP, automatic positive airway pressure; CPAP, continuous positive airway pressure; EPR, expiratory pressure relief; PAP, positive airway pressure; SD, standard deviation.
Significant difference (P < .05).
Short‐Term CPAP Adherence, Side Effects, and Time‐Varying Factors
Poor short‐term CPAP adherence was significantly more common among patients with poor long‐term adherence than among those with good adherence (52.9% vs 17.4%; P < .001). Pressure‐related discomfort during the initial 3 months was also more frequently reported in the poor adherence group than in the good adherence group (21.5% vs 4.8%; P < .001). Other CPAP‐related side effects, including nasal congestion, dry mouth, and claustrophobia, did not differ significantly between the groups. Time‐varying factors, such as BMI, ESS, and AHI changes, were not significantly associated with long‐term adherence (Table 4).
Table 4.
Comparisons of Time‐Varying Factors and Continuous Positive Airway Pressure Side Effects Between the Good and Poor Adherence Groups at 12 Months
| Long‐term CPAP compliance (12 mo) | |||
|---|---|---|---|
| Factors | Good adherence (n = 164) | Poor adherence (n = 89) | P‐value |
| ESS changes, mean (SD) | −4.1 (5.7) | −4.1 (5.3) | .990 |
| BMI changes, mean (SD) | −0.1 (2.5) | −0.3 (2.6) | .570 |
| AHI changes, mean (SD) | −43.2 (27.3) | −41.0 (29.9) | .570 |
| Pressure‐related discomfort, n (%) | 6 (4.8) | 14 (21.5) | <.001* |
| Mask problems, n (%) | 20 (16.0) | 13 (20.0) | .702 |
| Dry throat and mouth, n (%) | 12 (9.7) | 4 (6.2) | .727 |
| Claustrophobia, n (%) | 1 (11.0) | 0 (0) | 1.000 |
Abbreviations: AHI, apnea‐hypopnea index; BMI, body mass index; CPAP, continuous positive airway pressure; ESS, Epworth sleepiness scale.
Significant difference (P < .05).
Multivariate Analysis
In the multivariate logistic regression analysis, after adjusting for potential confounders, two variables remained independently associated with poor long‐term CPAP adherence. Age < 50 years (OR, 1.92; 95% CI, 1.01‐3.67; P = .046) and poor short‐term CPAP adherence (OR, 4.47; 95% CI, 2.33‐8.72; P < .001) were identified as independent predictors (Table 5).
Table 5.
Adjusted Odds Ratios for Associated Factors in Relation to Poor Long‐Term Continuous Positive Airway Pressure Adherence
| Associated factors | Multivariate analysis | ||
|---|---|---|---|
| Adjust OR | 95% CI | P‐value | |
| Age <50 y | 1.92 | 1.01‐3.67 | .046* |
| Single marital status | 1.52 | 0.76‐3.03 | .238 |
| Tonsillar hypertrophy | 1.37 | 0.36‐5.42 | .644 |
| Poor short‐term CPAP compliance | 4.47 | 2.33‐8.72 | <.001* |
| Pressure‐related discomfort at 3 mo | 2.05 | 0.80‐5.44 | .133 |
Abbreviations: CI, confidence interval; CPAP, continuous positive airway pressure; OR, odds ratio.
Significant difference (P < .05).
Model Performance
These two predictors were incorporated into a simple predictive model, referred to as the “AP” model (Age < 50 years and Poor short‐term CPAP adherence). The model demonstrated good discriminative ability, with an area under the receiver operating characteristic curve of 0.73 (95% CI, 0.66‐0.79), indicating acceptable performance. When both risk factors were present, the model predicted poor long‐term CPAP adherence, with a high specificity of 94% and a low sensitivity of 20% (Figure 3).
Figure 3.
Receiver operating characteristic curve of the predictive model (AP: Age < 50 years and Poor short‐term adherence) for identifying patients at risk of poor long‐term continuous positive airway pressure adherence.
Discussion
We demonstrated that only 47.8% of the patients achieved good long‐term adherence to CPAP therapy, defined as usage for ≥4 hours per night. The acceptance rate of PAP therapy was 73.8% (253 of 343 patients) at 12 months. The mean CPAP compliance rate at 12 months was 72.5%, with an average nightly usage of 5.5 ± 1.9 hours. Two clinical predictors of poor long‐term CPAP adherence were identified: age < 50 years and poor short‐term CPAP adherence. Conversely, polysomnographic parameters, CPAP settings, side effects, and changes in the ESS score, BMI, and AHI over time were not significantly associated with adherence.
This study demonstrated a relatively high acceptance rate, average PAP compliance, and good PAP adherence at 1 year compared with recent studies from Southeast Asia. Lee et al, in a study conducted within a privately funded healthcare system, reported that nearly half of all patients with significant OSA declined PAP treatment, and only 57.8% achieved adherence within 1 year. Moreover, only 30.4% of patients who initiated PAP therapy demonstrated good adherence. 27 Another large, publicly funded study from Singapore with a 5‐year follow‐up period, involved 751 patients treated with PAP therapy, found that only 381 patients (50.7%) accepted PAP therapy after 1 month, of whom 299 (78.5%) remained adherent at 1 year. CPAP adherence during the initial 1‐month trial was a significant predictor of long‐term adherence at 1 year. 28 Furthermore, younger age, lower BMI, and normal ESS scores were identified as predictors of treatment rejection. In Thailand and most other Asian countries, differences in healthcare financing structures, limited support from healthcare insurance systems, as well as variations in education level, socioeconomic status, and behavioral factors are important issues that significantly affect CPAP acceptance and adherence. 29
Consistent with the findings of Patel et al, 30 younger age was identified as a predictor of low adherence, possibly because younger patients perceive their symptoms as mild, experience few daytime consequences, or have lifestyle and social factors that negatively impact consistent CPAP use. Additionally, treatment‐related discomfort, such as mask fit, nasal congestion, and claustrophobia, contributes significantly to decreased adherence among younger patients who may prefer alternative treatment options, such as oral appliances or surgical interventions. Similarly, studies from Southeast Asia have shown that patients who accept PAP therapy are generally older. 27 , 28 , 29
The importance of early adherence as a predictor of long‐term CPAP use was reinforced by our findings and aligned with several studies from Western and Asian populations, which have emphasized that short‐term compliance is a strong determinant of treatment acceptance and sustained CPAP use. 23 , 28 , 29 , 31 , 32 In clinical practice, many patients experience nasal side effects such as nasal obstruction, which can negatively affect PAP usage. A previous study by Park et al highlighted that upper airway anatomical factors, such as high‐grade septal deviation, inferior turbinate hypertrophy, enlarged tonsils, and macroglossia, correlated with CPAP compliance. 32 The anatomical narrowing of the upper airway may lead to increased pressure requirements, greater discomfort, and consequently poor adherence. A study from Japan further reported that nasal obstruction was strongly associated with poor PAP compliance, underscoring the importance of comprehensive upper airway evaluation and surgical intervention has been shown to improve PAP adherence. 33 However, our study did not replicate these findings as no anatomical features were significantly associated with poor adherence.
The severity of OSA, measured using indices such as the AHI, ODI, and percentage of sleep time with oxygen saturation below 90% (T90), has yielded mixed results as a predictor of adherence in previous studies. 32 , 34 , 35 , 36 Campos‐Rodríguez et al reported that both AHI and T90 were positively associated with adherence, 34 whereas Kohler et al identified the ODI as an independent predictor. 35 Park et al further demonstrated that AHI predicted short‐term adherence, while T90 predicted long‐term adherence. 32 Gabryelska et al observed that an AHI < 15 was strongly associated with CPAP failure. 21 In contrast, our study did not identify any OSA severity index as a significant predictor of long‐term adherence.
Similarly, previous studies have suggested that CPAP‐related nasal side effects and technological features of the device, such as the auto‐adjusting PAP mode and modified pressure profiles, and high‐pressure setting may influence patient comfort and compliance. 32 , 37 , 38 Eguchi et al reported that automatic PAP mode and excessive mask leakage were associated with reduced adherence. 38 Nevertheless, our study found that these factors, as well as mask interface type, were not significantly associated with long‐term adherence. Notably, most of our patients used nasal masks and relatively low‐pressure settings, which may have contributed to the overall favorable compliance pattern observed. Additionally, time‐dependent factors, such as changes in body weight, daytime sleepiness, and AHI, were not significantly associated with adherence in our study. This finding suggests that baseline patient characteristics and early behavioral patterns may serve as stronger predictors of long‐term adherence than dynamic clinical variables.
Our data from Thailand demonstrate that younger age and poor short‐term adherence remain robust predictors of long‐term compliance, even within a Southeast Asian population. This finding supports the global applicability of these simple yet powerful predictors while contributing region‐specific evidence to an area where data remain limited. These results underscore the importance of early identification of patients at risk for poor CPAP adherence. Clinicians should pay particular attention to younger patients and those exhibiting suboptimal adherence during the first three 3 months of therapy. Targeted strategies, including personalized education, device troubleshooting, consistent follow‐up, and supportive interventions (telemedicine and technological solutions), should be prioritized to enhance long‐term compliance and optimize therapeutic outcomes.
This study has several limitations. The retrospective design limited causal inference. Unmeasured confounding factors, including psychological variables, patient attitudes toward CPAP, and comorbid conditions such as insomnia, were not assessed. The single‐center design and high loss‐to‐follow‐up rate may have introduced selection bias. Additionally, this study predominantly included nasal mask users, limiting the generalizability of the findings to users of other types of masks. Although tonsil grade and FTP were evaluated, more comprehensive craniofacial parameters were not systematically assessed. Future studies incorporating these variables could provide additional insight. Despite these limitations, this study also possesses notable strengths. First, CPAP adherence was objectively measured using device‐based compliance tracking, thereby minimizing reporting bias. Second, clinical, polysomnographic, and CPAP setting variables were comprehensively evaluated, providing a holistic assessment of adherence predictors. Finally, the development of a simple and pragmatic model enabled easy application in routine clinical practice. Future prospective, multicenter studies are warranted to validate this model and assess its generalizability across different populations. Incorporating modifiable predictors such as nasal obstruction scores, craniofacial morphology, psychosocial determinants, and behavioral support strategies may enhance predictive accuracy and facilitate individualized treatment strategies.
Conclusion
Age <50 years and poor short‐term CPAP adherence were independent predictors of poor long‐term CPAP adherence. The AP model offers a simple, pragmatic tool with high specificity that may be valuable in resource‐limited and primary care settings. Prospective, multicenter validation across Southeast Asia is warranted to confirm its clinical utility.
Author Contributions
Krongthong Tawaranurak, Design, conduct, analysis, editing final manuscript; Pannarat Kongtawee, Conduct, analysis, drafting; Nattarin Nilrat, Drafting.
Disclosures
Competing interests
The authors declare no conflicts of interest.
Funding source
This study was supported by the Medical Research Council (grant number 66‐124‐13‐1).
Acknowledgments
The authors would like to thank Miss Jirawan Jayuphan for data analysis.
References
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