
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39251631
63591
10.1038/s41598-024-63591-x
Article
Association between PM10 and respiratory diseases admission in peninsula Malaysia during haze
Mohd Zulkifli Siti Wafiah Hanin
Samsudin Humaida Banu humaida@ukm.edu.my

Majid Noriza
https://ror.org/00bw8d226 grid.412113.4 0000 0004 1937 1557 Department of Mathematical Science Faculty of Science and Technology, National University of Malaysia, Bangi, 43600 Selangor Malaysia
9 9 2024
9 9 2024
2024
14 210308 6 2023
30 5 2024
© The Author(s) 2024
2024
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Numerous studies have been conducted in other countries on the health effects of exposure to particulate matter with a diameter of 10 microns or less PM10, but little research has been conducted in Malaysia, particularly during the haze season. This study intends to investigate how exposure of PM10 influenced hospital admissions for respiratory diseases during the haze period in peninsula Malaysia and it was further stratified by age group, gender and respiratory diseases categories. The study includes data from all patients with respiratory diseases in 92 government hospitals, as well as PM10 concentration and meteorological data from 92 monitoring stations in Peninsula Malaysia starting from 1st January 2000 to 31st December 2019. A quasi-poison time series regression with distributed lag nonlinear model (DLNM) was employed in this study to examine the relationship between exposure of PM10 and hospital admissions for respiratory diseases during the haze period. Haze period for this study has been defined from June to September each year. According to the findings of this study, PM10 was positively associated with hospitalisation of respiratory disease within 30 lag days under various lag patterns, with lag 25 showing the strongest association (RR = 1.001742, CI 1.001029,1.002456). Using median as a reference, it was discovered that females were more likely than males to be hospitalized for PM10 exposure. Working age group will be the most affected by the increase in PM10 exposure with a significant cumulative RR from lag 010 to lag 030. The study found that PM10 had a significant influence on respiratory hospitalisation in peninsula Malaysia, particularly for lung diseases caused by external agents(CD5). Therefore, it is important to implement effective intervention measures to control PM10 and reduce the burden of respiratory disease admissions.

Keywords

Particulate matter (PM10)
Hospital admissions
Respiratory diseases
Relative risk
Subject terms

Environmental sciences
Diseases
Medical research
Risk factors
Mathematics and computing
Encouragement Research Grant National University of Malaysia, MalaysiaGGP-2020-027 issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Phenomena of haze occurred when there is a sufficient concentration of visible light scattering aerosols in the atmosphere, a detectable reduction in visual range1. Haze development is significantly influenced by pollutant emissions, the transformation of gases into particles, and meteorological variables. High levels of particles and gas-to-particle conversion cause haze to arise in stable weather circumstances like low wind speed and inverted thermodynamic structure2,3. Haze is associated with high levels of dry particles and smoke in the atmosphere, as well as a lower-than-normal relative humidity of less than 80% and visibility of fewer than 10 kilometers2,4. In the past few decades, Malaysia has experienced haze nearly every year, which is a frequent phenomenon in Southeast Asia (SEA). In Malaysia, the haze was first reported in 1983. Since then, severe haze occurrences have been observed, including in 1997, 2005, and 20155. Following a period of intense haze, the haze would occasionally appear between June and September each year. In comparison to non-haze periods, when PM10 concentrations are only about 40related to the increase in respiratory admission, the average PM10 concentration for 24 hours during haze periods exceeds 100μg/m3.In comparison to other months, PM10 concentration is higher from June through September (Fig. 1). The main causes of Malaysia’s transboundary haze have been deforestation, oil palm plantations on peatlands, and “slash and burn” farming methods, particularly in Sumatra and Kalimantan, Indonesia5–8.Figure 1 PM10 concentration from 2000 to 2019.

During haze episodes, there are high levels of air pollutants that reduce visibility and have an impact on human health. Hospital admissions have increased significantly during the haze period which is normally associated with respiratory health problems such as asthma, rhinitis, upper respiratory infections, and chronic obstructive pulmonary diseases. During severe haze in 1997, hospital admission rose to 8% if compared to the mean of hospital admission during the same month in 1995,1996 and 1998 and the number of outpatient visits due to respiratory diseases increased from 250 to 800 per day9,10. Another researcher Sahani et al.11 found that the risk of haze in the Klang Valley region between 2000 and 2007 showed that during the haze period, there is an increase of 19% in respiratory mortality. Although the relationship between air pollution and respiratory disease morbidity and mortality has been extensively studied, the interactive effect of PM10 and meteorological factors on respiratory diseases during the haze period in peninsula Malaysia has not been investigated. During haze periods, the levels of PM10 tend to rise significantly. Haze is often caused by the burning of biomass, such as forests and agricultural fields. As a result, large quantities of PM10 are released into the atmosphere. The particles can be transported over long distances and affect air quality in both urban and rural areas. PM10 consists of tiny particles or droplets in the air that are small enough to be inhaled into the respiratory system. These particles can penetrate deep into the lungs and, in some cases, even enter the bloodstream. Prolonged exposure to PM10 is associated with various respiratory and cardiovascular health issues including aggravated asthma, bronchitis, reduced lung function, and an increased risk of heart attacks and strokes. Hence, monitoring and controlling PM10 levels are essential to protect public health. Numerous researchers have investigated the issue of pollutants and their influence on the haze season in countries adjacent to Malaysia. One example is a study conducted in Brunei12, which revealed that visibility during haze episodes is notably affected by particulate matter with a diameter of 10 micrometers or less (PM10) and relative humidity (RH). Additionally, increased levels of PM10 and carbon monoxide (CO) observed during haze periods have been identified as significant factors contributing to an increased incidence of respiratory ailments such as asthma, acute respiratory infections (ARI), and influenza. A review of literature examining the association between haze and respiratory illnesses in Thailand, Indonesia, and Singapore consistently presents evidence of increased in respiratory morbidity, especially among vulnerable groups such as children and the elderly13,14. The 1997 haze in Singapore, caused by forest fires in Indonesia, had a significant impact on lung health14. In Northern Thailand, the annual haze poses a complex problem that requires coordinated policies to alleviate its effects on public health15. The transboundary haze in Southeast Asia has been linked to acute psychological, respiratory, cardiovascular, and neurological morbidity and mortality16.

The worsening of respiratory diseases due to PM10 pollution during haze periods has significant consequences for both public health and the economy. The impact towards public health will be increased in medical cost, poor quality of life and increased in illnesses and mortality. More people seek medical help during hazy periods, leading to higher healthcare expenses. People with respiratory issues suffer more, with worsened symptoms and less comfort. Haze can make respiratory problems worse, potentially causing more illness and, in severe cases, even premature death. On the other hand, in economic consequences, this will lower work productivity, higher healthcare expenses and government spending and affect business and tourism. Illnesses lead to missed workdays and reduced productivity, causing losses for employers. Employers and the government pay more for healthcare as people use more services. Haze can hurt businesses and tourism due to poor air quality and canceled events, resulting in lost income of the country.

This study intends to investigate the relationship between PM10 exposure and hospitalisation of respiratory illnesses in peninsula Malaysian according to gender,age and respiratory diseases categories. The lag-exposure-response relationship during the haze period of 13 states in Malaysia was investigated using a case-crossover analysis involving all respiratory diseases listed in Malaysia.

Methods

Study population

The data on respiratory disease cases listed in ICD (J00 – J99) from Jan 1st 2000 to Dec 31st 2019 were were obtained from the Health Informatics Center, Ministry of Health following the approval from the National Medical Register (NMRR). This study involved 92 government hospitals in peninsula Malaysia covering 13 states in Malaysia which are Kedah, Perak, Pulau Pinang, Perlis, Kelantan, Pahang, Terengganu, Melaka, Johor, Negeri Sembilan, Selangor, Wilayah Persekutuan Kuala Lumpur and Wilayah Persekutuan Putrajaya. Peninsula Malaysia covers 79% of the population in Malaysia and represents the majority of the population in Malaysia. peninsula Malaysia also indicates the speed of development with many industrial areas in Malaysia, they are likely to be most affected by air pollution from vehicular and industrial emissions. In addition, the Klang Valley area which includes around the Federal Territory of Kuala Lumpur,the Federal District of Putrajaya and Selangor has become a metropolitan city where there are much urbanization and people in Malaysia migrate here to improve their lifestyle. This study also includes the southern point of Johor, which is situated in front of Singapore and is exposed to relatively high pollution levels as a result of the rapid industrialisation in Johor. Future research will expand the investigation to examine how pollution affects Malaysia’s by including non peninsula regions’ respiratory health. The patient’s age, gender, admission date and diagnosis of respiratory diseases was obtained from the medical records. There are 3 age groups in this study (0–14 : young age, 15–64 : working age, 65 and above : old age). The age group classification follows the categorization of 3 main age groups in Malaysia listed in the Department of Statistics Malaysia. According to DOSM in the analysis of Labor Force Survey (LFC) in Malaysia, working age in Malaysia refers to the age structure of the economically active population, which is classified to be between the ages of 15 and 64. The respiratory diseases were classified following the Malaysia Health Indicators coded from J00 to J99. Respiratory diseases are classified into 9 code diseases which are (CD1 = Diseases of the upper respiratory tract, CD2 = Influenza, pneumonia and other acute lower respiratory infections, CD3 = Other diseases of the upper respiratory tract, CD4 = Chronic lower respiratory disease, CD5 = Lung diseases due to external agent, CD6 = Other respiratory diseases principally affecting the interstitium, CD7 = Suppurative and necrotic conditions of lower respiratory tract, CD8 = Other diseases of pleura, CD9 = other diseases of the respiratory system).

In this study, non-Malaysian patients have been deliberately excluded. The rationale behind this decision is the study’s exclusive focus on examining the impact of air pollution on the respiratory health of Malaysian citizens. This methodological approach of exclusively targeting Malaysians serves to enhance the uniformity of the sample, thereby allowing for better control of other potentially influential variables. For instance, individuals from other countries who have recently arrived in Malaysia, or those who plan to stay for a relatively short duration, might not be as profoundly affected by air pollution. Additionally, their attitudes towards air pollution could be shaped by their prior experiences in the countries they resided in before coming to Malaysia.From a practical standpoint, the implications of this study are notably more relevant to policymakers when it specifically pertains to the perspectives of Malaysian citizens. Malaysians are the demographic group directly impacted by the government’s regulations, plans, and incentives related to environmental protection, particularly in the context of tax-related factors. In addition, this study only involved patients who were admitted to government hospitals. Patients who were admitted to hospital due to respiratory diseases in private hospitals will be excluded from this study.

Air pollutants and meteorological data

For this study, historical daily data on pollutants such as Particulate Matter (PM10), and meteorological records such as humidity, wind speed, and temperature were collected at 92 monitoring stations across 13 states in peninsula Malaysia.. The southwest monsoon season in Malaysia, which lasts from June to September, and the dry season in the equatorial SEA region were used to define the haze period. The increased number of fires during the dry season contributes to the formation of haze. Thus, the hazy period for this study has been defined from June to September. Since fine particles comprise most of the particulate matter (PM) in haze samples, thus this study defined the haze period according to particulate matter PM10. As a result, it’s also shown in Fig. 1 that the concentration of PM10 increased most significantly between June and September every year. All these data were obtained from Malaysia’s Department of Environment (DOE). Some pollutants’ missing data (less than 7%) were treated by using the Multivariate Imputation via Chained Equation (MICE) package in R which assumes the missing data are missing at random. Consequently, the probability of the missing values depends on the observed value and can be predicted using them. Predicted Mean Matching (PMM), which is applicable to numerical variables, is the method used in the MICE imputation to predict the missing values.

The limited availability of data has constrained the depth of the analysis in this study, specifically in addressing area-level characteristics, and it is important to note this constraint in order to accurately interpret the associations between pollutants and hospitalisation outcomes.

Statistical analysis

Air pollutants and meteorological factors influence global human morbidity and mortality, and, and most of their effects on human health have lag and non linearity. In this study, quasi-Poisson Generalized Linear Model (GLM) with the distributed lag nonlinear model (DLNM) was used to analyze the lag-exposure-response of the association of daily respiratory diseases admissions and daily air pollutants and meteorological factors during the haze period17,18. Since the daily counts for respiratory diseases hospitalization follow Poisson distribution, hence quasi-Poisson statistical model was used to address the over dispersion of respiratory diseases hospitalization. Since many researchers found that there is a nonlinear association between environmental factors and health conditions, hence DLNM was used to explore the bi-dimensional exposure-lag-response relationship. The delayed effect of air pollutants on respiratory diseases during haze was predicted using the cross-basis function of the distributed lag nonlinear model (DLNM). The maximum lag for this study was set at 30 days and the degree of freedom for the exposure variables was determined by minimizing the generalized cross-validation score. The final model structure can be shown as follows;1 logEYt=α+βXt,p+nsTimet,df+ns(DOY,df)+nsTempt,df+nsRHt,df+nsWSt,df+factorDOWt

where Yt is the number of hospital visits for respiratory diseases at calendar day starting from t (1, 2, 3, ..., 3060) for the haze period ; [E(Yt )] represents the expected number of daily hospital visits for respiratory ; α is the intercept ;Xt,p refers to the cross-basis matrix for the pollutants produced by DLNM to fit the distributed lag effects ; ns is the smoother for natural cubic spline ; Time refers to calendar time to control for the long-term trend and seasonality of daily admission ; df was the degree of freedom ; DOY is the day of the year from day 1 to 365 days or 366 days for the leap year ; Temp is the temperature on the day t ; RH is the relative humidity ; WS is the windspeed of each day t and DOW is the day of the week starting from Monday to Saturday.

The degree of freedom for each cofounder was selected based on the lowest (Q-AIC) Akaike Information Criterion for quasi-Poisson which is ;2 QAIC=-2L(θ^)+2∅^k

where L is the log-likelihood of the fitted model with parameters θ^ and ∅^ is the estimated over dispersion parameter, where k is the number of the parameters. The degree of freedom for the equation are found to be 7 for Timet, WSt and DOYt. The degree of freedom for Tempt and RHt is 3 according to the previous study19–23. Air pollution may have a delayed effect, hence each single-pollutant model was evaluated for impacts on the current day (lag 0) and the exposure to the seven days prior (lag 7) or even 30 days prior (lag 30). This study fixed the maximum lag days to 30 days during the haze period because there is a lag influence of atmospheric particulates on human health during short-term exposure. The cumulative lag effects of the pollutant concentration were observed starting from the first two-day moving average (lag 01) to the eight days moving average (lag 07) and until one month (lag 030). This study explored the relative risk of respiratory diseases admissions according to the gender, age and 9 categories of respiratory diseases. The association of air pollutants and hospital admissions was presented as relative risk (RR) with 95% confidence interval correlated with 10μg/m3 increases in PM10. All data cleaning and statistical analyses were conducted in R Statistical Software Version 2.5.1. The R packages used for this study involve “dlnm”, “MICE”, “spline” and “mgcv”.

Sensitivity analysis

The sensitivity analysis results showed that the model was stable and consistent for a range of modifications made to the smoothers’ degree of freedom and was shown in Supplementary S1 and S2. The expected number of admission are unaffected by increasing the number of degree freedom by 3,4,5,6 that used to control seasonal and long-term trends as well as temperature.

Ethical approved

This is an observational study. We confirm that all methods employed in this study were conducted in strict accordance with the applicable guidelines and regulations. Informed consent was obtained from all subjects participating in the research, as well as their respective legal guardian(s) in the case of minors. The Medical Research and Ethics Committee Ministry of Health Malaysia has confirmed that no ethical approval is required. The Medical Research and Ethics Committee (MREC), Ministry of Health Malaysia (MOH) has provided ethical approval for this study. Please take note that all records and data are to be kept strictly CONFIDENTIAL and can only be used for the purpose of this study. All precautions are to be taken to maintain data confidentiality. Permission from the District Health Officer / Hospital Administrator / Hospital Director and all relevant heads of departments / units where the study will be carried out must be obtained prior to the study. Authors required to follow and comply with their decision and all other relevant regulations, including the Access to Biological and Benefit Sharing Act 2017.

Results

Descriptive results

Between 2000 to 2019, a total of 1,773,949 patients were admitted due to respiratory diseases during the haze period. The daily distribution of respiratory admission is shown in Fig. 2. The number of admissions for respiratory diseases increases year after year, from less than 1000 per day to more than 2000 per day. During the haze period in peninsula Malaysia, the average number of patients admitted per day was 727, with the first and third quantiles admitting 238 and 1176.5 patients per day, respectively. The highest number of patients admitted for respiratory disease was 2862 cases in January 2018 and the lowest number of admissions per day reported was 95 cases in January 2006. During the haze period, the 1st,50th and 99th percentiles of PM10 concentration were 18.37, 42.38 and 95.52. Figure 2 Time series plot between air pollutants with daily hospital admission due to respiratory diseases.

Table 1 comprises the descriptive statistics of hospitalisations due to respiratory diseases, the concentration of pollutants and the meteorological factors in peninsula, Malaysia from 2000 to 2019 during haze. The daily mean count of respiratory diseases admission was 727 with IQR (238 to 1176.6 of admission per day). The highest concentration for PM10 (226.12μg/m3) was on 14 September 2015 which is severe haze occurred in Malaysia, meanwhile the lowest concentration of PM10 (12.48μg/m3) was on 30 November 2015 which the second inter monsoonal period occurred. Daily average concentrations of pollutants were 719.3,18.93,34.77,7.02 for CO, NO2, O3 and SO2, respectively during the haze period. The daily average temperature during the period of study was 27.49 ∘ C, with humidity of 77.48% and wind speed of 4.97 m/s.Table 1 Descriptive Statistics of hospital admission for Respiratory Diseases,air pollutants and meteorological factors in peninsula Malaysia, 2000–2019.

	Min	Max	P25	P75	Median	Mean	SD	
Admissions (Days)	125	2622	238	1176.5	447.5	727	607.68	
CD1 (Days)	14	500	46	240	96	145.2	127.97	
CD2 (Days)	28	1474	83	548.5	190	336.6	327.59	
CD3 (Days)	0	116	6	22	10	18.12	19.93	
CD4 (Days)	0	26	0	2	1	2.47	4.07	
CD5 (Days)	37	578	88	127	97	186.6	122.39	
CD6 (Days)	0	72	5	23	10	15.03	13.30	
CD7 (Days)	0	20	1	4	2	2.63	2.89	
CD8 (Days)	0	50	4	16	8	11.08	9.42	
CD9 (Days)	0	44	4	12	7	9.34	7.27	
PM10 (μg/m3)	14.14	226.12	38.32	56.74	46.08	49.28	18.35	
CO(μg/m3)	0.40	2.25	0.62	0.79	0.69	0.72	0.16	
NO2(μg/m3)	8.31	27.46	17.11	20.73	18.88	18.93	2.65	
O3(μg/m3)	11.73	66.55	29.4	39.47	34.21	34.77	7.47	
SO2(μg/m3)	2.03	22.87	4.94	8.09	6.19	7.02	3.45	
Temperature (∘C)	23.62	32.19	26.92	28.16	27.57	27.49	0.98	
Humidity (%)	61.03	88.73	75.86	80.05	77.94	77.78	3.63	
Wind speed (m/s)	0.88	14.19	4.55	5.66	5.10	4.97	2.12	

Figure 2 shows the time series plot between air pollutants with daily hospital admission due to respiratory diseases in peninsula Malaysia, starting from January 1, 2000, to December 31, 2019. The intersection of daily air pollutants with daily hospital admission was clearly seen between 2013 to 2015 and severe haze happened in 2015. Other than that, the hospital admissions exhibit an increment in 2005 mainly due to severe haze happened. The concentration of all pollutants had a similar pattern of fluctuation every year, except for SO2 that has a higher concentration in 2000 than 2020. The daily concentration of PM10 has fluctuated ranging from 25 to 75 but also reach 150 to 200 in 2005 and 2015 which is exceed Malaysia Ambient Air Quality Standard for PM10 in interim target 2 (2018) that PM10 should be less than 45 μg/m3.

Table 2 shows the coefficient of spearman rank correlation between daily air pollutants with meteorological factors in peninsula Malaysia, from 2000 to 2019. All the pollutants and meteorological factors are significantly correlated with each other except for the correlation between NO2 with wind speed. The highest positive significant correlation was found between NO2 with PM10 and O3 with temperature with r=0.63 for both. Other than that, PM10 with CO and PM10 with 03 also have the positive correlation of 0.51 and 0.49 respectively. The lowest significant correlation was between NO2 with temperature with only 0.06. Humidity has the negative significant correlation with all pollutants and meteorological factors ranging from (-0.15 to -0.48). O3 has weakly and negatively correlated to SO2 with r= -0.09 and the highest negative significant correlation was between temperature and humidity with r= -0.48. The reason PM10 was chosen to be researched more thoroughly in this study is because it has a positive significant association with all pollutants and meteorological factors. Additionally, the air pollutant composition during the haze season is made up of atmospheric particulate matter (PM) .Table 2 Coefficient of spearman rank correlation (r) of air pollutants and meteorological factors in peninsula Malaysia, 2000–2019.

	CO	NO2	O3	PM10	SO2	Temp	Humidity	Wind speed	
Carvon monoxide (CO)	1	0.44**	0.34**	0.51**	0.15**	0.32**	-0.19**	-0.28**	
Nitrogen dioxide (NO2)		1	0.28**	0.63**	0.29**	0.06**	-0.15**	-0.02	
Oxygen (O3)			1	0.49**	-0.09**	0.63**	-0.45**	-0.09**	
Particulate matter (PM10)				1	0.42**	0.24**	-0.47**	0.26**	
Sulphur dioxide (SO2)					1	-0.13**	-0.4**	0.47**	
Temp						1	-0.48**	-0.28**	
Humidity							1	-0.24	
Windspeed								1	
Coefficient (Standard error) significant **0.001.

A single pollutant model of PM10 was built to investigate how changes in each concentration influence the risk of admission for respiratory diseases during the haze period. Figure 3 shows the lag effect of PM10 with morbidity of respiratory diseases in the haze period from lag 0 until lag 30. A nonlinear model of a single pollutant showed that every increase in PM10 concentration will contribute to the risk of respiratory admission, with the median concentration of PM10 as the reference for this model. The results of this study showed that the relative risk (RR) for respiratory diseases patients admitted on the same day of PM10 exposure was insignificant with 1.001012 for 10 μg/m3 increase of PM10 with CI of 0.998848 to 1.003151. The peak of the relative risk happened on the 25th day after the lag (RR = 1.001742, CI 1.001029,1.002456). The RR of the model was statistically significance starting from 17 days to 28 days. The highest cumulative RR of hospital admission is 1.018931 with 95% CI 1.008078,1.029901 on 30th day of lag and was statistically significance from lag 024 to lag 030. There were no cumulative lagged effects of seven days (lag07) and 18 days (lag018) after exposure for PM10 with RR of 1.000193(95% CI 0.9937977, 1.006629) and 1.000756(95% CI 00.9935564, 1.008008) respectively. In the lag-response association, it was found that RR increased when the concentration of PM10 increased during the haze period. The maximum concentration from this model was 226μg/m3 and reached the maximum RR= 1.401336 and 95% CI 1.155754,1.699101 in the period of haze. The relative risk of hospital admission varied with the lag days and the concentration level. Figure 3 showed that RR first increased with the increase of the lag period, then decreased and increase again until it reached its peak during 25th day of lag.Figure 3 Lag response curve for an increase in PM10 and cumulative effects.

Risk and cumulative risk according to gender

Figure 4 illustrates the association between the relative risk of individuals who were admitted and when there is a 10 μg/m3 increase in PM10 lag times by gender in the period of haze. For every increase of PM10 concentration during the haze period with median as the reference level, the relative risk of patients admitted with respiratory diseases increased in both men and women. The maximum relative risk for respiratory diseases in men peaked on the 24th day of lag (RR= 1.001383, 95% CI 1.000676, 1.002090) meanwhile, the relative risk for respiratory diseases in women reached its maximum on the 28th day of lag with (RR= 1.002330, 95% CI 1.000953, 1.003709). The relative risk of males admitted due to respiratory diseases was consistently higher in females. The significant time zone for RR in men was from 18 day to 27 days, while the significant time zone for women was from 16th day of lag to 29th day of lag and the lag period effect for women is longer if compared to men. Both genders had the cumulative effects in the lag period, the cumulative RR for men patient was not statistically significant but cumulative RR was significant in females in lag022 and lag030. As a result of the longer lag period, PM10 had a greater impact on the risk of respiratory disease admissions among women during the haze period than men, and women had a significant cumulative lag effect on the occurrence of respiratory diseases.Figure 4 Relative Risk (RR) and Cumulative Relative Risk of hospital admission according to age group.

Risk and cumulative risk according to age

The relative risk estimated according to the age group in different lag days was presented in Fig. 5. When there is a 10 μg/m3 increment of PM10 concentration, both young and old age group reach their maximum relative risk on the 25th lag day which is (RR= 1.001220, 95% CI 1.000413, 1.002024) and (RR= 1.001290, 95% CI 1.000573, 1.001925) respectively. For the young age group, the increment of PM10 was negatively associated with hospitalisation due to respiratory illnesses from the 5th lag day to 13th lag day and was positively associated in 21th lag day to 26th lag day. However, in cumulative RR for the young age group, PM10 was negatively significant during lag day 11 (lag011) to lag day 21 (lag021). The cumulative relative risk of the working age group was statistically significant starting from cumulative lag day 10 (010) to the end of the lag days (030). Similar to the young age group, the lag period of RR for the old age group was meaningful from 18th to 27th day of lag but was not statistically significant in the cumulative risk of the respiratory diseases except in lag 028 to lag 030. This proves that short-term exposure to PM10 affects the working age group longer if compared to the young and old group. The working age group also had the highest risk exposure that peaked during lag day 30 (RR= 1.003810 95% CI 1.001034, 1.006593).Figure 5 Relative Risk (RR) and Cumulative Relative Risk of hospital admission according to gender.

Relative risk according to respiratory diseases

Table 3 showed the results of the model with the exposure-response effect for each group of respiratory diseases. In the single pollutant model during the haze, an increase of PM10 concentration was associated with the significantly increased risk ratio of respiratory diseases such as diseases of the upper respiratory tract (CD1), influenza, pneumonia, and other acute lower respiratory infections (CD2), lung diseases due to external agent (CD5) and other respiratory diseases principally affecting the interstitium (CD6) appear later, with lags of 15-29, 19-28, and 15-29, respectively. The highest risk of lung diseases due to external agent (CD5) happened on the early lag day (lag 1) and remained significant until lag 4. The risk diseases, such as diseases of the upper respiratory tract (CD1), influenza, pneumonia, and other acute lower respiratory infections (CD2), and other respiratory diseases that primarily affect the interstitium (CD6),appear later, with lags of 15-29, 19-28, and 15-29, respectively. However, an increase of PM10 was associated with decreased risk for chronic lower respiratory disease (CD4) in lag 24 to lag 25. In the single pollutant model, there was no significant relationship between then PM10 exposure and the risk of other diseases of the upper respiratory tract (CD3), suppurative and necrotic conditions of the lower respiratory tract (CD7), other diseases of the pleura (CD8), and other diseases of the respiratory system (CD9).However, the risk of other diseases of the upper respiratory tract (CD3) was negatively significant in cumulative lag days 014 and 021. Aside from having a positive association with the risk of respiratory diseases that primarily affect the interstitium (CD6), PM10 has a negative association during present-day to lag 5. In terms of cumulative relative risk in Supplementary S3, lung diseases caused by external agents (CD5) are positively significant in all short-term periods (lag 07 to lag 030), whereas diseases of the upper respiratory tract (CD1) had significant relative risks during lag 021 and lag 031, and influenza, pneumonia, and other acute lower respiratory infections (CD2) had significant relative risks during lag 030. Other than that,the cumulative exposure of PM10 decrease the risk of hospitalization of diseases related to (CD4) and (CD6). The effect of PM10 exposure will increase the risk of getting lung diseases due to external agents (CD5) if compared to other groups of respiratory diseases.Table 3 Relative Risk of hospital admission according to the respiratory diseases.

Lag	CD1	CD2	CD3	CD4	CD5	CD6	CD7	CD8	CD9	
Lag 0	1.0012	1.0011	0.9966	0.9952	1.0019	0.9948 **	1.0032	0.9985	0.9991	
(0.9984,	(0.9982,	(0.9908,	(0.9799,	(0.9999,	(0.9901,	(0.9930,	(0.9932,	(0.9930,	
1.0040)	1.0041)	1.0025)	1.0108)	1.0039)	0.9995)	1.0137)	1.0038)	1.0052)	
Lag 1	1.0007	1.0006	0.9971	0.9955	1.0016 **	0.9956 **	1.0031	0.9986	0.9997	
(0.9986,	(0.9984,	(0.9928,	(0.9842,	(1.0001,	(0.9921,	(0.9954,	(0.9947,	(0.9953,	
1.0027)	1.0027)	1.0015)	1.0069)	1.0030)	0.9991)	1.0107)	1.0026)	1.0042)	
Lag 2	1.0002	1.0001	0.9976	0.9957	1.0012 **	0.9963**	1.0028	0.9987	1.0002	
(0.9987,	(0.9986,	(0.9986,	(0.9877,	(1.0002,	(0.9938,	(0.9973,	(0.9959,	(0.9970,	
1.0017)	1.0017	1.0007)	1.0038)	1.0023)	0.9989)	1.0084)	1.0016)	1.0034)	
Lag 3	0.9999	0.9997	0.998	0.9959	1.0010**	0.9971 **	1.0026	0.9989	1.0007	
(0.9988,	(0.9986,	(0.9986,	(0.9901,	(1.0002,	(0.9952,	(0.9986,	(0.9968,	(0.9983,	
1.0009)	1.0009)	1.0003)	1.0018)	1.0017)	0.9989)	1.0066)	1.0009)	1.0030)	
Lag 4	0.9996	0.9994	0.9983	0.996	1.0007 **	0.9978 **	1.0024	0.999	1.0009	
(0.9987	(0.9985,	(0.9985,	(0.9913,	(1.0001,	(0.9963,	(0.9991,	(0.9973,	(0.9990,	
1.0004)	1.0003)	1.0001)	1.0008)	1.0013)	0.9992)	1.0056)	1.0006)	1.0028)	
Lag 5	0.9994	0.9992	0.9986	0.9961	1.0005	0.9984 **	1.0021	0.9991	1.0011	
(0.9986,	(0.9984,	(0.9984,	(0.9916,	(0.9999,	(0.9970,	(0.9991,	(0.9975,	(0.9993,	
1.000257)	1.000112)	1.000342)	1.000754)	, 1.001113)	, 0.9998)	1.0052)	1.0007)	1.0029)	
Lag 6	0.9993	0.9991	0.9989	0.9962	1.0004	0.999	1.0019	0.9992	1.0013	
(0.9985,	(0.9982,	(0.9982,	(0.9913,	(0.9998,	(0.9975,	(0.9986,	(0.9975,	(0.9994,	
1.0002)	1.0001)	1.0007)	1.0012)	1.0010)	1.0004)	1.0051)	1.0008)	1.0031)	
Lag 7	0.9993	0.999	0.9991	0.9963	1.0003	0.9995	1.0016	0.9993	1.0013	
(0.9984,	(0.9980,	(0.9980,	(0.9909,	(0.9996,	(0.9979,	(0.9981,	(0.9975,	(0.9993,	
1.0003)	1.0001)	1.0010)	1.0016)	1.0009)	1.0011)	1.0051)	1.0011)	1.0033)	
Lag 8	0.9994	0.999	0.9992	0.9963	1.0002	1	1.0014	0.9993	1.0013	
(0.9984,	(0.9980,	(0.9980,	(0.9907,	(0.9995,	(0.9984,	(0.9977,	(0.9975,	(0.9992,	
1.0004)	1.0001)	1.0013)	1.0019)	1.0009)	( 1.0017)	1.0050)	1.0012)	1.0034)	
Lag 9	0.9995	0.999	0.9993	0.9962	1.0001	1.0005	1.0011	0.9994	1.0012	
(0.9984,	(0.9980,	(0.9980,	(0.9907,	(0.9994,	(0.9988,	(0.9975,	(0.9975,	(0.9991,	
1.0005)	1.0001)	1.0014)	1.0018)	1.0008)	1.0021)	1.0048)	1.0013)	1.0034)	
Lag 10	0.9996	0.9992	0.9994	0.9962	1.0001	1.0009	1.0009	0.9995	1.0011	
(0.9986	(0.9981,	(0.9981,	(0.9908,	(0.9994,	(0.9992,	(0.9973,	(0.9976,	(0.9990,	
1.0006)	1.0002)	, 1.0014)	( 1.0017)	1.0008)	1.0025)	1.0045)	1.0013)	1.0032)	
Lag 11	0.9998	0.9993	0.9995	0.9961	1	1.0012	1.0007	0.9995	1.001	
(0.9989,	(0.9983,	(0.9983,	(0.9910,	(0.9994	(0.9997,	(0.99731,	(0.9978,	(0.9990	
1.0007)	1.0003)	1.0014)	1.0013)	1.0007)	1.0027)	1.0041)	1.0013)	1.0029)	
Lag 12	1	0.9995	0.9995	0.996	1.0001	1.0015 **	1.0005	0.9996	1.0008	
(0.9992,	(0.9986,	(0.9986,	(0.9912,	(0.9995,	(1.0001,	(0.9973,	(0.9980,	(0.9989,	
1.000913)	1.000407)	1.0013)	1.0009)	1.0007)	1.0029)	1.0036)	1.0012)	1.0026)	
Lag 13	1.0003	0.9997	0.9996	0.9959	1.001	1.0018**	1.0003	0.9996	1.0006	
(0.9995,	(0.9988,	(0.9988,	(0.9915,	(0.9995,	(1.0004,	(0.9974,	(0.9981,	(0.9989, )	
1.0011)	1.0005)	1.0012)	1.0004)	1.0006)	1.0031)	1.0032)	, 1.0011)	1.0023)	
Lag 14	1.0006	0.9999	0.9996	0.9958	1.0001	1.0020**	1.0001	0.9997	1.0003	
(0.9998,	(0.9991,	(0.9991,	(0.9916,	(0.9996,	(1.0007,	(0.9974,	(0.9983,	(0.9987,	
1.0013)	1.0007)	1.0011)	1.0001)	1.0006)	1.0032)	1.0029)	1.0011)	1.0019)	
Lag 15	1.0009**	1.0002	0.9996	0.9957	1.0002	1.0021**	0.9999	0.9997	1.0001	
(1.0002,	(0.9994,	(0.9981,	(0.9915,	(0.9997,	(1.0009,	(0.9973,	(0.9983,	(0.9985,	
1.0016)	1.0009)	1.0011)	0.9999)	1.0007)	1.0033)	1.0026)	1.0011)	1.0016)	
Lag 16	1.0012**	1.0004	0.9996	0.9955	1.0002	1.0022 **	0.9999	0.9997	0.9998	
(1.0005,	(0.9996,	(0.9981,	(0.9911,	(0.9997,	(1.0010,	(0.9971,	(0.9983,	(0.9982,	
1.0020)	1.0012)	1.0012)	0.9999)	1.0007)	1.0034)	1.0026)	1.0011)	1.0014)	
Lag 17	1.0015**	1.0007	0.9996	0.9953	1.0003	1.0023 **	0.9998	0.9997	0.9996	
(1.0007,	(0.9998,	(0.9980,	(0.9906,	(0.9997,	(1.0009,	(0.9969,	(0.9982,	(0.9978,	
1.0023)	1.0015)	1.0013)	1.0001)	1.0008)	1.0036)	1.0027)	1.0012)	( 1.0013)	
Lag 18	1.0018**	1.0009	0.9996	0.9952	1.0003	1.0023 **	0.9998	0.9997	0.9993	
(1.0010,	(1.0000,	(0.9978,	(0.9900,	(0.9997,	(1.0008,	(0.9966,	(0.9981,	(0.9975,	
1.0027)	1.0019)	1.0014)	1.0004)	1.0009)	1.0037)	1.0029)	1.0014)	1.0012)	
Lag 19	1.0022**	1.0012**	0.9997	0.995	1.0004	1.0022 **	0.9997	0.9997	0.9991	
(1.0012,	(1.0002,	(0.9977,	(0.9893,	(0.9997,	(1.0007,	(0.9963,	(0.9980,	(0.9971,	
1.0031)	1.0022)	1.0016)	, 1.0006)	1.0010)	1.0037)	1.0032)	1.0015)	1.0011)	
Lag 20	1.0024**	1.0014**	0.9997	0.9948	1.0004	1.0021**	0.9998	0.9997	0.9989	
(1.0015,	(1.0004,	(0.9976,	(0.9888,	(0.9997,	(1.0005,	(0.9962,	(0.9979,	(0.9968,	
1.0034)	1.0025)	1.0018)	1.0008)	1.0011)	1.0037)	1.0034)	1.0016)	1.0011)	
Lag 21	1.0027**	1.0017**	0.9998	0.9946	1.0005	1.0019 **	0.9999	0.9997	0.9987	
(1.0017,	(1.0006,	(0.9976,	(0.9884,	(0.9997,	(1.0003,	(0.9961,	(0.9978,	(0.9965,	
1.0037)	1.0027)	1.0019)	1.0008)	1.0012)	1.0036)	1.0036)	1.0016)	1.0010)	
Lag 22	1.0029**	1.0018**	0.9998	0.9944	1.0005	1.0017**	1	0.9997	0.9986	
(1.0019,	(1.0008,	(0.9977,	(0.9882,	(0.9998,	(1.0000,	(0.9962,	(0.9977,	(0.9964,	
1.0039)	1.0029)	1.0020)	1.0006)	1.0012)	, 1.0034)	1.0038)	1.0016)	1.0008)	
Lag 23	1.0031**	1.0020**	0.9999	0.9942	1.0005	1.0015	1.0002	0.9996	0.9985	
(1.0021,	(1.0009,	(0.9979,	0.9882,	(0.9998,	(0.9998,	(0.9965,	(0.9977,	(0.9964,	
1.0041)	1.0031)	, 1.0020)	1.0002)	1.0012)	1.0031)	1.0039)	1.0015)	1.0007)	
Lag 24	1.0033**	1.0021**	1.0001	0.9940**	1.0005	1.0011	1.0004	0.9996	0.9985	
(1.0023,	(1.0011,	(0.9981,	(0.9882,	(0.9998,	(0.9996,	(0.9969,	(0.9978,	(0.9964,	
1.0042)	1.0031)	1.0021)	0.9998)	1.0011)	1.0027)	1.0040)	, 1.0014)	1.0006)	
Lag 25	1.0033**	1.0022**	1.0003	0.9938**	1.0004	1.0008	1.0007	0.9995	0.9985	
(1.0024,	(1.0012,	(0.9983,	(0.9882,	(0.9998,	(0.9993,	(0.9973,	(0.9978,	(0.9966,	
1.0043)	1.0032)	1.0022)	0.9994)	1.0011)	1.0023)	1.0041)	1.0012)	, 1.0005)	
Lag 26	1.0033**	1.0022**	1.0005	0.9936	1.0004	1.0004	1.0011	0.9994	0.9986	
(1.0024,	(1.0012,	(0.9984,	(0.9878,	(0.9997,	(0.9987,	(0.9975,	(0.9976,	(0.9965,	
1.0043)	1.0033)	1.0025)	0.9995)	1.0011)	1.0020)	1.0047)	1.0013)	1.0008)	
Lag 27	1.0033**	1.0022**	1.0007	0.9934	1.0003	0.9999	1.0015	0.9993	0.9988	
(1.0022,	(1.0010,	(0.9983,	(0.9865,	(0.9995,	(0.9979,	(0.9972,	(0.9971,	(0.9962,	
1.0045)	1.0035)	1.0032)	1.0004)	1.0011)	1.0019)	1.0058)	1.0016)	1.0014)	
Lag 28	1.0032**	1.0021**	1.0011	0.9933	1.0002	0.9993	1.002	0.9993	0.9991	
(1.0017,	(1.0005,	(0.9978,	(0.9842,	(0.9991,	(0.9967,	(0.9963,	(0.9963,	(0.9957,	
1.0048)	1.0038)	1.0043)	1.0025)	1.0013)	1.0020)	1.0077)	1.0022)	( 1.0025)	
Lag 29	1.0030**	1.002	1.0014	0.9931	1	0.9988	1.0026	0.9992	0.9994	
(1.0010,	(0.9997,	(0.9970,	(0.9809,	(0.9985,	(0.9952,	(0.9949,	(0.9951	(0.9948,	
1.0051	1.0042)	1.0059)	1.0055)	1.0015)	1.0023)	1.0104)	1.0032)	1.0040)	
Lag 30	1.0028	1.0017	1.0019	0.993	0.9998	0.9981	1.0032	0.999	0.9999	
(1.0000,	(0.9987,	(0.9960,	(0.9767,	(0.9978,	(0.9934,	(0.9929,	(0.9937,	(0.9938,	
1.0056)	1.0047)	1.0078)	1.0096)	1.0018)	1.0029)	1.0137)	1.0044)	1.0060)	
Coefficient (Standard error) significant **0.01.

Discussion

The DLNM model was used in the study to evaluate the link between PM10 exposure and hospital admission for respiratory diseases in peninsula Malaysia from 2000 to 2019 during the haze period.According to the study’s findings, average daily PM10 concentration exposure was significantly associated and had a delayed effect on the occurrence of hospital admissions for respiratory disorders, according to the study’s findings.

The relative risk of those patients who were admitted rose with an increase in PM10 of 10μg/m3 from the present day (lag 0) until exposure after 30 days (lag 30). In the present day, the immediate hazards of PM10 on respiratory admissions were not significant (RR = 1.001012, 95% CI 0.99888, 1.003151). Similar results were discovered by Tajudin et al.23 in the nearby city of KL over 5 years and Jin et al.22 in Lanzhou that PM10 was also insignificant in lag 0. Sofwan et al.19 on the other hand, found a significant association on the day of exposure, with a risk ratio of (RR = 1.0179, 95% CI 1.0059, 1.0300) that is comparable to studies in other South East Asian cities, including Bangkok (RR = 1.0209, 95% CI 1.0145, 1.0273) and Ho Chi Minh City (RR = 1.007, 95% CI = 1.002, 1.013)24,25. However,in this study there is a significant association delay effect that is noticeable from 17 days (lag 17) to 28 days (lag28) and peaks on the 25th day. An additional study conducted in Hefei discovered that a positive correlation between respiratory morbidity occurred in lag 12 with an RR of 1.06826. Unlike previous studies that discovered a substantial link in the early lag days, this study found that the association could still exist in the short term was slightly delayed but still occurred within the first 30 days of lag. This is due to the large sample size of this study and the fact that it only focuses on one pollutant. Other than that, Hoffmann27 stated that the inconsistency of association lag days depends on study design, sample sizes, diseases prevalence, cohort characteristics, access to healthcare and geographical differences. Predictions varied due to a variety of factors including demographic and socioeconomic characteristics, air pollution levels in various states or regions, and even meteorological factors20.

Gender-specific analysis showed that females were more likely to be affected by PM10 exposure during haze. This might be comparable to China, where women are known to spend more time in the kitchen hence could be more susceptible to particulate matter25,28,29. Thus, in addition to being exposed to haze, this exposed them to biomass burning20. Women may be more responsive to PM2.5 exposure because of greater airway activity and lung particle accumulation28. Nevertheless, both genders increase substantially the risk of admission for respiratory disorders. According to some studies, men are more vulnerable to pollution than women. This is because male smoking rates were substantially greater in some areas, making the individuals more sensitive towards PM1020. In addition to this study, a few other studies20,25,29 agreed that this inconsistency was caused by several factors. The factors that needed to be highlighted were diverse physiological characteristics in both genders, routine activities, smoking habits, occupations, comorbidity factors and others.

This study also looks into how age affects the likelihood of respiratory admission. Despite some researchers’ claims that older generations would increase the likelihood of admission, this study found the opposite. When compared to the young and old age groups, the working age group is found to be the most affected by an increase in PM10 concentration (10μg/m3), with an admission risk of R=1.004. Several factors could have caused the increasing association among the working-age population. First, those in the working age group spent more time outside while commuting to work and exercising, exposing themselves to air pollution for longer periods30. As a result of breathing the same air,overcrowding in public transport will cause infectious diseases to spread easily, including diseases related to breathing. Second, they breathe more air than the elderly group because they have larger lung capacities31. This is also supported by the data from this study that, the difference in elderly group admission due to respiratory diseases was 12.32% lower than the working age group. Thirdly, because air pollution has become a severe public health issue, younger generations may experience health issues as a result of pollution exposure earlier than anticipated32. Although the working age group has a higher risk of admission, the severity of the respiratory diseases was differing among each group. In addition, differences in lung function, dermal absorption, physical activity, and personal care products can also contribute as risk modifiers.

Other than stratification of gender and age, this study also focused on the risk of respiratory admission according to the categories of respiratory disorders. The results reported that lung diseases caused by external agents (CD5) are positively significant in all short-term periods (lag 07 to lag 030), whereas diseases of the upper respiratory tract (CD1) had significant relative risks during lag 021 and lag 031, and influenza, pneumonia, and other acute lower respiratory infections (CD2) had significant relative risks during lag 030. The diseases listed in lung diseases caused by external agents were Pneumococcus and Other lung diseases due to external agents. Meanwhile, the diseases listed under influenza, pneumonia, and other acute lower respiratory infections (CD2) were influenza, viral pneumonia, other pneumonia, acute bronchitis , acute bronchiolitis and Unspecified acute lower respiratory infections. For diseases of the upper respiratory tract (CD1), the diseases were Acute nasopharyngitis, Acute sinusitis, Acute pharyngitis, Acute tonsillitis, Acute laryngitis and tracheitis and other diseases related to upper respiratory tracts. The total of these three categories of respiratory diseases accounts for 91.93% of hospitalized patients. As a result, the majority of patients fell into one of these three diagnosis categories. According to this classification, exposure to PM10 increases the likelihood of developing these disease categories.

Though there is a relationship between PM10 and respiratory diseases that occurred during haze season, this study also has some limitations. If the study matched the hospitals with the closest monitoring stations in each state, it may identify which region is most accountable for hospital admissions and resulting in a more precise effect of exposure-response of PM10. Other than that, this study can be improved by including admission to private hospitals rather than only admission to government hospitals. The data also has a restriction on accessing whether a patient’s admission due to the same respiratory diseases has occurred previously. Additionally, the comorbidity factors are omitted due to a lack of data. This study had the number of contributions despite of these limitations. There is no study that explores the association of PM10 exposure specifically in peninsula Malaysia as a whole. In this large time series study during haze in all states of peninsula Malaysia between 2000 and 2019, increases in PM10 exposure were associated with an increase in hospitalization for respiratory illnesses. The respiratory admission, lag impact, and PM10 exposure were all comprehensively evaluated using the DLNM adopted in this study. This study further explored the association according to the age group, gender and respiratory diseases categories.

To enhance the comprehensiveness of the study, it is recommended to broaden the focus beyond a single pollutant and explore the effects of multiple pollutants. Additionally, expanding the geographical scope from peninsula Malaysia to encompass the entire country would provide a more holistic understanding of the relationship between pollutants and respiratory diseases, considering potential regional variations. Furthermore, conducting research within specific subpopulations, such as rural communities, industrial areas, and vulnerable groups, within peninsula Malaysia can unveil nuanced patterns and disparities in the impact of pollutants on respiratory health, contributing to a more thorough and representative analysis of environmental influences on public health.

Conclusion

This study found that short-term exposure to PM10 prior to 30 days significantly increased the risk of hospitalization for respiratory diseases in peninsula Malaysia during haze period, particularly for lung diseases caused by external agents, influenza, pneumonia, and other acute lower respiratory infections and diseases of the upper respiratory tract. It was discovered that women and people in the working age group were more sensitive to exposure and more susceptible to it.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-63591-x.

Acknowledgements

This study was funded by The National University of Malaysia (UKM) under Encouragement Research Grant (Geran Galakan Penyelidikan: GGP-2020-027). Sincere appreciation for the cooperation in data collection from the National Medical Research Register (NMRR), the Health Information Centre Malaysia, the Ministry of Higher Education Malaysia, and the Ministry of Health Malaysia. The authors thank the Department of Environment (DOE) for the provisions to utilize the data for this study. Our sincere gratitude goes out to Dr. Nurzawani Md Sofwan from UITM Kuching for her guidance and advice on the model development employed in this study.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection, analysis and editing were performed by Siti Wafiah Hanin Mohd Zulkifli, Humaida Banu Samsudin and Noriza Majid. Conceptualization: Siti Wafiah Hanin Mohd Zulkifli ; Methodology: Siti Wafiah Hanin Mohd Zulkifli; Formal analysis and investigation: Siti Wafiah Hanin Mohd Zulkifli, Humaida Banu Samsudin, Noriza Majid ; Writing - original draft preparation: Siti Wafiah Hanin Mohd Zulkifli ; Writing -review and Editing : Humaida Banu Samsudin, Noriza Majid ; Funding acquisition: Humaida Banu Samsudin; Resources: Siti Wafiah Hanin Mohd Zulkifli, Humaida Banu Samsudin ; Supervision: Humaida Banu Samsudin, Noriza Majid.

Data availability

The datasets generated during and analysed during the current study are not publicly available due to agreement from Ministry of Health Malaysia that the data cannot be published elsewhere but are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Siti Wafiah Hanin Mohd Zulkifli and Noriza Majid.
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