Exploring the link between ambient PM_2.5 concentrations and respiratory diseases in the elderly: a study in the Muang district of Khon Kaen, Thailand

Background

The World Health Organization (WHO) estimates that 90 % of the world’s population is at risk due to excessive exposure to air pollution [1]. Diseases related to air pollution are currently the sixth leading cause of death, with a mortality rate of 9 %, or about 4.51 million people worldwide each year [2], 3]. Of these, 30.6 % are stroke deaths, ischemic heart disease (IHD) (26.8 %), lung cancer (LC) (23.9 %), and chronic obstructive pulmonary disease (COPD) (18.7 %) [4], 5]. In Thailand, mortality stemming from air pollution has been steadily increasing since 2017. It is the seventh leading cause of death with a mortality rate of 34.68 per 100,000 people, or about 33,772 deaths with a morbidity rate of 2,363.24 per 100,000 people [6]–8]. The most at-risk group is the elderly, aged 70 years and older, with a mortality rate of 338.95 per 100,000 people [9].

One of the major air pollutants is particulate matter having diameters less than 2.5 μm (PM_2.5). When these particulates are inhaled deeply into the lungs, they penetrate the bronchi and alveoli, causing short-term symptoms, including nose, throat and eye irritation [10]–12], as well as coughing, sneezing, runny nose and shortness of breath. When accumulated over a long period, PM_2.5 exposure can lead to respiratory diseases as well [13]. In Thailand, the morbidity rate for respiratory diseases arising from exposure to PM_2.5 is 1,446.87 per 100,000 people [14], 15]. In Khon Kaen Province (Thailand), the morbidity rate for respiratory diseases aggravated by PM_2.5 exposure among the elderly is 10.1 %. The Muang (City) district has the highest proportion of sick people, 22.76 %, with a morbidity rate of 13.2 % [7], 8]. When classified by disease, the highest prevalence was pneumonia (4.9 %), chronic obstructive pulmonary disease (3.6 %), acute pharyngitis (2.2 %), acute bronchitis (1.3 %), asthma (0.8 %), chronic rhinitis (0.1 %) and influenza (0.1 %) [16].

There are several primary sources of PM_2.5. It has been estimated that burning plant material produces 58,200–407,400 metric tons of particulate matter annually [17]. Also, the transportation sector produces particulates through the use of internal combustion engines that emit hydrocarbon compounds as particulate matter. This is a major cause of air pollution [18]. Additionally, factories of various industries release particulate matter. These industries include iron smelting, rock blasting and soil excavation during mining, as well as oil refining, construction, demolition, mixing operations and transportation of raw materials and finished products [17]. Additionally, waste incineration generates much particulate matter. It was found that incineration of 1 kg of solid waste can generate 45.7 g of fine particulate matter [19]. Finally, there are many development projects such as the construction of railways, roads, and bridges, among others that generate particulate matter and other forms of air pollution [17]. Meteorological conditions are contributing factors to PM_2.5 levels. It has been found that there is a positive correlation between higher daily temperatures and the amount of fine particulate matter in the air [20]. Relative humidity is statistically correlated (negatively and directly) with the level of particulate matter in the atmosphere (p<0.01) [21]. Wind tends to disperse particulate matter, reducing its concentration [20]. Finally, precipitation impacts particulate matter in the air. Increased rainfall reduces the amount of particulate matter in the air [19].

Considering the above-mentioned issues and the persistent PM_2.5 problem in the area (Khon Kaen), the researchers see the importance of studying factors that are related to and responsible for respiratory diseases among the elderly in the Muang district of Khon Kaen. The results of the current study will be used by government officials to set regulatory guidelines and monitor air pollution, especially PM_2.5. This will be useful to reduce air pollution and to provide information for area management with respect to geography and meteorology, as well as improving personal health and hygiene for the purpose of reducing respiratory illnesses among the elderly in the Muang district of Khon Kaen.

Methods

Measures

There were two datasets (Figure 1). Both are specific for Thailand. The first contained primary data about the concentration of PM_2.5 in the atmosphere. These data were directly collected using real-time sensors (Public Health Particulate Matter Sensor: PHPM) [25] (May 2021 to April 2022) that had been calibrated using US EPA methods [22]. The other data set, from the Ministry of Public Health, contained personal data about the study participants who suffered from respiratory diseases made worse by PM_2.5, i.e., asthma (ICD10=J45.0, J45.1, J45.2, J45.3, J45.4 and J44.2), (chronic obstructive pulmonary disease; COPD) (ICD10=J44.1, J44.8 and J44.9), pneumonia (ICD10=J12 and J18), influenza (ICD10=J10 and J11), acute pharyngitis (ICD10=J02.0, J02.8 and J02.9), chronic rhinitis (ICD10=J31.0) and acute bronchitis (ICD10=J20.9) [26], 27]. All of these diseases fall under the category of illnesses with long-term symptoms (January 2020 to December 2021) discussed above, as well as land use data of Khon Kaen Province [28] (January 2018 to December 2019). Transportation data consisted of the cumulative number of registered vehicles, separated by sub-district [29] (January 2020 to December 2021). Industrial plant information for Khon Kaen Province was obtained from the Department of Industrial Works [30] (January 2020 to December 2021). Construction activity was supplied by the various sub-districts of Khon Kaen City Municipality [31] (January 2020 to December 2021). Solid waste incineration and meteorological data were obtained from the Pollution Control Department [32] (January 2020 to December 2021).

Figure 1:

Inclusion and exclusion criteria diagram.

Statistical analysis

The data were analyzed using Stata Version 17. Descriptive statistics were used to analyze demographic data such as gender, age, marital status, educational level, occupation, body mass index (BMI), and comorbidities, as well as tobacco and alcohol consumption. They were analyzed and presented in terms of standard deviation (SD), median, minimum-maximum values, frequency, and percentages. Inferential statistics were used to present geographical and meteorological factors as well as the concentration of ambient PM_2.5. This was done to determine the factors associated with respiratory diseases. The generalized linear mixed model (GLMM) was used by controlling cluster effects (sub-district level) with a binomial distribution and placing them in the random effects equations using an exchangeable correlation structure linked sub-equations of the Logit family of binomials to yield an adjusted odds ratio (AOR) with a 95 % confidence interval (95 % CI).

This research was reviewed and approved by the Human Research Ethics Committee of Khon Kaen University (No. HE642184) on July 3, 2021 (Figure 2 and Table 1).

Figure 2:

Conceptual framework.

Table 1:

The power of test analysis for estimating proportions in the sample group.

Power	n	Pcnt n	P0	P1	Odds ratio	ρ	Alpha	Beta
		X=1 (R)
1.00000	43,534	32.000	0.480	0.740	3.083	0.500	0.05000	0.00000
1.00000	43,534	32.000	0.480	0.740	3.083	0.600	0.05000	0.00000
1.00000	43,534	32.000	0.480	0.740	3.083	0.700	0.05000	0.00000
1.00000	43,534	32.000	0.480	0.740	3.083	0.800	0.05000	0.00000

Results

Demographic characteristics of the elderly in the Muang district of Khon Kaen

Of these 43,534 people, the prevalence of respiratory diseases was 16.8 % (n=7,328), with the most common being pneumonia (8.6 %), acute pharyngitis (4.1 %), and COPD (2.2 %) (Figure 3). About half (50.1 %) were female, with a range of early elderly (41.1 %). About 80 % were married and had less than a secondary level of education (93.1 %). Also, most of them were above the BMI threshold (58.3 %) and often unwell with diabetes (19.5 %), hypertension (5.2 %), and cardiovascular disease (2.7 %). At the same time, most of the factors had a statistically significant relationship (p<0.05) (see Table 2).

Figure 3:

Prevalence of respiratory diseases of the elderly.

Table 2:

Demographic characteristics of the elderly population in the study.

Variables	No illnesses (n=36,206)	Illnesses (n=7,328)	Total (n=43,534)	p-Value
Gender, female, n (%)	18,497 (51.1)	3,319 (45.3)	21,816 (50.1)	<0.001
Age, years, n, %				0.368
Early elderly (60–69)	14,914 (41.2)	2,978 (40.6)	17,892 (41.1)
Middle elderly (70–79)	12,362 (34.1)	2,486 (33.9)	14,848 (34.1)
Late elderly (≥80)	8,930 (24.7)	1,864 (25.4)	10,794 (24.8)
Marital status, married, n (%)	28,981 (80.1)	5,736 (78.3)	34,717 (79.8)	<0.001
Educational level, lower secondary school, n (%)	33,647 (92.9)	6,887 (94)	40,534 (93.1)	<0.001
Occupation, n (%)				<0.001
Government service or state enterprise	3,173 (8.8)	403 (5.5)	3,576 (8.2)
Farmer	6,820 (18.8)	1,055 (14.4)	7,875 (18.1)
General employee	11,403 (31.5)	2,115 (28.9)	13,518 (31.1)
Self-employed	14,810 (40.9)	3,755 (51.2)	18,565 (42.6)
Body mass index, n (%)				<0.001
Below threshold (<18.5 kg m−2)	2,810 (7.8)	569 (7.8)	3,379 (7.8)
Normal (18.5–22.9 kg m−2)	12,652 (34.9)	2,122 (29)	14,774 (33.9)
Above threshold (≥23.0 kg m−2)	20,744 (57.3)	4,637 (63.3)	25,381 (58.3)
Comorbidities
Diabetes, n (%)	6,274 (17.3)	2,262 (30.9)	8,536 (19.5)	<0.001
High blood pressure, n (%)	1,853 (5.1)	408 (5.6)	2,261 (5.2)	0.117
Cardiovascular disease, n (%)	946 (2.6)	210 (2.9)	1,156 (2.7)	0.224
Hyperlipidemia, n (%)	239 (0.7)	50 (0.7)	541 (0.7)	0.832
Smoking, smoker, n (%)	5,486 (15.2)	1,631 (22.3)	7,117 (16.4)	<0.001
Alcohol consumption, drink alcohol, n (%)	6,626 (18.3)	1,419 (19.4)	8,045 (18.5)	0.033

1. Boldface indicates statistical significance (p<0.25) for bivariable analysis.

Discussion

The study findings indicate that males face a higher susceptibility to respiratory diseases. Consistent with previous research, increased exposure to elevated levels of PM_2.5 contributes to a heightened risk of lung cancer, especially in males. This disparity may be attributed to males engaging in riskier behaviors such as smoking and alcohol consumption than females [33]. Additionally, individuals below the age of 80 tend to engage in more outdoor activities, resulting in greater exposure to PM_2.5 and a subsequent risk of developing respiratory diseases like COPD [34], 35]. Unmarried elderly individuals constitute a high-risk group due to lower levels of neurotransmitters, including dopamine, oxytocin, and norepinephrine, which can increase stress and vulnerability to illness [36]. Furthermore, the self-employed, contractors and farmers who work outside and rarely use protective equipment. They are therefore more susceptible to chemical and PM_2.5 exposure, which can irritate the respiratory tract [37], 38].

Individuals with non-standard body mass index (BMI) face elevated health risks. This aligns with earlier studies that demonstrated a reduction in lung function, specifically decreased forced expiratory volume (FEV1) with every 1 kg m⁻² increase in BMI. Females experience a 9 % decrease in FEV1, while there is no notable difference observed for males [39]. The respiratory muscles of overweight and underweight individuals are weaker compared to those of individuals with normal weight, leading to lower respiratory function and more severe respiratory diseases [40]. Non-communicable diseases (NCDs) such as high blood pressure, diabetes, and cardiovascular diseases often coexist with the primary diagnosis, resulting in more severe illnesses and subsequent complications, especially among smokers [41]. Smoking significantly heightens the risk of developing respiratory diseases (AOR: 2.56; 95 % CI [1.92–3.41]) [42], as it causes inflammation of lung tissue and airways, rendering individuals more susceptible to respiratory infections [43], 44].

Regarding PM_2.5 concentration, an increase of 10 μg m⁻³ corresponds to a higher likelihood of developing respiratory diseases (AOR: 3.98; 95 % CI [1.53–10.31]). Furthermore, a 10 μg m⁻³ increase in ambient PM_2.5 elevates the risk of lung cancer (AOR: 1.13; 95 % CI [1.11–1.15]) [45] and asthma (AOR: 1.03; 95 % CI [1.01–1.05]) [46]. PM_2.5 is often contaminated with other substances such as PM₁₀, ozone, nitrogen dioxide, and sulfur dioxide, all of which can cause irritation to the respiratory system. When these particles are inhaled deeply into the lungs, they can penetrate the bronchi and alveoli, leading to nose, throat, and eye irritation [10]–12], as well as symptoms like coughing, sneezing, runny nose, shortness of breath, and respiratory diseases [13].

Individuals residing in sub-districts where more than 5 % of the area is dedicated to sugarcane cultivation exhibit a higher likelihood of developing respiratory diseases. The reason behind this is the common practice of burning sugarcane leaves immediately before harvest, which generates a substantial amount of PM_2.5 [17]. Likewise, biomass power plants pose a similar risk in specific sub-districts, with people residing near these facilities facing an increased susceptibility to respiratory diseases (AOR: 1.28; 95 % CI [1.02–1.62]). Particularly for those living within a 1 km radius of a biomass power plant, there is a heightened risk of allergies (AOR: 2.4; 95 % CI [1.5–4.0]), asthma (AOR: 2.1; 95 % CI [1.0–4.4]), and COPD (AOR: 2.7; 95 % CI [1.0–8.4]) compared to those residing farther away [47]. Biomass power plants burn diverse waste materials as fuel to generate electricity, resulting in the production of fine particulates that contribute to subsequent illnesses [48].

Conclusions

The findings of this study demonstrate that meteorological factors significantly enhance disease occurrence, which will be useful information for the general public, especially the elderly, assisting them in taking proactive measures to safeguard themselves against PM_2.5 exposure. These benefits extend to various groups, including farmers, general contractors, and individuals at risk due to personal factors and non-communicable diseases (NCDs). To effectively address the PM_2.5 issue, it is imperative for relevant agencies to actively monitor and urgently tackle the problem. Implementing concrete policies at the local level is essential. For instance, adoption of modern technology, such as low-emission machines and tractors, should be prioritized to reduce exhaust emissions. Additionally, economic factors that contribute to the illegal burning of sugarcane must be addressed, and solutions for issues in biowaste power plants should be sought to mitigate the PM_2.5 levels. Constant monitoring of particulate matter removal systems is necessary to ensure their effectiveness. Furthermore, fostering collaboration with communities and localities is crucial, as it allows for stakeholder feedback and collaborative problem-solving. By working together, effective solutions can be formulated and implemented.