Correlational study of air pollution-related diseases (asthma, conjunctivitis, URTI and dengue) in Johor Bahru, Malaysia

Jamaludin, Aaishah Radziah; Yusof, Fadhilah; Lokoman, Rahmah Mohd; Noor, Zainura Zainoon; Alias, Noreliza; Aris, Nurazimah M.

doi:10.11113/mjfas.v13n4-1.897

Cited by 6 publications

(6 citation statements)

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“…This finding motivates our contribution, in the sense that PIT based on negative binomial distributions shows the better fit of the data than the corresponding PIT based on Poisson. This study at the same time could solve the limitation found in the previous study, as proposed [9]. Another advantage of this study is by using QMLE we avoid the complicated likelihood functions and at the same time it is still possible to obtain consistent estimators whose standard error can be robustly estimated [15].…”

Section: Performance Measuresmentioning

confidence: 80%

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Modelling Asthma Cases by Count Analysis Approach: Poisson INGARCH and Negative Binomial INGARCH

Jamaludin

Yusof

Suhartono³

2020

MATEMATIKA

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Johor Bahru with its rapid development where pollution is an issue that needs to be considered because it has contributed to the number of asthma cases in this area. Therefore, the goal of this study is to investigate the behaviour of asthma disease in Johor Bahru by count analysis approach namely; Poisson Integer Generalized Autoregressive Conditional Heteroscedasticity (Poisson-INGARCH) and Negative Binomial INGARCH (NB-INGARCH) with identity and log link function. Intervention analysis was conducted since the outbreak in the asthma data for the period of July 2012 to July 2013. This occurs perhaps due to the extremely bad haze in Johor Bahru from Indonesian fires. The estimation of the parameter will be done by quasi-maximum likelihood estimation. Model assessment was evaluated from the Pearson residuals, cumulative periodogram, the probability integral transform (PIT) histogram, log-likelihood value, Akaike’s Information Criterion (AIC) and Bayesian information criterion (BIC). Our result shows that NB-INGARCH with identity and log link function is adequate in representing the asthma data with uncorrelated Pearson residuals, higher in log likelihood, the PIT exhibits normality yet the lowest AIC and BIC. However, in terms of forecasting accuracy, NB-INGARCH with identity link function performed better with the smaller RMSE (8.54) for the sample data. Therefore, NB-INGARCH with identity link function can be applied as the prediction model for asthma disease in Johor Bahru. Ideally, this outcome can assist the Department of Health in executing counteractive action and early planning to curb asthma diseases in Johor Bahru.

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Section: Performance Measuresmentioning

confidence: 80%

“…However, warmer temperatures were not related to asthma. Our previous work [9], applied Poisson Generalized Linear Model and Negative Binomial Model to model the asthma disease in Johor Bahru for the period of 2012 to 2013. They found that humidity and temperature have a significant relationship with asthma diseases.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling Asthma Cases by Count Analysis Approach: Poisson INGARCH and Negative Binomial INGARCH

Jamaludin

Yusof

Suhartono³

2020

MATEMATIKA

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“…Twelve articles from 10 regions met the inclusion criteria and were included in the meta-analysis (see Figure 1), covering 30,103,982 conjunctivitis patients. Among the 12 included studies, five were case-crossover studies [4,5,9,27,28], four were time-series studies [6,14,29,30], and three were other studies (e.g., spatial analysis and multi-level regression). Table 1 and Table A2 summarize the basic characteristics of the included studies.…”

Section: Resultsmentioning

confidence: 99%

“…However, the evidence so far is inconsistent (Figure 2). For instance, Fu et al [5] revealed that the risk of NO 2 and conjunctivitis in the population was significant, with an RR value of 1.0403 (95%CI: 1.0228, 1.0581), while Jamaludin et al [30] did not find any significant effects on the risk of conjunctivitis in the population, with an RR value of 0.9989 (95%CI: 0.9205, 1.0840). For PM 10 , Chang et al’s [4] study revealed that PM 10 was significantly associated with the conjunctivitis risk among people, with an RR value of 1.0020 (95%CI: 1.0005, 1.0036).…”

Section: Discussionmentioning

confidence: 99%

“…For SO 2 , Fu et al’s study [5] revealed that the risk of conjunctivitis between SO 2 and the population was significant, with an RR value of 1.0480 (95%CI: 1.0040, 1.0939). In the study of Jamaludin et al [30], SO 2 had a protective effect on the conjunctivitis risk among people, with an RR value of 0.8468 (95%CI: 0.7371, 0.9730). Air pollution is gradually occupying an important position in the risk factors of conjunctivitis.…”

Section: Discussionmentioning

confidence: 99%

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Global Associations of Air Pollution and Conjunctivitis Diseases: A Systematic Review and Meta-Analysis

Chen

Yang

Zhang

et al. 2019

IJERPH

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(1) Background: As the most common eye disease diagnosed in emergency departments, conjunctivitis has caused serious health and economic burdens worldwide. However, whether air pollution may be a risk factor for conjunctivitis is still inconsistent among current evidence. (2) Methods: We searched the literature on the relationship between air pollution and conjunctivitis in multiple English databases before 18 March 2019. Meta-analysis, meta-regression, and funnel plots were used to integrate the data, identify the sources of bias, and determine the publication bias, respectively. (3) Results: A total of 2450 papers were found, 12 of which were finally included. The pooled relative risk for each 10 μg/m3 increase of air pollution on conjunctivitis was 1.0006 (95%CI: 0.9993–1.0019) for CO, 1.0287 (1.0120–1.0457) for NO2, 1.0089 (1.0030–1.0149) for O3, 1.0004 (0.9976–1.0032) for PM2.5, 1.0033 (0.9982–1.0083) for PM10, and 1.0045 (0.9908–1.0185) for SO2. In the subgroup, PM2.5 and O3 had a greater impact on conjunctivitis risk in women than in men, and people <18 years old than those ≥18 years old. Relative humidity significantly modified the risk of O3 on conjunctivitis (p = 0.023), explaining 45% of the between-study heterogeneity. (4) Conclusion: Globally, air pollution has considerable health risks for conjunctivitis. Females and the youth were more vulnerable to PM2.5, NO2, and O3. Reductions of air pollution levels are still warranted to protect the vulnerable populations.

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Correlation between air pollution and prevalence of conjunctivitis in South Korea using analysis of public big data

Nam

Shin

Han

et al. 2022

Sci Rep

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This study investigated how changes in weather factors affect the prevalence of conjunctivitis using public big data in South Korea. A total of 1,428 public big data entries from January 2013 to December 2019 were collected. Disease data and basic climate/air pollutant concentration records were collected from nationally provided big data. Meteorological factors affecting eye diseases were identified using multiple linear regression and machine learning analysis methods such as extreme gradient boosting (XGBoost), decision tree, and random forest. The prediction model with the best performance was XGBoost (1.180), followed by multiple regression (1.195), random forest (1.206), and decision tree (1.544) when using root mean square error (RMSE) values. With the XGBoost model, province was the most important variable (0.352), followed by month (0.289) and carbon monoxide exposure (0.133). Other air pollutants including sulfur dioxide, PM10, nitrogen dioxides, and ozone showed low associations with conjunctivitis. We identified factors associated with conjunctivitis using traditional multiple regression analysis and machine learning techniques. Regional factors were important for the prevalence of conjunctivitis as well as the atmosphere and air quality factors.

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Correlational study of air pollution-related diseases (asthma, conjunctivitis, URTI and dengue) in Johor Bahru, Malaysia

Cited by 6 publications

References 0 publications

Modelling Asthma Cases by Count Analysis Approach: Poisson INGARCH and Negative Binomial INGARCH

Modelling Asthma Cases by Count Analysis Approach: Poisson INGARCH and Negative Binomial INGARCH

Global Associations of Air Pollution and Conjunctivitis Diseases: A Systematic Review and Meta-Analysis

Correlation between air pollution and prevalence of conjunctivitis in South Korea using analysis of public big data

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