Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia

GIS-based spatiotemporal mapping of malaria prevalence and exploration of environmental inequalities

Malaria poses a significant threat to global health, with particular severity in Nigeria. Understanding key factors influencing health outcomes is crucial for addressing health disparities. Disease mapping plays a vital role in assessing the geographical distribution of diseases and has been instrumental in epidemiological research. By delving into the spatiotemporal dynamics of malaria trends, valuable insights can be gained into population dynamics, leading to more informed spatial management decisions. This study focused on examining the evolution of malaria in Nigeria over twenty years (2000-2020) and exploring the impact of environmental factors on this variation. A 5-year-period raster map was developed using malaria indicator survey data for Nigeria's six geopolitical zones. Various spatial analysis techniques, such as point density, spatial autocorrelation, and hotspot analysis, were employed to analyze spatial patterns. Additionally, statistical methods, including Principal Component Analysis, Spearman correlation, and Ordinary Least Squares (OLS) regression, were used to investigate relationships between indicators and develop a predictive model. The study revealed regional variations in malaria prevalence over time, with the highest number of cases concentrated in northern Nigeria. The raster map illustrated a shift in the distribution of malaria cases over the five years. Environmental factors such as the Enhanced Vegetation Index, annual land surface temperature, and precipitation exhibited a strong positive association with malaria cases in the OLS model. Conversely, insecticide-treated bed net coverage and mean temperature negatively correlated with malaria cases in the same model. The findings from this research provide valuable insights into the spatiotemporal patterns of malaria in Nigeria and highlight the significant role of environmental drivers in influencing disease transmission. This scientific knowledge can inform policymakers and aid in developing targeted interventions to combat malaria effectively.

Modelling of tuberculosis dynamics incorporating indirect transmission of contaminated environment and infectivity of smear-negative individuals: A case study for Xinjiang, China

Xinjiang has been one of the high incidence areas of pulmonary tuberculosis (PTB) in China. Besides being infected by direct contacting with active PTB individuals (direct infection), the susceptible would be infected because of the exposure to the environment contaminated by Mycobacterium tuberculosis (indirect infection). Active PTB individuals include not only the smear-positive PTB (PTB+) but also the smear-negative PTB (PTB-) who are infectious due to their ability to release tiny Mycobacterium tuberculosis particles even in the absence of visible Mycobacterium tuberculosis in sputum. By taking account of direct/indirect infection and the difference between PTB+ and PTB- individuals in transmission capability, a periodic dynamical PTB transmission model is proposed. The model is fitted to the newly monthly PTB+ and PTB- cases in Xinjiang from 2008 to 2017 by Markov Chain Monte Carlo algorithm. Moreover, global sensitivity analysis is constructed to address the uncertainty of some key parameters by using Latin hypercube sampling and partial rank correlation coefficient methods. Basic reproduction number R0 for PTB transmission in Xinjiang is estimated to be 2.447 (95% CrI:(1.203, 3.844)), indicating that PTB has been prevalent in Xinjiang over the study period. Our results suggest that reducing the direct/indirect transmission rates, early screening, isolating and treating the latent, PTB+ and PTB- individuals, and enhancing the clearance of Mycobacterium tuberculosis in the environment could more effectively control PTB transmission in Xinjiang. The model fits the reported PTB data well and achieves acceptable prediction accuracy. We believe that our model can provide heuristic support for controlling PTB transmission in Xinjiang.

The association between humidex and tuberculosis: a two-stage modelling nationwide study in China

BACKGROUND

Under a changing climate, the joint effects of temperature and relative humidity on tuberculosis (TB) are poorly understood. To address this research gap, we conducted a time-series study to explore the joint effects of temperature and relative humidity on TB incidence in China, considering potential modifiers.

METHODS

Weekly data on TB cases and meteorological factors in 22 cities across mainland China between 2011 and 2020 were collected. The proxy indicator for the combined exposure levels of temperature and relative humidity, Humidex, was calculated. First, a quasi-Poisson regression with the distributed lag non-linear model (DLNM) was constructed to examine the city-specific associations between humidex and TB incidence. Second, a multivariate meta-regression model was used to pool the city-specific effect estimates, and to explore the potential effect modifiers.

RESULTS

A total of 849,676 TB cases occurred in the 22 cities between 2011 and 2020. Overall, a conspicuous J-shaped relationship between humidex and TB incidence was discerned. Specifically, a decrease in humidex was positively correlated with an increased risk of TB incidence, with a maximum relative risk (RR) of 1.40 (95% CI: 1.11-1.76). The elevated RR of TB incidence associated with low humidex (5th humidex) appeared on week 3 and could persist until week 13, with a peak at approximately week 5 (RR: 1.03, 95% CI: 1.01-1.05). The effects of low humidex on TB incidence vary by Natural Growth Rate (NGR) levels.

CONCLUSION

A J-shaped exposure-response association existed between humidex and TB incidence in China. Humidex may act as a better predictor to forecast TB incidence compared to temperature and relative humidity alone, especially in regions with higher NGRs.

Geo-spatial high-risk clusters of Tuberculosis in the global general population: a systematic review

INTRODUCTION

The objective of this systematic review is to identify tuberculosis (TB) high-risk among the general population globally. The review was conducted using the following steps: elaboration of the research question, search for relevant publications, selection of studies found, data extraction, analysis, and evidence synthesis.

METHODS

The studies included were those published in English, from original research, presented findings relevant to tuberculosis high-risk across the globe, published between 2017 and 2023, and were based on geospatial analysis of TB. Two reviewers independently selected the articles and were blinded to each other`s comments. The resultant disagreement was resolved by a third blinded reviewer. For bibliographic search, controlled and free vocabularies that address the question to be investigated were used. The searches were carried out on PubMed, LILACS, EMBASE, Scopus, and Web of Science. and Google Scholar.

RESULTS

A total of 79 published articles with a 40-year study period between 1982 and 2022 were evaluated. Based on the 79 studies, more than 40% of all countries that have carried out geospatial analysis of TB were from Asia, followed by South America with 23%, Africa had about 15%, and others with 2% and 1%. Various maps were used in the various studies and the most used is the thematic map (32%), rate map (26%), map of temporal tendency (20%), and others like the kernel density map (6%). The characteristics of the high-risk and the factors that affect the hotspot's location are evident through studies related to poor socioeconomic conditions constituting (39%), followed by high population density (17%), climate-related clustering (15%), high-risk spread to neighbouring cities (13%), unstable and non-random cluster (11%).

CONCLUSION

There exist specific high-risk for TB which are areas that are related to low socioeconomic conditions and spectacular weather conditions, these areas when well-known will be easy targets for intervention by policymakers. We recommend that more studies making use of spatial, temporal, and spatiotemporal analysis be carried out to point out territories and populations that are vulnerable to TB.

The association between air pollutants, meteorological factors and tuberculosis cases in Beijing, China: A seven-year time series study

BACKGROUND

Tuberculosis (TB) is a severe public health problem globally. Previous studies have revealed insufficient and inconsistent associations between air pollutants, meteorological factors and TB cases. Yet few studies have examined the associations between air pollutants, meteorological factors and TB cases in Beijing.

OBJECTIVE

The purpose of this study was to explore the impact of air pollutants and meteorological factors on TB in Beijing, and to provide novel insights into public health managers to formulate control strategies of TB.

METHODS

Data on the daily case of TB in Beijing during 2014-2020 were obtained from Chinese tuberculosis information management system. Concurrent data on the daily PM₁₀, PM_2.5, SO₂, NO₂, CO and O₃, were obtained from the online publication platform of the Chinese National Environmental Monitoring Center. Daily average temperature, average wind speed, relative humidity, sunshine duration and total precipitation were collected from the China Meteorological Science Data Sharing Service System. A distributed lag non-linear model was fitted to identify the non-linear exposure-response relationship and the lag effects between air pollutions, meteorological factors and TB cases in Beijing.

RESULTS

In the single-factor model, the excess risk (ER) of TB was significantly positively associated with every 10 μg/m³ increase in NO₂ in lag 1 week (ER: 1.3%; 95% confidence interval [CI]: 0.4%, 2.3%) and every 0.1 m/s increase in average wind speed in lag 5 weeks (ER: 0.3%; 95% CI: 0.1%, 0.5%), and was negatively associated with every 10 μg/m³ increase in O₃ in lag 1 week (ER: -1.2%; 95% CI: -1.8%, -0.5%), every 5 °C increase in average temperature (ER: -1.7%; 95% CI: -2.9%, -0.4%) and every 10% increase in average relative humidity (ER: -0.4%; 95% CI: -0.8%, -0.1%) in lag 10 weeks, respectively. In the multi-factor model, the lag effects between TB cases and air pollutants, meteorological factors were similar. The subgroup analysis suggests that the effects of NO₂, O₃, average wind speed and relative humidity on TB were greater in male or labor age subgroup, while the effect of CO was greater in the elderly. In addition, no significant associations were found between PM_2.5, SO_2, sunshine duration and TB cases.

CONCLUSION

Our findings provide a better understanding of air pollutants and meteorological factors driving tuberculosis occurrence in Beijing, which enhances the capacity of public health manager to target early warning and disease control policy-making.

Survival Trend of Tuberculosis Patients and Risk Factors Associated with Mortality and Developing Drug-Resistant Tuberculosis in Hospital Pulau Pinang, Malaysia: A Retrospective Study

BACKGROUND

Multidrug resistance TB (MDR-TB) has emerged as a public health issue worldwide, and the mortality rate is worrying. Therefore, this study was conducted to investigate the factors related to MDR-TB occurrence and the survival experience of TB patients.

METHODS

A retrospective cohort study was conducted at Hospital Pulau Pinang in Malaysia. Medical records of active TB patients from 2014-2018 were reviewed. Cox regression was used to identify the factors associated with MDR-TB development and mortality among TB patients.

RESULTS

The patients had a mean age of 48.84 ± 16.713 years, and a majority of the Chinese race (46.4%). Out of 351 TB patients, 325 (92.6%) were drug-susceptible TB, and 26 (7.4%) were diagnosed with MDR-TB. Among drug-susceptible TB patients, 245 (75.4%) achieved successful outcomes, and 73 (22.5%) passed away. In multivariable Cox regression, drug addiction, levels of white blood cells, urea, platelets, and albumin were significantly associated with death. Relapsed TB, alcohol consumption, and being single were significant risk factors for MDR-TB development.

CONCLUSION

Patients achieved a success rate of 75.4%, which is encouraging but still far below the WHO target (at least an 85% success rate) and has room for further improvement.

Association between averaged meteorological factors and tuberculosis risk: A systematic review and meta-analysis

Inconsistencies were discovered in the findings regarding the effects of meteorological factors on tuberculosis (TB). This study conducted a systematic review of published studies on the relationship between TB and meteorological factors and used a meta-analysis to investigate the pooled effects in order to provide evidence for future research and policymakers. The literature search was completed by August 3rd, 2021, using three databases: PubMed, Web of Science and Embase. Relative risks (RRs) in included studies were extracted and all effect estimates were combined together using meta-analysis. Subgroup analyses were carried out based on the resolution of exposure time, regional climate, and national income level. A total of eight studies were included after screening for inclusion and exclusion criteria. Our results show that TB risk was positively correlated with precipitation (RR = 1.32, 95% CI: 1.14, 1.51), while temperature (RR = 1.15, 95% CI: 1.00, 1.32), humidity (RR = 1.05, 95% CI: 0.99, 1.10), air pressure (RR = 0.89, 95% CI: 0.69, 1.14) and sunshine duration (RR = 0.95, 95% CI: 0.80, 1.13) all had no statistically significant correlation. Subgroup analysis shows that quarterly measure resolution, low and middle Human Development Index (HDI) level and subtropical climate increase TB risk not only in precipitation, but also in temperature and humidity. Moreover, less heterogeneity was observed in "high and extremely high" HDI areas and subtropical areas than that in other subgroups (I² = 0%). Precipitation, a subtropical climate, and a low HDI level are all positive influence factors to tuberculosis. Therefore, residents and public health managers should take precautionary measures ahead of time, especially in extreme weather conditions.

Effects and Interaction of Meteorological Factors on Pulmonary Tuberculosis in Urumqi, China, 2013–2019

Background

Most existing studies have only investigated the delayed effect of meteorological factors on pulmonary tuberculosis (PTB). However, the effect of extreme climate and the interaction between meteorological factors on PTB has been rarely investigated.

Methods

Newly diagonsed PTB cases and meteorological factors in Urumqi in each week between 2013 and 2019 were collected. The lag-exposure-response relationship between meteorological factors and PTB was analyzed using the distributed lag non-linear model (DLNM). The generalized additive model (GAM) was used to visualize the interaction between meteorological factors. Stratified analysis was used to explore the impact of meteorological factors on PTB in different stratification and RERI, AP and SI were used to quantitatively evaluate the interaction between meteorological factors.

Results

A total of 16,793 newly diagnosed PTB cases were documented in Urumqi, China from 2013 to 2019. The median (interquartile range) temperature, relative humidity, wind speed, and PTB cases were measured as 11.3°C (−5.0–20.5), 57.7% (50.7–64.2), 4.1m/s (3.4–4.7), and 47 (37–56), respectively. The effects of temperature, relative humidity and wind speed on PTB were non-linear, which were found with the “N”-shaped, “L”-shaped, “N”-shaped distribution, respectively. With the median meteorological factor as a reference, extreme low temperature was found to have a protective effect on PTB. However, extreme high temperature, extreme high relative humidity, and extreme high wind speed were found to increase the risk of PTB and peaked at 31.8°C, 83.2%, and 7.6 m/s respectively. According to the existing monitoring data, no obvious interaction between meteorological factors was found, but low temperature and low humidity (RR = 1.149, 95%CI: 1.003–1.315), low temperature and low wind speed (RR = 1.273, 95%CI: 1.146–1.415) were more likely to cause the high incidence of PTB.

Conclusion

Temperature, relative humidity and wind speed were found to play vital roles in PTB incidence with delayed and non-linear effects. Extreme high temperature, extreme high relative humidity, and extreme high wind speed could increase the risk of PTB. Moreover, low temperature and low humidity, low temperature and low wind speed may increase the incidence of PTB.

Prediction of tuberculosis cases based on sociodemographic and environmental factors in gombak, Selangor, Malaysia: A comparative assessment of multiple linear regression and artificial neural network models

Background

Early prediction of tuberculosis (TB) cases is very crucial for its prevention and control. This study aims to predict the number of TB cases in Gombak based on sociodemographic and environmental factors.

Methods

The sociodemographic data of 3325 TB cases from January 2013 to December 2017 in Gombak district were collected from the MyTB web and TB Information System database. Environmental data were obtained from the Department of Environment, Malaysia; Department of Irrigation and Drainage, Malaysia; and Malaysian Metrological Department from July 2012 to December 2017. Multiple linear regression (MLR) and artificial neural network (ANN) were used to develop the prediction model of TB cases. The models that used sociodemographic variables as the input datasets were referred as MLR1 and ANN1, whereas environmental variables were represented as MLR2 and ANN2 and both sociodemographic and environmental variables together were indicated as MLR3 and ANN3.

Results

The ANN was found to be superior to MLR with higher adjusted coefficient of determination (R²) values in predicting TB cases; the ranges were from 0.35 to 0.47 compared to 0.07 to 0.14, respectively. The best TB prediction model, that is, ANN3 was derived from nationality, residency, income status, CO, NO₂, SO₂, PM₁₀, rainfall, temperature, and atmospheric pressure, with the highest adjusted R² value of 0.47, errors below 6, and accuracies above 96%.

Conclusions

It is envisaged that the application of the ANN algorithm based on both sociodemographic and environmental factors may enable a more accurate modeling for predicting TB cases.