Households living in the close vicinity of shoreline are constantly threatened by various climate change impacts. Community awareness towards climate change is a subject of considerable study as adequate knowledge is a preliminary step for adaptation decision making. An important question is how coastal communities perceive climatic variation, sea level rise and coastal hazard impacts and the socio-economic factors that affect their level of awareness. Thus, this research measures the level of awareness and the factors influencing it based on a household survey (n = 1016) that was conducted 10 critically eroded coastal areas in Selangor. Descriptive statistical analysis reveals that more than half of the households have high level of awareness about climatic variation and sea level, however, there is moderate awareness about the coastal hazard impacts such as human causalities and disease transmission. Even though households are more aware of direct coastal hazard impact such as damages to properties and disruption of daily activities. An independent sample T test indicates that respondents who are male, at working age, educated, involve in natural resource dependent occupations, and had prior exposure to extreme coastal hazards have higher levels of awareness. Research indicated about 55% of all sampled households reflected awareness of climate change, 60% households were aware of sea level rise and 47% households were aware of coastal hazard impact. This study recommends that households in Selangor coast need capacity building and climate change awareness initiatives which would assist household to build adaptive capacity, increase resilience and reduce vulnerability to climate change.
The east coast of Malaysia is frequently hit by monsoon floods every year that severely impact people, particularly those living close to the river bank, which is considered to be the most vulnerable and high-risk areas. We aim to determine the most vulnerable area and understand affected residents of this community who are living in the most sensitive areas caused by flooding events in districts of Temerloh, Pekan, and Kuantan, Pahang. This study involved collecting data for vulnerability index components. A field survey and face-to-face interviews with 602 respondents were conducted 6 months after the floods by using a questionnaire evaluation based on the livelihood vulnerability index (LVI). The findings show that residents in the Temerloh district are at higher risk of flooding damage compared to those living in Pekan and Kuantan. Meanwhile, the contribution factor of LVI-Intergovernmental Panel on Climate Change (IPCC) showed that Kuantan is more exposed to the impact of climate change, followed by Temerloh and Pekan. Among all the principal components shown, food components were considered to be the most vulnerable. Meanwhile, water components were categorised as the most invulnerable. Preventive planning involves preserving human life, minimising damage to household products, preserving crops and animals, adequate supply of clean water and food, good health and ensuring financial sustainability as an indication of changing livelihoods, sustainable food-storing systems, and other protective steps to curb damage and injury caused by annual flood strikes. Information generated on LVI assessment and adaptation procedures will help policymakers reduce people’s vulnerability in the face of floods and ensure proper plans are put in place in all relevant areas.
The need for an emergency disaster management related to dam has risen up in recent years. This is due to uncertainties in global weather predictions which also affect local Malaysian area. With unpredictable prolonged rainy weather, concerns on events that could lead to flooding has triggered the authority to review the evacuation strategies in critical locations. This paper describes an investigation on the effect of early warning system and people response delay to the rate of fatality in the event of flooding due to dam breach. The Life Safety Model is utilized as a tool for the simulation of people vehicle and building response to 2D hydraulic flow of the river originated from the dam. The study area is based on Kenyir Dam and its surrounding vicinity. A number of scenarios are simulated namely cases with and without early warning system. For the case with early warning system, different triggering time is also investigated. On top of that, the effect of people response delay to the warning system is simulated. It was found that early warning system plays a critical role in reducing the number of fatalities due to flooding. Equally important is the time taken for the community to start evacuating when triggered by the early warning system. From the result LSM, optimum evacuation parameters could be identified and used for the purpose of design, planning and implementation of local emergency evacuation plan in the event of dam-related flooding.
BACKGROUND: Environmental factors have been associated with adverse health effects in epidemiological studies. The main exposure variable is usually determined via prior knowledge or statistical methods. It may be challenging when evidence is scarce to support prior knowledge, or to address collinearity issues using statistical methods. This study aimed to investigate the importance level of environmental variables for the under-five mortality in Malaysia via random forest approach. METHOD: We applied a conditional permutation importance via a random forest (CPI-RF) approach to evaluate the relative importance of the weather- and air pollution-related environmental factors on daily under-five mortality in Malaysia. This study spanned from January 1, 2014 to December 31, 2016. In data preparation, deviation mortality counts were derived through a generalized additive model, adjusting for long-term trend and seasonality. Analyses were conducted considering mortality causes (all-cause, natural-cause, or external-cause) and data structures (continuous, categorical, or all types [i.e., include all variables of continuous type and all variables of categorical type]). The main analysis comprised of two stages. In Stage 1, Boruta selection was applied for preliminary screening to remove highly unimportant variables. In Stage 2, the retained variables from Boruta were used in the CPI-RF analysis. The final importance value was obtained as an average value from a 10-fold cross-validation. RESULT: Some heat-related variables (maximum temperature, heat wave), temperature variability, and haze-related variables (PM10, PM10-derived haze index, PM10- and fire-derived haze index, fire hotspot) were among the prominent variables associated with under-five mortality in Malaysia. The important variables were consistent for all- and natural-cause mortality and sensitivity analyses. However, different most important variables were observed between natural- and external-cause under-five mortality. CONCLUSION: Heat-related variables, temperature variability, and haze-related variables were consistently prominent for all- and natural-cause under-five mortalities, but not for external-cause.
The impacts of climate change and degradation are increasingly felt in Malaysia. While everyone is vulnerable to these impacts, the health and wellbeing of children are disproportionately affected. We carried out a study composed of two major components. The first component is an environmental epidemiology study comprised of three sub-studies: (i) a global climate model (GCM) simulating specific health-sector climate indices; (ii) a time-series study to estimate the risk of childhood respiratory disease attributable to ambient air pollution; and (iii) a case-crossover study to identify the association between haze and under-five mortality in Malaysia. The GCM found that Malaysia has been experiencing increasing rainfall intensity over the years, leading to increased incidences of other weather-related events. The time-series study revealed that air quality has worsened, while air pollution and haze have been linked to an increased risk of hospitalization for respiratory diseases among children. Although no clear association between haze and under-five mortality was found in the case-crossover study, the lag patterns suggested that health effects could be more acute if haze occurred over a longer duration and at a higher intensity. The second component consists of three community surveys on marginalized children conducted (i) among the island community of Pulau Gaya, Sabah; (ii) among the indigenous Temiar tribe in Pos Kuala Mu, Perak; and (iii) among an urban poor community (B40) in PPR Sg. Bonus, Kuala Lumpur. The community surveys are cross-sectional studies employing a socio-ecological approach using a standardized questionnaire. The community surveys revealed how children adapt to climate change and environmental degradation. An integrated model was established that consolidates our overall research processes and demonstrates the crucial interconnections between environmental challenges exacerbated by climate change. It is recommended that Malaysian schools adopt a climate-smart approach to education to instill awareness of the impending climate change and its cascading impact on children’s health from early school age.
The spatial distribution of environmental conditions may influence the dynamics of vectorborne diseases like leptospirosis. This study aims to investigate the global and localised relationships between leptospirosis with selected environmental variables. The association between environmental variables and the spatial density of geocoded leptospirosis cases was determined using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR). A higher prevalence of leptospirosis was detected in areas with higher water vapour pressure (exp(â): 1.12; 95% CI: 1.02 – 1.25) and annual precipitation (exp(â): 1.15; 95% CI: 1.02 – 1.31), with lower precipitation in the driest month (exp(â): 0.85; 95% CI: 0.75 – 0.96) and the wettest quarter (exp(â): 0.88; 95% CI: 0.77 – 1.00). Water vapor pressure (WVP) varied the most in the hotspot regions with a standard deviation of 0.62 (LQ: 0.15; UQ; 0.99) while the least variation was observed in annual precipitation (ANNP) with a standard deviation of 0.14 (LQ: 0.11; UQ; 0.30). The reduction in AICc value from 519.73 to 443.49 indicates that the GWPR model is able to identify the spatially varying correlation between leptospirosis and selected environmental variables. The results of the localised relationships in this study could be used to formulate spatially targeted interventions. This would be particularly useful in localities with a strong environmental or socio-demographical determinants for the transmission of leptospirosis.
Dengue is a complex disease with an increasing number of infections worldwide. This study aimed to analyse spatiotemporal dengue outbreaks using geospatial techniques and examine the effects of the weather on dengue outbreaks in the Klang Valley area, Kuala Lumpur, Malaysia. Daily weather variables including rainfall, temperature (maximum and minimum) and wind speed were acquired together with the daily reported dengue cases data from 2001 to 2011 and converted into geospatial format to identify whether there was a specific pattern of the dengue outbreaks. The association between these variables and dengue outbreaks was assessed using Spearman’s correlation. The result showed that dengue outbreaks consistently occurred in the study area during a 11-year study period. And that the strongest outbreaks frequently occurred in two high-rise apartment buildings located in Kuala Lumpur City centre. The results also show significant negative correlations between maximum temperature and minimum temperature on dengue outbreaks around the study area as well as in the area of the high-rise apartment buildings in Kuala Lumpur City centre.
Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980’s, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.
Plasmodium knowlesi is an emerging species for malaria in Malaysia, particularly in East Malaysia. This infection contributes to almost half of all malaria cases and deaths in Malaysia and poses a challenge in eradicating malaria. The aim of this study was to develop a predictive model for P. knowlesi susceptibility areas in Sabah, Malaysia, using geospatial data and artificial neural networks (ANNs). Weekly malaria cases from 2013 to 2014 were used to identify the malaria hotspot areas. The association of malaria cases with environmental factors (elevation, water bodies, and population density, and satellite images providing rainfall, land surface temperature, and normalized difference vegetation indices) were statistically determined. The significant environmental factors were used as input for the ANN analysis to predict malaria cases. Finally, the malaria susceptibility index and zones were mapped out. The results suggested integrating geospatial data and ANNs to predict malaria cases, with overall correlation coefficient of 0.70 and overall accuracy of 91.04%. From the malaria susceptibility index and zoning analyses, it was found that areas located along the Crocker Range of Sabah and the East part of Sabah were highly susceptible to P. knowlesi infections. Following this analysis, targetted entomological mapping and malaria control programs can be initiated.
In the Southeast Asian region, the Philippines and Malaysia are two of the most affected by Harmful Algal Blooms (HABs). Using long-term observations of HAB events, we determined if these are increasing in frequency and duration, and expanding across space in each country. Blooms of Paralytic Shellfish Toxin (PST)-producing species in the Philippines did increase in frequency and duration during the early to mid-1990s, but have stabilized since then. However, the number of sites affected by these blooms continue to expand though at a slower rate than in the 1990s. Furthermore, the type of HABs and causative species have diversified for both toxic blooms and fish kill events. In contrast, Malaysia showed no increasing trend in the frequency of toxic blooms over the past three decades since Pyrodinium bahamense was reported in 1976. However, similar to the Philippines, other PST producers such as Alexandrium minutum and Alexandrium tamiyavanichii have become a concern. No amnesic shellfish poisoning (ASP) has been confirmed in either Philippines or Malaysia thus far, while ciguatera fish poisoning cases are known from the Philippines and Malaysia but the causative organisms remain poorly studied. Since the 1990s and early 2000s, recognition of the distribution of other PST-producing species such as species of Alexandrium and Gymnodinium catenatum in Southeast Asia has grown, though there has been no significant expansion in the known distributions within the last decade. A major more recent problem in the two countries and for Southeast Asia in general are the frequent fish-killing algal blooms of various species such as Prorocentrum cordatum, Margalefidinium polykrikoides, Chattonella spp., and unarmored dinoflagellates (e.g., Karlodinium australe and Takayama sp.). These new sites affected and the increase in types of HABs and causative species could be attributed to various factors such as introduction through mariculture and eutrophication, and partly because of increased scientific awareness. These connections still need to be more concretely investigated. The link to the El Niño Southern Oscillation (ENSO) should also be better understood if we want to discern how climate change plays a role in these patterns of HAB occurrences.
Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran’s I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.
BACKGROUND: Studies on the association between smoke haze (hereafter ‘haze’) and adverse health effects have increased in recent years due to extreme weather conditions and the increased occurrence of vegetation fires. The possible adverse health effects on under-five children (U5Y) is especially worrying due to their vulnerable condition. Despite continuous repetition of serious haze occurrence in Southeast Asia, epidemiological studies in this region remained scarce. Furthermore, no study had examined the association accounting for three important aspects (time lag, duration and intensity) concurrently. OBJECTIVE: This study aimed to examine the association between haze and U5Y mortality in Malaysia, considering time lag, duration and intensity of exposure. METHODS: We performed a time-stratified case-crossover study using a generalized additive model to examine the U5Y mortality related to haze in 12 districts in Malaysia, spanning from 2014 to 2016. A ‘haze day’ was characterized by intensity [based on concentrations of particulate matter (PM)] and duration (continuity of haze occurrence, up to 3 days). RESULTS: We observed the highest but non-significant odds ratios (ORs) of U5Y mortality at lag 4 of Intensity-3. Lag patterns revealed the possibility of higher acuteness at prolonged and intensified haze. Stratifying the districts by the 95th-percentile of PM distribution, the ‘low’ category demonstrated marginal positive association at Intensity-2 Duration-3 [OR: 1.210 (95% confidence interval: 1.000, 1.464)]. CONCLUSIONS: We found a null association between haze and U5Y mortality. The different lag patterns of the association observed over different duration and intensity suggest consideration of these aspects in future studies.
Small-scale flash flood events are climate-related disasters which can put multiple aspects of the system at risk. The consequences of flash floods in densely populated cities are increasingly becoming problematic around the globe. However, they are largely ignored in disaster impact assessment studies, especially in assessing socioeconomic loss and damage, which can provide a significant insight for disaster risk reduction measures. Using a structured questionnaire survey, this study applied a statistical approach and developed a structural equation model (SEM) for assessing several socioeconomic dimensions including physical impacts, mobility disruption, lifeline facilities, health and income-related impacts. The study reveals that respondents have experienced a stronger impact on direct tangible elements such as household contents and buildings as well as direct intangible elements with ? coefficients 0.703, 0.576 and 0.635, respectively, at p?
This study aims to examine the relationship between daily temperature and mortality in the Klang Valley, Malaysia, over the period 2006-2015. A quasi-Poisson generalized linear model combined with a distributed lag non-linear model (DLNM) was used to estimate the association between the mean temperature and mortality categories (natural n=69,542, cardiovascular n= 15,581, and respiratory disease n=10,119). Particulate matter with an aerodynamic diameter below 10 ?m (PM(10)) and surface ozone (O(3)) was adjusted as a potential confounding factor. The relative risk (RR) of natural mortality associated with extreme cold temperature (1st percentile of temperature, 25.2 °C) over lags 0-28 days was 1.26 (95% confidence interval (CI): 1.00, 1.60), compared with the minimum mortality temperature (28.2 °C). The relative risk associated with extremely hot temperature (99th percentile of temperature, 30.2 °C) over lags 0-3 days was 1.09 (95% CI: 1.02, 1.17). Heat effects were immediate whereas cold effects were delayed and lasted longer. People with respiratory diseases, the elderly, and women were the most vulnerable groups when it came to the effects of extremely high temperatures. Extreme temperatures did not dramatically change the temperature-mortality risk estimates made before and after adjustments for air pollutant (PM(10) and O(3)) levels.
Difficulties in controlling the effects of outdoor thermal environment on the human body are attracting considerable research attention. This study investigated the outdoor thermal comfort of urban pedestrians by assessing their perceptions of the tropical, micrometeorological, and physical conditions via a questionnaire survey. Evaluation of the outdoor thermal comfort involved pedestrians performing various physical activities (sitting, walking, and standing) in outdoor and semi-outdoor spaces where the data collection of air temperature, globe temperature, relative humidity, wind speed, solar radiation, metabolic activity, and clothing insulation data was done simultaneously. A total of 1011 participants were interviewed, and the micrometeorological data were recorded under outdoor and semi-outdoor conditions at two Malaysian university campuses. The neutral temperatures obtained which were 28.1 °C and 30.8 °C were within the biothermal acceptable ranges of 24-34 °C and 26-33 °C of the PET thermal sensation ranges for the outdoor and semi-outdoor conditions, respectively. Additionally, the participants’ thermal sensation and preference votes were highly correlated with the PET and strongly related to air and mean radiant temperatures. The findings demonstrated the influence of individuals’ thermal adaptation on the outdoor thermal comfort levels. This knowledge could be useful in the planning and designing of outdoor environments in hot and humid regions to create better thermal environments.
Air pollution has been a rising concern of the 21st due to its effects to public health. Air Monitoring Stations are state-of-the-art equipment used to measure airborne pollutants concentration i.e. carbon monoxide, nitrogen oxide, sulphur dioxide, particulate matter (PM10) and ozone (O-3), as well as the meteorological parameters (i.e. ambient air temperature, relative humidity, wind speed and wind direction). Effects of climate change will affect the ambient temperature and humidity, which may induce a direct effect on air quality. In light of this, feed forward artificial neural network was employed to simulate the dynamic variations of PM10 and O-3 with relative humidity, temperature, and windspeed data being the inputs under 12 different training algorithms. Based on the results obtained, Bayesian regularization with 12 hidden neurons is the optimized network structure, with mean absolute percentage error in testing dataset of O-3 and PM10 at 51.31% and 36.49%, respectively. The models performed better in O-3 prediction as it is a photochemical reaction where ozone concentration varies according to temperature, the effect of meteorological parameters is significant. On the other hand, PM10 is not heavily dependent on meteorological parameters as the diversity of particulate matter components where most of its sources are dormant to changes in climate.
Introduction: Altered weather patterns and changes in precipitation, temperature and humidity resulting from climate change could affect the distribution and incidence of cholera. This study is to quantify climate-induced increase in morbidity rates of cholera. Material and Methods: Monthly cholera cases and monthly temperature, precipitation, and relative humidity data from 2004 to 2014 were obtained from the Malaysian Ministry of Health and Malaysian Meteorological Department, respectively. Poisson generalized linear models were developed to quantify the relationship between meteorological parameters and the number of reported cholera cases. Results: The findings revealed that the total number of cholera cases in Malaysia during the 11 year study period was 3841 cases with 32 deaths. Out of these, 45.1% of the cases were among children below 12 years old and 75% of the cases were from Sabah. Temperature and precipitation gave significant impact on the cholera cases in Sabah, (p<0.001) while precipitation were significant in Terengganu (p<0.001), and Sarawak (p=0.013). Monthly lag temperature data at Lag 0, 1, and 2 months were associated with the cholera cases in Sabah (p<0.001). The change in odds of having cholera cases were by the factor of 3.5 for every 1 degrees C increase in temperature. However, the contribution of rainfall was very mild, whereby an increase of 1 mm in precipitation will increase the excess risk of cholera by up to 0.8%. Conclusion: This study concludes that climate does influence the number of cholera cases in Malaysia.
Leptospirosis outbreaks in various parts of the world have been linked to changes in the weather. Furthermore, the effects have been shown to occur at different lags of up to 10 months, affecting the performance of simulation models that predict leptospirosis occurrence. In Malaysia, the link between different weather parameters, at different time lags, has yet to be established despite an increasing number of cases in recent years. In this study, a combination of data mining and machine learning is used to analyze, capture, and predict the relation between leptospirosis occurrence and temperature, rainfall, and relative humidity using the Seremban district in Malaysia as a case study. First, the optimal time lags for rainfall were determined using graphical exploratory data analysis (EDA) while non-graphical EDA was used for temperature. Then, an artificial neural network (ANN) model is developed to classify the combination of selected features into disease occurrence and non-occurrence using back-propagation training, optimizing the number of hidden layers and hidden nodes. The success is measured using accuracy, sensitivity, and specificity of each model. EDA has shown that leptospirosis occurrence in Seremban is highly correlated with weekly average temperature at lag 16 weeks and weekly rainfall amount at lag 12-20 weeks. Using these selected features, the ANN model achieved the highest accuracy, sensitivity, and specificity at 84.00, 86.44, and 79.33%, respectively. Overall, the EDA approach has increased the accuracy of the predictive model by 13.30-31.26% from the baseline models.
Aims: Leptospirosis is an infectious disease that is endemic to many tropical regions. Large epidemics usually happen after heavy rainfall and flooding. This potentially fatal zoonosis is caused by pathogenic bacteria belonging to the genus Leptospira. Leptospirosis can be diagnosed using specific biomarkers such as target genes and virulence indicators that are well preserved across various Leptospira spp., including those that are prevalent in clinical samples and in the environment. To date, several pathogenicity-determinant genes, including lipL32 and lipL41, have been described and used for diagnosing leptospirosis. However, prevalence of these genes in leptospiral strains is unclear. Methodology and results: In the present study, we assessed the distribution of eight pathogenicity-determinant genes in reference Leptospira strains and environmental isolates in Malaysia, by polymerase chain reaction (PCR). We found that only lipL32 and ligB were consistently expressed in all pathogenic Leptospira strains compared with the other tested genes. Moreover, our results suggested that the use of lipL41, lipL21, ompL1, lfb1, ligA, and ligC as biomarkers could incorrectly misdetect pathogenic Leptospira strains present in the environment. Conclusion: Thus, our results suggest that the pathogenicity-determinant genes lipL32 and ligB can be used as biomarkers for detection pathogenic Leptospira.
Climate change is an incessant global phenomenon and has turned contentious in the present century. Malaysia, a developing Asian country, has also undergone significant vicissitudes in climate, which has been projected with significant deviations in forthcoming decades. As per the available studies, climate changes may impact on the fertility, either via direct effects on the gonadal functions and neuroendocrine regulations or via several indirect effects on health, socioeconomic status, demeaning the quality of food and water. Malaysia is already observing a declining trend in the Total fertility rate (TFR) over the past few decades and is currently recorded below the replacement level of 2.1 which is insufficient to replace the present population. Moreover, climate changes reportedly play a role in the emergence and cessation of various infectious diseases. Besides its immediate effects, the long-term effects on health and fertility await to be unveiled. Despite the huge magnitude of the repercussion of climate changes in Malaysia, research that can explain the exact cause of the present reduction in fertility parameters in Malaysia or any measures to preserve the national population is surprisingly very scarce. Thus, the present review aims to elucidate the possible missing links by which climate changes are impairing fertility status in Malaysia.
