Leishmaniasis is a vector-borne disease of which the transmission is highly influenced by climatic factors, whereas the nature and magnitude differ between geographical regions. The effects of climatic variables on leishmaniasis in Sri Lanka are poorly investigated. The present study focused on time-series analysis of leishmaniasis cases reported from Sri Lanka with selected climatic variables. Variance stabilized time series of leishmaniasis patients of entire Sri Lanka and major districts from 2014 to 2018 was fitted to autoregressive integrated moving average (ARIMA) models. All the possible models were generated by assigning different values for autoregression and moving average terms using a function written in R statistical program. The top ten models with the lowest Akaike information criterion (AIC) values were selected by writing another function. These models were further evaluated using RMSE and MAPE error parameters to select the optimal model for each area. Cross-autocorrelation analyses were performed to assess the associations between climate and the leishmaniasis incidence. Most associated lags of each variable were integrated into the optimal models to determine the true effects imposed. The optimal models varied depending on the area. SARIMA (0,1,1) (1,0,0)(12) was optimal for the country level. All the forecasts were within the 95% confidence intervals. Humidity was the most notable factor associated with leishmaniasis, which could be attributed to the positive impacts on sand fly activity. Rainfall showed a negative impact probably as a result of flooding of sand fly larval habitats. The ARIMA-based models performed well for the prediction of leishmaniasis in the short term.
Hydro-geochemistry of drinking water was characterized in chronic kidney disease of unknown etiology (CKDu) endemic areas in Girandurukotte (GK) and Dehiattakandiya (DH) and non-endemic areas in GK, DH, and Sewanagala (SW) in Sri Lanka to comprehend any potential risk factors for CKDu. Groundwater (n = 142) and surface water (n = 08) were sampled during wet and dry seasons and analyzed for major anions, cations and stable isotopes of hydrogen and oxygen (δ(2)H and δ(18)O). Besides the typical water quality analysis, the water quality status was determined using the weighted arithmetic water quality index (WQI) and Hofmeister ion exposure levels. The measured average groundwater F(-) level was higher than the permissible level assigned by regulatory agencies for tropical countries at CKDu locations in GK, DH and non-CKDu locations in DH and SW. Significant differences in the content of total hardness (p = 0.017) and total dissolved solids (p = 0.003) were observed between CKDu and non-CKDu locations whereas the differences were insignificant for F(-) (p = 0.985) and alkalinity (p = 0.203). Weathering of silicate and carbonate minerals was found to be the main governing factor of groundwater compositions in both CKDu and non-CKDu areas, while recharging of groundwater is mainly determined by the rainfall than the surface water inputs. Higher ionic strength of groundwater in non-CKDu areas suggested that the potential environmental CKDu risk factors might be suppressed from dissolution into groundwater. The WQI calculations revealed that the both CKDu and non-CKDu locations were frequently presented with poor water quality. This study highlights the water quality status of the CKDu and non-CKDu locations and signifies the potential health risks that could arise even in non-CKDu areas due to the consumption of poor quality water. Accordingly, regular monitoring of water quality and assessment of Hofmeister ions exposure from food and beverages are highly warranted.
Two riverside communities in Kolonnawa, Colombo district, and Thawalama, Galle district, of Sri Lanka are flood-prone communities that, not only have experiences of flood disasters of different scales, but also face a number of disaster risks and vulnerabilities intertwined in their everyday lives. Using a mixed methods approach, the article draws on household surveys, in-depth interviews and focus group discussions with flood-affected people, and on semi-structured interviews with community leaders in the two-flood-prone-communities. The article critically explores how the understanding of disaster risks and vulnerabilities is overly focused on “incapacities,” “weaknesses,” and “victimization” of affected people, and emphasizes that vulnerability should not be treated as an antonym of community resilience. The article argues how community resilience still exists within vulnerable communities and it highlights the need for developing a more holistic approach to understand the vulnerability paradigm and better disentangle the complexity of vulnerability and disaster risk in local contexts.
Prolonged heat exposure during outdoor physical exertion can result in adverse renal health outcomes, and it is also supposed to be a driver of chronic kidney disease of uncertain etiology (CKDu) in tropical regions. School students are more likely to experience high heat exposure during outdoor sports practices, and the current knowledge on potential renal health outcomes associated with heat exposure carries many knowledge gaps. Hence, the present study aimed to perform biomarker-based assessment of the likelihood of pediatric renal injury focusing the communities in the dry climatic zone in Sri Lanka, where it prevails relatively harsh climate and high prevalence of CKDu. School students who engaged in regular outdoor sports practices (high-heat exposure), and an age-matched control of students who did not engage in sports practices (low-heat exposure) from four educational zones: Padavi Sripura (N = 159) and Medirigiriya (N = 171), Uhana (N = 165) and Thanamalwila (N = 169) participated in this cross-sectional study representing CKDu endemic and non-endemic regions. Effective temperature (ET), wet-bulb globe temperature (WBGT), heat index (HI) and humidex were used for comparison of thermal comfort in the environment. The intensity of environmental heat measured by thermal comfort indices showed no significant difference (p > 0.05) among the study regions. Urinary kidney injury molecule (KIM-1) and albumin-creatinine ratio (ACR) in participants with high heat exposure did not differ significantly from those in the control groups in the four study zones, where urinary neutrophil gelatinase-associated lipocalin showed substantial differences in some groups. Irrespective of heat exposure, increased KIM-1 excretion was observed (p < 0.01) in participants of CKDu endemic regions compared to those in non-endemic areas. Within the context of our findings, there is no plausibly strong evidence to establish potential association of heat exposure with the likelihood of developing renal injury or abnormal renal outcomes in dry zone school students in Sri Lanka.
Urban dwellers experience overheating due to both global and urban warming. The rapid urbanisation, especially in hot, humid cities, lead to greater exposure to heat risk, both due to increasing urban populations as well as overheating due to global/urban warming. However, a nation-wide exploration of thermal comfort trends, especially in the hot, humid tropics, remains relatively unexplored. In this paper, we explore the recent historical trends (1991-2020) in outdoor thermal comfort across the entire island of Sri Lanka and evaluate the likely effects of known urban climate mitigation strategies – shade and vegetative cover. We find that ‘very strong heat stress’ is moving towards ‘extreme heat stress’ that was barely registered in 1990s and is now common across two-thirds of the landmass of Sri Lanka in the hottest month (April). Even in the coolest month (January), ‘moderate heat stress’ unknown in the 1990s is now becoming a common trend across the most densely populated parts of the country. High shading and vegetation could reduce heat stress, even in the hottest month, but its utility will diminish as the warming continues in future. As such, policies to reduce global warming needs to be urgently pursued while simultaneously adapting to urban warming in Sri Lanka.
Leptospirosis is considered a neglected tropical disease despite its considerable mortality and morbidity. Lack of prediction remains a major reason for underestimating the disease. Although many models have been developed, most of them focused on the districts situated in the wet zone due to higher case numbers in that region. However, leptospirosis remains a major disease even in the dry zone of Sri Lanka. The objective of this study is to develop a time series model to predict leptospirosis in the Anuradhapura district situated in the dry zone of Sri Lanka. Time series data on monthly leptospirosis incidences from January 2008 to December 2018 and monthly rainfall, rainy days, temperature, and relative humidity were considered in model fitting. The first 72 months (55%) were used to fit the model, and the subsequent 60 months(45%) were used to validate the model. The log-transformed dependent variable was employed for fitting the Univariate seasonal ARIMA model. Based on the stationarity of the mean of the five variables, the ARDL model was selected as the multivariate time series technique. Residuals analysis was performed on normality, heteroskedasticity, and serial correlation to validate the model. The lowest AIC and MAPE were used to select the best model. Univariate models could not be fitted without adjusting the outliers. Adjusting seasonal outliers yielded better results than the models without adjustments. Best fitted Univariate model was ARIMA(1,0,0)(0,1,1)12,(AIC-1.08, MAPE-19.8). Best fitted ARDL model was ARDL(1, 3, 2, 1, 0),(AIC-2.04,MAPE-30.4). The number of patients reported in the previous month, rainfall, rainy days, and temperature showed a positive association, while relative humidity was negatively associated with leptospirosis. Multivariate models fitted better than univariate models for the original data. Best-fitted models indicate the necessity of including other explanatory variables such as patient, host, and epidemiological factors to yield better results.
This study explored food security and climate change issues and assessed how food sovereignty contributes to addressing the climate change impacts on entire food systems. The study aimed to contextualise food security, climate change, and food sovereignty within Sri Lanka’s current development discourse by bringing global learning, experience, and scholarship together. While this paper focused on many of the most pressing issues in this regard, it also highlighted potential paths towards food sovereignty in the context of policy reforms. This study used a narrative review that relied on the extant literature to understand the underlying concepts and issues relating to climate change, food security and food sovereignty. Additionally, eight in-depth interviews were conducted to obtain experts’ views on Sri Lanka’s issues relating to the thematic areas of this study and to find ways forward. The key findings from the literature review suggest that climate change has adverse impacts on global food security, escalating poverty, hunger, and malnutrition, which adversely affect developing nations and the poor and marginalised communities disproportionately. This study argues that promoting food sovereignty could be the key to alleviating such impacts. Food sovereignty has received much attention as an alternative development path in international forums and policy dialogues while it already applies in development practice. Since the island nation has been facing many challenges in food security, poverty, climate change, and persistence of development disparities, scaling up to food sovereignty in Sri Lanka requires significant policy reforms and structural changes in governance, administrative systems, and wider society.
Internally displaced people (IDP) due to conflict and violence were estimated as 41.3 million in 55 countries as the end of the year 2019, the highest figure ever recorded. Sri Lanka has not yet prioritized the health and wellbeing of households in building designing, with the emerging heat island effect making the lives more desperate for IDP. This study focused on the effect of energy poverty on occupant comfort in determining the quality of life of people and adaptive behaviors to manage heat strain in overheated interiors of rehabilitated residences in Jaffna, Sri Lanka. Field investigations consisted of personal monitoring, questionnaire surveying and physical measurements in four clusters of rehabilitation residence programmes in four regions. The study found that IDP were suffering from hidden energy poverty, with mean electricity consumption of 52 kWh per household per month. Residents have marginal (29%) access to clean fuels for cooking and accountable for an abnormal particulate matter count of 360 951 particles per cubic centimeter. Findings explicitly revealed the presence of overheated spaces with mean thermal preference of-0.6 conveying the need of cooler indoor environment. People tend to exhibit behavioral adjustments to cope up with prevailing extreme temperatures. Severity of heat stress informed by modified wet bulb globe temperature (WBGT) reporting 90% (28-31 degrees C) of households facing higher risk of heat strain while remaining 10% (>31 degrees C) are in hazardous situation. Predicted mean vote (PMV) was 1.29 explains warm sensation with predicted percentage of dissatisfied (PPD) 44.1% not complying to ASHRAE 55 standards. This detrimental combination of fuel poverty, lack of thermal comfort, and unacceptable indoor air quality has been a significant factor for 62% of the residences reporting at least one type of illness and being more prone to cardiovascular and respiratory disorders (37%). Thus, the study evidenced the presence of energy poverty and overheated interiors in the IDP’s residences in hot tropics of Sri Lanka. (c) 2021 Elsevier B.V. All rights reserved.
In tropical countries such as Sri Lanka, where leptospirosis-a deadly disease with a high mortality rate-is endemic, prediction is required for public health planning and resource allocation. Routinely collected meteorological data may offer an effective means of making such predictions. This study included monthly leptospirosis and meteorological data from January 2007 to April 2019 from Sri Lanka. Factor analysis was first used with rainfall data to classify districts into meteorological zones. We used a seasonal autoregressive integrated moving average (SARIMA) model for univariate predictions and an autoregressive distributed lag (ARDL) model for multivariable analysis of leptospirosis with monthly average rainfall, temperature, relative humidity (RH), solar radiation (SR), and the number of rainy days/month (RD). Districts were classified into wet (WZ) and dry (DZ) zones, and highlands (HL) based on the factor analysis of rainfall data. The WZ had the highest leptospirosis incidence; there was no difference in the incidence between the DZ and HL. Leptospirosis was fluctuated positively with rainfall, RH and RD, whereas temperature and SR were fluctuated negatively. The best-fitted SARIMA models in the three zones were different from each other. Despite its known association, rainfall was positively significant in the WZ only at lag 5 (P = 0.03) but was negatively associated at lag 2 and 3 (P = 0.04). RD was positively associated for all three zones. Temperature was positively associated at lag 0 for the WZ and HL (P < 0.009) and was negatively associated at lag 1 for the WZ (P = 0.01). There was no association with RH in contrast to previous studies. Based on altitude and rainfall data, meteorological variables could effectively predict the incidence of leptospirosis with different models for different climatic zones. These predictive models could be effectively used in public health planning purposes.
Dengue fever is a mosquito-borne viral disease caused by the dengue virus of the Flaviviridae family and is responsible for colossal health and economic burden worldwide. This study aimed to investigate the effect of environmental, seasonal, and spatial variations on the spread of dengue fever in Sri Lanka. The study used secondary data of monthly dengue infection and the monthly average of environmental parameters of 26 Sri Lankan regions from January 2015 to December 2019. Besides the descriptive measurements, Kendall’s tau_b, Spearman’s rho, and Kruskal-Wallis H test have been performed as bivariate analyses. The multivariate generalized linear negative binomial regression model was applied to determine the impacts of meteorological factors on dengue transmission. The aggregate negative binomial regression model disclosed that precipitation (odds ratio: 0.97, p < 0.05), humidity (odds ratio: 1.05, p < 0.01), and air pressure (odds ratio: 1.46, p < 0.01) were significantly influenced the spread of dengue fever in Sri Lanka. The bioclimatic zone is the vital factor that substantially affects the dengue infection, and the wet zone (odds ratio: 6.41, p < 0.05) was more at-risk than the dry zone. The climate season significantly influenced dengue fever transmission, and a higher infection rate was found (odds ratio: 1.46, p < 0.01) in the northeast monsoon season. The findings of this study facilitate policymakers to improve the existing dengue control strategies focusing on the meteorological condition in the local as well as global perspectives.
BACKGROUND: Dengue, transmitted by Aedes mosquitoes, is a major public health problem in Sri Lanka. Weather affects the abundance, feeding patterns, and longevity of Aedes vectors and hence the risk of dengue transmission. We aimed to quantify the effect of weather variability on dengue vector indices in ten Medical Officer of Health (MOH) divisions in Kalutara, Sri Lanka. METHODS: Monthly weather variables (rainfall, temperature, and Oceanic Niño Index [ONI]) and Aedes larval indices in each division in Kalutara were obtained from 2010 to 2018. Using a distributed lag non-linear model and a two-stage hierarchical analysis, we estimated and compared division-level and overall relationships between weather and premise index, Breteau index, and container index. FINDINGS: From Jan 1, 2010, to Dec 31, 2018, three El Niño events (2010, 2015-16, and 2018) occurred. Increasing monthly cumulative rainfall higher than 200 mm at a lag of 0 months, mean temperatures higher than 31·5°C at a lag of 1-2 months, and El Niño conditions (ie, ONI >0·5) at a lag of 6 months were associated with an increased relative risk of premise index and Breteau index. Container index was found to be less sensitive to temperature and ONI, and rainfall. The associations of rainfall and temperature were rather homogeneous across divisions. INTERPRETATION: Both temperature and ONI have the potential to serve as predictors of vector activity at a lead time of 1-6 months, while the amount of rainfall could indicate the magnitude of vector prevalence in the same month. This information, along with knowledge of the distribution of breeding sites, is useful for spatial risk prediction and implementation of effective Aedes control interventions. FUNDING: None.
BACKGROUND: More than 80,000 dengue cases including 215 deaths were reported nationally in less than 7 months between 2016 and 2017, a fourfold increase in the number of reported cases compared to the average number over 2010-2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission. METHODS: We present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016-2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1?km?×?1?km spatial resolution and a weekly temporal resolution. RESULTS: Our results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions. CONCLUSIONS: Our study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.
Sri Lanka is experiencing various social and environmental challenges, including drought, storms, floods, and landslides, due to climate change. One of Sri Lanka’s biggest cities, Kurunegala, is a densely populated city that is gradually turning into an economic revitalization area. This fast-growing city needs to establish an integrated urban plan that takes into account the risks of climate change. Thus, a climate change risk assessment was conducted for both the water and heat wave risks via discussions with key stakeholders. The risk assessment was conducted as a survey based on expert assessment of local conditions, with awareness surveys taken by residents, especially women. The assessment determined that the lack of drinking water was the biggest issue, a problem that has become more serious due to recent droughts caused by climate change and insufficient water management. In addition, the outbreak of diseases caused by heat waves was identified as a serious concern. Risk assessment is integral to developing an action plan for minimizing the damage from climate change. It is necessary to support education and awareness in developing countries so that they can perform risk assessment well and develop both problem-solving and policy-making abilities to adapt to a changing climate.
