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.
