WHO reference number: WHO/WKC/UHEM/14.01
Technical Report
Climatic factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
2 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Submitted by:
About the Project
This technical report was the final product of a research project funded by the World Health Organization (WHO). This was a joint collaboration between the WHO Centre for Health Development (WHO Kobe Centre) and the WHO Regional Office for South-East Asia (WHO SEARO).
The research project was reviewed by the WHO SEARO’s Research Review Committee in April 2011 and was then implemented and completed by the Health Systems Research Unit (HSRU), Faculty of Medicine, University of Colombo in Sri Lanka in 2012 through a technical service agreement with WHO SEARO.
The research project provided a case study to address the knowledge gap on the impact of climate change on the occurrence of vector-borne diseases and diarrhoeal diseases in Sri Lanka, using data from both urban and rural settings. It was based on a generic research protocol entitled “Assessing the relationship between climatic factors and diarrhoeal and vector- borne diseases – a retrospective study generic research protocol” that was published by WHO SEARO in 2010.
Project Objectives
The objectives of the project were to: (1) describe the relationship between incidence of dengue fever, leptospirosis and diarrhoeal diseases with rainfall, temperature and relative humidity in five selected areas in Sri Lanka; (2) develop models to predict dengue, leptospirosis and dysentery incidence with changes in climatic factors; and (3) identify the availability and quality of data on potential co-variables for feedback to the relevant organizations.
Project Team Members (HSRU, University of Colombo) Professor Rohini de A Seneviratne, Department of Community Medicine, Faculty of Medicine University of Colombo, Sri Lanka Professor A Pathmeswaran, Department of Community Medicine, Faculty of Medicine University of Colombo, Sri Lanka Ms Kantha Lankatileke, Department of Community Medicine, Faculty of Medicine University of Colombo, Sri Lanka
Project Team Members (WHO) Dr Zakir Hussain, Regional Adviser, Environmental Health and Climate Change (EHC), WHO SEARO Dr Jostacio M. Lapitan, Technical Officer, Urban Health Emergency Management, WHO Kobe Centre
WHO Reference Number: 3
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
WHO reference number: WHO/WKC/UHEM/14.01
Acknowledgements
The research project would not have been possible without the area of work on climate change and health in urban settings at the WHO Kobe Centre initiated in 2008-2013 and the regional cooperation and support led by Dr Jai Narain, Director, Department of Sustainable Development and Healthy Environments, WHO SEARO. WHO SEARO convened in 2009 an informal consultation “Research to assess the impact of climate change on communicable diseases”, Kolkata, India, 24-26 August 2009 which paved the way for the formulation of the generic research protocol (retrospective) on the relationship between climatic factors and diarrhoeal and vector-borne diseases which was used in pilot studies conducted by the WHO Kobe Centre in Kolkata, India and Japha district, Nepal.
© World Health Organization 2014
All rights reserved. Requests for permission to reproduce or translate WHO publications –whether for sale or for non-commercial distribution– should be addressed to WHO Press through the WHO web site (www.who.int/about/licensing/copyright_form/en/index.html), or to the WHO Centre for Health Development, I.H.D. Centre Building, 9th Floor, 5-1, 1-chome, Wakinohama-Kaigandori, Chuo-ku, Kobe City, Hyogo Prefecture, 651-0073, Japan (fax: +81 78 230 3178; email: [email protected]).
The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted lines on maps represent approximate border lines for which there may not yet be full agreement.
The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by the World Health Organization in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters.
All reasonable precautions have been taken by the World Health Organization to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall the World Health Organization be liable for damages arising from its use.
The named authors alone are responsible for the views expressed in this publication. 4 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
TABLE OF CONTENTS
Tables of contents………………………………………………………………………………… 4 List of figures……………………………………………………………………………………… 5 List of graphs……………………………………………………………………………………… 6 List of tables………………………………………………………………………………………. 6 List of abbreviations……………………………………………………………………………… 6 7 1. Introduction……………………………………………………………………………………... 8
2. Background…………………………………………………………………………………….. 10 2.1 Dengue fever…………………………………………………………………………………. 11 2.2 Leptospirosis…………………………………………………………………...... 12 2.3 Diarrhoeal diseases…………………………………………………………………………. 13
3. Rationale……………………………………………………………………………………….. 14
4. General Objective……………………………………………………………………………… 15 4.1 Specific objectives…………………………………………………………………………… 15
5. Materials and methods………………………………………………………………………... 16 5.1 Study design………………………………………………………………………………….. 16 5.2 Study areas…………………………………………………………………………………… 16 5.3 Data sources………………………………………………………………………………….. 17 5.4 Data management……………………………………………………………………………. 17 5.5 Data processing………………………………………………………………………………. 18 5.6 Data analysis…………………………………………………………………………………. 19
6. Results………………………………………………………………………………………….. 20 6.1 Dengue fever…………………………………………………………………………………. 20 6.2 Dysentery……………………………………………………………………………………… 35 6.3 Leptospirosis………………………………………………………………………………….. 41 6.4 Meteorology data of study areas…………………………………………………………… 47
7. Discussion……………………………………………………………………………………… 53
8. Conclusions…………………………………………………………………………………….. 56
9. References……………………………………………………………………………………... 57
5
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
LIST OF FIGURES
1 Global temperature, 1880-2011…………………………………………………………. 8
2 Dengue cases and deaths in Sri Lanka, 1992 – 2009………………………………... 11
3 Annual distribution of dengue cases – 2004-2010……………………………………. 12
4 Notification of leptospirosis cases from 1995 to 2010………………………………… 13
5 Distribution of cases of diarrhoeal diseases by district, 2003………………………... 14
6 Monthly dengue case rate in Colombo Municipal Council area: 1996-2010……… 20
7 Monthly rainfall, dengue case rate and average temperature in CMC: 1996-2000.. 22
8 Monthly dengue case rate in Gampaha Medical Officer of Health area: 1996-2010 23
9 Monthly rainfall, dengue case rate and average temperature for 24 Gampaha Medical Officer of Health area: 1996 to 2010……………………………..
10 Monthly dengue case rate in Kandy Municipal Council area: 1996-2010………….. 25
11 Monthly rainfall, dengue case rate and average temperature reported from 26 Kandy Municipal Council area: 1996-2010……………………………………………..
12 Monthly dengue case rate in Anuradhapura (NE) Medical Officer of Health area: 27 1996- 2010…………………………………………………………………………………
13 Monthly rainfall, dengue case rate and average temperature in Anuradhapura 28 Medical Officer of Health area: 1996 – 2010…………………………………………..
14 Monthly dengue case rate in Puttalam Medical Officer of Health area: 1996-2010.. 29
15 Monthly rainfall, dengue case rate and average temperature reported from 31 Puttalam Medical Officer of Health area during 1996 to 2010……………………….
16 Monthly dysentery case rate in Colombo Municipal Council: 1996 to 2010………... 35
17 Monthly dysentery case rate in Gampaha Medical Officer of Health area: 36 1996 to 2010……………………………………………………………………………….
18 Monthly dysentery case rate in Kandy Municipal Council area: 1996 to 2010…….. 37
19 Monthly dysentery case rate in Anuradhapura (NE) Medical Officer of Health 38 area: 1996 to 2010………………………………………………………………………..
20 Monthly dysentery case rate in Puttalam Medical Officer of Health area: 39 1996 to 2010………………………………………………………………………………. 21 Monthly leptospirosis case rate in Colombo Municipal Council: 1996 to 2010…….. 41
22 Monthly leptospirosis case rate in Gampaha MOH area: 1996 to 2010……………. 42 6 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
23 Monthly leptospirosis case rate in Kandy MC: 1996 to 2010 ………………………… 43
24 Monthly leptospirosis case rate in Anuradhapura MOH area: 1996 to 2010………. 44
25 Monthly leptospirosis case rate in Puttalam MOH area: 1996 to 2010……………... 45
LIST OF GRAPHS
1 Summary of weather data, CMC……………………………………………………… 48 2 Summary of weather data, Gampaha………………………………………………… 49 3 Summary of weather data, Kandy…………………………………………………….. 50 4 Summary of weather data, Anuradhapura…………………………………………… 51 5 Summary of weather data, Puttalam…………………………………………...... 51
LIST OF TABLES
1 Distribution of population, population density in study areas by climatic zones 16 and province………………………………………………………………………………
2 Summary of weather data for the study areas for the period 1996 to 2010………. 18
3 Time series correlation between dengue notifications in CMC Area and predictor 21 variables (weekly data)………………………………………………………………….
4 Time series correlation between dengue notifications in Gampaha MOH area 23 and predictor variables (weekly data)………………………………………………….
5 Time series correlation between dengue notifications in Kandy Municipal 26 Council area and predictor variables (weekly data)………………………………….
6 Time series correlation between dengue notifications in Anuradhapura 27 MOH area and predictor variables (weekly data)……………………………………..
7 Time series correlation between dengue notifications in Anuradhapura 30 MOH area and predictor variables (weekly data)…………………………………….. 8 Time series correlation between dengue notifications and predictor variables 33 7
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
(weekly data)……………………………………………………………………………...
9 Time series regression of dengue rate by study area……………………………….. 34
10 Time series correlation between dysentery notifications and predictor variables 35 (weekly data)……………………………………………………………………………..
11 Time series correlation between dysentery notifications and predictor variables 36 (weekly data)……………………………………………………………………………..
12 Time series correlation between dysentery notifications and predictor variables 37 (weekly data)……………………………………………………………………………..
13 Time series correlation between dysentery notifications and predictor 38 variables (weekly data)…………………………………………………………………..
14 Time series correlation between dysentery notifications and predictor 39 variables (weekly data)…………………………………………………………………..
15 Time series regression of dysentery rate by study area…………………………….. 40
16 Time series correlation between leptospirosis notifications and predictor 41 variables (weekly data) – CMC…………………………………………………………
17 Time series correlation between leptospirosis notifications and predictor 42 variables (weekly data) - Gampaha MOH area……………………………………….
18 Time series correlation between leptospirosis notifications and predictor 43 variables (weekly data) - Kandy MC……………………………………………………
19 Time series correlation between leptospirosis notifications and predictor 44 variables (weekly data) - Anuradhapura MOH area………………………………….
20 Time series correlation between leptospirosis notifications and predictor 45 variables (weekly data) - Puttalam MOH area………………………………………...
21 Time series regression of leptospirosis rate by study area…………………………. 46
LIST OF ABBREVIATIONS
ARIMA AutoRegressive Integrated Moving Average CFR Case Fatality Rate DF Dengue Fever DHF Dengue Haemorrhagic Fever IPCC Intergovernmental Panel on Climate Change MOH Medical Officer of Health; Ministry of Health RH Relative Humidity
8 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: a Retrospective Study
1. Introduction The Intergovernmental Panel on Climate Change (Intergovernmental Panel on Climate Change/IPCC 2007) categorically states that, ‘Warming of the climate system is unequivocal, as is now evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice and rising global average sea level.’
Figure 1 shows trends in 5-year global temperature mean and anomaly.
Figure 1. Global temperature, 1880-2011
The temperature anomaly from 1880-1935 has been being consistently negative and the opposite is true from 1980 onwards where it is positive. More recently, the highest mean rise in temperature of +0.60C per year is evident. It is the belief of climate scientists that climate change is the result of emission of greenhouse gases from burning of fossil fuel and resultant global warming (McMichael and Haines 1997, McMichael, Woodruff and Hales 2006). The weather variability experienced in many parts of the world is also considered to be an outcome of global warming.
9
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
The changes in climate and the variability of the weather have impacts on physical, biological and ecological systems. Humans over time have adapted and adjusted to these systems and any change in these have the potential to influence the health and well-being of people, both directly and indirectly. Climate change could affect health through direct and indirect pathways. The increased frequency and intensity of heat waves, reduction in intensity and duration of cold spells, recurrent and more severe flooding, drought, higher risk of disasters have direct impacts on morbidity and mortality.
Climate change and changes in ecological systems enhance the geographical range, breeding sites, reproductive and biting rates and activities of vectors of disease (Patz 1996). The occurrence of mosquito-borne diseases, including malaria, dengue, and viral encephalitic diseases, are identified to be sensitive to effects of climate (Nerlander N, Commission on Climate Change and Development 2009). In addition, urbanization, population expansion and movement, and the increasing population density along with human behaviour changes have resulted both in the formation of vector breeding sites and the higher risk of exposure of peoples to the vectors. The changes in the distribution of vector-borne diseases, their introduction and re-emergence, as well as spread of infectious diseases, cardiovascular mortality and respiratory illnesses, malnutrition are some of the indirect effects of climate changes (Patz 2005).
The overall balance of effects on health is regarded as likely to be negative and populations in low-income countries are likely to be particularly vulnerable to the adverse effects (Haines et al 2006). The warming and precipitation trends due to anthropogenic climate change of the past 30 years are attributed to have already claimed over 150,000 lives annually (Patz et al 2008).
In 1990, almost 30% of the world’s population lived in regions where the estimated risk of dengue transmission was greater than 50% (Hales et al 2002). They modelled the current geographical limits of dengue fever transmission with 89% accuracy on the basis of long-term average vapour pressure and predict that with climate change projections for 2085, and the estimate for the projected global population at risk of dengue transmission is about 50–60% of the projected population, compared with 35% of the population, if climate change did not happen. They concluded that, ‘climate change is likely to increase the area of land with a climate suitable for dengue fever transmission, and that if no other contributing factors were to change, a large proportion of the human population would then be put at risk’.
The progressive development of research on climate change and health effects is also characterized by the inclusion of non-climatic determinants of vulnerability to climate change, 10 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
including adaptive capacity, and the shift from estimating expected damages to attempting to reduce them (Füssel and Klein 2006).
The collection and compilation of meteorological data in Sri Lanka commenced in 1861. Since then data rainfall, minimum and maximum temperature have been gathered regularly and are available on request and payment of a fee. Currently there are 20 functioning stations for collection of data. Increasing trends in minimum and maximum temperature have been reported by the Meteorology Department of Sri Lanka for all stations with differences in rates of change for the different areas (Meteorology Department 2010). The rates of temperature rise have also been reported to have been higher for the past 40 years than for the entire century of 1900- 2000. The recent analyses of climate data also have shown an increase in the number of warm days and nights, an increase of the number of consecutive dry days and a decrease in the number of wet days. The average rainfall analysis for the whole country shows that there is a slight decrease in annual total rainfall in Sri Lanka but the variability is higher during the 1961-1990 than 1931-1960, the two averaging thirty year periods. The more important feature of the change identified in the report is the gradually enhancing variability of rainfalls, as the higher variability of rainfall adversely affects all climate-sensitive activities and events.
The future climate scenario of Sri Lanka prepared for the third assessment report of IPCC, projects an average temperature increase of 2.40C by 2100, and 0.400C, 0.90C and 1.60C, for the years 2025, 2050 and 2075, respectively. The projected average rainfall increases are 173mm, 402mm and 1061mm for the years 2025, 2050 and 2100, respectively.
The mean annual temperature and rainfall trends for the areas under study are given in Annex 1 (Meteorology Department 2010).
2. Background
Sri Lanka is an island situated in the Indian Ocean at the southern end of the Indian peninsula. The island has a maximum length of 435 km and width of 225 km with a land area of 65,000 square kilometers. It has a central mountainous region with peaks as high as 2,500 meters. The mean temperature ranges from 26º C to 28º C in the low country, and from 14º C to 24º C in the central hill country.
11
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
Sri Lanka is currently facing a burden of diseases on several fronts, related to the rapid epidemiological transition, demographic transition, economic and socio-cultural and political transitions. Sri Lanka still faces a substantial morbidity burden from communicable diseases mainly from dengue fever, diarrhoeal diseases and tuberculosis. In the recent past, leptospirosis has also shown an increasing trend with occurrence of outbreaks in some areas (Annual Health Statistics Sri Lanka 2007).
Most of the risk factors of these diseases are known although the role of climate change in relation to the increase in trends, occurrence of outbreaks and increase in incidence in some areas, and not in others, has not been studied so far.
i. Dengue fever Dengue fever (DF) was first reported in Sri Lanka in 1965 from the Western Province and 2 years later, in 1967, the first epidemic occurred with 29 cases and 8 deaths. The case fatality rate (CFR) was 27.6%. Following this, in 1990 a resurgence of DF was observed. In 1996, Ministry of Health made dengue fever a notifiable disease. Distribution of DF from 1990-2009 is shown in Figure 2. Dengue fever has now become endemic with periodic outbreaks and has spread to affect both urban and rural areas and the entire island. Case fatality rate has been high during epidemics.
Figure 2. Dengue cases and deaths in Sri Lanka, 1992 - 2009
18000 200 15463 16000 180 14000 160 11980 12000 140 124120 10000 8931 8647 100 7327
8000 87 6560 Deaths Number 5986 5994 80 6000 5203 644749 54 54 60 46 4000 40 37 32 1294 28 28 27 2000 756 656 15 582 440 346 17 421 628 14 20 7 11 8 0 3 0 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Years
Cases Deaths
Source: Epidemiology Unit, Ministry of Health, Sri Lanka, 2010
Figure 3 shows the annual distribution of DF by week for the period 2004-2010. Two clear peaks are observed consistently, in the middle of the year, associated with the South West monsoon and the other one at the end of the year continuing into the beginning of the following year related to the North East monsoon. In 2009 and 2010 the incidence of DF remains 12 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
relatively high throughout the year with the two peaks corresponding to the two monsoon periods being more pronounced. Sri Lanka experienced the largest epidemic of DF/DHF with as many as 70,000 cases and almost 600 deaths during 2009-10.
Figure 3. Annual distribution of dengue cases – 2004-2010
ii. Leptospirosis From late 1990s the disease has remained endemic in Sri Lanka. However, in 2008, Sri Lanka witnessed its biggest outbreak of leptospirosis with a total of 7,423 cases and 207 reported deaths (CFR = 2.8%). From the 2008 outbreak onwards the notification of leptospirosis cases has indicated a higher level of endemicity compared to the previous years. The notification of leptospirosis cases from 1995 to 2010 to the Epidemiology Unit, Ministry of Health, Sri Lanka is shown in Figure 4.
Figure 4. Notification of leptospirosis cases from 1995 to 2010
Source: Epidemiology Unit, Ministry of Health, Sri Lanka, 2010 13
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study iii. Diarrhoeal diseases
Diseases of the gastrointestinal tract were the fifth leading cause of hospitalization in Sri Lanka in 2003. The incidence of diarrhoeal diseases was highest in Moneragala district with a rate exceeding 1000 cases per 100,000 population (Annual Health Bulletin, 2003).
According to the Demographic and Health Survey (2007) the two-week period prevalence of diarrhoea was 3.3% while the prevalence of diarrhoea with blood was 0.3%. The highest prevalence was reported from districts of Ampara (8.4%), Polonnaruwa (8.2%), Batticaloa (6.5%), Nuwaraeliya (5.1%) and Anuradhapura (5%).
During the period from 1985 to 2003 the mortality of diarrhoeal diseases has shown a dramatic decline from 10 deaths to 0.7 deaths per 100,000 population. The case fatality rate also shows a similar decline from 1% to 0.1% and this remarkable improvement in mortality can be attributed mainly to improved fluid management at household level with the introduction of oral re-hydration solution.
S Source: Epidemiological Unit, Ministry of Health, Sri Lanka
14 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
The morbidity rate however has remained static at a rate of around 1,000 cases per 100,000 population (Annual Health Bulletin, 2003). In recent years, many diarrhoeal disease high risk situations have been faced due to the occurrence of floods, and displacement related to landslides etc.
3. Rationale
The underlying causes for the situation regarding DF, leptospirosis and diarrhoeal diseases are many. Unplanned urbanization, industrialization, water storage practices, poor disposal of solid waste, especially non-biodegradable packaging materials have contributed to the creation of breeding sites. A clear relationship has been observed between the increase of cases of DF following South West and North East monsoons. Leptospirosis, too, is seen to be associated with seasonal cultivation and rainfall, especially paddy areas and with severe weather events such as flooding. The exact relationship between the increased incidence of DF and the vector breeding and leptospirosis with meteorological factors such as rainfall, temperature and relative humidity is not well understood. The vectors of DF, Aedes aegypti and Aedes albopictus, their breeding sites, localities, geographic range and vector activities which are crucial to the transmission of the virus of vectors of DF, too, have not been studied adequately. The availability of entomological data, the impact of climate change on these parameters, and their role in forecasting DF has not been explored yet in Sri Lanka.
Diarrhoeal diseases are known to be related to poor sanitation, unhygienic practices related to food and water, low level of education, poverty, malnutrition, population density and overuse of amenities related to migration and unplanned urbanization.
Hence, this research project is designed to shed information on the relationship between climate factors of rainfall, and temperature on the occurrence of dengue fever, leptospirosis and diarrhoeal diseases in Sri Lanka for the period from 1996 – 2010, develop a predictive model if feasible and to identify information gaps that should be addressed to enable the use of information for prevention and control. It is also hoped that it will provide an insight to the relationship between the climate changes and the selected infectious diseases. The findings could also help in informing the prevention and control of the diseases.
15
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
4. General objective
To describe the relationship between climatic factors and incidence of dengue fever, leptospirosis and diarrhoeal diseases in selected urban settings in Sri Lanka.
4.1 Specific objectives
1. To describe the relationship between incidence of dengue fever, leptospirosis and diarrhoeal diseases with rainfall, temperature and relative humidity in four selected areas in Sri Lanka;
2. To develop models to predict dengue, leptospirosis and dysentery incidence with changes in climatic factors; and
3. To identify the availability and quality of data on potential co-variables for feedback to the relevant organizations.
16 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
5. Materials and methods
5.1 Study design Retrospective study using routinely collected data on dengue fever, leptospirosis and diarrhoeal diseases and climatic data.
5.2 Study areas The study was conducted by using data from the following five areas: 1. Colombo Municipal Council (urban) – commercial capital of Sri Lanka; 2. Gampaha Divisional Health area (semi urban) in the Western Province; 3. Kandy Municipal Council (urban) in the Central Province from the central hills; 4. Anuradhapura Divisional Health Area (urban & rural mixed), North Central Province; and 5. Puttalam Divisional Health Area (rural), North Western Province.
The first three areas benefit from the South West monsoon with an average rainfall of over 2,500 mm per year. Anuradhapura and Puttalam benefit from the North East monsoon with an average rainfall of 1,200 mm per year. The population and population density of the five areas selected for the study are shown in Table 1.
Table 1: Distribution of population, population density in study areas by climatic zones and province Population (in 2010) Population density Province & climate Area Colombo MC area 700,000 (resident) 3,330 persons /sq Western Province and 500 000 daily km Wet zone migrants for work Gampaha Divisional 170,000 1340 persons /sq Western Province Health area km Wet zone Kandy MC area 105,000 1917 persons /sq Central province, km Wet zone, hills Anuradhapura 80,000 112 persons /sq km North Central Divisional Health Province, Dry zone area Puttalam Divisional 118,000 246 persons / sq km North Western Health area Province, Dry zone
Source: Census of Population and Housing 2011
17
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
5.3 Data sources
Notification data Data on weekly notifications of dengue for the study areas for the period 1996 to 2010 were obtained from the Epidemiological Unit of the Ministry of Health, Sri Lanka. From the year 1996 onwards, dengue fever has been made a notifiable disease. The notification data are collated by the Epidemiological Unit of the Ministry of Health. The data were available on weekly basis from the areas selected for this study. The notifications are based on clinical diagnosis made by qualified physicians. Since data for DF were available from 1996, for leptospirosis from 1995 and for diarrhoeal diseases for a much longer period, it was decided to confine the analysis for all three diseases from 1996-2010.
In addition similar data were obtained regarding leptospirosis. Since the notification of diarrhoeal diseases was only confined to dysentery; and hepatitis and enteric fever were not classified with diarrheoal diseases, the analysis of the category diarrheoal diseases is confined to notified dysentery only and hence forth will be referred to as such.
Climatic data Daily data on rainfall and temperature (minimum and maximum) for the study areas were obtained from the Department of Meteorology of Sri Lanka. Data on relative humidity were not being computed by this department and hence this independent variable had to be excluded.
Other data Data pertaining to vector dynamics, breeding and activities have not been routinely collected and thus could not be used as a variable in the analysis.
5.4 Data Management Data format The weekly notification data for the different areas were obtained in MS Excel format and converted to Stata format. The daily weather data for the different areas were obtained as text files and converted to Stata format. Weekly average temperature, total rainfall and number of rainy days were obtained from the daily data.
18 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Quality of data Notified data from the primary source (hospital) are investigated by the medical officers of health (MOH) and confirmed ones are forwarded to the Epidemiological Unit of the Ministry of Health. The completeness of notification though not 100%, has remained relatively constant over the years.
5.5 Data processing
Epidemiological data Incidence rate per week from 1996 onwards was calculated for study areas using notifications received by the epidemiological unit and the estimated population of the respective areas as the denominator. This weekly data was converted to monthly data and used to create time series graphs. Diarrhoeal diseases were not notifiable. Only dysentery was being notified, the scope of the study including the title and objectives were changed to reflect this and diarrhoeal diseases were changed to dysentery only.
Computation of climatic data The weekly averages of rainfall, number of rainy days per week and weekly average temperature were calculated from the daily data (Table 2).
Table 2: Summary of weather data for the study areas for the period 1996 to 2010 CMC Gampaha Kandy A’pura Puttalam Daily temperature (o C) Min 22.8 23.2 19.6 22.1 22.8 Av 27.9 27.8 25.0 28.4 28.1 Max 31.5 31.5 29.4 34.3 32.2 Daily temperature difference (o C) Min 0.4 0.6 0.5 0.3 0.3 Av 6.1 7.1 8.7 8.8 7.2 Max 14.0 16.1 20.1 16.9 16.3 Annual rainfall (mm) Min 1933 1540 1504 1068 918 Av 2383 2162 1856 1282 1160 Max 3370 3224 2666 1665 1585 Rainy days Min 138 142 169 87 84 Av 174 161 187 105 107 Max 199 173 201 122 129 Source: Department of Meteorology, summarized data
Table 2 indicates that the mean daily temperature, mean temperature difference for all study areas is similar. CMC, Gampaha and Kandy do show much variability in this parameter. The 19
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study variability for the same meteorological parameters for Anuradhapura and Puttalam is much less. The mean annual rain fall for Anuradhapura ranges from 1068-1665mm and for Puttalam from 918-1585mm. The range for CMC (1933-3370mm), for Gampaha (1540-3224mm) and for Kandy (1504-2666mm) in contrast, is high indicating high level of variability as well as high levels of rainfall. The average number of rainy days for Puttalam and Anuradhapura, too, are lower, being 105 and 107 days respectively, in contrast to the other three study areas. It can be assumed that Puttalam and possibly Anuradhapura could be suitable as control areas in studying the different diseases.
5.6 Data analysis
Descriptive charts In order to visualize the relationship between dengue notification rate and the meteorological variables, a set of time series graphs were produced separately for the five geographic areas under consideration, using monthly data.
Time series analysis
In a time series the correlations between successive values of the time would give a wrong estimation for predictions. A better predictive model can be done by taking correlations in the data into account. Autoregressive Integrated Moving Average (ARIMA) models include an explicit statistical model for the irregular component of a time series that allows for non-zero autocorrelations in the irregular component.
Formal time series analysis was carried out using weekly epidemiological and meteorological data. Initially bivariate time series correlations between weekly notifications and the different meteorological variables and their lagged terms were carried out to identify the appropriate lag terms for the ARIMAX model.
The ARIMAX model was used for regression analysis as this allows for adjusting for autocorrelations in the dependent variable and use of moving average terms and lagged terms in the predictors.
20 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
6. Results
6.1 Dengue fever
i. Colombo Municipal Council (CMC) area
In order to visualize the relationship between dengue notification rate and the meteorological variables, a set of time series graphs were produced separately for the five geographic areas under consideration.
The annual dengue case rate in the CMC area showed an increasing trend with short peaks at the beginning of the period of study (Figure 6). More consistent and frequent peaks of high case rates of DF per 100,000 population are seen in 2008-2010. For the past decade, the monthly case rates have shown clear peaks in May, June and July and for 1996-2005, the peaks corresponding to the South West monsoon. In the later years, another peak is discernible at the end of the year, in November and December, corresponding with the South West monsoon.
Figure 6. Monthly dengue case rate in Colombo Municipal Council area: 1996-2010
CMC - Dengue - 1996 to 2010
2.5
60
2
1.5
40
1
.5
0
20 Dengue100 cases/ 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Dengue100 cases/ 000
1996 1997 0 1998 1999
Jan 1995 Jan 2000 Jan 2005 Jan 2010
50
60
40
40
30
20
20
10
0 0
Dengue100 cases/ 000 Dengue100 cases/ 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was a significant correlation between dengue case rate and rainfall with a lag of 7 weeks (r = 0.1711), number of rainy days with a lag of 8 weeks (r = 0.1365) and average temperature with a lag of 12-16 weeks (r = 0.16) (Table 3).
21
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
The autocorrelation of dengue case rate with that of the previous week was very high (r = 0.82).
Table 3: Time series correlation between dengue notifications in CMC area and predictor variables (weekly data)
Area Average Rainfall Dengue rate Temperature CMC Lag r Lag r Lag r L9 0.13 L6 0.14 L1 0.82 L10 0.14 L7 0.17 L11 0.15 L8 0.14 L12 0.16 L9 0.13 L13 0.14 L10 0.14 L14 0.15 L15 0.14 L16 0.17
The monthly rainfall, dengue case rates and average temperature are shown in Figure 7. Rainfall clearly showed two peaks every year (Figure 7) as was observed for DF case rates which correspond to the monsoon rains, during the later years. Temperature showed an annual pattern.
ii. Gampaha MOH area
As with the CMC areas, a gradual rise in the trends of DF case rate was observed, with more higher and more frequent peaks in the last few years. A consistent pattern of monthly variation in case rate was not observed (Figure 8).
22 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Figure 7. Monthly rainfall, dengue case rate and average temperature in CMC: 1996-2010
CMC: Dengue, Rainfall & Temperatue
1996 - 2010
1000
800
600
Rainfall
400
200 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
60
40
20
Dengue cases per 100 000 Dengue cases 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
30
29
28
27
Ave. Temperature Ave. 26 Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
Figure 8 shows the relationship of monthly dengue case notification rate in Gampaha Medical Officer of Health area and the monthly variation for the period, 1996-2010.
23
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
Figure 8. Monthly dengue case rate in Gampaha Medical Officer of Health area: 1996-2010
Gampaha - Dengue - 1996 to 2010
2
40
1.5
30
1
.5
20
0 Dengue100 cases/ 000
10 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Dengue100 cases/ 000
1996 1997 0 1998 1999
Jan 1995 Jan 2000 Jan 2005 Jan 2010
40 40
30 30
20 20
10 10
0 0
Dengue100 cases/ 000 Dengue100 cases/ 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was no significant correlation between dengue case rate and rainfall or the number of rainy days. But there was a significant correlation between dengue case rate and average temperature with a lag of 10 weeks (r = 0.1301) (Table 4). The autocorrelation of dengue case rate with that of the previous week was relatively high (r = 0.58).
Table 4. Time series correlation between dengue notifications in Gampaha MOH area and predictor variables (weekly data) Area Average Rainfall Dengue rate Temperature Gampaha Lag r Lag r Lag r L10 0.13 -- L1 0.58
Rainfall clearly shows two peaks every year (Figure 9). Dengue case rates during the later years also show two peaks per year and there is an increasing trend as well. Temperature shows an annual pattern.
24 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Figure 9. Monthly rainfall, dengue case rate and average temperature for Gampaha Medical Officer of Health area: 1996 to 2010
Gampaha: Dengue, Rainfall & Temperatue
1996 - 2010
800
600
400
Rainfall
200 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
40
30
20
10
Dengue cases per 100 000 Dengue cases 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
30
29
28
27
Ave. Temperature Ave. 26 Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
25
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
iii. Kandy Municipal Council area
The same pattern of annual dengue case rates observed for CMC and Gampaha was observed for this area too (Figure 10).
Figure 10. Monthly dengue case rate in Kandy Municipal Council area: 1996-2010
Kandy - Dengue - 1996 to 2010
5
100
4
80
3
2
60
1
40
0 Dengue100 cases/ 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
20 Dengue100 cases/ 000
1996 1997 0 1998 1999
Jan 1995 Jan 2000 Jan 2005 Jan 2010
80
100
80
60
60
40
40
20
20
0 0
Dengue100 cases/ 000 Dengue100 cases/ 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was no significant correlation between dengue case rate and rainfall or the number of rainy days (Table 5). But there was a significant correlation between dengue case rate and average temperature with a lag of 11 weeks (r = 0.1657). A relatively high level of autocorrelation (r = 0.61) of occurrence of dengue in the current week with that of the previous week of observed for this area too.
Table 5. Time series correlation between dengue notifications in Kandy Municipal Council area and predictor variables (weekly data)
Area Average Rainfall Dengue rate Temperature Kandy MC area Lag r Lag r Lag r L9 0.14 -- L1 0.61 L10 0.15 L11 0.17 L12 0.16 26 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
L13 0.14 L14 0.16 L15 0.14 L16 0.13 Rainfall clearly showed two peaks every year (Figure 11). From 2001 / 2002, the peak case rate was similar during most years and at the peak, case rates appear to last a longer period. Temperature showed an annual pattern of variation.
Figure 11. Monthly rainfall, dengue case rate and average temperature reported from Kandy Municipal Council area: 1996-2010 Kandy: Dengue, Rainfall & Temperatue
1996 - 2010
600
400
Rainfall
200 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
100
80
60
40
20
Dengue cases per 100 000 Dengue cases 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
28
27
26
25
24
Ave. Temperature Ave. 23
Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
27
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
iv. Anuradhapura MOH area
The case rate of DF over the years showed the same increasing trend, more marked in the last few years of the study period (Figure 12). No consistent pattern in the monthly variation was observed.
Figure 12. Monthly dengue case rate in Anuradhapura (NE) Medical Officer of Health area: 1996- 2010
Anuradapura - Dengue - 1996 to 2010
150 3
2
100
1
0
50 Dengue100 cases/ 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Dengue100 cases/ 000
1996 1997 0 1998 1999
Jan 1995 Jan 2000 Jan 2005 Jan 2010
150
80
60
100
40
50
20
0 0
Dengue100 cases/ 000 Dengue100 cases/ 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was a significant correlation between dengue case rate and rainfall with a lag of 7 weeks (r = 0.1411) (Table 6), number of rainy days with a lag of 8 weeks (r = 0.1263). There was a significant negative relationship between dengue case rate and average temperature in the same week (lag of 0 weeks, r = -0.13) and two weeks before (r = -0.13). The autocorrelation of dengue case rate with that of the previous week was moderate (r = 0.43).
Table 6. Time series correlation between dengue notifications in Anuradhapura MOH area and predictor variables (weekly data) Area Average Rainfall Dengue rate Temperature Anuradhapura Lag r Lag r Lag r MOH area L0 -0.13 L7 0.14 L1 0.43 L2 -0.13 28 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Rainfall showed two clear peaks every year (Figure 13) with the prominent peak towards the end of the year reflecting the fact that the area gets most of the rain during the North East monsoon. Figure 13: Monthly rainfall, dengue case rate and average temperature in Anuradhapura Medical Officer of Health area: 1996 - 2010
Anuradapura: Dengue, Rainfall & Temperatue
1996 - 2010
400
300
200
Rainfall
100 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
150
100
50
Dengue cases per 100 000 Dengue cases 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
32
30
28
Ave. Temperature Ave. 26
Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
29
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
Dengue shows a peak during the later years and towards the end of the year and another peak around the middle of the year. There is an increasing trend as well. Temperature shows an annual pattern.
v. Puttalam MOH area
As shown in Figure 14, the dengue case rate was very low throughout the study period except for two peaks of DF in 2002 and 2009, and there was no monthly variation.
Figure 14. Monthly dengue case rate in Puttalam Medical Officer of Health area: 1996-2010
Puttalam - Dengue - 1996 to 2010
1
150
.8
.6
.4
100
.2
0
50 Dengue100 cases/ 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Dengue100 cases/ 000
1996 1997 0 1998 1999
Jan 1995 Jan 2000 Jan 2005 Jan 2010
80
150
60
100
40
50
20
0 0
Dengue100 cases/ 000 Dengue100 cases/ 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was no significant correlation between dengue case rate and rainfall or the number of rainy days. However, there was a significant negative relationship between dengue case rate and average temperature within the same week (lag 0 weeks, r=- 0.1692) as shown in Table 7.
30 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Table 7. Time series correlation between dengue notifications in Puttalam MOH area and predictor variables (weekly data)
Area Average Rainfall Dengue rate Temperature Puttalam MOH Lag R Lag r Lag R area L0 -0.17 -- -- L1 -0.14 L2 -0.15 L3 -0.14 L4 -0.13
Rainfall clearly showed two peaks every year (Figure 15) with the higher peaks towards the end of the year reflecting the fact that the area gets most of the rain during the North East monsoon. There had been two outbreaks of dengue in 2002 and 2009. Both outbreaks were during the rainy season towards the end of the year and may have been related to unusually high rainfall and flooding. Temperature shows an annual pattern. Detailed analysis was not carried out as the case rate was lower.
31
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
Figure 15. Monthly rainfall, dengue case rate and average temperature reported from Puttalam Medical Officer of Health area during 1996 to 2010
Puttalam: Dengue, Rainfall & Temperatue
1996 - 2010
500
400
300
Rainfall
200
100 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
150
100
50
Dengue cases per 100 000 Dengue cases 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
32
30
28
26
Ave. Temperature Ave. 24 Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
32 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Table 8 shows the summary of dengue notification rate and the correlation with predictor variables of rainfall and average temperature.
For Kandy, Gampaha and Puttalam, no correlation of DF notification is seen with average rainfall. But there was a significant correlation between dengue case rate and average temperature with a lag of 11 weeks and even 14 weeks for Kandy (r = 0.1657). A negative correlation was observed with average temperature for the Anuradhapura and Puttalam MOH areas.
A high autocorrelation was seen with previous week’s DF notification rate, being highest for CMC (0.82), followed by Kandy (0.61) and Gampaha (0.58), all being densely populated urban or semi-urban areas. There was only a moderate correlation with the previous week’s dengue cases for Anuradhapura. This was not observed for Puttalam, where the variability of weather parameters and their intensity was low (Table 2, Figure 2). Climate factors and past climate factors and DF did not have a relationship for Puttalam. However, there was a significant negative relationship between dengue case rate and average temperature with a lag of 0 weeks (r = - 0.1692).
In summary, there was some correlation between average temperature and rainfall and notification of DF in CMC area (Table 8). There was a significant correlation between dengue case rate and rainfall with a lag of 7 weeks (r = 0.17). There was a low positive correlation of average rainfall for Anuradhapura too with a significant correlation with a lag of 7 weeks as well.
33
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
Table 8. Time series correlation between dengue notifications and predictor variables (weekly data) Area Average Rainfall Dengue rate Temperature Lag r Lag r Lag r CMC L9 0.13 L6 0.14 L1 0.82 L10 0.14 L7 0.17 L11 0.15 L8 0.14 L12 0.16 L9 0.13 L13 0.14 L10 0.14 L14 0.15 L15 0.14 L16 0.17
Gampaha L10 0.13 -- L1 0.58
Kandy L9 0.14 -- L1 0.61
L10 0.15 L11 0.17 L12 0.16
L13 0.14 L14 0.16 L15 0.14 L16 0.13
Anuradhapura L0 -0.13 L7 0.14 L1 0.43 L2 -0.13
Puttalam L0 -0.17 -- -- L1 -0.14 L2 -0.15 L3 -0.14 L4 -0.13
34 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Table 9 shows the results of the time series regression analysis for occurrence of dengue fever of the study areas by climate variables.
Table 9: Time series regression of dengue rate by study area
Study area Variable Β SE P CMC Dengue rate (L1) 0.813 0.011 <0.001 Temperature (L16) 0.318 0.080 <0.001 Rainfall (L6) 0.00215 0.000678 0.001 Rainfall (L7) 0.00337 0.000909 <0.001 Rainfall (L8) 0.00200 0.001077 0.064 Rainfall (L9) 0.00139 0.000924 0.132 Rainfall (L10) 0.00182 0.000780 0.019
Gampaha Dengue rate (L1) 0.619 0.015 <0.001 Temperature (L10) 0.128 0.095 0.178
Kandy Dengue rate (L1) 0.603 0.021 <0.001 Temperature (L14) 0.571 0.233 0.014
Anuradhapura Dengue rate (L1) 0.418 0.0176 <0.001 Temperature (L0) -0.433 0.187 0.021 Rainfall (L7) 0.00792 0.0040 0.048
Puttalam Dengue rate (L1) 0.639 0.00449 <0.001 Temperature (L0) -0.700 0.430 0.103
The lags for the predictors were selected based on the cross correlations as displayed in Table 8.
All areas showed a significant autocorrelation with the occurrence of DF in the week before (L1), the highest observed being for CMC. Significant positive relationship with average temperature was observed for CMC (lag of 16 weeks), Kandy (lag of 10 weeks) and Kandy (lag of 14 weeks), while a negative correlation with temperature was observed for Anuradhapura and Puttalam for the week of notification.
No consistent relationship was observed with average rainfall, except for CMC where average rainfall in the 7-12 weeks preceding notification was significant. Only Anuradhapura showed a similar relationship with the average rainfall 7 weeks before notification.
35
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
6.2 Dysentery
i. Colombo Municipal Council area
The CMC area had a relatively low monthly case rate of dysentery per 100,000 population (Figure 16). There appears to be a slight increase in the case rate during the middle of the year but overall there had been a decrease in the rate during the past five years. The higher rates observed for the months June and July for 1996-2005 corresponding to the South West monsoon, were not observed for the 2006-2011 period, showing that the monsoon related high rates associated with floods had declined. This may be related to the coverage of CMC areas with potable safe drinking water.
Figure 16. Monthly dysentery case rate in Colombo Municipal Council: 1996 to 2010
CMC - Dysentery - 1996 to 2010
4 4
3
3
2
2
1
Dysenterycases/ 100 000
1 Dysenterycases/ 100 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1996 1997 1998 1999
Jan 1996 Dec 2000 Dec 2005 Dec 2010
4 4
3 3
2 2
1 1
Dysenterycases/ 100 000 Dysenterycases/ 100 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was minimal correlation between either rainfall or temperature and dysentery rate and almost no autocorrelation was seen with lagged dysentery rate (Table 10).
Table 10: Time series correlation between dysentery notifications and predictor variables (weekly data) Area Average Rainfall Dysentery rate Temperature CMC Lag R Lag R Lag R L1 0.06 L2 0.04 L1 0.01
36 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
ii. Gampaha MOH area
The monthly dysentery rate in the Gampaha MOH area was higher than that of the CMC. The higher rates observed indicated by peaks for the months June and July corresponding to the South West monsoon for 1996-2003, were not observed for the later years (Figure 17). There was some indication of a decline in rates over the past decade.
Figure 17: Monthly dysentery case rate in Gampaha Medical Officer of Health area: 1996 to 2010
Gampaha MOH - Dysentery - 1996 to 2010
10 10
8
8
6
6
4
4
2
Dysenterycases/ 100 000
2 Dysenterycases/ 100 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1996 1997 1998 1999
Jan 1996 Dec 2000 Dec 2005 Dec 2010
10 10
8 8
6 6
4 4
2 2
Dysenterycases/ 100 000 Dysenterycases/ 100 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was some correlation between lagged terms of temperature (L5) and rainfall (L3) and autocorrelation with the preceding week’s dysentery rate in Gampaha MOH area (Table 11).
Table 11: Time series correlation between dysentery notifications and predictor variables (weekly data)
Area Average Rainfall Dysentery rate Temperature Gampaha Lag R Lag R Lag R L5 0.13 L3 0.11 L1 0.21 37
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
iii. Kandy Municipal Council area
There was no evidence of an annual pattern in the dysentery case rate in the Kandy Municipal Council area except for a few outbreaks during the 1996-1998 and the early part of last decade (Figure 18). Overall monthly case rates were relatively low. Peaks are observed for the months December and January and February of the following year, for a few years during the period. This pattern is different to the one observed in the CMC and Gampaha MOH area.
Figure 18. Monthly dysentery case rate in Kandy Municipal Council area: 1996 to 2010
Kandy - Dysentery - 1996 to 2010
10
10
8
8
6
4
6
2
4
Dysenterycases/ 100 000 Dysenterycases/ 100 000 2 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1996 1997 1998 1999
Jan 1996 Dec 2000 Dec 2005 Dec 2010
10
10
8
8
6
6
4
4
2
2
Dysenterycases/ 100 000 Dysenterycases/ 100 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was minimal correlation between either rainfall or temperature and dysentery rate and almost no autocorrelation was seen with lagged dysentery rate in Kandy (Table 12).
Table 12: Time series correlation between dysentery notifications and predictor variables (weekly data) Area Average Rainfall Dysentery rate Temperature Kandy Lag R Lag R Lag R L3 0.04 L1 -0.06 L2 0.08
38 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
iv. Anuradhapura MOH area
There appears to be higher case rates for dysentery in the Anuradhapura MOH area during the North East monsoon period (Figure 19). There were outbreaks of dysentery during November and December of 2001 and 2002. But since then the rate has been relatively low.
Figure 19. Monthly dysentery case rate in Anuradhapura (NE) Medical Officer of Health area: 1996 to 2010
Anuradhapura MOH - Dysentery - 1996 to 2010
30 10
8
25
6
20
4
15
2
Dysenterycases/ 100 000
10 Dysenterycases/ 100 000 5 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1996 1997 1998 1999
Jan 1996 Dec 2000 Dec 2005 Dec 2010
30 10
25
8
20
6
15
4
10
2
5
Dysenterycases/ 100 000 Dysenterycases/ 100 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was a weak negative correlation between terms of temperature (L0 & L2) and positive correlation with rainfall (L3) and autocorrelation with the preceding weeks’ dysentery rate (Table 13).
Table 13: Time series correlation between dysentery notifications and predictor variables (weekly data) Area Average Rainfall Dysentery rate Temperature Anuradhapura Lag R Lag R Lag R L0 -0.13 L3 0.15 L1 0.28 L2 -0.12 L2 0.37 39
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
There was no evidence of an annual pattern in the dysentery case rate in the Puttalam MOH area (Figure 20) and the rate have been declining over the past decade. Outbreaks have been observed in some years and do not show a particular monthly pattern of variation.
Figure 20. Monthly dysentery case rate in Puttalam Medical Officer of Health area:1996 to 2010
Puttalam - Dysentery - 1996 to 2010
20
20
15
15
10
10
5
Dysenterycases/ 100 000
5 Dysenterycases/ 100 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1996 1997 1998 1999
Jan 1996 Dec 2000 Dec 2005 Dec 2010
20 10
8
15
6
10
4
5
2
Dysenterycases/ 100 000 Dysenterycases/ 100 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was minimal correlation between either temperature (LO and L2) or rainfall (L3) and dysentery rate but there was autocorrelation with lagged dysentery rate in Puttalam MOH area (L1 and L2) (Table 14).
Table 14: Time series correlation between dysentery notifications and predictor variables (weekly data) Area Average Rainfall Dysentery rate Temperature Puttalam Lag R Lag R Lag R L0 -0.13 L3 0.15 L1 0.28 L2 -0.12 L2 0.37 40 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
The only consistent finding in the time series regression models for the different areas was the presence of significant autocorrelation with lagged terms of dysentery rate (Table 15). Neither rainfall nor temperature was a significant factor in predicting dysentery rate in Colombo, Kandy and Puttalam. Table 15: Time series regression of dysentery rate by study area
Study area Variable Β SE P CMC Dysentery rate (L1) 0.091 0.038 0.018 Temperature (L1) 0.027 0.015 0.080 Rainfall (L6) 0.00029 0.00016 0.066
Gampaha Dysentery rate (L1) 0.192 0.026 <0.001 Temperature (L15) 0.102 0.033 0.002 Rainfall (L3) 0.00128 0.00041 0.002
Kandy Dysentery rate (L2) 0.079 0.035 0.023 Temperature (L3) 0.028 0.024 0.242 Rainfall (L1) -0.0011 0.0011 0.348
Anuradhapura Dysentery rate (L1) 0.185 0.025 <0.001 Dysentery rate (L2) 0.312 0.014 <0.001 Temperature (L2) -0.039 0.50 0.436 Rainfall (L3) 0.00245 0.00093 0.009
Puttalam Dysentery rate (L1) 0.191 0.0121 <0.001 Dysentery rate (L2) 0.115 0.0233 <0.001 Temperature (L0) -0.041 0.0384 0.285 Rainfall (L1) 0.000856 0.00098 0.387
The lags for the predictors were selected based on the cross correlations as displayed in Tables 10-14.
It is also clear that in all areas the case rate of dysentery has declined in the past 5-10 years. The improvement of sanitation, public health programmes would have played a key role. However, there appears a consistent positive relationship of case rate with the monsoon rains as seen in Figure 17 for Gampaha MOH area, and Anuradhapura and to a lesser extent for CMC, and Kandy. The role of floods in spreading dysentery during these weather events cannot be ruled out. This requires further study relating occurrence of dysentery with severe flooding.
There is a positive autocorrelation of case rates with the previous week’s case rates in Anuradhapura and Puttalam. The coverage of these two areas with safe drinking water is poor, which may account for the spread of dysentery from one case to another. This is not seen in CMC, Gampaha, and Kandy, where coverage with safe water supply is much higher.
41
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
6.3 Leptospirosis
i. Colombo Municipal Council area
The number of cases of leptospirosis reported from the Colombo Municipal Council area was much lower than either the cases of dengue or dysentery. There was no obvious annual pattern but there was an increase in the case rate during the last five years. During this period the rates were higher during June to September (Figure 21).
Figure 21. Monthly leptospirosis case rate in Colombo Municipal Council: 1996 to 2010
CMC - Leptospirosis - 1996 to 2010
2 2
1.5
1.5
1
1
.5
Leptospirosiscases/ 100 000 .5
Leptospirosiscases/ 100 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1996 1997 1998 1999
Jan 1996 Dec 2000 Dec 2005 Dec 2010
2 2
1.5 1.5
1 1
.5 .5
Leptospirosiscases/ 100 000 Leptospirosiscases/ 100 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was no correlation with temperature but some correlation with rainfall (lags 3 to 5 weeks) and some autocorrelation (Table 16). Table 16. Time series correlation between leptospirosis notifications and predictor variables (weekly data) - CMC Average Temperature Rainfall Leptospirosis rate (auto correlation) Lag r Lag r Lag R - - 3 0.14 1 0.14 4 0.15 5 0.21
42 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
ii. Gampaha MOH area
The leptospirosis rate in the Gampaha MOH area had been increasing over the 15-year period under consideration. It also shows two peaks, one around February and March and the other around September and October during most years (Figure 21).
Figure 22. Monthly leptospirosis case rate in Gampaha MOH area 1996 to 2010
Gampaha - Leptospirosis - 1996 to 2010
12 12
10
10
8
8
6
6
4
2
4
Leptospirosiscases/ 100 000 2 Leptospirosiscases/ 100 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1996 1997 1998 1999
Jan 1996 Dec 2000 Dec 2005 Dec 2010
12 12
10 10
8 8
6 6
4 4
2 2
Leptospirosiscases/ 100 000 Leptospirosiscases/ 100 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was no correlation between the leptospirosis rate and either temperature or rainfall but there was some degree of autocorrelation (Table 17).
Table 17. Time series correlation between leptospirosis notifications and predictor variables (weekly data) - Gampaha MOH area Average Temperature Rainfall Leptospirosis rate (auto correlation) Lag r Lag r Lag R 1 0.25 - - 2 0.31
43
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
iii. Kandy Municipal Council area
Leptospirosis had been reported only sporadically in the Kandy MOH area until around 2005. After 2005 there had been a steady increase in the case rate for a couple of years followed by a decline in 2010. Even during the years of relatively high case rate there was no obvious annual pattern (Figure 23).
Figure 23. Monthly leptospirosis case rate in Kandy MC 1996 to 2010
Kandy - Leptospirosis - 1996 to 2010
4
4
3
3
2
1
2
Leptospirosiscases/ 100 000 1
Leptospirosiscases/ 100 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1996 1997 1998 1999
Jan 1996 Dec 2000 Dec 2005 Dec 2010
4
4
3
3
2
2
1
1
Leptospirosiscases/ 100 000 Leptospirosiscases/ 100 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was no correlation between the leptospirosis rate and either temperature or rainfall but there was some degree of autocorrelation (Table 18). Table 18. Time series correlation between leptospirosis notifications and predictor variables (weekly data) - Kandy MC Average Temperature Rainfall Leptospirosis rate (auto correlation) Lag r Lag r Lag R 1 0.08 2 0.10 3 0.10 5 0.14 10 0.23
44 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
iv. Anuradhapura MOH area
The leptospirosis case rate was lower than the dysentery rate in Anuradhapura. There was no evidence of an increasing trend over the 15-year study period (Figure 24).
Figure 24. Monthly leptospirosis case rate in Anuradhapura MOH area 1996 to 2010
Anuradhapura - Leptospirosis - 1996 to 2010
5
5
4
4
3
3
2
1
2
Leptospirosiscases/ 100 000 1
Leptospirosiscases/ 100 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1996 1997 1998 1999
Jan 1996 Dec 2000 Dec 2005 Dec 2010
5
5
4
4
3
3
2
2
1
1
Leptospirosiscases/ 100 000 Leptospirosiscases/ 100 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There probably is a slight increase in the case rate during the months of November, December and January. There was a weak negative correlation with temperature and weak positive correlation with rainfall (Table 19).
Table 19. Time series correlation between leptospirosis notifications and predictor variables (weekly data) - Anuradhapura MOH area Average Temperature Rainfall Leptospirosis rate (auto correlation) Lag r Lag r Lag R 4 -0.13 8 0.13 1 0.11 2 0.10
45
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
v. Puttalam MOH area
In the Puttalam MOH area, there had been sporadic cases of leptospirosis over the 15-year period under consideration (Figure 25).
Figure 25. Monthly leptospirosis case rate in Puttalam MOH area 1996 to 2010
Puttalam - Leptospirosis - 1996 to 2010
1 1
.75
.75
.5
.5
.25
Leptospirosiscases/ 100 000 .25
Leptospirosiscases/ 100 000 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1996 1997 1998 1999
Jan 1996 Dec 2000 Dec 2005 Dec 2010
1 1
.75 .75
.5 .5
.25 .25
Leptospirosiscases/ 100 000 Leptospirosiscases/ 100 000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 2001 2002 2006 2007 2008 2003 2004 2005 2009 2010
There was no correlation between the leptospirosis rate and either temperature or rainfall and there was minimal autocorrelation with case rate of the previous week (Table 20).
Table 20:Time series correlation between leptospirosis notifications and predictor variables (weekly data) - Puttalam MOH area Average Temperature Rainfall Leptospirosis rate (auto correlation) Lag R Lag r Lag R - - 5 0.10
The only consistent finding in the time series regression models for the different areas was the presence of significant autocorrelation with lagged terms of leptospirosis rate (Table 21). Neither rainfall nor temperature was a significant factor in predicting leptospirosis rate in Gampaha, Kandy and Puttalam. 46 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Table 21. Time series regression of leptospirosis rate by study area Study area Variable β SE P CMC Leptospirosis rate (L1) 0.116 0.021 <0.001 Rainfall (L3) 0.00014 0.000034 <0.001 Rainfall (L4) 0.00014 0.000026 <0.001 Rainfall (L5) 0.00027 0.000030 <0.001
Gampaha Leptospirosis rate (L1) 0.172 0.029 <0.001 Leptospirosis rate (L2) 0.300 0.023 <0.001
Kandy Leptospirosis rate (L1) 0.0645 0.027 0.018 Leptospirosis rate (L2) 0.0782 0.028 0.005 Leptospirosis rate (L3) 0.0771 0.025 0.002 Leptospirosis rate (L5) 0.128 0.018 <0.001
Anuradhapura Leptospirosis rate (L1) 0.0887 0.023 <0.001 Leptospirosis rate (L2) 0.0793 0.027 0.004 Temperature (L4) -0.0232 0.011 0.038 Rainfall (L8) 0.000805 0.00030 0.007
Puttalam Leptospirosis rate (L5) 0.106 0.016 <0.001
The lags for the predictors were selected based on the cross correlations as displayed in Tables 16-20.
47
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
6.4 Meteorology data of study areas
The study areas had a reasonably diverse pattern of temperature and rainfall. These probably represent most of the climatic patterns seen in the country except the higher elevations of the hill country.
The minimum daily temperature of 19.6˚ Centigrade was observed in Kandy and the maximum daily temperature of 34.3˚ Centigrade was observed in Anuradhapura (Table 2). But the daily temperature difference in the five study areas had a relatively narrow range from 6.1˚ Centigrade to 8.8˚ Centigrade. Due to this narrow range of the daily temperature it was decided to use the average temperature as the explanatory variable in the time series models.
Time series graphs of minimum, maximum and average temperature, and rainy days per month and monthly rainfall were constructed to visualize their pattern. In all areas there was an obvious annual pattern of both temperature and rainfall but there was no obvious evidence of a secular trend during the relatively short period of 15 years (graphs 1 - 5).
48 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Graph 1: Summary of weather data, CMC Summary of met data - CMC
January 1996 to December 2010
34
32
30
28
Temperature
26
24 22 Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
Min. temperature/Max. temperature Ave. Temperature
1000
800
600
Rainfall
400
200 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
20
15
10
5
Number ofNumber rainydays 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
Source: Dept of meteorology, Colombo
49
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
Graph 2. Summary of weather data, Gampaha Summary of met data - Gampaha
January 1996 to December 2010
34
32
30
28
26
Temperature
24 22
Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
Min. temperature/Max. temperature Ave. Temperature
800
600
400
Rainfall
200 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
20
15
10
5
Number ofNumber rainydays 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
Source: Dept of meteorology, Colombo
50 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Graph 3: Summary of weather data, Kandy Summary of met data - Kandy
January 1996 to December 2010
34
32
30
28
26
24
Temperature
22
20 18
Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
Min. temperature/Max. temperature Ave. Temperature
600
400
Rainfall
200 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
20
15
10
5
Number ofNumber rainydays 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
Source: Dept of meteorology, Colombo
51
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
Graph 4: Summary of weather data, Anuradhapura Summary of met data - Anuradhapura
January 1996 to December 2010
36
34
32
30
28
Temperature
26
24 22
Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
Min. temperature/Max. temperature Ave. Temperature
400
300
200
Rainfall
100 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
15
10
5
Number ofNumber rainydays 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
Source: Dept of meteorology, Colombo
52 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Graph 5: Summary of weather data, Puttalam Summary of met data - Puttalam
January 1996 to December 2010
36
34
32
30
28
26
Temperature
24 22
Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
Min. temperature/Max. temperature Ave. Temperature
500
400
300
Rainfall
200
100 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010
Year & month
20
15
10
5
Number ofNumber rainydays 0
Jan 1996 Dec 2000 Dec 2005 Dec 2010 Year & month
Source: Dept of meteorology, Colombo
53
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
7. Discussion
Methods and Data
The areas chosen for the study covered four major provinces of Sri Lanka: Western, Central, North Central and the North Western Provinces, giving population coverage of 60%. The areas benefitted from the two monsoons which bring heavy rain, the South West and North East, the relevant data are given in Table 2. The settings comprised urban (Colombo and Kandy Municipalities), semi urban (Gampaha), urban rural mix (Anuradhapura) and rural (Puttalam). Three were located in the wet zone (Colombo, Gampaha and Kandy) with a higher annual rainfall and two in the dry zone (Anuradhapura and Puttalam). The availability of data on a weekly basis for the three diseases being studied in an electronic format was a strength. The data were notified by the doctor diagnosing the case to the Epidemiological Unit of the Ministry of Health and confirmation by Medical Officer of Health in the field contributed to the reliability of data. Verification though discussed was not attempted as data used, the latest, were for 2010. The availability in electronic format made the data analyses much easier.
Duration for which data were available was relatively short, from 1996 for dengue and for a longer period for diarrhoeal diseases. The disease entities included under diarrhoeal diseases were notifiable gastro-intestinal infections, namely, enteric fever and dysentery. Thus, it was decided to analyze data from 1996 to 2010 for the two diseases being studied. Although this is too short a period (15 years) to study climate change and the impact on diseases, we believe, this analysis will provide useful insight into data constraints, issues in analysis, in the findings and analysis.
Several interventions instituted by the Ministry of Health and other relevant sectors, too, have the potential to influence the relationship, between dengue and climate factors. These interventions which occurred in the past 2-3 years were not applicable to the period for which data were used in the study. The inability to quantify these interventions and their outcomes, too, need to be borne in mind.
We were unable to obtain data on floods and displacement for the study areas for the period of study. Formal time series analysis was carried out with bivariate time series correlations between weekly notifications and the different meteorological variables being computed initially with their lagged terms. These were carried out to identify the appropriate lag terms for the ARIMAX model. The ARIMAX model was used for regression analysis as this allows for adjusting for autocorrelations in the dependent variable and use of moving average terms and lagged terms in the predictors. 54 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
The graphical presentation of the dependent and independent variables allowed for descriptive interpretation of the occurrence of the dependent variable in relation to the distribution of independent variables in the same time period. One of the areas provided a useful input as a control, as the area, Puttalam had very low climate variability and low rates of the notification of the diseases studied.
Results The bivariate correlations with lag time analysis results (Table 3) showed that autocorrelations of the dependent variable (i.e., dengue rate in the previous week, lag 1) itself was significant predictor of dengue fever (DF) in the present week for all five study areas. Rainfall, in CMC, up to a lag period of 6-7 weeks lag period is significant in predicting DF notification case rates. Research has shown that the incidence and epidemics of dengue, in particular, have a relationship with the rainy season (Guha-Sapir D, Schimmer B 2005). This is not observed for the other study areas. The pattern of distribution of DF in Puttalam with very low case rates with a relatively low variability of climate factors highlight the importance of the climate variability factors in the occurrence of DF. The high level of rainfall, the occurrence of high case rates corresponding with South West monsoon with heavy rainfall, and high population density (Table 1, 3330 persons/sq km) appear to contribute to the high level of dengue transmission.
The significant positive correlation between dengue case rate and average temperature with a lag varying between 10-16 weeks was observed for Colombo, Gampaha and Kandy. The relationship with Anuradhapura and Puttalam DF case rate with temperature was negative. As shown in Table 2, the average and maximum temperatures of both Anuradhapura and Puttalam were higher than the other areas by 1-20C. In a study carried out in Puerto Rico from 1988 to 1992, the model of data of DF transmission and seasonal temperature revealed weak relationship between monthly mean temperature and incidence of DF (Guha-Sapir and Schimmer 2005). A relationship of DF with temperature variability was also shown in Taiwan (Pei-Chih Wu 2007).
The vectors of DF play a crucial intermediary role in the complex dynamics in the relationship between DF transmission and climate factors. Research also highlights the relationship between the vector dynamics and climate factors. Studies which have shown that the development, growth, and survival of Culex quinquefasciatus and Aedes aegypti, the median developmental rates of both mosquito species and the temperature-dependent body size which generally 55
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study decreased as temperature increased may provide the reason for the relationship of DF seen in this study with the temperature.
The role played by temperature, where high temperature has shown to increase vector efficiency by reducing the period of viral replication in mosquitoes is also an important issue in the complex relationship between temperature and occurrence of DF (Rueda et al 1990). The proportion of houses in a community with larva on the premises was found to be significantly associated with the proportion of people in the community with diseases (odds ratio (ORadj = 1.9; 95% confidence interval (CI) 1.4-2.5). The relationship to water storage practices was also seen in this study where there was a significant risk of occurrence of cases with the proportion of households with uncovered water containers (ORadj = 1.9; 95% CI 1.4-2.7) (Koopman 1991). The lack of entomological data is a shortcoming of data availability which has to be highlighted, considering the crucial role played by the Aedes mosquito species in the transmission of the disease, and the potential for the use of entomological data to predict and prevent DF, before transmission occurs.
In all study areas, case rate of dysentery has declined in the past 5-10 years. The improvement of sanitation, public health programmes would have played a key role. However, there appears a consistent positive relationship of case rate with the monsoon rains as seen in Gampaha and Anuradhapura MOH areas and to a lesser extent for CMC and Kandy. The role of floods in spreading dysentery during these weather events cannot be ruled out. This requires further study relating occurrence of dysentery with severe flooding.
There is a positive autocorrelation of case rates with the previous week’s case rates in Anuradhapura and Puttalam. The coverage of these two areas with safe drinking water is poor, which may account for the spread of dysentery from one case to another. This is not seen in CMC, Gampaha, and Kandy, where coverage with safe water supply is much higher.
In all study areas the case rate of leptospirosis was very low except Puttalam which showed sporadic cases. An inconsistent autocorrelation with previous weeks’ case rate was observed for Gampaha and Kandy. A similar pattern was seen for rainfall for the study areas of CMC and Kandy. A low negative correlation with temperature was observed for Anuradhapura. The consistent finding is the sporadic nature of leptospirosis and a lack of any relationship with climate factors or with previous week’s case rate in Puttalam MOH area.
56 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
8. Conclusions
1. The period of study (15 years) was relatively short and inadequate to draw meaningful conclusions. If it were longer (at least 30 years), the research conclusions would be more apparent. The constraint was mainly related to the relatively short period of time for which data on DF and leptospirosis were available. The constraints faced in climate and impacts on infectious studies have been highlighted by Lafferty (2009). He concludes that though there is global warming, there is little evidence that climate change has already favored infectious diseases and recent models predict range shifts in disease distributions, with little net increase in area. He also highlights that many factors affect infectious disease, and some may play a more dominant role and ‘overshadow’ the effects of climate.
2. Climate data were available for over 100 years in electronic format. We were not able to obtain data on relative humidity, although we propose to do so based on certain calculations from original data available.
3. The occurrence of DF appears to be related to rainfall, the previous week’s case load as well as population density. Rainfall occurring after 6-7 weeks was related to dengue fever in the current week. This can be accounted for by the duration of the lifecycle of the mosquito and the need for an adequate number of cases for the spread which in turn is affected by the population density. These were not evaluated in this study.
4. In all areas the case rate of dysentery has declined in the past 5-10 years. There appears a consistent positive relationship of case rate with the monsoon rains. The role of floods in spreading dysentery during these weather events cannot be ruled out.
5. The positive auto correlation of case rates of dysentery with the previous week’s case rates in Anuradhapura and Puttalam where the coverage of these two areas with safe drinking water is poor, may account for the spread of dysentery from one case to another. This was not seen in CMC, Gampaha, and Kandy, where coverage with safe water supply is much higher.
6. Entomological data should be collected routinely and made available. Since this precedes the occurrence of cases, it would be useful to support implementation of effective preventive measures very early.
57
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
9. References
Department of Census and Statistics. 2007, Sri Lanka- Demographic and Health Survey – 2006-2007, Colombo: Department of Census and Statistics.
Department of Meteorology, Sri Lanka. Climatic Analysis for Preparation of the Second National Conference 2007.
Guha-Sapir D, Schimmer B. Dengue fever: new paradigms for a changing epidemiology: A Review. Emerging Themes in Epidemiology, 2005, 2:1 doi:10.1186/1742-7622-2-1.
Haines A, Kovats RS, Campbell-Lendrum D, Corvalan C. 2006. Climate change and human health: impacts, vulnerability, and mitigation. The Lancet, 367(9528), p.2101–2109.
Hales S, de Wet N, Maindonald J, Woodward A. Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. The Lancet 2002, Volume 360 Issue 9336. pp 830–834.
Hans-Martin Füssel, Richard J. T. Klein. Climate Change Vulnerability Assessments: An Evolution of Conceptual Thinking. Climatic Change. 2006, Volume 75, Issue 3, pp 301-329
Intergovernmental Panel on Climate Change. Climate Change: Synthesis Report 2007. An Assessment of the Intergovernmental Panel on Climate Change (IPCC).
Koopman JS, Prevots DR, Vaca Marin MA, Gomez Dantes H, Zarate Aquino ML, Longini IM Jr, Sepulveda Amor J. Determinants and predictors of dengue infection in Mexico. American Journal of Epidemiology, 1991 Jun 1, 133(11), 168-78.
McMichael A J, Haines A. Global climate change: the potential effects on health. British Medical Journal, 1997, 315, p.805.
McMichael A J, Woodruff R and Hales S. Climate change and human health: present and future risks. The Lancet, March 2006, 367 Issue 9513, 11–17 pp 859–869
Ministry of Health Sri Lanka. 2007, Annual Health Statistics, Colombo: Ministry of Healthcare & Nutrition.
Ministry of Health Sri Lanka. 2003, Annual Health Bulletin, Colombo: Ministry of Healthcare & Nutrition. 58 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Nerlander N. Commission on Climate Change and Development 2009. http://www.ccdcommission.org/Filer/commissioners/Health.pdf.
Patz JA, Campbell-Lendrum D, Holloway T & Foley JA. Impact of regional climate change on human health Nature, 438, 310-317 (17 November 2005) | doi:10.1038/nature04188.
Patz JA, Epstein PR, Burke TA, Balbus JM. Global Climate Change and Emerging Infectious Disease. Journal of American Medical Association, 1996, 275(3), p.217-223.
Patz JA, Campbell-Lendrum D, Gibbs H, Woodruff R. Health Impact Assessment of Global Climate Change: Expanding on Comparative Risk Assessment Approaches for Policy Making. Annual Review of Public Health, 2008, Vol. 29, pp 27-39. DOI: 10.1146/annurev.publhealth.29.020907.090750.
Pei-Chih W, How-Ran G, Shih-Chun Lung, Chuan-Yao Lin, Huey-Jen Sue. Weather as an effective predictor for occurrence of dengue fever in Taiwan. Acta Tropica, July 2007, Volume 103, Issue 1, Pages 50–57
Rueda LM, Patel KJ, Axtell RC, Stinner RE. Temperature-dependent development and survival rates of Culex quinquefasciatus and Aedes aegypti (Diptera: Culicidae). Journal of Medical Entomology, 1990 Sep, 27(5), 892-8.
WHO (2010). Assessing the relationship between climatic factors and diarrhoeal and vector- borne diseases – a retrospective study generic research protocol. WHO Regional Office for South-East Asia. http://203.90.70.117/PDS_DOCS/B4517.pdf [Accessed on 29 March 2013].
59
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
Annex 1
Trends in Annual Mean Temperature Anomaly: Sri Lanka – 1961-2000
Colombo
Gampaha
60 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Kandy
Anuradhapura
61
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
Puttalam
Trends in Annual Mean Rainfall Anomaly: Sri Lanka – 1961-2000
Sri Lanka
62 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Colombo
Gampaha
63
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
Kandy
Anuradhapura
64 Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka 1996-2010: A Retrospective Study
Puttalam
65
Climatic Factors and the Occurrence of Dengue Fever, Dysentery and Leptospirosis in Sri Lanka: 1996-2010: A Retrospective Study
World Health Organization Centre for Health Development (WHO Kobe Centre – WKC)
I.H.D. Centre Building, 9th Floor1-5-1 Wakinohama-Kaigandori Chuo-ku, Kobe 651-0073 Japan Telephone: +81 78 230 3100 Facsimile: +81 78 230 3178 E-mail: [email protected] URL: http://www.who.int/kobe_centre/
WHO reference number: WHO/WKC/UHEM/14.01