Technical Report

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Technical Report

Climatic factors and the Occurrence of

Dengue Fever, Dysentery and Leptospirosis

in Sri Lanka 1996-2010: A Retrospective Study

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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:

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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.

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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.

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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.

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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 Gampaha Medical Officer of Health area: 1996 to 2010……….. 24 10 Monthly dengue case rate in Kandy Municipal Council area: 1996-2010………….. 25

11 Monthly rainfall, dengue case rate and average temperature reported from Kandy Municipal Council area: 1996-2010……….. 26 12 Monthly dengue case rate in Anuradhapura (NE) Medical Officer of Health area: 1996- 2010……… 27 13 Monthly rainfall, dengue case rate and average temperature in Anuradhapura Medical Officer of Health area: 1996 – 2010……….. 28 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 Puttalam Medical Officer of Health area during 1996 to 2010………. 31 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: 1996 to 2010………. 36 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 area: 1996 to 2010……….. 38 20 Monthly dysentery case rate in Puttalam Medical Officer of Health area: 1996 to 2010………. 39 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

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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 and province……… 16 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 variables (weekly data)………. 21 4 Time series correlation between dengue notifications in Gampaha MOH area and predictor variables (weekly data)………. 23 5 Time series correlation between dengue notifications in Kandy Municipal Council area and predictor variables (weekly data)………. 26 6 Time series correlation between dengue notifications in Anuradhapura

MOH area and predictor variables (weekly data)………..

27

7 Time series correlation between dengue notifications in Anuradhapura

MOH area and predictor variables (weekly data)………..

30 8 Time series correlation between dengue notifications and predictor variables 33

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(weekly data)………...

9 Time series regression of dengue rate by study area……….. 34 10 Time series correlation between dysentery notifications and predictor variables

(weekly data)………..

35

11 Time series correlation between dysentery notifications and predictor variables (weekly data)………..

36

37

38

14 Time series correlation between dysentery notifications and predictor

variables (weekly data)………..

39

15 Time series regression of dysentery rate by study area……….. 40 16 Time series correlation between leptospirosis notifications and predictor

variables (weekly data) – CMC………

41

17 Time series correlation between leptospirosis notifications and predictor

variables (weekly data) - Gampaha MOH area……….

42

18 Time series correlation between leptospirosis notifications and predictor variables (weekly data) - Kandy MC………

43

19 Time series correlation between leptospirosis notifications and predictor variables (weekly data) - Anuradhapura MOH area……….

44

20 Time series correlation between leptospirosis notifications and predictor variables (weekly data) - Puttalam MOH area………...

45

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

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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.

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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,

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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.

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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

656 756 582 440 1294

346 421 628

5203 5986 8931

4749 15463

5994 11980

7327 6560 8647

15 3 7 11

54

17 8 14

37

54 64 32

87

28 46

28 27 124

0 2000 4000 6000 8000 10000 12000 14000 16000 18000

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Years

Number

0 20 40 60 80 100 120 140 160 180 200

Deaths

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

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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

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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

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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.

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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.

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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

Area

Population (in 2010) Population density Province & climate Colombo MC area 700,000 (resident)

and 500 000 daily migrants for work

3,330 persons /sq km

Western Province Wet zone

Gampaha Divisional Health area

170,000 1340 persons /sq km

Western Province Wet zone

Kandy MC area 105,000 1917 persons /sq km

Central province, Wet zone, hills Anuradhapura

Divisional Health area

80,000 112 persons /sq km North Central Province, Dry zone Puttalam Divisional

Health area

118,000 246 persons / sq km North Western Province, Dry zone Source: Census of Population and Housing 2011

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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.

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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 (^oC)

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 (^oC)

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

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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.

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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

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).

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Dengue cases/ 100 000

Jan 1995 Jan 2000 Jan 2005 Jan 2010

0.5 11.5 22.5

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1996 1997

1998 1999

01020304050

2000 2001 2002

2003 2004 2005

0204060

2006 2007 2008

2009 2010

CMC - Dengue - 1996 to 2010

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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

Temperature

Rainfall Dengue rate

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).

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Figure 7. Monthly rainfall, dengue case rate and average temperature in CMC: 1996-2010

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.

0

2004006008001000

Rainfall

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

0204060

Dengue cases per 100 000

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

2627282930

Ave. Temperature

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

CMC: Dengue, Rainfall & Temperatue

1996 - 2010

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Figure 8. Monthly dengue case rate in Gampaha Medical Officer of Health area: 1996-2010

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

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.

010203040

Jan 1995 Jan 2000 Jan 2005 Jan 2010

0.5 11.5 2

1996 1997

1998 1999

010203040

2000 2001 2002

2003 2004 2005

010203040

2006 2007 2008

2009 2010

Gampaha - Dengue - 1996 to 2010

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Figure 9. Monthly rainfall, dengue case rate and average temperature for Gampaha Medical Officer of Health area: 1996 to 2010

0

200400600800

Rainfall

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

010203040

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

2627282930

Ave. Temperature

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

Gampaha: Dengue, Rainfall & Temperatue

1996 - 2010

(25)

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

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

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

020406080100

Jan 1995 Jan 2000 Jan 2005 Jan 2010

012345

1996 1997

1998 1999

020406080

2000 2001 2002

2003 2004 2005

020406080100

2006 2007 2008

2009 2010

Kandy - Dengue - 1996 to 2010

(26)

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

0

200400600

Rainfall

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

020406080100

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

232425262728

Ave. Temperature

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

Kandy: Dengue, Rainfall & Temperatue

1996 - 2010

(27)

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

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

Temperature

Rainfall Dengue rate Anuradhapura

MOH area

Lag r Lag r Lag r

L0 -0.13 L7 0.14 L1 0.43

L2 -0.13

050100150

Jan 1995 Jan 2000 Jan 2005 Jan 2010

0123

1996 1997

1998 1999

020406080

2000 2001 2002

2003 2004 2005

050100150

2006 2007 2008

2009 2010

Anuradapura - Dengue - 1996 to 2010

(28)

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

0

100200300400

Rainfall

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

050100150

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

26283032

Ave. Temperature

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

Anuradapura: Dengue, Rainfall & Temperatue

1996 - 2010

(29)

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

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.

050100150

Jan 1995 Jan 2000 Jan 2005 Jan 2010

0.2.4.6.8 1

1996 1997

1998 1999

020406080

2000 2001 2002

2003 2004 2005

050100150

2006 2007 2008

2009 2010

Puttalam - Dengue - 1996 to 2010

(30)

Table 7. Time series correlation between dengue notifications in Puttalam MOH area and predictor variables (weekly data)

Area Average

Temperature

Rainfall Dengue rate Puttalam MOH

area

Lag R Lag r Lag R

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)

Figure 15. Monthly rainfall, dengue case rate and average temperature reported from Puttalam Medical Officer of Health area during 1996 to 2010

0

100200300400500

Rainfall

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

050100150

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

2426283032

Ave. Temperature

Jan 1996 Dec 2000 Dec 2005 Dec 2010

Year & month

Puttalam: Dengue, Rainfall & Temperatue

1996 - 2010

(32)

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)

Table 8. Time series correlation between dengue notifications and predictor variables (weekly data)

Area Average

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)

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)

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

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

Temperature

Rainfall Dysentery rate

CMC Lag R Lag R Lag R

L1 0.06 L2 0.04 L1 0.01

1234

Dysentery cases/ 100 000

Jan 1996 Dec 2000 Dec 2005 Dec 2010

1234

1996 1997

1998 1999

1234

2000 2001 2002

2003 2004 2005

1234

2006 2007 2008

2009 2010

CMC - Dysentery - 1996 to 2010

(36)

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

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

Temperature

Rainfall Dysentery rate

Gampaha Lag R Lag R Lag R

L5 0.13 L3 0.11 L1 0.21

246810

Jan 1996 Dec 2000 Dec 2005 Dec 2010

246810

1996 1997

1998 1999

246810

2000 2001 2002

2003 2004 2005

246810

2006 2007 2008

2009 2010

Gampaha MOH - Dysentery - 1996 to 2010

Technical Report