Climate Change Health Risk Mapping Sub-National Climate Risk Maps for Climate Change Health Risk Mapping

i i || Climate Change Health Risk Mapping

Background

About the Project Climate change, including climate variability, has multiple influences on human health. Both direct and indirect impacts are expected. These include altera- tions in the geographic range and intensity of transmission of vector-, tick-, and rodent-borne and food- and waterborne diseases, and changes in the prevalence of diseases associated with air pollutants and aeroallergens. Climate change could alter or disrupt natural systems, making it possible for diseases to spread or emerge in areas where they had been limited or had not existed, or for diseases to disappear by making areas less hospitable to the vector or the pathogen. The World Health Organization (WHO) estimates that climate change may already be causing over 150,000 deaths globally per year. While direct and immediate impacts such as deaths in heat waves and floods can often be dra- matic and provoke immediate policy-responses, the most important long-term influences will likely act through changes in natural ecosystems and their impacts on vectors, waterborne pathogens, and contaminants.

Despite the increasing understanding of health risks associated with climate change, there has been limited identification and implementation of strategies, policies, and measures to protect the health of the most vulnerable populations. Reasons for this include the relatively recent appreciation of the links between climate change and health, which means that existing public health related poli- cies and practices globally do not reflect needs with respect to managing likely climate change-related health impacts.

Recognizing the fact that Ghana experiences an extremely high burden of climate-sensitive diseases such as , diarrhoeal, cerebrospinal meningitis and other infectious diseases and given the fact that Ghana is significantly vul- nerable to climatic changes, The Ministry of Health (MOH), Ghana in partnership of United Nations Development Programme (UNDP) is implementing a Global Environment Facility (GEF) funded project to pilot climate change adaptation for health in Ghana.

|| iii Climate Change Health Risk Mapping

Malaria, Cerebrospinal Meningitis and Diarrhoeal Diseases, were identified as cli- mate sensitive diseases of interest for the pilot project. The pilot will cover three districts – Bongo in the Upper East Region, Keta in the Volta region and Gomoa West in the Central region.

The proposed project will develop systems and response mechanisms to strength- en the integration of climate change risks into the health sector. Critical barriers will be overcome to shift the current response capacity of the health sector from being reactive towards being more anticipatory, deliberate and systematic. Project actions will identify, implement, monitor, and evaluate adaptations to reduce likely future burdens of malaria, diarrhoeal diseases, and cerebrospinal meningitis (CSM), priority climate change-related health issues identified by national stakeholders.

i v || Climate Change Health Risk Mapping

Acknowledgement

The production of this report was facilitated by the Climate Change and Health Project Implementation Unit, Ministry of Health, led by Benjamin Yaw Manu, the Project Manger, with the support of Abena Nakawa, the Project Associate, and in consultation with Mr. Isaac Adams, Director, Research, Statistics, Information Management, Ghana.

The content of this report was developed, discussed and validated through extensive consultations led by the Ministry of Health with stakeholders from government agencies including , Ghana Meteorological Service, National Malaria Control Programme, National Development Planning Commission, National Disaster Management Organization, Ministry of Local Government and Rural Development, Environmental Protection Agency, Ministry of Environment Science and Technology, National Disease Control Programme, Health Promotion Unit, Ministry of Health, Ministry of Finance and Economic Planning (External Relations Unit)

REPUBLIC OF GHANA MINISTRY OF HEALTH

INTEGRATING CLIMATE CHANGE INTO THE MANAGEMENT OF PRIORITY HEALTH RISKS IN GHANA

www.climatehealthghana.org

‘Photos used in this report were taken by the project and as such are the property of the project’

|| v Climate Change Health Risk Mapping

v i || Climate Change Health Risk Mapping Contents

Background iii Acknowledgement v Climate Change Health Risk Mapping 1 Sub-National Climate Risk Maps for Ghana Executive summary 3 1.0 Introduction 5 1.1 Aim of the study 8 1.2 Specific objectives 8 1.3 Rationale 8 1.4 Project deliverables 9 2.0 the linkages between climate and health risks 11 2.1 The linkages between climate and health risks 13 2.2 Malaria and climate change 13 2.3 Meningitis and climate change 15 2.4 Diarrhoea and climate change 16 2.5 Climate change health risk mapping 18 3.0 conceptual Framework 21 3.1 Environmental health risk mapping 23 4.0 Approach and methodology 25 4.1 Study area 27 4.2 Bongo profile 27 4.3 Gomoa West Municipal profile 29 4.4 Keta Municipal profile 30 4.5 Data 32 4.5.1 Climatic, environmental and population data 32 4.5.2 Disease incidence data 33 4.6 GIS Analysis 35 4.6.1 Health hazard and risk mapping 35 4.6.2 Health risk models 37 4.6.3 Health risk maps 43 4.7 Challenges 52 5.0 Summary overview and status of the deliverables 53 5.1 Conclusion 55 5.2 Recommendations 56 Policy Brief 59 Executive Summary 61 Methodology 62

|| v i i Climate Change Health Risk Mapping

List of Tables Table 1: Health facilities in Bongo District 28 Table 2: Health staffing in Bongo District 28 Table 3: Health facilities in Gomoa Municipal 30 Table 4: Health facilities in Keta Municipal 31 Table 5: Keta Municipal health staff strength 31 Table 6: OPD cases for malaria, diarrhoea and CSM in Bongo District 2009-2011 33 Table 7: OPD cases for malaria, diarrhoea and CSM in Keta Municipal Area 2009-2011 34 Table 8: OPD cases for malaria, diarrhoea and CSM in Gomoa West Municipal Area 2009-2011 35 Table 9: Data layers used in health hazard/risk mapping 37 Table 10: CSM Hazard /Risk layer ranking and weighting 38 Table 11: Diarrhoea Hazard / Risk layer ranking and weighting 41 Table 12: Malaria Hazard / Risk layer ranking and weighting 42 Table 13: Status of project deliverables 55

List of figures Figure 1: Relationships between environmental health hazard environmental health risk and environmental health impact (source: Briggs, 2000). 23 Figure 2: Map of Ghana showing the study districts 27 Figure 3: Map of Bongo District 28 Figure 4: Map of Gomoa West Municipal Area 29 Figure 5: Map of Keta Municipal Area 30 Figure 6: Environmental Health Risk Mapping Methodological Flow Chart 37 Figure 7: Malaria Risk Model 38 Figure 8: Diarrhoea Risk Model 39 Figure 9: Meningitis Risk Model 42 Figure 10: Bongo District CSM risk map 45 Figure 11: Bongo District malaria risk map 46 Figure 12: Bongo District diarrhoea risk map 47 Figure 13: Gomoa West Municipal diarrhoea risk map 48 Figure 14: Gomoa West Municipal malaria risk map 49 Figure 15: Keta Municipal malaria risk map 50 Figure 16: Keta Municipal diarrhoea risk map 51

viii || Climate Change Health Risk Mapping

Climate Change Health Risk Mapping Sub-National Climate Risk Maps for Ghana

Final Report

Submitted to UNDP/GEF Climate Change and Health Project. By: Philip Mantey Foster Mensah Kofi Nyarko

The Centre for Remote Sensing and Geographic Information Services (CERSGIS), University of Ghana, Legon. PMB L17, Tel: +233 302 500301/50796, Fax: +233 302 500310, email: [email protected]

|| 1 Climate Change Health Risk Mapping

2 || Climate Change Health Risk Mapping

Executive summary Due to Ghana’s significant vulnerability to climatic changes and consequently, the prevalence of an extremely high burden of climate-sensitive diseases, the Ministry of Health (MOH), Ghana in partnership of United Nations Development Programme (UNDP) is implementing a Global Environment Facility (GEF) funded project to pilot climate change adaptation for health in Ghana. The project focuses on three selected diseases of Malaria, Cerebrospinal Meningitis and Diarrhoeal Diseases in three pilot districts of Bongo in the Upper East region, Keta in the Volta region and Gomoa West in the Central Region.

CERSGIS was tasked with the development human factors in modeling health risks in the of climate change health risk maps at sub- pilot districts. Meningitis risk in the Bongo national scales (3 pilot districts, namely district was high within the district capital Keta, Gomoa West, and Bongo) showing where there was high population density current and future severity and incidence and low in the rural and sparsely populated of climate-sensitive diseases. This involved communities. Generally, malaria risk was seen among other things; a review of the existing fairly distributed within the districts studied literature showing linkages between climate but was highest within the densely populated and health risks in Ghana, the development of communities, forest areas and along the a methodology for developing and updating coast. Diarrhoeal risk was also high along the climate change health risk maps, as well as coast and densely in populated areas. the use of appropriate surveillance data on climate-sensitive diseases and other relevant Capacity building activities on the use of GIS information to map current health risks in technology in disease epidemiology and pilot districts. CERSGIS was also required health risk mapping for selected staff of the to build the capacity of selected national, beneficiary districts as well as their regional regional and district health team members and national counterparts were undertaken to develop, interpret, update, and apply risk to transfer these essential skills to the health maps for planning, monitoring, surveillance, administrators. and response. Challenges had to do with the level of The development of the risk maps was aggregation of disease incidence, coarseness undertaken using GIS techniques in multi- of the meteorological network as well as the criteria evaluation involving the overlay of non-availability of sub-district spatial data disease-specific environmental, climatic and on other essential confounding factors that would improve modeling results.

|| 3 Climate Change Health Risk Mapping

4 || Climate Change Health Risk Mapping

1.0 Introduction

|| 5 Climate Change Health Risk Mapping

6 || Climate Change Health Risk Mapping

Scientists tell us that the evidence that the Earth is warming is “unequivocal.” Indicators such as increases in global average air and sea temperature, melting polar ice caps and rising global sea levels all help us understand and prepare for the coming challenges. In addition to these observed changes are climate-sensitive impacts on human health which have become of great concern in recent years. They are attacking the pillars of public health and are providing a glimpse of the challenges public health will have to confront on a large scale. These challenges include changes in the spatial distribution and intensity of disease transmission vectors as well the prevalence and spread of air and water borne diseases which are highly susceptible to pollution from weather related events such as drought and flooding.

Climate change induced alteration and resources that, in many regions, are already change to natural ecosystems may result under severe stress. The core concern is in the emergence or spread of diseases to succinctly stated: “climate change endangers previously unaffected geographic regions human health,” (WHO). Global warming may or disease prevalence in areas where they be gradual, but the effects of extreme weather had been limited. In other cases, changes in events - more storms, floods, droughts and climate could also result in the disappearance heat waves will be abrupt and acutely felt. of diseases from areas as they become Both trends can affect some of the most less hospitable to the disease vector or the fundamental determinants of health, namely pathogen. air, water, food, shelter and freedom from disease. Though a global phenomenon, the consequences of climate change will not be Human beings are already exposed to the evenly distributed. In short, climate change effects of climate-sensitive diseases and can affect problems that are already huge, these diseases today kill millions. They include largely concentrated in the developing world, , which causes over 3.5 million and difficult to control. In the tropics, examples deaths per year, diarrhoeal diseases, which kill already provide us with images of the future: over 1.8 million, and malaria, which kills almost 1 million. But malaria, along with , ƒƒ Rift Valley fever in Africa: Major outbreaks meningitis and dengue fever, are only a few are usually associated with rains, which of the 14 communicable diseases recently are expected to become more frequent identified as being climate-sensitive by the as the climate changes; World Health Organization, which estimates ƒƒ Malaria in the East African highlands: In that climate change may already be directly the last 30 years, warmer temperatures responsible for over 150,000 deaths globally have also created more favourable per year. conditions for mosquito populations in the region and therefore for transmission While direct and immediate impacts such as of malaria; deaths in heat waves and floods can often be dramatic and provoke immediate policy, long- ƒƒ of cholera in Bangladesh: They term influences will likely act through changes are closely linked to flooding and in natural ecosystems and their impacts on unsafe water. disease vectors, waterborne pathogens, and Although these trends and events cannot be contaminants. With the appreciation of the attributed solely to climate change, they are linkages between climate change and health nonetheless the types of challenges expected only being a recent development, existing to become more frequent and intense with public health policies and practices are viewed climate changes. They will further strain health as largely inadequate in meeting the needs

|| 7 Climate Change Health Risk Mapping

with respect to potential climate change ƒƒ Transfer data, skills, software, and all related health impacts. necessary tools for updating the maps to the pilot districts In Ghana, three climate-sensitive diseases ƒƒ Train selected national, regional and namely; malaria, diarrhoeal disease and district health team members to develop, meningitis are identified as accounting for interpret, update, and apply risk maps for almost half the total disease burden with planning, monitoring, surveillance, and malaria still the leading cause of outpatient response. morbidity in all ages and sex groups. Public health interventions that address these three diseases particularly through improved risk mapping as a case in point are therefore 1.3 Rationale viewed as very essential to realising existing Adequate health infrastructure with universal health goals. access to primary health is crucial to reducing a population’s vulnerability to the impact of changing patterns of diseases due to climate 1.1 Aim of the study change. A well functioning health system not only provides treatment, but, together The main objective of the consultancy was to with laboratory services and standardized develop climate change health risk maps at diagnosis and reporting systems, is a crucial sub-national scales (3 pilot districts, namely component of a national surveillance system. Keta, Gomoa West, and Bongo) showing Health professionals must be better trained to current and future severity and incidence of understand the potential impacts of climate climate-sensitive diseases change on health

One of the main requirements identified in the 1.2 Specific objectives existing national response to climate change is the strengthening of disease surveillance ƒƒ Review existing literature showing systems to better avoid (through early warning linkages between climate and health systems), prepare for, and effectively respond risks in Ghana to climate change related health risks. The use ƒƒ Develop a methodology for developing of appropriate visualization tools can assist and updating climate change health risk in risk assessment and forecasting. Currently maps, as well as quantifying relationships health risk maps for the climate sensitive between climate variations and disease diseases are non-existent at the district level. outbreaks. Coupled with a limited capacity for developing ƒƒ Use appropriate surveillance data on risk maps at all levels of the health sector, this climate-sensitive diseases and other presents as inadequacy in capacity to forecast relevant information to map current risk patterns. health risks in pilot districts In order to develop coherent responses to ƒƒ Downscale climate change projections the increasing incidence of climate-related available for Ghana to the scale/level outbreaks, and to longer-term changing appropriate for the pilot districts disease patterns, there is the need for ƒƒ Identify suitable models to produce improved information upon which to base projection maps of vulnerability to the mainstreaming of climate change into climate-sensitive diseases (including but health planning. Public health interventions not limited to diarrhoeal disease, malaria, that address these three diseases particularly and CSM) in the three pilot districts through improved risk mapping as a case in point are therefore viewed as very essential to ƒƒ Communicate findings to policymakers, realizing existing health goals. planners, and other stakeholders

8 || Climate Change Health Risk Mapping

Due to Ghana’s significant vulnerability to 1.4 Project deliverables climatic changes and consequently, the prevalence of an extremely high burden of The consultant was tasked with providing the climate-sensitive diseases, the Ministry of following under the assignment: Health (MOH), Ghana in partnership with the ƒƒ Project inception report describing the United Nations Development Programme methodology and approach, (UNDP) is implementing a Global Environment Facility (GEF) funded project to pilot climate ƒƒ Transfer of data, skills, software, and change adaptation for health in Ghana. The all tools required in developing and project focuses on three selected diseases updating the maps to the districts, namely: Malaria, Cerebrospinal Meningitis and ƒƒ Submit a draft report providing a clear Diarrhoeal Diseases in three pilot districts of description of what exists and an Bongo in the Upper East region, Keta in the analysis of the strength, weaknesses, Volta region and Gomoa West in the Central opportunities and potential, Region. ƒƒ An in-person presentation to the project This will therefore provide a spatial management committee and identified representation of the risk and warning signs stakeholder groups, which would be updated regularly to improve ƒƒ Final technical report (5 hard copies and surveillance and response to these climate- a soft copy on CD including the maps and sensitive diseases. This information will help in interpretation of findings) packaged into setting up early warning systems and improve a publication ready format, the districts’ preparedness and ƒƒ Policy Brief based on the technical response. The development of the climate report, change risk map will therefore serve as a baseline data to show the risk of the disease ƒƒ Workshop with stakeholders, including a transmission in the pilot areas. This will also high-level policy briefing. serve as reference for scaling up nationally or for other districts.

|| 9 Climate Change Health Risk Mapping

1 0 || Climate Change Health Risk Mapping

The linkages 2.0 between climate and health risks

|| 1 1 Climate Change Health Risk Mapping

1 2 || Climate Change Health Risk Mapping

2.1 The linkages between diseases. Some of the non communicable climate and health risks disease conditions affected by the effect of climate change are cardiovascular diseases, Climate change refers to a statistically cancers and chronic respiratory diseases significant variation in either the mean state such as asthma. The communicable disease of the climate or in its variability, persisting conditions known to be affected by climate for an extended period (typically decades change are food borne diseases and vector or longer). Climate change may be due borne diseases such as malaria, diarrhoea to natural internal processes or external and meningitis. The three top climate forces, or to persistent anthropogenic sensitive diseases of concern in Ghana with changes (man-made) in the composition high disease burden and as such the need of the atmosphere or in land use. Over the to develop adaptive strategies are Meningitis, last 50 years, human activities – particularly Malaria and Diarrhoea. These diseases have the burning of fossil fuels – have released been identified as accounting for almost half sufficient quantities of carbon dioxide and the total disease burden with malaria still the other greenhouse gases to trap additional leading cause of outpatient morbidity in all heat in the lower atmosphere and affect the ages and sex groups. global climate. The World Health Organization estimates that, in the last 100 years, the world has warmed by approximately 0.75oC. Over the last 25 years, the rate of global warming 2.2 Malaria and climate has accelerated at over 0.18oC per decade. change Sea levels are rising, glaciers are melting and Malaria is a disease of gargantuan proportions precipitation patterns are changing. Extreme and is one of the most prevalent human weather events are becoming more intense infectious diseases. It is caused by protozoan and frequent. parasite of the plasmodium genus that is vectored (transmitted) from person to person Climate change endangers human health, through the bite of infected female anopheline affecting all sectors of society, both mosquitoes, which usually occurs at dusk and domestically and globally. The environmental dawn. There are four Plasmodium species consequences of climate change, that is responsible for causing malaria in humans and those already observed and those that are these are P. falciparum, P. vivax, P. ovale and P. anticipated, such as sea-level rise, changes in Malariae, P. knowlesi. Plasmodium falciparum precipitation resulting in flooding and drought, causes the largest burden of disease. The heat waves, more intense hurricanes and parasite infects red blood cells and Malaria storms, and degraded air quality, will affect is characterized by cycles of chills, fever, pain, human health both directly and indirectly. and sweating. Malaria is the most important Addressing the effects of climate change on tropical disease, remaining widespread human health is especially challenging because throughout the tropics. both the surrounding environment and the decisions that people make influence health. Globally, there were an estimated 216 In an analysis that included just four climate- million episodes of malaria in 2010, of which sensitive diseases (cardiovascular disease, approximately 81%, were in the African Region. malnutrition, diarrhoea, and malaria) as well Malaria deaths were estimated at 655 000 of as floods, the World Health Organization which 91% were in Africa and approximately (WHO) estimated 166,000 deaths and about 86% of malaria deaths were among children 5.5 million disability-adjusted life years (DALYs, under 5 years of age. The estimated incidence a measure of overall disease burden) were of malaria globally has reduced by 17% since attributable to climate change in the year 2000 and malaria-specific mortality rates by 2000. 26% but this could change with the imminent threat of climate change. In Ghana, climate is known to be related to many disease conditions including both communicable and non-communicable

|| 1 3 Climate Change Health Risk Mapping

The Ghana Health Service health facility data a mosquito infectious. If more time is needed indicates that malaria is the number one cause for the parasite to mature, then the probability of morbidity, accounting for about 38 percent that a mosquito will live long enough for the of all outpatient illnesses, 36 percent of all parasite to spread the is dramatically admissions, and 33 percent of all deaths in reduced. Thus the rates of insect biting and children under five years. Between 3.1 and 3.5 the maturation of microorganisms within million cases of clinical malaria are reported them are temperature-dependent, and both in public health facilities each year, of which rates increase when the air warms, enhancing 900,000 cases are in children under five years. the chances for disease transmission.

The transmission of malaria is dependent on Water available for breeding sites is the second the presence of ecological conditions, which primary environmental factor that has been have been linked with the development of associated with malaria. Water provides a both the parasite and vector mosquito. The habitat for mosquitoes to lay their eggs and for geographical and temporal distributions the development of the Anopheles larvae and as well as the incidence of many vector pupae. The presence of permanent bodies of borne diseases such as malaria and dengue water creates mosquito breeding sites and are sensitive to temperature and rainfall. the possibility of malaria transmission year Temperature is an important factor that affects round. The bodies of water can be either the distribution and rate of development natural, like swamps or man-made structures, of both the malaria parasite and the vector like dams. Living in close proximity to these (mosquito). An increase in temperature of types of water bodies has been found to the environment where they breed boosts be a risk factor for malaria. However, the their rates of reproduction and the number of importance of a body of water as a mosquito blood meals they take, prolongs their breeding habitat depends on the preference of a season, and shortens the maturation period particular mosquito species, the proximity to for the microbes they disperse. Temperature blood meals, and the presence of predator affects the mosquito at each stage of the species that prey on the immature stages of mosquito lifecycle. If the temperature of the the vector. It has been found that Anopheles water where mosquitoes lay their eggs is have the ability to breed in sites where water too hot or cold, then fewer eggs hatch. For is present for at least 10–14 days, depending example, the ideal water temperature for on the time required for the mosquito life Anopheles Gambiae egg hatching has been cycle to take place. Therefore, the frequency shown to range between 24oC and 30oC. After and amount of precipitation is an indicator the egg stage, mosquitoes develop into larvae commonly used to approximate the formation then pupae. Temperature has also been of temporary bodies of water that may be shown to affect the time it takes to transition important breeding sites for mosquitoes. between these stages with the optimum water temperature for survival and shortest On the other hand, drought may reduce transition between larvae and pupae ranging the transmission of some mosquito borne between 22oC and 26oC. diseases, leading to reduction in the proportion of immune persons and therefore Specific temperature ranges are also a larger amount of susceptible people once important for the development of the parasite the drought breaks. After a flood event, rates in the mosquito. It has been shown that the of vector borne diseases such as malaria can optimum range for parasite development increase as mosquitoes breed in stagnant is between 25oC and 30oC. The minimum or slow moving pools of water. However the temperature observed for survival for relationship is complex, as flood events can Plasmodium falciparum is 18oC and the also wash away breeding sites. maximum has been reported at 40oC. At a temperature of 25oC, Plasmodium falciparum Adult mosquitoes are also dependent on requires only 12 days for parasite development specific moisture content in the air and will but, at a temperature of 20oC, over 30 days are desiccate if the climate is too dry. Therefore, needed to undergo development and render adequate humidity is also an important

1 4 || Climate Change Health Risk Mapping environmental condition related to mosquito into the equation. If these variables are not survival. Despite the biological plausibility of accounted for when assessing the relationship temperature and water to determine malaria between the environment and malaria, there risk, there have been inconsistent results are likely many variables confounding any true when comparing studies at the community association present. A better understanding level. There are different ecological niches for of the ecological processes that modify local the various species of Anopheles mosquito temperature and water availability is needed that transmit human malaria with some and specific thresholds for how malaria preferring dryer climates while others transmission will be affected by different are prone to areas that are more humid. sets of environmental conditions need to The differences in preferred habitat may be identified for all the species of Anopheles explain some of the variability in attempts mosquitoes that transmit human malaria so to assess malaria risk using environmental that researchers can better understand the factors. However, the environment–malaria interactions amongst and within the ecological association is not this simple as there are also variables and their anticipated impact on many other environmental conditions that malaria risk in each community. modify temperature and precipitation that must be taken into account.

Many other factors can affect malaria, and 2.3 Meningitis and climate while some can increase the transmission, change others may counteract the effects of weather Cerebrospinal Meningitis (CSM) is an infection and climate. In the last decade, an increase of the meninges, caused by the bacteria called in financing for malaria control has facilitated Neisseria meningitides. It causes inflammation efforts to combat malaria, including the of the lining of the brain of spinal cord called distribution of insecticide-treated nets. A the meninges. Outbreaks of CSM usually lead recent study in 2010 used evidence-based to high death rates in African communities. malaria maps to show that despite global The agent is highly contagious and person- warming during the 20th century, a global to-person aerial transmission occurs through recession of malaria was observed due respiratory and throat secretions. The main to the other malaria intervention being stay of prevention is vaccination. Meningitis implemented. Therefore, the indirect effects can result from many causes, both infectious of climate change on malaria are important to and non-infectious. Bacterial meningitis is consider in determining the key associations. a life-threatening condition that requires prompt recognition and treatment. The Signs Factors such as indoor air temperature, and symptoms of CSM are as follows: Severe the ability for water to pool and persist, headache; Stiff neck; Fever (38 OC axillary water quality, elevation, deforestation, and and 38.5OC rectal); Vomiting; drowsiness or agriculture can all modify the local temperature unconsciousness. or water availability and affect malaria risk. Not taking the modifying factors into account and Cerebrospinal meningitis is the only form of focusing on simply the primary effects is likely bacterial meningitis which causes epidemics confounding the true association between which can occur in any part of the world. malaria and the environment and explains However, the largest epidemics occur mainly why there has been so much inconsistency in the semi-arid areas of sub-Saharan Africa, between studies. designated the African meningitis belt. Apart from epidemics, meningococcal meningitis The many interactions between temperature occurs sporadically throughout the world, and water, numerous other factors that modify with seasonal variations, and accounts for temperature and water, and the specific a variable proportion of endemic bacterial species of Anopheles mosquito present in meningitis. Recent analysis of reported each community all have an important role epidemics indicated that there appears to to play in malaria transmission, but human be a southward shift in the distribution of and parasite behaviour must also be factored

|| 1 5 Climate Change Health Risk Mapping

epidemics over time, with new areas affected geographical distribution of disease cases south of the current belt area consistent with is called the “Meningitis Belt” and is roughly changes in the region’s climate ,e.g. Southern circumscribed to the biogeographical Sahelo- Province in Ethiopia. Sudanian band. This Sahelo-Sudanian region has a dry winter, dominated by northern Over 1.2 million cases of bacterial meningitis winds, called the Harmattan, followed by a wet are estimated to occur worldwide each year. season starting in spring with the monsoon. The incidence and case-fatality rates for The co-occurrence in both space and time of bacterial meningitis vary by region, country, meningitis cases and climate variability within pathogen, and age group. Without treatment, the Sahelo-Sudanian area suggests that the the case-fatality rate can be as high as 70% occurrence of the disease might be directly and one in five survivors of bacterial meningitis related to climate. So far, very few studies have may be left with permanent sequel including tried to quantify the potential linkages that hearing loss, neurologic disability, or loss of a could exist between climate and meningitis limb. outbreaks.

Cerebrospinal Meningitis has affected Sahelian CSM outbreaks in West Africa usually start Africa for centuries and has been known to be at the beginning of February, and then endemic over the past 30 years. During the disappear in late May. These hot, dry northerly 1980s, the World Health Organization (WHO) winds blow from the Sahara over all of West registered between 25,000 and 200,000 Africa, violently from December to February. disease cases per year, with about 10% During this period, the dust particle which case fatality rate, with the highest infection are almost impalpable particles of quartz rates observed in younger children. (CSM), and clay, colloids or fine mika flakes and can therefore, became a public health concern remain airborne for days combined with in the poorest regions in the world within the the cold night temperatures and encourage meningitis belt. respiratory . The Harmattan climate causes damage to the mucous membranes of In Ghana, meningitis which is a climate- the oral cavity through dry air and strong dust sensitive disease is prevalent in the northern winds, and creates propitious conditions for part of the country which forms part of the transmission of the bacteria responsible the meningitis belt of sub-Saharan Africa for CSM; low absolute humidity and dust spanning from Senegal in the East to Ethiopia may enhance meningococcal invasion by in the West. There was a major outbreak of damaging the mucosal barrier directly or meningitis in Ghana between 1996 and 1997 by inhibiting mucosal immune defences. In and the WHO AFRO regional Assembly in 1998 contrast, higher humidity during the rainy adopted a strategy to strengthen diseases season strongly reduces the disease risk by surveillance using the Integrated Diseases decreasing the transmission capacity of the Surveillance and Response (IDSR) to improve bacteria. CSM epidemics thus generally stop surveillance systems in Africa. The strategy with the onset of rainfall. aimed at enhancing early detection, reporting and timely response.

The spatial distribution, intensity of 2.4 Diarrhoea and climate transmission and seasonality of Cerebrospinal change Meningitis in the semi-arid areas of sub- Saharan Africa have been linked to climatic Diarrhoea is the frequent passing of loose factors, particularly drought and hot, dry or watery stools. Acute diarrhoea, which and dusty conditions, although the causal is a common cause of death in developing mechanism and relationship is not clear. countries, appears rapidly and may last from five to ten days. Chronic diarrhea lasts much Epidemics have been rarely reported from the longer and is the second cause of childhood humid forested or coastal regions, even when death in the developing world. Diarrhea neighboring areas are severely affected. The is sometimes accompanied by abdominal

1 6 || Climate Change Health Risk Mapping cramps or fever. It may be caused by infection, during the rainy seasons and along the coastal allergy, or could be a sign of a serious disorder, regions. such as IBD (inflammatory bowel disease). Most diarrheal cases in developing countries Rainfall, temperature and other climatic including Ghana are of infectious origin. factors affect the transmission of diarrheal disease. High temperatures, water scarcity The main causative agents include viruses, and water abundance resulting from flooding bacteria, protozoa, and helminths that or heavy precipitation have all been shown to are transmitted through the faeco-oral be related to diarrheal diseases. route. Among the principal bacterial agents of diarrhoeal diseases are Vibrio After a flood-event, rates of diarrheal disease, cholerae (cholera), Salmonella spp (typhoid including cholera, may increase, especially fever), Shigella spp, (bacterial ), in areas where facilities are Campylobacter spp and a variety of poor. Heavy rainfall, even without flooding, enteropathogenic Escherichia coli strains. may increase rates of diarrheal disease as Diarrhoeal diseases of viral origin include latrines or sewage systems overflow and rotavirus which is the most common and also faecal contaminants from pastures and enteric adenoviruses, astroviruses and dwellings are flushed into water supplies caliciviruses. Protozoa such as Giardia, thereby contaminating them. Increases in Cyclospora and Cryptosporidium spp and soil run-off may contaminate water sources. Entamoeba histolytica also cause diarrhoea. For surface water sources, heavy rainfall can lead to overflow of storm drains that According to the World Health Organization may be combined with the sewage system. (WHO) approximately 3.5 million deaths each This can then allow substantial amounts of year are attributable to diarrhoea. About 80% faecally polluted water into rivers. Surface of those deaths occur in children under the water turbidity can also increase dramatically age of 5 years and 70% of all diarrhoeal cases during heavy rainfall events and this can are attributable to inadequate water and cause additional stress on inadequate water sanitation. Children are more susceptible to the treatment systems. Further evidence of the complications of diarrhoea because a smaller impact of heavy rainfall on the epidemiology amount of fluid loss leads to dehydration, of enteric pathogen comes from studies of compared to adults. An average morbidity the presence of various organisms in water. attack rate of 3.2 episodes of diarrhoea per For example, there is a correlation between year per child has been reported, but in some rainfall and the likelihood of detecting Giardia settings in developing countries, this number or Cryptosporidium oocytes in river water. A can be as high as 12 episodes per year per further issue related to heavy rainfall events child. Evidence has been accumulating for is the additional nutrient input into water long-term consequences of such heavy bodies that accompany heavy rainfall. When disease burden in early childhood on physical other conditions are appropriate, this can lead and mental development of children that may to the rapid growth and blooming of various eventually translate into costly impairment of planktonic species which can cause diarrhoea. human fitness and productivity at an adult age. Flooding may follow heavy rainfall. For developing nations there is evidence of Outbreaks of cholera, shigellosis and typhoid disease outbreaks following floods. Outbreaks fever most often occur in resource-poor of diarrhoeal disease have followed floods countries, adding to the burden of disease in Khartoum and in Nicaragua, following among the most vulnerable such as refugees, Hurricane Mitch and the associated flooding. internally displaced populations and groups Even in Ghana, outbreaks of cholera usually living in shanty towns. Cholera is a type of occur after heavy rainfall and flood events diarrhoea which is related to poor hygienic especially in the Greater Accra, Eastern and condition. In Ghana, outbreaks of cholera Central regions. which is caused by Vibrio Cholerae and has a high case fatality rate, occurs frequently

|| 1 7 Climate Change Health Risk Mapping

Water scarcity on the other hand is also likely at different timescales. Already today, we need to have consequences for public health. A lack to be better at dealing with climate variability of availability of water for personal hygiene and its related health effects. Improving our and washing of food may lead to an increase capacity to prepare and respond, through in diarrheal disease and other diseases using for example, early warning systems and associated with poor hygiene. Water scarcity is seasonal forecasts, will allow us to be better associated with droughts and contamination positioned to address the challenges that of water bodies can occur during this season climate change will bring. Long-term climate as a result of stagnation. Water pollution has projections will be increasingly important to become a major environmental problem, and ensure that we are prepared for risks changing excessive use of groundwater is adversely over time when planning resource allocation, affecting the availability of safe drinking water building infrastructure and ensuring that in some countries. surveillance systems are able to detect changing patterns of diseases. Reducing Cholera outbreaks in the Amazon have been vulnerabilities and increasing resilience in linked to low river flows in the dry season, general will help populations cope with the which may be due to pathogen concentration health effects of climate change. in pools. A high concentration of pathogens may also overload water treatment plants. Mapping uneven events, such as disease cases or risk factors, makes it possible for Temperature also has an effect on diarrhoeal public administrators to discover the origin diseases and probably the most obvious of pollution or source of epidemic outbreaks link between diarrhoea and increased and generate more hypotheses for further temperature relates to the blooms of various investigations. More importantly, it may planktonic species that are directly or spatially identify the high-risk areas, which indirectly hazardous to human health. The can be targeted for environmental hazards most evidence of the effect of temperature and public health prevention activities. on risk from waterborne disease is in relation Mapping has been made easier and become to cholera. There is now good evidence that more widely used with the development of V. cholerae survives in marine waters in a geographical information systems (GIS). viable but non-cultural form that seems to be associated with algae and plankton. When During the last years, increasingly powerful temperatures rise, plankton bloom and, in and versatile geostatistical tools have been appropriate areas, such blooms are followed developed for spatial analysis. Geostatistics is a by increases in reported cases of cholera. It methodology for incorporating the spatial and is also known that bacterial diarrhoea usually temporal coordinates of observations in data peak in hot months whiles viral diarrhoea processing. A key feature of epidemiological may have some peak in cooler months, but data is their location in a space-time transmission continues all year round. This is continuum. GIS has emerged as an innovative because high temperatures favour the growth and important component of many projects of food spoilage bacteria which contaminates in public health and epidemiology: GIS has foods and hence consumption of these proved to be useful for epidemiological spoiled foods can cause diarrhoea. research purposes, decision-making, planning, management and dissemination of information. GIS can be used to map and analyse the geographical distribution of 2.5 Climate change health population at risk, health outcomes and risk risk mapping factors; to explore associations between risk Public health planning and decision making has factors and health status; to plan public health mainly been focusing on relatively short term services. risks. However, there is the need to now focus Proponents of the use of GIS have pointed on the projected long term impacts of climate out that its architecture is ideal for handling change. It will be increasingly important to the complexities of a relatively large number address the links between climate and health 1 8 || Climate Change Health Risk Mapping of spatially distributed variables, and should and autocorrelation that are ever so present in emerge as a powerful tool in ecologic studies both environmental and malaria transmission of exposure to environmental hazards and systems. These powerful tools provide new disease etiology. In spite of the vigorous methods for a more integrated approach to debate surrounding the extent to which assessing health risk and could be used to GIS-assisted ecologic studies can establish account for the interactions inherent in the causation, there is no doubt, however, that environment and eliminate any potentially these studies can be invaluable in placing confounding effects in determining ecological disease in context, generating hypotheses, risk factors for diseases. and justifying more expensive individual-level studies. To date, studies attempting to model the occurrence of climate-sensitive diseases using As GIS combines individual-level data with ecological risk factors have largely focused aggregate data collected at a wide range on temperature and precipitation, with more of resolutions, these ecologic studies have recent studies using land use data based on the potential to contribute information remote sensing imagery. Using the ecological undiscoverable by any other means, but data and digital mapping technologies, maps they also introduce opportunities for bias, have been created at the country and regional inadvertent misrepresentation, or violation of levels based on ecological risk factors and are confidentiality if used improperly. relatively accurate at this small scale. However, results of studies focusing at the local level Many studies deal with the integrated use have been inconsistent: there is still a high level of GIS techniques (geocoding, buffers, of uncertainty surrounding the association overlay, distance functions) and classical between disease incidence, temperature, statistics (chi-square test, multiple logistic and precipitation at the microclimate level. regression) examining the relation between Environmental systems are complex and it risk factors and population health status. A is possible that modifying factors may be classical approach involves the use of GIS as present that are confounding any associations a tool to classify population with reference to that exist between ecology and malaria risk. geographical items. Then classical statistics is applied to assess the hypothesis that inspired Being able to tease apart the complexities of classification. GIS techniques have also been the environment–disease relationship and applied in the management of environmental identifying thresholds also allows for predictive and epidemiological data in respiratiory health tools to be developed that can better enable risk mapping. control operations to target their efforts when and where they will be most effective with the GIS software can be used to map the spatial appropriate interventions. distribution of disease risk and overlay climatic and environmental features such as However, mapping provides only a visual the presence of water bodies, precipitation, display of the uneven cases, but cannot temperature, or land use to identify possible definitively confirm clustering of cases or ecological factors associated with specific spatial correlations. Risk surface estimation, climate-sensitive diseases. The application including kernel estimation and geostatistical of remote sensing is similar, in that images methods, produces continuous surfaces of a community coded by land use, amount of risk across the whole study areas and of moisture, or other features can then be potentially offer more insight into the nature compared to disease incidence and used to of the clusters. However, mapping the spatial identify possible land uses related to risk of clusters of uneven events is a static snapshot, diseases as well as how this may change if which ignores the temporal kinetics of these the land use changes. Modelling has enabled uneven events, and it is difficult to evaluate research to go beyond spatial analysis whether the hazards or epidemics have and conduct statistical analysis on spatial been broken out or kept under control by correlations between disease and ecological policymakers. factors, to assess issues like over dispersion

|| 1 9 Climate Change Health Risk Mapping

In addition to straightforward questions of models and repeated analyses testing of accuracy and precision, types of data different scenarios. GIS technology is an ideal related errors arising from unavoidable data tool to take advantage of such information, manipulation which are likely to affect ecologic as it has the power to manage temporally as studies include measurement error, effect well as spatially referenced datasets, and can modification (where the meaning of an effect generate a series of spatial images reflecting variable changes from one scale to another) the changes in an area’s contamination over and misclassification (where an observation time. Conceptualizing the environment/public is grouped into the wrong category). health relationship as one which occurs over Although change is central to questions of temporal space as well as geographic space exposure and health impact, few studies promotes good database design. Having a performed to date are more than snapshots longitudinal database handy for assessments of current conditions in the environment of potential hazard also permits more and the health of its inhabitants. Ongoing statistical stability for the detection of change. data collection permits the development

2 0 || Climate Change Health Risk Mapping

Conceptual 3.0 Framework

|| 2 1 Climate Change Health Risk Mapping

2 2 || Climate Change Health Risk Mapping

3.1 Environmental health risk Mapping refers to more than simply the mapping production of maps. The map is merely the end product of an often lengthy and complex The concept of environmental health analytical process – a model of reality. How risk mapping is relatively new. The terms well it expresses this reality depends upon environmental health, hazard, risk and the decisions made, and the methods used, mapping are also open to different during this mapping process. Maps may interpretations. Environmental health may take many different forms. In portraying be more or less narrowly defined to refer environmental health risks, maps will often be exclusively to the effects of the ‘natural’ thematic – i.e. they will show the distribution or ambient environment on health. More of one or more features or themes, graded or broadly, it or may be taken also to include the classified according to their type or degree of social and cultural environment. In this study, risk. Environmental health is defined as those aspects of the living environment of humans, insofar as these may affect health. As such, it focuses on the tangible (physical, chemical, organic) environment, including both the ambient and indoor environment, but it excludes social and cultural factors which are not expressed in some way through the tangible world.

Hazard refers to those factors or conditions which have the potential to pose a threat to human wellbeing and, more specifically in the context of this study, to health. As such, it is important to differentiate between hazards and risks, and to understand the relationship of both to human health. Hazards represent the presence of an environmental risk factor: risk only occurs if humans are in some way exposed to this factor at levels which might affect their health. ƒƒFigure 1: Relationships between A health effect occurs only if individuals within environmental health hazard the exposed population are susceptible to the environmental health risk and effects of the hazard, and if they accumulate environmental health impact (source: sufficient exposures to experience an effect. Briggs, 2000). The relationship between environment and health can be seen as a chain, comprising Environmental health hazard mapping involves three distinct links: the environment, the mapping of the distribution and magnitude population and health (figure 1). The chain of environmental hazards with the capacity can be characterised as follows: certain to affect health, without consideration of the environmental conditions create hazards; if population. This is based solely on information people come into contact with and are thus on the environment. exposed to these hazards, then health risks occur and if individuals within the exposed population accumulate exposures which exceed their resistance or tolerance then health effects occur.

|| 2 3 Climate Change Health Risk Mapping

Environmental health risk mapping involves Environmental health mapping comprises mapping of the potential risk to human health, mapping the actual health outcome which either by estimating the numbers of people can be attributed to exposures to the exposed to the environmental hazards, or the environmental hazards of concern. The scope likely health burden. This is based on informa- of this assignment deals with environmental tion both on the environmental hazard and on risk mapping. the distribution (and possibly susceptibility) of the population or on direct estimates of popu- lation exposure (may include other confound- ing/non-environmental factors).

2 4 || Climate Change Health Risk Mapping

Approach and 4.0 methodology

|| 2 5 Climate Change Health Risk Mapping

2 6 || Climate Change Health Risk Mapping

4.0 Approach and methodology The assignment seeks to investigate the linkages between climate change and health with a focus on Malaria, CSM and Diarrhoeal disease and to demonstrate advantages in using climate information to either reduce exposures in vulnerable populations or to guide more effective interventions. The study applies geographic information systems techniques in developing climate change health risk maps for the specified climate- sensitive diseases for the three (3) selected districts in the country.

Geographic Information Systems (GIS) has emerged as an innovative and important component of many projects in public health and epidemiology. A Geographical Information System is a computer-based system for capturing, storing, manipulating, ƒƒFigure 2: Map of Ghana showing the analyzing and displaying huge amounts of study districts spatial data. GIS has proved to be useful for epidemiological research purposes, decision-making, planning, management and 4.2 Bongo profile dissemination of information. GIS technology can be used to map and analyse the Bongo is one of the nine districts in Upper geographical distribution of population at risk, East Region and occupies an area of about 2 health outcomes and risk factors; to explore 459.5km . The district is divided into six sub associations between risk factors and health districts namely; Bongo central, Bongo Beo, status and to plan public health services. Bongo Soe, Namoo, Zorko and Valley Zone. There are 132 communities in Bongo with a total projected population of 86,889 in 2011, who are predominantly peasant farmers. The 4.1 Study area topography is generally flat or low lying with The study was undertaken in 3 pilot districts outcrops of granite and Birimian rocks. Areas of Bongo in the Upper East Region, Gomoa occupied by granites are generally of low, West in the Central Region and Keta in the gently rolling relief 90 to 300 metres above Volta Region (see figure 2). sea level.

The district is drained by the and its main tributaries namely, the Ayedama and Kulumasa Rivers, which flow into the Red Volta. The district has one large dam at Vea, nine small dams and 5 dugouts. Apart from the district capital, Bongo, all the other communities are made up of small farm settlements scattered around the district.

|| 2 7 Climate Change Health Risk Mapping

ƒƒFigure 3: Map of Bongo District

Figure 3 is a map of Bongo District showing the major roads, settlements and drainage features. The type and number of health facilities as well as the number and category of health staff available in the district are presented in tables 1 and 2 respectively.

ƒƒTable 1: Health facilities in Bongo District

Facility Type Number

District Hospital 1

health centre (Dua,Namoo, Soe, Zorko and Vea) 5

Reproductive Clinic (Anafobisi) 1

Nutrition Rehabilitation Centre 1

functional CHPS compounds + 1 13

feeding centres 5

Outreach centres 66

ƒƒTable 2: Health staffing in Bongo District

Health Staff Number Public Health Nurse 1

Midwifes 20

Community health nurses 73

TBAs 109

CIMCI volunteers 264

2 8 || Climate Change Health Risk Mapping

4.3 Gomoa West Municipal numerous streams with the notable ones profile being rivers Brushing and Ayensu. The Gomoa West District is one of the 17 Mean annual rainfall ranges between 700 and districts within the Central Region of Ghana. 900mm in the southern coastal belt and 900 It lies within latitude 5°14 north and 5 °35 to 1,100mm in the northern and northwestern and longitude 0o22 and 0o54 west on the semi-deciduous forest areas. However eastern part of the Central Region of Ghana. available statistics reveal a fluctuating rainfall The district covers an area of 1,022km2 and pattern. The mean annual maximum and a total projected population of 112,285 in minimum temperatures of 29°C and 26°C 97 settlements or localities. This makes it occur in February to March and August the district with the highest population and respectively. The relative humidity ranges surface area next to the Assin district (fig. 4). between 70% and 80% for the northern and southern sectors of the district respectively The land slopes gently from south to north and is influenced by the presence of large with isolated hills on forest dissected plateau water bodies like the ocean, rivers, lagoons in the north and coastal plains in the south. and streams. The area is drained by a few rivers and

ƒƒFigure 4: Map of Gomoa West Municipal Area

Economically, the district is one of the most private maternity home. The Doctor -Patient deprived in the Central region. Most of the ratio is 1:27493 and the Nurse - Patient ratio is inhabitants are engaged in fishing, farming 1:1099. The district has 102 Trained Traditional and petty trading. The health infrastructure Birth Attendants and 96 community-based consists of a District Hospital (St Luke’s surveillance volunteers. Catholic Hospital), 4 Health centers, 13 CHPS Zones, a Nutrition Rehabilitation Centre and a

|| 2 9 Climate Change Health Risk Mapping

ƒƒTable 3: Health facilities in Gomoa Municipal

Facility Type Number

Hospital 1

Health Centre 4

Community Clinic 1

CHPS Centre 13

Reproductive and Child Health (RCH) Centre 1

Nutrition Rehabilitation Centre 1

Private Maternity Home 1

4.4 Keta Municipal profile The lowest point is between 1.0-3.5 metres below sea level along the coast. The Keta Municipality is one of the eighteen (18) Municipality is in the dry coastal equatorial districts in the Volta Region of Ghana and climate with an annual average rainfall of lies at the south-eastern corner of the Volta less than 1,000mm. The amount of rainfall Region and Ghana; between longitude 0°30E reduces as one travels from the north to the and 1°05E. The total population of the area south, where the annual average is about projected from the 2000 census is 164,407 800mm.which makes it one of the driest based on annual growth rate of 1.9%. Keta along the coast of Ghana. High temperatures Municipality has a total land area of about are experienced all the year round. The 1,086km2, about a third of which is covered average temperature experienced is 30°C. with water bodies (362km2.). Among the water Keta is a mainly agricultural Municipality, with bodies, the Keta lagoon is the largest and the the majority of the people engaged in crop most important. It is about 1.2km wide and farming, sea and/ or lagoon fishing, livestock 32km long and divides the municipality into keeping and other related trading activities. two, North and South. The whole of the Keta Municipality is a low lying coastal plain, with the highest point of about 53 metres above sea level in the North.

ƒƒFigure 5: Map of Keta Municipal Area

3 0 || Climate Change Health Risk Mapping

A map of the municipality is presented in figure 5. The details of health facilities within the Keta Municipality are also presented in table 4. Table 5 also highlights the health personnel resources available at the different levels of system within the Keta Municipal area. It presents the differenc personnel as well as their respective grades within these facilities.

ƒƒTable 4: Health facilities in Keta Municipal

Facility type Number Hospital 2 Health centres (public) 11 Health centre (mission) 1 Private clinic (general services) 3 Private maternity 4 Functional CHPS zone 5 RCH centre 1 CHPS zone yet to be operationalised 2

ƒƒTable 5: Keta Municipal health staff strength

Grade Keta Hosp Abor Hosp Sub- District Directorate Total Medical Officers 2 3 0 0 5 DDNS 0 1 0 2 3 Nursing Officers 11 2 0 0 13 Dental Clinic Assistant 2 0 0 0 2 Nurse Anesthetist 1 1 0 0 2 Supt. Comm. Nurse 0 3 1 0 4 Prin. Comm. Nurse 0 0 2 0 2 Senior Comm. Nurse 0 2 5 0 7 Comm. Health Nurse 0 3 43 2 48 Public Health Nurse 0 2 0 1 3 Physiotherapist 0 1 0 0 1 Staff Midwife 13 2 2 0 17 Senior Staff Midwife 2 0 2 0 4 Midwifery Officers 4 2 2 0 8 Prin. Midwifery Officer 0 7 2 0 9 Ward Assistant (Health Aide) 2 5 5 0 12 Health Assistant (clinical) 2 5 0 0 7 Prin. Health Asst. 4 5 5 14 Senior Health Asst. 2 8 0 0 10 Technical Officer - Lab. 1 1 0 0 2 Laboratory Asst. 0 3 0 0 3 Technical Officer - X’Ray 1 1 0 0 2

|| 3 1 Climate Change Health Risk Mapping

Grade Keta Hosp Abor Hosp Sub- District Directorate Total Technical Officer - DC 0 1 0 4 5 Technical Officer -H/Inf. 0 2 0 1 3 Technical Officer (Biost) 6 4 4 0 14 Field Technician 0 0 1 2 3 Nutrition Officer 0 0 0 1 1 Senior Executive Officer 1 1 0 1 3 Prin. Accountant 0 2 0 0 2 Accountant 2 3 0 1 6 Accounts officers 1 3 0 1 5 Finance officer 2 2 0 0 4 Biomedical Scientist 0 2 0 0 2 Estate Officers 1 1 0 0 2 Health Serv. Administrator 2 2 0 0 4 Human Resource Manager 0 1 0 0 1 Pharmacist 1 0 0 0 1 Dispensing Assistant 2 2 1 0 5 Storekeeper 1 0 0 1 2 Enrolled Nurses 15 3 3 0 21 Pharmacist Tech. Asst 2 2 0 0 4 Medical Assistant 1 1 6 0 8 Senior Staff Nurse 6 3 0 0 9 Staff Nurse 13 14 0 0 27 Senior Health Educator 0 1 0 0 1 Support Staff 35 79 20 14 148 Grand Total 130 182 106 31 449

4.5 Data information on the population and population density of the towns within the pilot districts Data for the study came from secondary is also available from the Ghana Statistical sources consisting of climatic and Service as well as projected figures from the environmental data sets as well as those on districts. Environmental data relating to the the total burden of the diseases of interest in boundaries of the pilot districts, location pilot districts. of settlements, drainage, altitude are also available from the digital mapping section 4.5.1 Climatic, environmental of the Ghana Survey Departments. Derived data sets such as proximity of towns to water and population data bodies for example and digital elevation Climate related data is available from the model (DEM) were obtained from GIS analysis. Ghana Meteorological Agency, which holds Additionally, data on land use information for historical data sets from their weather the pilot districts were derived from satellite monitoring stations from as far back as the image analysis. 1970s. Historical data on temperature, rainfall and relative humidity were of importance in developing the health risk maps. Current

3 2 || Climate Change Health Risk Mapping

4.5.2 Disease incidence data Total number of reported cases for malaria, CSM and Diarrhoea for each of the pilot districts was obtained from the respective district health directorates of the Ghana Health Service for the period of 2009 -2011. The data is aggregated at the health facility level which is the lowest level of reporting within the Ghana Health Service and is presented in tables 6, 7 and 8.

ƒƒTable 6: OPD cases for malaria, diarrhoea and CSM in Bongo District 2009-2011

2009 2010 2011 CHPS Center Malaria Diarrhoea CSM Malaria Diarrhoea CSM Malaria Diarrhoea CSM Adaboya CHPS 685 97 0 1200 226 0 1595 301 0 Akolposiga CHPS 349 53 0 353 46 0 297 53 0 Anafobisi CHPS 50 11 0 Anafobisi Clinic 150 12 0 488 65 0 609 128 0 Atampisi CHPS 977 164 0 907 153 5 1174 195 5 Balungu CHPS 575 104 0 1258 204 0 1216 256 0 Boko CHPS 486 31 0 611 90 0 868 124 0 Bongo Hospital 14713 792 0 9261 633 0 14426 2050 0 Bongo Soe Health 3184 157 0 4722 242 0 5740 348 0 Centre Dua Clinic 1441 65 0 1664 143 0 3710 394 0 Feo CHPS 402 47 0 512 87 0 557 38 0 Gamborongo 678 74 0 Gowrie CHPS 674 75 0 1129 120 0 1250 115 0 Kabre CHPS 134 3 0 273 17 0 574 47 0 Kadare CHPS 212 35 0 274 64 0 567 89 0 Kodorogo CHPS 213 5 0 389 50 0 680 128 0 Kunkua CHPS 278 53 0 272 114 0 379 57 0 Lungu CHPS 583 54 0 1305 225 0 Namoo Health 4275 211 0 3264 425 0 4846 642 2 Centre Vea Health Centre 2592 97 0 2491 106 0 4026 197 0 Wagliga CHPS 722 129 0 1311 204 0 2236 380 0 Zorko Health Centre 3819 270 0 4514 1243 8 5752 687 0 TOTAL 35931 2411 0 35476 4286 13 52485 6528 7

|| 3 3 Climate Change Health Risk Mapping

ƒƒTable 7: OPD cases for malaria, diarrhoea and CSM in Keta Municipal Area 2009-2011

2009 2010 2011 No Health Facility Malaria Diarrhoea Malaria Diarrhoea Malaria Diarrhoea 1 Abor Clinic 0 0 1864 243 0 0 2 Afiadenyigba Health Centre 4511 229 5216 197 3322 283 3 Agbledomi Health Centre 19 2 0 0 0 0 4 Anloga Health Centre 6299 860 7903 825 5339 794 6 Anyako Health Centre 1552 146 1742 26 1359 22 7 Anyanui Health Centre 2053 188 3341 290 2557 274 8 Asadame Health Centre 1415 192 792 87 803 178 9 Atiavi Health Centre 1108 146 758 87 271 76 10 Atorkor Health Centre 816 77 805 53 783 64 11 Crown Maternity Home 344 16 659 31 1049 88 13 Dziedzove CHPS 0 0 0 0 139 24 14 Golo-Sota Health Centre 556 87 797 59 1027 113 15 Hatogodo Health Centre 513 61 725 24 735 39 16 Holy Maternity Home 2 0 97 10 62 2 17 Kafui Maternity Home 116 0 388 0 129 0 18 Kedzi Health Centre 330 29 19 Keta Hospital 11543 936 14897 1126 16712 2649 20 Kodzi Health Centre 689 77 978 99 1073 82 21 Lebene Clinic 227 23 248 34 209 19 22 Mother Mary Maternity Home 83 0 23 Nancy Maternity Home 919 96 578 64 433 71 24 Sacred Heart Catholic Hospital 4259 503 5887 565 6216 1109 25 Sesiame CHPS 687 77 807 81 1107 145 26 St. Francis Clinic 1862 184 1741 117 2384 192 27 St. Rapheal Clinic 1557 37 1940 48 1299 51 28 Tegbi Health Centre 8737 96 7427 521 8982 713 29 Tregui Health Centre 1084 168 339 13 327 19 Total 50951 4201 59929 4600 56647 7036

3 4 || Climate Change Health Risk Mapping

ƒƒTable 8: OPD cases for malaria, diarrhoea and CSM in Gomoa West Municipal Area 2009-2011 ƒƒTable 7: OPD cases for malaria, diarrhoea and CSM in Keta Municipal Area 2009-2011 2009 2010 2011 No Name of health facility 2009 2010 2011 Malaria Diarrhoea malaria Diarrhoea malaria Diarrhoea No Health Facility Malaria Diarrhoea Malaria Diarrhoea Malaria Diarrhoea 1 St Luke’ s Catholic Hospital 9832 336 9336 630 12,603 689 1 Abor Clinic 0 0 1864 243 0 0

2 Afiadenyigba Health Centre 4511 229 5216 197 3322 283 2 Oguaa Health centre 723 105 932 114 1476 56 3 Agbledomi Health Centre 19 2 0 0 0 0 3 Osedze Health Centre 261 57 756 122 1,944 153 4 Anloga Health Centre 6299 860 7903 825 5339 794 4 Noguchi Health Centre 1204 153 6 Anyako Health Centre 1552 146 1742 26 1359 22 5 Assin CHPS 401 68 1110 251 1317 280 7 Anyanui Health Centre 2053 188 3341 290 2557 274 6 Ngyiresi CHPS 347 79 8 Asadame Health Centre 1415 192 792 87 803 178 7 Dago health centre 127 16 9 Atiavi Health Centre 1108 146 758 87 271 76 8 Apam RCH 43 1 10 Atorkor Health Centre 816 77 805 53 783 64 9 Mankoadze clinic 122 36 159 33 156 26 11 Crown Maternity Home 344 16 659 31 1049 88 10 Sampa CHPS 252 49 246 23 231 29 13 Dziedzove CHPS 0 0 0 0 139 24 11 Brofo CHPS 412 88 14 Golo-Sota Health Centre 556 87 797 59 1027 113 12 Eshiem CHPS 657 78 977 64 1003 59 13 Tarkwa CHPS 611 78 738 47 1034 97 15 Hatogodo Health Centre 513 61 725 24 735 39 14 Fomena CHPS 80 9 88 12 230 30 16 Holy Maternity Home 2 0 97 10 62 2 15 Mumford CHPS 136 1 17 Kafui Maternity Home 116 0 388 0 129 0 18 Kedzi Health Centre 330 29 Total 13,648 999 14,549 1,325 21,436 1,573 19 Keta Hospital 11543 936 14897 1126 16712 2649 20 Kodzi Health Centre 689 77 978 99 1073 82 21 Lebene Clinic 227 23 248 34 209 19 4.6 GIS Analysis ranked from low to very high based on degree 22 Mother Mary Maternity Home 83 0 of vulnerability to the factor. Every layer is GIS analysis involved the application of 23 Nancy Maternity Home 919 96 578 64 433 71 then re-classified based on these ranks. geostatistical techniques in the development 24 Sacred Heart Catholic Hospital 4259 503 5887 565 6216 1109 Re-classified layers are multiplied by their and modelling of disease specific risk maps 25 Sesiame CHPS 687 77 807 81 1107 145 standard weight and then added to others through the combination of climatic and for providing the output hazard maps for the 26 St. Francis Clinic 1862 184 1741 117 2384 192 environmental with other ancillary data layers respective diseases. 27 St. Rapheal Clinic 1557 37 1940 48 1299 51 in multi-criteria evaluation. 28 Tegbi Health Centre 8737 96 7427 521 8982 713 The output hazard map then serve as an 29 Tregui Health Centre 1084 168 339 13 327 19 input factor into risk mapping, by combining 4.6.1 Health hazard and risk Total 50951 4201 59929 4600 56647 7036 them with other non-environmental factors mapping in producing the risk layers/maps for the Hazard maps, i.e. a map that highlights areas respective diseases. The final risk maps were which have a potential to pose significant then reclassified into high, moderate and low threats to human health are prepared by risk areas as three strata for planning control weighting and overlaying the disease specific interventions. The accuracy of hot spots in the environmental and climatic factors (see table risk maps were assessed by comparison with 8). Point based source data were converted the disease-specific incidence map of the pilot into their surface equivalent using the districts. All data processing and analysis were appropriate interpolation routines available undertaken with the ArcGIS GIS software and in ArcGIS10. The selection and weighting of semi-automated using the model maker utility. different factors for hazard and risk maps All data used in health hazard/risk mapping is were informed by the literature and expert presented in table 9 below. input. Ratings and classes for each factor are

|| 3 5 Climate Change Health Risk Mapping

3 6 || Climate Change Health Risk Mapping

ƒƒTable 9: Data layers used in health hazard/risk mapping

Disease Hazard Map Risk Map

Temperature Malaria hazard map Rainfall Population density Relative humidity Land use Malaria Altitude Malaria incidence Slope Proximity to water bodies Proximity to water bodies Diarrhoeal disease hazard map Altitude Population density Diarrhoeal disease Temperature Land use Slope Diarrhoea incidence Temperature CSM hazard map Altitude Population density CSM Relative humidity Land use CSM incidence

ƒƒFigure 6 presents the methodological work flow adopted in the development of the risk maps.

ƒƒFigure 6: Environmental Health Risk Mapping Methodological Flow Chart

4.6.2 Health risk models Climate change environmental/ecological health risk models developed for producing the risk maps for malaria, diarrhoea and meningitis are presented in figures 7, 8 and 9 respectively. The associated layer rankings and weightings for each disease are also given in tables 10 through 12.

|| 3 7 Climate Change Health Risk Mapping

ƒƒFigure 7: Malaria Risk Model

ƒƒTable 10: CSM Hazard /Risk layer ranking and weighting

Layer/Factor Class Rank Weight

Hazard map <800mm 3 Rainfall 800-900mm 2 0.2 >900mm 1 <50% 2 Relative humidity 0.35 >50% 1 <28 o 1 Temperature 0.3 >28 o 2 <600m 3 Elevation 600-800m 2 0.15 >800m 1

3 8 || Climate Change Health Risk Mapping

Layer/Factor Class Rank Weight

Risk Map <2 1 2.0-3.9 2 4.0-8.9 3 Mean CSM incidence 0.4 9.0-15.9 4 16-26 5 >26 6 <1900 1 1900-299 2 2300-2599 3 Population density 0.2 2600-3099 4 3100-3899 5 >3900 6 <2.3 1 2.3-2.7 2 2.7-3.0 3 CSM hazard 0.2 3.0-3.4 4 3.4-3.9 5 >3.9 6 Water body 1 Closed forest 2 Open forest 3 ƒƒFigure 7: Malaria Risk Model Land use Farmland/shrubs 4 0.2 Open spaces /Bare ƒƒTable 10: CSM Hazard /Risk layer ranking and weighting land 5 settlement 6 Layer/Factor Class Rank Weight

Hazard map <800mm 3 Rainfall 800-900mm 2 0.2 >900mm 1 <50% 2 Relative humidity 0.35 >50% 1 <28 o 1 Temperature 0.3 >28 o 2 <600m 3 Elevation 600-800m 2 0.15 >800m 1

ƒƒFigure 8: Diarrhoea Risk Model

|| 3 9 Climate Change Health Risk Mapping

4 0 || Climate Change Health Risk Mapping

ƒƒTable 11: Diarrhoea Hazard / Risk layer ranking and weighting

Layer/Factor Class Rank Weight

Hazard map

<800mm 1 Rainfall 800-900mm 2 0.3 >900mm 3

<2o 3 Slope 2-8o 2 0.2 >8o 1

<26o 1 Temperature 26-28o 2 0.3 >28o 3

<200m 4 200-600m 3 Elevation 0.2 600-800m 2 >800m 1

Risk Map

<134 1 134-239 2 240-374 3 Mean diarrhoea incidence 0.2 375-589 4 590-960 5 >960 6

<1900 1 1900-299 2 2300-2599 3 Population density 0.1 2600-3099 4 3100-3899 5 >3900 6

<1.6 1 1.6-1.9 2 2.0-2.3 3 Diarrhoea hazard 0.4 2.4-2.6 4 2.7-3.0 5 >3.0 6

Water body 1 Closed forest 2 Open forest 3 Land use Farmland/shrubs 4 0.2 Open spaces /Bare land 5 Settlement 6

<250m 6 250-500m 5 501-900m 4 Distance to Water bodies 0.1 901-1450m 3 1251-2000m 2 >2000m 1

|| 4 1 Climate Change Health Risk Mapping

ƒƒFigure 9: Meningitis Risk Model

ƒƒTable 12: Malaria Hazard / Risk layer ranking and weighting

Layer/Factor Class Rank Weight

Hazard map

<800mm 1 Rainfall 800-900mm 2 0.2 >900mm 3

<50% 1 Relative humidity 50-70% 2 0.2 >70% 3

<23 o 1 Temperature 23-26 o 2 0.3 >26 o 3

<100m 5 100-300m 4 Elevation 301-500m 3 0.10 501-800m 2 >800m 1

<2o 3 slope 2-8o 2 0.10 >8o 1

4 2 || Climate Change Health Risk Mapping

Layer/Factor Class Rank Weight

<250m 6 250-500m 5 501-900m 4 Distance to Water bodies 0.10 901-1450m 3 1251-2000m 2 >2000m 1

Risk Map

<1400 1 1401-2500 2 2501-4000 3 Mean Malaria incidence 0.2 4001-6000 4 6001-9000 5 >9000 6

<1900 1 1900-299 2 2300-2599 3 Population density 0.2 2600-3099 4 3100-3899 5 >3900 6

<2 1 2-2.4 2 2.41-2.6 3 Malaria hazard 0.4 2.61-2.8 4 2.81-2.9 5 >2.9 6

Water body 1 Closed forest 2 Open forest 3 Land use 0.2 Farmland/shrubs 4 Open spaces /Bare land 5 settlement 6

4.6.3 Health risk maps along the coast, the well drained as well as the forested areas. In Bongo district, malaria risk Disease specific health risk maps for CSM, was fairly well spread in the entire district bar Malaria and Diarrhoea for each of the selected the north eastern quarter which appeared to pilot district are presented in figures 10 to 17. show very low risk. Risk was highest in central and southern parts of the district. The spatial distribution of meningitis risk in Bongo district shows a concentration of risk Malaria risk distribution in the Gomoa West from the mid to western part of the district. Municipality of the Central region showed a Meningitis risk was highest within the district greater concentration in the northern forest capital where there was high population areas and the densely populated settlements. density and low in the rural and sparsely Risk was generally relatively less in the western populated communities in general. part of the municipality. Generally malaria risk was fairly distributed In the Keta Municipality, malaria risk within the pilot districts but was concentrated distribution show highest risk along the within the densely populated communities, densely populated coastal areas and medium

|| 4 3 Climate Change Health Risk Mapping

risk in the middle and western parts. Areas to Diarrhoea risk distribution in the Gomoa West the north of the municipality showed the least Municipality of the Central region showed levels of malaria risk. the densely populated settlements along the coast, the east and mid north with the western Diarrhoeal risk for the three pilot districts parts showing relatively lower risk. shows a concentration within the densely populated areas, the well drained and the In the Keta Municipality, diarrhoea risk coastal areas. In Bongo, diarrhoeal risk was distribution shows the densely populated mainly concentrated in the central, southern coastal areas as well as the northern part as and western parts of the district whereas the having the highest risk. Areas to the west and northern and eastern parts showed relatively middle of the municipality showed relatively lower risks lower risk.

4 4 || Climate Change Health Risk Mapping

ƒƒFigure 10. Bongo District CSM risk map

|| 4 5 Climate Change Health Risk Mapping

ƒƒFigure 11. Bongo District malaria risk map

4 6 || Climate Change Health Risk Mapping

ƒƒFigure 11. Bongo District malaria risk map ƒƒFigure 12. Bongo District diarrhoea risk map

|| 4 7 Climate Change Health Risk Mapping

ƒƒFigure 13. Gomoa West Municipal diarrhoea risk map

4 8 || Climate Change Health Risk Mapping

ƒƒFigure 13. Gomoa West Municipal diarrhoea risk map ƒƒFigure 14. Gomoa West Municipal malaria risk map

|| 4 9 Climate Change Health Risk Mapping

ƒƒFigure 15. Keta Municipal malaria risk map

5 0 || Climate Change Health Risk Mapping

ƒƒFigure 16. Keta Municipal diarrhoea risk map

|| 5 1 Climate Change Health Risk Mapping

4.7 Challenges ƒƒ Lack of spatial data on some confounding/non-environmental Major challenges in developing the risk maps data: data on some non climatic had to do with a variety of factors as discussed but important confounding factors below: important for health risk mapping such ƒƒ The level of spatial aggregation as inoculation rates, larviciding, ITN use, of disease incidence data: the total indoor spraying as well personal hygiene number of cases reported for the and sanitation factors in addition to diseases of interest obtained from the economic and physical vulnerabilities GHS was aggregated at the facility/sub- at the sub district level also limits the district level. This presented challenges in wholeness of the models developed. the fact that the ideal level of aggregation ƒƒ Additionally, most of the data on climatic for mapping the spatial incidence of the factors and reported disease burdens respective diseases is at the community for the different pilot regions required level where the predisposing factors geo-rectification prior to analysis and are deemed to have acted. Mapping at model development. the health facility level introduces some ƒƒ Other challenges for the projects had generalization into the data and does to do with data requirements for the not allow for the spatial differentiation of risk mapping which were indicated disease incidence to the communities. in the inception report and were to ƒƒ Coarseness of climatic data: the low be facilitated by the client proved a density of network of the meteorological challenge as these data were held by stations operated by the Ghana third parties who were oblivious to the Meteorological Agency also presents time-bounded nature of the study. Thus another challenge in the fact that climate in all, these served to delay the output of data is highly generalized without any the consultant unduly. significant sub-district variations. ƒƒ Scale of digital environmental data: the digital environmental data layers obtained from the Ghana Survey Department was mapped at the scale of 1:50,000 which for sub-district mapping purposes should be at a larger scale. This therefore introduces some degree of generalization into the environmental layers as well as derived layers such as the DEM and proximity to water bodies.

5 2 || Climate Change Health Risk Mapping

Summary 5.0 overview and status of the deliverables

|| 5 3 Climate Change Health Risk Mapping

5 4 || Climate Change Health Risk Mapping

There are Eight (8) deliverables under the project, the description and status of which is presented in table 4 below.

ƒƒTable 13: Status of project deliverables

Deliverable Deliverable title Status No.

Project inception report describing the 1 Completed methodology and approach

An in-person presentation to the project 2 management committee and identified Completed stakeholder groups,

Development of climate change health risk maps 3 at sub-national scales (3 pilot districts, namely Completed Keta, Gomoa West, and Bongo).

Capacity building for national and district teams to 4 Completed develop, interpret, and update the maps

5 Draft technical report Completed

6 Final technical report Completed

7 Policy Brief based on the technical report, Completed

Workshop with stakeholders, including a high-level 8 Completed policy briefing.

5.1 Conclusion produced using disease-specific climate data sets, epidemiologic data as well as related Health risk mapping makes it possible for human environmental data. public administrators to discover the origin or source and spread of epidemic outbreaks Capacity building activities involving the while ensuring that we are prepared for training of beneficiary district and regional risks changing over time when planning health officers in health risk mapping were resource allocation, building infrastructure also undertaken and this would go a long and ensuring that surveillance systems are way to improving our capacity to prepare and able to detect changing patterns of disease. respond to climate-sensitive diseases and But more importantly, it may spatially identify will also allow us to be better positioned to the high-risk areas, which can be targeted address the challenges that climate change for environmental hazards and public health will bring. Long-term climate projections prevention. will be increasingly important to reducing vulnerabilities and increasing resilience which Climate sensitive health risk models and maps will help populations cope with the health for three pilot districts namely; Bongo in the effects of climate change. Upper East, Keta in the Volta and Gomoa West in the Central have been

|| 5 5 Climate Change Health Risk Mapping

5.2 Recommendations Guerrant RL, Kosek M, Moore S, Lorntz B, Brantley R, Lima AA. Magnitude and impact ƒƒ Disease incidence data for risk modelling of diarrheal diseases. Arch Med Res 2002;33: should be disaggregated at the level of 351-5 the community from which the patients hail. Hunter P.R. Climate change and water-borne ƒƒ Climate data sets should be a lot and vector-borne diseases. Journal of Applied more localised rather than the highly Microbiology 2003;94:37-46 generalised data derived from the rather sparse meteorological network. Islam, M.S., Draser, B.S. and Bradley, D.J. (1990) Long persistence of toxigenic Vibrio ƒƒ Environmental data sets should be cholerae O1 in the mucilage sheath of a mapped a much larger scale than the blue-green alga, Anabaena variabilis. Journal 1:50,000 for sub-district representation of Tropical Medicine and Hygiene 93, 133–139. at least 1:10,000. ƒƒ Community level data relating to non- J. P. Besancenot, M. Boko, P.C. Oke, 1997, environmental/confounding factors weather conditions and cerebrospinal such as economic vulnerability, control meningitis in Benin (Gulf of Guinea, West activities like larviciding, ITN use, indoor Africa). European Journal of Epidemiology 13: spraying and personal/environmental 807–815 hygiene will help improve the modelling results. McCarthy, M.C., He, J., Hyams, K.C., el-Tigani, A., Khalid, I.O. and Carl, M. (1994) Acute References hepatitis E infection during the 1988 floods in Khartoum, Sudan. Transactions of the Royal Atherbolt, T.B., LeChevallier, M.W., Norton, Society of Tropical Medicine & Hygiene 88, 177 W.D. and Rosen, J.S.(1998) Effect of rainfall on Giardia and Cryptosporidium. Journal of the Miossec, L., Le Guyader, F., Haugarreau, L. American Water Works Association 90, 66–80 and Pommepuy, M. (2000) Magnitude of rainfall on viral contamination of the marine Briggs, D. (2000). Environmental health hazard environment during gastroenteritis epidemics mapping for Africa. in human coastal population. Revue d Epidemiologie et de Sante Publique 48(suppl Campanella, N. (1999) Infectious diseases and 2), 2S62–2S71 natural disasters: the effects of Hurricane Mitch over Villanueva municipal area, Nicar- Nuckols, J.R., J.K.Berry, and L.Stallones. 1994. agua. Public Health Reviews 27, 311–319 Defining populations potentially exposed to chemical waste mixtures using computer- Colwell, R.R. (1996) Global climate change aided mapping analysis. In Toxicology of and infectious disease: the cholera paradigm. Chemical Science 274, 2025–2031. Mixtures,R.S.H.Yang (Ed.), pp. 473—504. Croner, C.M., J.S.Sperling, and F.R.Broome. Orlando, FL: Academic Press. 1996. Geographic information systems (GIS): New Rosenstein, N. E., B. A. Perkins, D. S. Stephens, T. Popovic, and J. M. Hughes. 2001. perspectives in understanding human health Meningococcal Disease. New England Journal and environmental relationships. Statistics in of Medicine 344:1378-1388. Medicine15(17—18):1961—1977. Singh RBK, Hales S, Wet N, Rishi R, Hearnden Greenland, S. and H.Morgenstern. 1989. M, Weinstein P. The Influence of Climate Ecological bias, confounding, and effect Variation and Change on Diarrheal Disease modification. in the Pacific Islands. Environmental Health Perspectives 2001; 109:2 International Journal of Epidemiology 18(1):269—274. 5 6 || Climate Change Health Risk Mapping

Waller, L.A.1996a. Epidemiologic uses of geographic information systems. Statistics in Epidemiology Report 3(1):1—7.

World Health Organization. 1988. Control of epidemic meningococcal disease. WHO Practical Guidelines. Second Edition. Geneva.

WHO (2004) Inheriting the world: atlas of children’s health and environment

|| 5 7 Climate Change Health Risk Mapping

5 8 || Climate Change Health Risk Mapping

Policy Brief

September, 2012

INTEGRATING CLIMATE CHANGE INTO THE MANAGEMENT OF PRIORITY HEALTH RISKS IN GHANA

|| 5 9 Climate Change Health Risk Mapping

6 0 || Climate Change Health Risk Mapping

Executive Summary The World Health Organization estimates that, in the last 100 years the world has warmed by approximately 0.75ºC.This means there has been an increase in the mean temperature. Sea levels are rising, rainfall patterns are changing and extreme weather events are becoming frequent as a result. This change in climatic conditions is what is referred to as climate change. There are known health risks associated with climate change. Some diseases are known to thrive well in some peculiar climates whereas others are practically nonexistent in some climatic conditions. Some changes in climate bring about reduction in incidence of certain diseases and increase in the occurrence of other diseases.

In Ghana, climate change is known to affect incidence of diarrhea is high as compared to both communicable and non communicable the dry season. diseases. Malaria, cerebrospinal meningitis (CSM) and diarrhea are known to be among Public health interventions that address these the top climate sensitive communicable diseases particularly through improved risk diseases in Ghana. They are known to be mapping as a case in point, are therefore responsible for almost half the total disease viewed as very essential to realizing existing burden. health goals.

Malaria is a vector borne disease. It is As a nation, we do have strong systems to transmitted by the female anopheles monitor and respond to situations which mosquito. The life processes and activities of will occur as a result of climate change. this vector are affected by climatic conditions. Currently, there are no health risk maps for Temperature, rainfall and humidity are the climate sensitive diseases and there is factors that affect the reproductive patterns limited capacity for developing risk maps at of the mosquito hence affect the transmission all levels of the health sector. These hinder of the disease. During the rainy season there the capacity to forecast risks and respond to is provision of ideal breeding grounds for the those risks associated with climate change. vector therefore malaria is more prevalent. There is therefore the need to provide a spatial representation of the risk and warning CSM is caused by bacteria. It is transmitted signs which would be regularly updated to through respiratory and throat secretions. enhance quick and efficient response to these It is known to be prevalent in the Northern climate sensitive diseases. parts of Ghana. It is linked to the dry, hot and dusty climatic conditions that prevail there. The aim of this assignment was to develop The incidence reduces drastically with the climate change health risk maps at sub – onset of rain. Diarrhoea is a food borne national scales (three pilot districts namely disease common in areas with unhygienic Keta, Gomoa West and Bongo) showing sanitary conditions. During the rainy season, current and future severity and incidence of climate - sensitive diseases.

|| 6 1 Climate Change Health Risk Mapping Methodology Bongo district in the Upper East Region, Keta in the volta Region and Gomoa West in the Central Region were the districts selected for the study. Secondary data sets on climate and environment were obtained from the Ghana Meteorological Agency and Ghana Survey Department respectively. Derived data sets such as proximity of towns to water bodies and digital elevation model were obtained from GIS analysis. Total number of reported cases in the districts was obtained from the District Health Directorates of the Ghana Health Service for the period of 2009 – 2011. The development of the disease specific risk maps were under- taken using GIS techniques in multi-criteria evaluation involving the overlay of disease-specific, environmental, climatic and human factors in modelling health risks in the pilot districts

Statement of Issue: ƒƒ There is lack of spatial data on some ƒƒ Meningitis risk in the Bongo district was non-climatic but important confounding high within the district capital where factors such as ITN use indoor spraying there was high population density and as well as personal in addition to low in the rural and sparsely populated economic and physical vulnerabilities communities. at the sub district level and community levels. ƒƒ Generally malaria risk was seen fairly distributed within the districts studied ƒƒ There are no systems in place that detect but was high within the densely high risk communities. populated communities. Diarrheal risk ƒƒ Policy makers, administrators, health was also high along the coast. workers are not well trained and ƒƒ There were adequate information on equipped to draw and efficiently analyze the three diseases at regional and these risk maps even if it exits. district level, however the data is not There is therefore a lack of clear policy disaggregated at the lower levels such as direction and there is generally no response the communities and this is a challenge. measures in place to handle surge of ƒƒ Climate datasets are not localized but are climate sensitive diseases in case there is an rather highly generalized data derived unexpected variation in the climatic condition. from the rather sparse meteorological network. There are no well documented data on climatic patterns and its impact on disease burdens in the districts.

6 2 || Climate Change Health Risk Mapping

Key Policy Options: €€ Advantages Health risk mapping makes it possible for yy It will help reduce vulnerabilities policy makers have a clearer understanding and increase resilience which will of disease risks based on the relevant factors yy help populations cope with the and therefore enable them prepare for risks health effects of climate change. changing over time when planning, allocating yy It will improve our capacity to resources, building infrastructure and prepare and respond to climate ensuring that surveillance systems are able to sensitive diseases. Risk mapping will detect changing patterns of disease. allow us to be better ƒƒ Morbidity and mortality data should be yy positioned to address the disaggregated at the community level challenges that climate change will (where the patients hail from) to enhance bring. proper risk modeling. It is therefore extremely important to design €€ Advantages risk maps for climate sensitive diseases It allows for determination of risk maps for high risk areas and the country profile at the community level and as a whole. this helps to target interventions.

€€ Disadvantages Costly in terms of human resources Policy Recommendations and logistics. Ghana Health Service ƒƒ Climate data sets (rainfall, temperature, ƒ humidity etc) should be more localized ƒ Morbidity and mortality data should be rather than the highly generalized disaggregated at the community level data derived from the rather sparse (where the patients hail from) to enhance meteorological network to allow for proper risk modeling. sub-district or lower level analysis. ƒƒ Health professional should be trained in €€ Advantages the interpretation and use of health risk maps This allows for development of localized climate change health risk Ghana Meteorological Agency maps. ƒƒ To collect climate data sets at a €€ Disadvantages more localized rather than the highly Costly in terms of human generalized data derived from the rather resources and logistics such as sparse meteorological network to allow rain gauges and setting up climate for sub-district or lower level analysis stations.

ƒƒ We should develop long term climate projections Sources: 1. Ghana Meteorological Agency data 2. GHS data, Ghana Health Service (GHS) facility data.

|| 6 3 Climate Change Health Risk Mapping

6 4 || Climate Change Health Risk Mapping

|| 6 5 REPUBLIC OF GHANA MINISTRY OF HEALTH

INTEGRATING CLIMATE CHANGE INTO THE MANAGEMENT OF PRIORITY HEALTH RISKS IN GHANA

www.climatehealthghana.org