Climate Change Health Risk Mapping - UNDP · 2020-05-05 · Climate Change Health Risk Mapping 1 Sub-National Climate Risk Maps for Ghana Executive summary 3 1.0 Introduction 5 1.1

Climate Change Health Risk MappingSub-National Climate Risk Maps for Ghana

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Climate Change health Risk mapping

BackgroundAbout the ProjectClimate 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 diseases 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 disease 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 malaria, 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.

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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.

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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 Health Service, 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)

Acknowledgement

REPUBLIC OF GHANAMINISTRY 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’

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ContentsBackground iiiAcknowledgement vClimate Change Health Risk Mapping 1Sub-National Climate Risk Maps for GhanaExecutive summary 31.0 Introduction 5 1.1 Aim of the study 8 1.2 Specific objectives 8 1.3 Rationale 8 1.4 Project deliverables 92.0 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 183.0 conceptual Framework 21 3.1 Environmental health risk mapping 234.0 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 525.0 Summary overview and status of the deliverables 53 5.1 Conclusion 55 5.2 Recommendations 56Policy brief 59Executive Summary 61Methodology 62

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

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

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Climate Change Health Risk MappingSub-National Climate Risk Maps for GhanaFinal report

Submitted to UNDP/GEF Climate Change and Health Project.

By: Philip Mantey Foster Mensah KofiNyarko

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]

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Executive summaryDue 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 of climate change health risk maps at sub-national scales (3 pilot districts, namely Keta, Gomoa West, and Bongo) showing current and future severity and incidence of climate-sensitive diseases. This involved among other things; a review of the existing literature showing linkages between climate and health risks in Ghana, the development of a methodology for developing and updating climate change health risk maps, as well as the use of appropriate surveillance data on climate-sensitive diseases and other relevant information to map current health risks in pilot districts. CERSGIS was also required to build the capacity of selected national, regional and district health team members to develop, interpret, update, and apply risk maps for planning, monitoring, surveillance, and response.

The development of the risk maps was undertaken using GIS techniques in multi-criteria evaluation involving the overlay of disease-specific environmental, climatic and

human factors in modeling health risks in the pilot districts. Meningitis risk in the Bongo district was high within the district capital where there was high population density and low in the rural and sparsely populated communities. Generally, malaria risk was seen fairly distributed within the districts studied but was highest within the densely populated communities, forest areas and along the coast. Diarrhoeal risk was also high along the coast and densely in populated areas.

Capacity building activities on the use of GIS technology in disease epidemiology and health risk mapping for selected staff of the beneficiary districts as well as their regional and national counterparts were undertaken to transfer these essential skills to the health administrators.

Challenges had to do with the level of aggregation of disease incidence, coarseness of the meteorological network as well as the non-availability of sub-district spatial data on other essential confounding factors that would improve modeling results.

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Introduction1.0

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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 change to natural ecosystems may result in the emergence or spread of diseases to previously unaffected geographic regions or disease prevalence in areas where they had been limited. In other cases, changes in climate could also result in the disappearance of diseases from areas as they become less hospitable to the disease vector or the pathogen.

Though a global phenomenon, the consequences of climate change will not be evenly distributed. In short, climate change can affect problems that are already huge, largely concentrated in the developing world, and difficult to control. In the tropics, examples already provide us with images of the future:

� Rift Valley fever in Africa: Major outbreaks are usually associated with rains, which are expected to become more frequent as the climate changes;

� Malaria in the East African highlands: In the last 30 years, warmer temperatures have also created more favourable conditions for mosquito populations in the region and therefore for transmission of malaria;

� Epidemics of cholera in Bangladesh: They are closely linked to flooding and unsafe water.

Although these trends and events cannot be attributed solely to climate change, they are nonetheless the types of challenges expected to become more frequent and intense with climate changes. They will further strain health

resources that, in many regions, are already under severe stress. The core concern is succinctly stated: “climate change endangers human health,” (WHO). Global warming may be gradual, but the effects of extreme weather events - more storms, floods, droughts and heat waves will be abrupt and acutely felt. Both trends can affect some of the most fundamental determinants of health, namely air, water, food, shelter and freedom from disease.

Human beings are already exposed to the effects of climate-sensitive diseases and these diseases today kill millions. They include malnutrition, which causes over 3.5 million deaths per year, diarrhoeal diseases, which kill over 1.8 million, and malaria, which kills almost 1 million. But malaria, along with cholera, meningitis and dengue fever, are only a few of the 14 communicable diseases recently identified as being climate-sensitive by the World Health Organization, which estimates that climate change may already be directly responsible for over 150,000 deaths globally per year.

While direct and immediate impacts such as deaths in heat waves and floods can often be dramatic and provoke immediate policy, long-term influences will likely act through changes in natural ecosystems and their impacts on disease vectors, waterborne pathogens, and contaminants. With the appreciation of the linkages between climate change and health only being a recent development, existing public health policies and practices are viewed as largely inadequate in meeting the needs

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with respect to potential climate change related health impacts.

In Ghana, three climate-sensitive diseases namely; malaria, diarrhoeal disease and meningitis are identified as accounting for almost half the total disease burden with malaria still the leading cause of outpatient 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 viewed as very essential to realising existing health goals.

1.1 Aim of the studyThe main objective of the consultancy was to develop climate change health risk maps at sub-national scales (3 pilot districts, namely Keta, Gomoa West, and Bongo) showing current and future severity and incidence of climate-sensitive diseases

1.2 Specificobjectives � Review existing literature showing

linkages between climate and health risks in Ghana

� Develop a methodology for developing and updating climate change health risk maps, as well as quantifying relationships between climate variations and disease outbreaks.

� Use appropriate surveillance data on climate-sensitive diseases and other relevant information to map current health risks in pilot districts

� Downscale climate change projections available for Ghana to the scale/level appropriate for the pilot districts

� Identify suitable models to produce projection maps of vulnerability to climate-sensitive diseases (including but not limited to diarrhoeal disease, malaria, and CSM) in the three pilot districts

� Communicate findings to policymakers, planners, and other stakeholders

� Transfer data, skills, software, and all necessary tools for updating the maps to the pilot districts

� Train selected national, regional and district health team members to develop, interpret, update, and apply risk maps for planning, monitoring, surveillance, and response.

1.3 RationaleAdequate health infrastructure with universal access to primary health is crucial to reducing a population’s vulnerability to the impact of changing patterns of diseases due to climate change. A well functioning health system not only provides treatment, but, together with laboratory services and standardized diagnosis and reporting systems, is a crucial component of a national surveillance system. Health professionals must be better trained to understand the potential impacts of climate change on health

One of the main requirements identified in the existing national response to climate change is the strengthening of disease surveillance systems to better avoid (through early warning systems), prepare for, and effectively respond to climate change related health risks. The use of appropriate visualization tools can assist in risk assessment and forecasting. Currently health risk maps for the climate sensitive diseases are non-existent at the district level. Coupled with a limited capacity for developing risk maps at all levels of the health sector, this presents as inadequacy in capacity to forecast risk patterns.

In order to develop coherent responses to the increasing incidence of climate-related outbreaks, and to longer-term changing disease patterns, there is the need for improved information upon which to base the mainstreaming of climate change into health planning. Public health interventions that address these three diseases particularly through improved risk mapping as a case in point are therefore viewed as very essential to realizing existing health goals.

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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 with the 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 namely: 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.

This will therefore provide a spatial representation of the risk and warning signs which would be updated regularly to improve surveillance and response to these climate-sensitive diseases. This information will help in setting up early warning systems and improve the districts’ epidemic preparedness and response. The development of the climate change risk map will therefore serve as a baseline data to show the risk of the disease transmission in the pilot areas. This will also serve as reference for scaling up nationally or for other districts.

1.4 ProjectdeliverablesThe consultant was tasked with providing the following under the assignment:

� Project inception report describing the methodology and approach,

� Transfer of data, skills, software, and all tools required in developing and updating the maps to the districts,

� Submit a draft report providing a clear description of what exists and an analysis of the strength, weaknesses, opportunities and potential,

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

� Final technical report (5 hard copies and a soft copy on CD including the maps and interpretation of findings) packaged into a publication ready format,

� Policy Brief based on the technical report,

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

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The linkages between climate and health risks

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2.1 The linkages between climate and health risks

Climate change refers to a statistically significant variation in either the mean state of the climate or in its variability, persisting for an extended period (typically decades or longer). Climate change may be due to natural internal processes or external forces, or to persistent anthropogenic changes (man-made) in the composition of the atmosphere or in land use. Over the last 50 years, human activities – particularly the burning of fossil fuels – have released sufficient quantities of carbon dioxide and other greenhouse gases to trap additional heat in the lower atmosphere and affect the 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 has accelerated at over 0.18oC per decade. Sea levels are rising, glaciers are melting and precipitation patterns are changing. Extreme weather events are becoming more intense and frequent.

Climate change endangers human health, affecting all sectors of society, both domestically and globally. The environmental consequences of climate change, that is those already observed and those that are anticipated, such as sea-level rise, changes in precipitation resulting in flooding and drought, heat waves, more intense hurricanes and storms, and degraded air quality, will affect human health both directly and indirectly. Addressing the effects of climate change on human health is especially challenging because both the surrounding environment and the decisions that people make influence health. In an analysis that included just four climate-sensitive diseases (cardiovascular disease, malnutrition, diarrhoea, and malaria) as well as floods, the World Health Organization (WHO) estimated 166,000 deaths and about 5.5 million disability-adjusted life years (DALYs, a measure of overall disease burden) were attributable to climate change in the year 2000.

In Ghana, climate is known to be related to many disease conditions including both communicable and non-communicable

diseases. Some of the non communicable disease conditions affected by the effect of climate change are cardiovascular diseases, cancers and chronic respiratory diseases such as asthma. The communicable disease conditions known to be affected by climate change are food borne diseases and vector borne diseases such as malaria, diarrhoea and meningitis. The three top climate sensitive diseases of concern in Ghana with high disease burden and as such the need to develop adaptive strategies are Meningitis, Malaria and Diarrhoea. These diseases have been identified as accounting for almost half the total disease burden with malaria still the leading cause of outpatient morbidity in all ages and sex groups.

2.2 Malaria and climate change

Malaria is a disease of gargantuan proportions and is one of the most prevalent human infectious diseases. It is caused by protozoan parasite of the plasmodium genus that is vectored (transmitted) from person to person through the bite of infected female anopheline mosquitoes, which usually occurs at dusk and dawn. There are four Plasmodium species responsible for causing malaria in humans and these are P. falciparum, P. vivax, P. ovale and P. Malariae, P. knowlesi. Plasmodium falciparum causes the largest burden of disease. The parasite infects red blood cells and Malaria is characterized by cycles of chills, fever, pain, and sweating. Malaria is the most important tropical disease, remaining widespread throughout the tropics.

Globally, there were an estimated 216 million episodes of malaria in 2010, of which approximately 81%, were in the African Region. Malaria deaths were estimated at 655 000 of which 91% were in Africa and approximately 86% of malaria deaths were among children under 5 years of age. The estimated incidence of malaria globally has reduced by 17% since 2000 and malaria-specific mortality rates by 26% but this could change with the imminent threat of climate change.

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The Ghana Health Service health facility data indicates that malaria is the number one cause of morbidity, accounting for about 38 percent of all outpatient illnesses, 36 percent of all admissions, and 33 percent of all deaths in children under five years. Between 3.1 and 3.5 million cases of clinical malaria are reported in public health facilities each year, of which 900,000 cases are in children under five years.

The transmission of malaria is dependent on the presence of ecological conditions, which have been linked with the development of both the parasite and vector mosquito. The geographical and temporal distributions as well as the incidence of many vector borne diseases such as malaria and dengue are sensitive to temperature and rainfall. Temperature is an important factor that affects the distribution and rate of development of both the malaria parasite and the vector (mosquito). An increase in temperature of the environment where they breed boosts their rates of reproduction and the number of blood meals they take, prolongs their breeding season, and shortens the maturation period for the microbes they disperse. Temperature affects the mosquito at each stage of the mosquito lifecycle. If the temperature of the water where mosquitoes lay their eggs is too hot or cold, then fewer eggs hatch. For example, the ideal water temperature for Anopheles Gambiae egg hatching has been shown to range between 24oC and 30oC. After the egg stage, mosquitoes develop into larvae then pupae. Temperature has also been shown to affect the time it takes to transition between these stages with the optimum water temperature for survival and shortest transition between larvae and pupae ranging between 22oC and 26oC.

Specific temperature ranges are also important for the development of the parasite in the mosquito. It has been shown that the optimum range for parasite development is between 25oC and 30oC. The minimum temperature observed for survival for Plasmodium falciparum is 18oC and the maximum has been reported at 40oC. At a temperature of 25oC, Plasmodium falciparum requires only 12 days for parasite development but, at a temperature of 20oC, over 30 days are needed to undergo development and render

a mosquito infectious. If more time is needed for the parasite to mature, then the probability that a mosquito will live long enough for the parasite to spread the infection is dramatically reduced. Thus the rates of insect biting and the maturation of microorganisms within them are temperature-dependent, and both rates increase when the air warms, enhancing the chances for disease transmission.

Water available for breeding sites is the second primary environmental factor that has been associated with malaria. Water provides a habitat for mosquitoes to lay their eggs and for the development of the Anopheles larvae and pupae. The presence of permanent bodies of water creates mosquito breeding sites and the possibility of malaria transmission year round. The bodies of water can be either natural, like swamps or man-made structures, like dams. Living in close proximity to these types of water bodies has been found to be a risk factor for malaria. However, the importance of a body of water as a mosquito habitat depends on the preference of a particular mosquito species, the proximity to blood meals, and the presence of predator species that prey on the immature stages of the vector. It has been found that Anopheles have the ability to breed in sites where water is present for at least 10–14 days, depending on the time required for the mosquito life cycle to take place. Therefore, the frequency and amount of precipitation is an indicator commonly used to approximate the formation of temporary bodies of water that may be important breeding sites for mosquitoes.

On the other hand, drought may reduce the transmission of some mosquito borne diseases, leading to reduction in the proportion of immune persons and therefore a larger amount of susceptible people once the drought breaks. After a flood event, rates of vector borne diseases such as malaria can increase as mosquitoes breed in stagnant or slow moving pools of water. However the relationship is complex, as flood events can also wash away breeding sites.

Adult mosquitoes are also dependent on specific moisture content in the air and will desiccate if the climate is too dry. Therefore, adequate humidity is also an important

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environmental condition related to mosquito survival. Despite the biological plausibility of temperature and water to determine malaria risk, there have been inconsistent results when comparing studies at the community level. There are different ecological niches for the various species of Anopheles mosquito that transmit human malaria with some preferring dryer climates while others are prone to areas that are more humid. The differences in preferred habitat may explain some of the variability in attempts to assess malaria risk using environmental factors. However, the environment–malaria association is not this simple as there are also many other environmental conditions that modify temperature and precipitation that must be taken into account.

Many other factors can affect malaria, and while some can increase the transmission, others may counteract the effects of weather and climate. In the last decade, an increase in financing for malaria control has facilitated efforts to combat malaria, including the distribution of insecticide-treated nets. A recent study in 2010 used evidence-based malaria maps to show that despite global warming during the 20th century, a global recession of malaria was observed due to the other malaria intervention being implemented. Therefore, the indirect effects of climate change on malaria are important to consider in determining the key associations.

Factors such as indoor air temperature, the ability for water to pool and persist, water quality, elevation, deforestation, and agriculture can all modify the local temperature or water availability and affect malaria risk. Not taking the modifying factors into account and focusing on simply the primary effects is likely confounding the true association between malaria and the environment and explains why there has been so much inconsistency between studies.

The many interactions between temperature and water, numerous other factors that modify temperature and water, and the specific species of Anopheles mosquito present in each community all have an important role to play in malaria transmission, but human and parasite behaviour must also be factored

into the equation. If these variables are not accounted for when assessing the relationship between the environment and malaria, there are likely many variables confounding any true association present. A better understanding of the ecological processes that modify local temperature and water availability is needed and specific thresholds for how malaria transmission will be affected by different sets of environmental conditions need to be identified for all the species of Anopheles mosquitoes that transmit human malaria so that researchers can better understand the interactions amongst and within the ecological variables and their anticipated impact on malaria risk in each community.

2.3 Meningitis and climate change

Cerebrospinal Meningitis (CSM) is an infection of the meninges, caused by the bacteria called Neisseria meningitides. It causes inflammation of the lining of the brain of spinal cord called the meninges. Outbreaks of CSM usually lead to high death rates in African communities. The agent is highly contagious and person-to-person aerial transmission occurs through respiratory and throat secretions. The main stay of prevention is vaccination. Meningitis can result from many causes, both infectious and non-infectious. Bacterial meningitis is a life-threatening condition that requires prompt recognition and treatment. The Signs and symptoms of CSM are as follows: Severe headache; Stiff neck; Fever (38 OC axillary and 38.5OC rectal); Vomiting; drowsiness or unconsciousness.

Cerebrospinal meningitis is the only form of bacterial meningitis which causes epidemics which can occur in any part of the world. However, the largest epidemics occur mainly in the semi-arid areas of sub-Saharan Africa, designated the African meningitis belt. Apart from epidemics, meningococcal meningitis occurs sporadically throughout the world, with seasonal variations, and accounts for a variable proportion of endemic bacterial meningitis. Recent analysis of reported epidemics indicated that there appears to be a southward shift in the distribution of

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epidemics over time, with new areas affected south of the current belt area consistent with changes in the region’s climate ,e.g. Southern Province in Ethiopia.

Over 1.2 million cases of bacterial meningitis are estimated to occur worldwide each year. The incidence and case-fatality rates for bacterial meningitis vary by region, country, pathogen, and age group. Without treatment, the case-fatality rate can be as high as 70% and one in five survivors of bacterial meningitis may be left with permanent sequel including hearing loss, neurologic disability, or loss of a limb.

Cerebrospinal Meningitis has affected Sahelian Africa for centuries and has been known to be endemic over the past 30 years. During the 1980s, the World Health Organization (WHO) registered between 25,000 and 200,000 disease cases per year, with about 10% case fatality rate, with the highest infection rates observed in younger children. (CSM), therefore, became a public health concern in the poorest regions in the world within the meningitis belt.

In Ghana, meningitis which is a climate-sensitive disease is prevalent in the northern part of the country which forms part of the meningitis belt of sub-Saharan Africa spanning from Senegal in the East to Ethiopia in the West. There was a major outbreak of meningitis in Ghana between 1996 and 1997 and the WHO AFRO regional Assembly in 1998 adopted a strategy to strengthen diseases surveillance using the Integrated Diseases Surveillance and Response (IDSR) to improve surveillance systems in Africa. The strategy aimed at enhancing early detection, reporting and timely response.

The spatial distribution, intensity of transmission and seasonality of Cerebrospinal Meningitis in the semi-arid areas of sub-Saharan Africa have been linked to climatic factors, particularly drought and hot, dry and dusty conditions, although the causal mechanism and relationship is not clear.

Epidemics have been rarely reported from the humid forested or coastal regions, even when neighboring areas are severely affected. The

geographical distribution of disease cases is called the “Meningitis Belt” and is roughly circumscribed to the biogeographical Sahelo-Sudanian band. This Sahelo-Sudanian region has a dry winter, dominated by northern winds, called the Harmattan, followed by a wet season starting in spring with the monsoon. The co-occurrence in both space and time of meningitis cases and climate variability within the Sahelo-Sudanian area suggests that the occurrence of the disease might be directly related to climate. So far, very few studies have tried to quantify the potential linkages that could exist between climate and meningitis outbreaks.

CSM outbreaks in West Africa usually start at the beginning of February, and then disappear in late May. These hot, dry northerly winds blow from the Sahara over all of West Africa, violently from December to February. During this period, the dust particle which are almost impalpable particles of quartz and clay, colloids or fine mika flakes and can remain airborne for days combined with the cold night temperatures and encourage respiratory infections. The Harmattan climate causes damage to the mucous membranes of the oral cavity through dry air and strong dust winds, and creates propitious conditions for the transmission of the bacteria responsible for CSM; low absolute humidity and dust may enhance meningococcal invasion by damaging the mucosal barrier directly or by inhibiting mucosal immune defences. In contrast, higher humidity during the rainy season strongly reduces the disease risk by decreasing the transmission capacity of the bacteria. CSM epidemics thus generally stop with the onset of rainfall.

2.4 Diarrhoea and climate change

Diarrhoea is the frequent passing of loose or watery stools. Acute diarrhoea, which is a common cause of death in developing countries, appears rapidly and may last from five to ten days. Chronic diarrhea lasts much longer and is the second cause of childhood death in the developing world. Diarrhea is sometimes accompanied by abdominal

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cramps or fever. It may be caused by infection, allergy, or could be a sign of a serious disorder, such as IBD (inflammatory bowel disease). Most diarrheal cases in developing countries including Ghana are of infectious origin.

The main causative agents include viruses, bacteria, protozoa, and helminths that are transmitted through the faeco-oral route. Among the principal bacterial agents of diarrhoeal diseases are Vibrio cholerae (cholera), Salmonella spp (typhoid fever), Shigella spp, (bacterial dysentery), Campylobacter spp and a variety of enteropathogenic Escherichia coli strains. Diarrhoeal diseases of viral origin include rotavirus which is the most common and enteric adenoviruses, astroviruses and caliciviruses. Protozoa such as Giardia, Cyclospora and Cryptosporidium spp and Entamoeba histolytica also cause diarrhoea.

According to the World Health Organization (WHO) approximately 3.5 million deaths each year are attributable to diarrhoea. About 80% of those deaths occur in children under the age of 5 years and 70% of all diarrhoeal cases are attributable to inadequate water and sanitation. Children are more susceptible to the complications of diarrhoea because a smaller amount of fluid loss leads to dehydration, compared to adults. An average morbidity attack rate of 3.2 episodes of diarrhoea per year per child has been reported, but in some settings in developing countries, this number can be as high as 12 episodes per year per child. Evidence has been accumulating for long-term consequences of such heavy disease burden in early childhood on physical and mental development of children that may eventually translate into costly impairment of human fitness and productivity at an adult age.

Outbreaks of cholera, shigellosis and typhoid fever most often occur in resource-poor countries, adding to the burden of disease among the most vulnerable such as refugees, internally displaced populations and groups living in shanty towns. Cholera is a type of diarrhoea which is related to poor hygienic condition. In Ghana, outbreaks of cholera which is caused by Vibrio Cholerae and has a high case fatality rate, occurs frequently

during the rainy seasons and along the coastal regions.

Rainfall, temperature and other climatic factors affect the transmission of diarrheal disease. High temperatures, water scarcity and water abundance resulting from flooding or heavy precipitation have all been shown to be related to diarrheal diseases.

After a flood-event, rates of diarrheal disease, including cholera, may increase, especially in areas where sanitation facilities are poor. Heavy rainfall, even without flooding, may increase rates of diarrheal disease as latrines or sewage systems overflow and also faecal contaminants from pastures and dwellings are flushed into water supplies thereby contaminating them. Increases in soil run-off may contaminate water sources. For surface water sources, heavy rainfall can lead to overflow of storm drains that may be combined with the sewage system. This can then allow substantial amounts of faecally polluted water into rivers. Surface water turbidity can also increase dramatically during heavy rainfall events and this can cause additional stress on inadequate water treatment systems. Further evidence of the impact of heavy rainfall on the epidemiology of enteric pathogen comes from studies of the presence of various organisms in water. For example, there is a correlation between rainfall and the likelihood of detecting Giardia or Cryptosporidium oocytes in river water. A further issue related to heavy rainfall events is the additional nutrient input into water bodies that accompany heavy rainfall. When other conditions are appropriate, this can lead to the rapid growth and blooming of various planktonic species which can cause diarrhoea.

Flooding may follow heavy rainfall. For developing nations there is evidence of disease outbreaks following floods. Outbreaks of diarrhoeal disease have followed floods in Khartoum and in Nicaragua, following Hurricane Mitch and the associated flooding. Even in Ghana, outbreaks of cholera usually occur after heavy rainfall and flood events especially in the Greater Accra, Eastern and Central regions.

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Water scarcity on the other hand is also likely to have consequences for public health. A lack of availability of water for personal hygiene and washing of food may lead to an increase in diarrheal disease and other diseases associated with poor hygiene. Water scarcity is associated with droughts and contamination of water bodies can occur during this season as a result of stagnation. Water pollution has become a major environmental problem, and excessive use of groundwater is adversely affecting the availability of safe drinking water in some countries.

Cholera outbreaks in the Amazon have been linked to low river flows in the dry season, which may be due to pathogen concentration in pools. A high concentration of pathogens may also overload water treatment plants.

Temperature also has an effect on diarrhoeal diseases and probably the most obvious link between diarrhoea and increased temperature relates to the blooms of various planktonic species that are directly or indirectly hazardous to human health. The most evidence of the effect of temperature on risk from waterborne disease is in relation to cholera. There is now good evidence that V. cholerae survives in marine waters in a viable but non-cultural form that seems to be associated with algae and plankton. When temperatures rise, plankton bloom and, in appropriate areas, such blooms are followed by increases in reported cases of cholera. It is also known that bacterial diarrhoea usually peak in hot months whiles viral diarrhoea may have some peak in cooler months, but transmission continues all year round. This is because high temperatures favour the growth of food spoilage bacteria which contaminates foods and hence consumption of these spoiled foods can cause diarrhoea.

2.5 Climate change health risk mapping

Public health planning and decision making has mainly been focusing on relatively short term risks. However, there is the need to now focus on the projected long term impacts of climate change. It will be increasingly important to address the links between climate and health

at different timescales. Already today, we need to be better at dealing with climate variability and its related health effects. Improving our capacity to prepare and respond, through using for example, early warning systems and seasonal forecasts, will allow us to be better positioned to address the challenges that climate change will bring. Long-term climate projections will be increasingly important to ensure that we are prepared for risks changing over time when planning resource allocation, building infrastructure and ensuring that surveillance systems are able to detect changing patterns of diseases. Reducing vulnerabilities and increasing resilience in general will help populations cope with the health effects of climate change.

Mapping uneven events, such as disease cases or risk factors, makes it possible for public administrators to discover the origin of pollution or source of epidemic outbreaks and generate more hypotheses for further investigations. More importantly, it may spatially identify the high-risk areas, which can be targeted for environmental hazards and public health prevention activities. Mapping has been made easier and become more widely used with the development of geographical information systems (GIS).

During the last years, increasingly powerful and versatile geostatistical tools have been developed for spatial analysis. Geostatistics is a methodology for incorporating the spatial and temporal coordinates of observations in data processing. A key feature of epidemiological data is their location in a space-time continuum. GIS has emerged as an innovative and important component of many projects in public health and epidemiology: GIS has proved to be useful for epidemiological research purposes, decision-making, planning, management and dissemination of information. GIS can be used to map and analyse the geographical distribution of population at risk, health outcomes and risk factors; to explore associations between risk factors and health status; to plan public health services.

Proponents of the use of GIS have pointed out that its architecture is ideal for handling the complexities of a relatively large number

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of spatially distributed variables, and should emerge as a powerful tool in ecologic studies of exposure to environmental hazards and disease etiology. In spite of the vigorous debate surrounding the extent to which GIS-assisted ecologic studies can establish causation, there is no doubt, however, that these studies can be invaluable in placing disease in context, generating hypotheses, and justifying more expensive individual-level studies.

As GIS combines individual-level data with aggregate data collected at a wide range of resolutions, these ecologic studies have the potential to contribute information undiscoverable by any other means, but they also introduce opportunities for bias, inadvertent misrepresentation, or violation of confidentiality if used improperly.

Many studies deal with the integrated use of GIS techniques (geocoding, buffers, overlay, distance functions) and classical statistics (chi-square test, multiple logistic regression) examining the relation between risk factors and population health status. A classical approach involves the use of GIS as a tool to classify population with reference to geographical items. Then classical statistics is applied to assess the hypothesis that inspired classification. GIS techniques have also been applied in the management of environmental and epidemiological data in respiratiory health risk mapping.

GIS software can be used to map the spatial distribution of disease risk and overlay climatic and environmental features such as the presence of water bodies, precipitation, temperature, or land use to identify possible ecological factors associated with specific climate-sensitive diseases. The application of remote sensing is similar, in that images of a community coded by land use, amount of moisture, or other features can then be compared to disease incidence and used to identify possible land uses related to risk of diseases as well as how this may change if the land use changes. Modelling has enabled research to go beyond spatial analysis and conduct statistical analysis on spatial correlations between disease and ecological factors, to assess issues like over dispersion

and autocorrelation that are ever so present in both environmental and malaria transmission systems. These powerful tools provide new methods for a more integrated approach to assessing health risk and could be used to account for the interactions inherent in the environment and eliminate any potentially confounding effects in determining ecological risk factors for diseases.

To date, studies attempting to model the occurrence of climate-sensitive diseases using ecological risk factors have largely focused on temperature and precipitation, with more recent studies using land use data based on remote sensing imagery. Using the ecological data and digital mapping technologies, maps have been created at the country and regional levels based on ecological risk factors and are relatively accurate at this small scale. However, results of studies focusing at the local level have been inconsistent: there is still a high level of uncertainty surrounding the association between disease incidence, temperature, and precipitation at the microclimate level. Environmental systems are complex and it is possible that modifying factors may be present that are confounding any associations that exist between ecology and malaria risk.

Being able to tease apart the complexities of the environment–disease relationship and identifying thresholds also allows for predictive tools to be developed that can better enable control operations to target their efforts when and where they will be most effective with the appropriate interventions.

However, mapping provides only a visual display of the uneven cases, but cannot definitively confirm clustering of cases or spatial correlations. Risk surface estimation, including kernel estimation and geostatistical methods, produces continuous surfaces of risk across the whole study areas and potentially offer more insight into the nature of the clusters. However, mapping the spatial clusters of uneven events is a static snapshot, which ignores the temporal kinetics of these uneven events, and it is difficult to evaluate whether the hazards or epidemics have been broken out or kept under control by policymakers.

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In addition to straightforward questions of accuracy and precision, types of data related errors arising from unavoidable data manipulation which are likely to affect ecologic studies include measurement error, effect modification (where the meaning of an effect variable changes from one scale to another) and misclassification (where an observation is grouped into the wrong category). Although change is central to questions of exposure and health impact, few studies performed to date are more than snapshots of current conditions in the environment and the health of its inhabitants. Ongoing data collection permits the development

of models and repeated analyses testing different scenarios. GIS technology is an ideal tool to take advantage of such information, as it has the power to manage temporally as well as spatially referenced datasets, and can generate a series of spatial images reflecting the changes in an area’s contamination over time. Conceptualizing the environment/public health relationship as one which occurs over temporal space as well as geographic space promotes good database design. Having a longitudinal database handy for assessments of potential hazard also permits more statistical stability for the detection of change.

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Conceptual Framework3.0

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3.1 Environmentalhealthriskmapping

The concept of environmental health risk mapping is relatively new. The terms environmental health, hazard, risk and mapping are also open to different interpretations. Environmental health may be more or less narrowly defined to refer exclusively to the effects of the ‘natural’ or ambient environment on health. More broadly, it or may be taken also to include the social and cultural environment. In this study, 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.

A health effect occurs only if individuals within the exposed population are susceptible to the effects of the hazard, and if they accumulate sufficient exposures to experience an effect. The relationship between environment and health can be seen as a chain, comprising three distinct links: the environment, the population and health (figure 1). The chain can be characterised as follows: certain environmental conditions create hazards; if people come into contact with and are thus 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.

Mapping refers to more than simply the production of maps. The map is merely the end product of an often lengthy and complex analytical process – a model of reality. How well it expresses this reality depends upon the decisions made, and the methods used, during this mapping process. Maps may take many different forms. In portraying environmental health risks, maps will often be thematic – i.e. they will show the distribution of one or more features or themes, graded or classified according to their type or degree of risk.

� Figure 1: relationships between environmental health hazard environmental health risk and environmental health impact (source: briggs, 2000).

Environmental health hazard mapping involves mapping of the distribution and magnitude of environmental hazards with the capacity to affect health, without consideration of the population. This is based solely on information on the environment.

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Environmental health risk mapping involves mapping of the potential risk to human health, either by estimating the numbers of people exposed to the environmental hazards, or the likely health burden. This is based on informa-tion both on the environmental hazard and on the distribution (and possibly susceptibility) of the population or on direct estimates of popu-lation exposure (may include other confound-ing/non-environmental factors).

Environmental health mapping comprises mapping the actual health outcome which can be attributed to exposures to the environmental hazards of concern. The scope of this assignment deals with environmental risk mapping.

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Approach and methodology4.0

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26 ||


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, analyzing and displaying huge amounts of spatial data. GIS has proved to be useful for epidemiological research purposes, decision-making, planning, management and dissemination of information. GIS technology can be used to map and analyse the geographical distribution of population at risk, health outcomes and risk factors; to explore associations between risk factors and health status and to plan public health services.

4.1 Study areaThe study was undertaken in 3 pilot districts of Bongo in the Upper East Region, Gomoa West in the Central Region and Keta in the Volta Region (see figure 2).

� Figure 2: Map of Ghana showing the study districts

4.2 BongoprofileBongo is one of the nine districts in Upper East Region and occupies an area of about 459.5km2. The district is divided into six sub districts namely; Bongo central, Bongo Beo, 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 topography is generally flat or low lying with outcrops of granite and Birimian rocks. Areas occupied by granites are generally of low, gently rolling relief 90 to 300 metres above sea level.

The district is drained by the Red Volta 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.

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� Figure 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.

� table 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

� Table 2: Health staffing in Bongo District

HealthStaff Number

Public Health Nurse 1

Midwifes 20

Community health nurses 73

TBAs 109

CIMCI volunteers 264

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4.3 Gomoa West Municipal profile

The Gomoa West District is one of the 17 districts within the Central Region of Ghana. It lies within latitude 5°14 north and 5 °35 and longitude 0o22 and 0o54 west on the eastern part of the Central Region of Ghana. The district covers an area of 1,022km2 and a total projected population of 112,285 in 97 settlements or localities. This makes it the district with the highest population and surface area next to the Assin district (fig. 4).

The land slopes gently from south to north with isolated hills on forest dissected plateau in the north and coastal plains in the south. The area is drained by a few rivers and

numerous streams with the notable ones being rivers Brushing and Ayensu.

Mean annual rainfall ranges between 700 and 900mm in the southern coastal belt and 900 to 1,100mm in the northern and northwestern semi-deciduous forest areas. However available statistics reveal a fluctuating rainfall pattern. The mean annual maximum and minimum temperatures of 29°C and 26°C occur in February to March and August respectively. The relative humidity ranges between 70% and 80% for the northern and southern sectors of the district respectively and is influenced by the presence of large water bodies like the ocean, rivers, lagoons and streams.

� Figure 4: Map of Gomoa West Municipal Area

Economically, the district is one of the most deprived in the Central region. Most of the inhabitants are engaged in fishing, farming and petty trading. The health infrastructure consists of a District Hospital (St Luke’s Catholic Hospital), 4 Health centers, 13 CHPS Zones, a Nutrition Rehabilitation Centre and a

private maternity home. The Doctor -Patient ratio is 1:27493 and the Nurse - Patient ratio is 1:1099. The district has 102 Trained Traditional Birth Attendants and 96 community-based surveillance volunteers.

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� table 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 KetaMunicipalprofileKeta Municipality is one of the eighteen (18) districts in the Volta Region of Ghana and lies at the south-eastern corner of the Volta Region and Ghana; between longitude 0°30E and 1°05E. The total population of the area projected from the 2000 census is 164,407 based on annual growth rate of 1.9%. Keta Municipality has a total land area of about 1,086km2, about a third of which is covered with water bodies (362km2.). Among the water bodies, the Keta lagoon is the largest and the most important. It is about 1.2km wide and 32km long and divides the municipality into 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.

The lowest point is between 1.0-3.5 metres below sea level along the coast. The Municipality is in the dry coastal equatorial climate with an annual average rainfall of less than 1,000mm. The amount of rainfall reduces as one travels from the north to the south, where the annual average is about 800mm.which makes it one of the driest along the coast of Ghana. High temperatures are experienced all the year round. The average temperature experienced is 30°C. Keta is a mainly agricultural Municipality, with the majority of the people engaged in crop farming, sea and/ or lagoon fishing, livestock keeping and other related trading activities.

� Figure 5: Map of Keta Municipal Area

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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 health care system within the Keta Municipal area. It presents the differenc personnel as well as their respective grades within these facilities.

� table 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

� Table 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

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Grade Keta hosp Abor hosp Sub- District Directorate total

TechnicalOfficer-DC 0 1 0 4 5

TechnicalOfficer-H/Inf. 0 2 0 1 3

TechnicalOfficer(Biost) 6 4 4 0 14

Field Technician 0 0 1 2 3

NutritionOfficer 0 0 0 1 1

SeniorExecutiveOfficer 1 1 0 1 3

Prin. Accountant 0 2 0 0 2

Accountant 2 3 0 1 6

Accountsofficers 1 3 0 1 5

Financeofficer 2 2 0 0 4

Biomedical Scientist 0 2 0 0 2

EstateOfficers 1 1 0 0 2

HealthServ.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

SeniorStaffNurse 6 3 0 0 9

StaffNurse 13 14 0 0 27

Senior Health Educator 0 1 0 0 1

SupportStaff 35 79 20 14 148

Grand Total 130 182 106 31 449

4.5 Data Data for the study came from secondary sources consisting of climatic and environmental data sets as well as those on the total burden of the diseases of interest in pilot districts.

4.5.1 Climatic, environmental and population data

Climate related data is available from the Ghana Meteorological Agency, which holds historical data sets from their weather monitoring stations from as far back as the 1970s. Historical data on temperature, rainfall and relative humidity were of importance in developing the health risk maps. Current

information on the population and population density of the towns within the pilot districts is also available from the Ghana Statistical Service as well as projected figures from the districts. Environmental data relating to the boundaries of the pilot districts, location of settlements, drainage, altitude are also available from the digital mapping section of the Ghana Survey Departments. Derived data sets such as proximity of towns to water bodies for example and digital elevation model (DEM) were obtained from GIS analysis. Additionally, data on land use information for the pilot districts were derived from satellite image analysis.

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4.5.2 Disease incidence dataTotal 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.

� table 6: oPD cases for malaria, diarrhoea and cSM in bongo District 2009-2011

chPS center2009 2010 2011

Malaria Diarrhoea cSM Malaria Diarrhoea cSM Malaria Diarrhoea cSM

Adaboya CHPS 685 97 0 1200 226 0 1595 301 0Akolposiga CHPS 349 53 0 353 46 0 297 53 0Anafobisi CHPS 50 11 0 Anafobisi Clinic 150 12 0 488 65 0 609 128 0Atampisi CHPS 977 164 0 907 153 5 1174 195 5Balungu CHPS 575 104 0 1258 204 0 1216 256 0Boko CHPS 486 31 0 611 90 0 868 124 0Bongo Hospital 14713 792 0 9261 633 0 14426 2050 0Bongo Soe Health Centre 3184 157 0 4722 242 0 5740 348 0

Dua Clinic 1441 65 0 1664 143 0 3710 394 0Feo CHPS 402 47 0 512 87 0 557 38 0Gamborongo 678 74 0Gowrie CHPS 674 75 0 1129 120 0 1250 115 0Kabre CHPS 134 3 0 273 17 0 574 47 0Kadare CHPS 212 35 0 274 64 0 567 89 0Kodorogo CHPS 213 5 0 389 50 0 680 128 0Kunkua CHPS 278 53 0 272 114 0 379 57 0Lungu CHPS 583 54 0 1305 225 0Namoo Health Centre 4275 211 0 3264 425 0 4846 642 2

Vea Health Centre 2592 97 0 2491 106 0 4026 197 0Wagliga CHPS 722 129 0 1311 204 0 2236 380 0Zorko Health Centre 3819 270 0 4514 1243 8 5752 687 0totAL 35931 2411 0 35476 4286 13 52485 6528 7

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� table 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

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� table 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

� table 8: oPD cases for malaria, diarrhoea and cSM in Gomoa West Municipal Area 2009-2011

No Name of health facility2009 2010 2011

Malaria Diarrhoea malaria Diarrhoea malaria Diarrhoea

1 St Luke’ s Catholic Hospital 9832 336 9336 630 12,603 689

2 Oguaa Health centre 723 105 932 114 1476 56

3 Osedze Health Centre 261 57 756 122 1,944 1534 Noguchi Health Centre

1110 251 1317 280

1204 1535 Assin CHPS 401 68

6 Ngyiresi CHPS 347 79

7 Dago health centre 127 16

8 Apam RCH 43 19 Mankoadze clinic 122 36 159 33 156 2610 Sampa CHPS 252 49 246 23 231 2911 Brofo CHPS 412 8812 Eshiem CHPS 657 78 977 64 1003 5913 Tarkwa CHPS 611 78 738 47 1034 97

14 Fomena CHPS 80 9 88 12 230 30

15 Mumford CHPS 136 1

total 13,648 999 14,549 1,325 21,436 1,573

4.6 GIS AnalysisGIS analysis involved the application of geostatistical techniques in the development and modelling of disease specific risk maps through the combination of climatic and environmental with other ancillary data layers in multi-criteria evaluation.

4.6.1 Health hazard and risk mapping

Hazard maps, i.e. a map that highlights areas which have a potential to pose significant threats to human health are prepared by weighting and overlaying the disease specific environmental and climatic factors (see table 8). Point based source data were converted into their surface equivalent using the appropriate interpolation routines available in ArcGIS10. The selection and weighting of different factors for hazard and risk maps were informed by the literature and expert input. Ratings and classes for each factor are

ranked from low to very high based on degree of vulnerability to the factor. Every layer is then re-classified based on these ranks. Re-classified layers are multiplied by their standard weight and then added to others for providing the output hazard maps for the respective diseases.

The output hazard map then serve as an input factor into risk mapping, by combining them with other non-environmental factors in producing the risk layers/maps for the respective diseases. The final risk maps were then reclassified into high, moderate and low risk areas as three strata for planning control interventions. The accuracy of hot spots in the risk maps were assessed by comparison with the disease-specific incidence map of the pilot districts. All data processing and analysis were undertaken with the ArcGIS GIS software and semi-automated using the model maker utility. All data used in health hazard/risk mapping is presented in table 9 below.

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� table 9: Data layers used in health hazard/risk mapping

Disease hazard Map risk Map

Malaria

Temperature Malaria hazard map

Rainfall Population density

Relative humidity Land use

Altitude Malaria incidence

Slope

Proximity to water bodies

Diarrhoeal disease

Proximity to water bodies Diarrhoeal disease hazard map

Altitude Population density

Temperature Land use

Slope Diarrhoea incidence

CSM

Temperature CSM hazard map

Altitude Population density

Relative humidity Land use

CSM incidence

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

� Figure 6: environmental health risk Mapping Methodological Flow chart

4.6.2 Health risk modelsClimate 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.

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� Figure 7: Malaria risk Model

� table 10: cSM hazard /risk layer ranking and weighting

Layer/Factor class rank Weight

Hazard map

Rainfall<800mm800-900mm>900mm

321

0.2

Relative humidity<50%>50%

21

0.35

Temperature <28 o>28 o

12

0.3

Elevation <600m600-800m>800m

321

0.15

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� Figure 7: Malaria risk Model

� table 10: cSM hazard /risk layer ranking and weighting


Hazard map


321

0.2

Relative humidity<50%>50%

21

0.35

Temperature <28 o>28 o

12

0.3

Elevation <600m600-800m>800m

321

0.15


Risk Map

Mean CSM incidence

<22.0-3.94.0-8.99.0-15.916-26>26

123456

0.4

Population density

<19001900-2992300-25992600-30993100-3899>3900

123456

0.2

CSM hazard

<2.32.3-2.72.7-3.03.0-3.43.4-3.9>3.9

123456

0.2

Land use

Water bodyClosed forestOpen forestFarmland/shrubsOpen spaces /Bare landsettlement

1234

56

0.2

� Figure 8: Diarrhoea risk Model

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40 ||


� table 11: Diarrhoea hazard / risk layer ranking and weighting


hazard map


123

0.3

Slope <2o2-8o>8o

321

0.2

Temperature <26o26-28o>28o

123

0.3

Elevation

<200m200-600m600-800m>800m

4321

0.2

risk Map

Mean diarrhoea incidence

<134134-239240-374375-589590-960>960

123456

0.2

Population density

<19001900-2992300-25992600-30993100-3899>3900

123456

0.1

Diarrhoea hazard

<1.61.6-1.92.0-2.32.4-2.62.7-3.0>3.0

123456

0.4

Land use

Water bodyClosed forestOpen forestFarmland/shrubsOpen spaces /Bare landSettlement

1234

56

0.2

Distance to Water bodies

<250m250-500m501-900m901-1450m1251-2000m>2000m

654321

0.1

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� Figure 9: Meningitis risk Model

� table 12: Malaria hazard / risk layer ranking and weighting


hazard map


123

0.2

Relative humidity<50%50-70%>70%

123

0.2

Temperature <23 o23-26 o>26 o

123

0.3

Elevation

<100m100-300m301-500m501-800m>800m

54321

0.10

slope<2o2-8o>8o

321

0.10

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Distance to Water bodies

<250m250-500m501-900m901-1450m1251-2000m>2000m

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risk Map

Mean Malaria incidence

<14001401-25002501-40004001-60006001-9000>9000

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Population density

<19001900-2992300-25992600-30993100-3899>3900

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Malaria hazard

<22-2.42.41-2.62.61-2.82.81-2.9>2.9

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0.4

Land use

Water bodyClosed forestOpen forestFarmland/shrubsOpen spaces /Bare landsettlement

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0.2

4.6.3 Health risk mapsDisease specific health risk maps for CSM, Malaria and Diarrhoea for each of the selected pilot district are presented in figures 10 to 17.

The spatial distribution of meningitis risk in Bongo district shows a concentration of risk from the mid to western part of the district. Meningitis risk was highest within the district capital where there was high population density and low in the rural and sparsely populated communities in general.

Generally malaria risk was fairly distributed within the pilot districts but was concentrated within the densely populated communities,

along the coast, the well drained as well as the forested areas. In Bongo district, malaria risk was fairly well spread in the entire district bar the north eastern quarter which appeared to show very low risk. Risk was highest in central and southern parts of the district.

Malaria risk distribution in the Gomoa West Municipality of the Central region showed a greater concentration in the northern forest areas and the densely populated settlements. Risk was generally relatively less in the western part of the municipality.

In the Keta Municipality, malaria risk distribution show highest risk along the densely populated coastal areas and medium

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risk in the middle and western parts. Areas to the north of the municipality showed the least levels of malaria risk.

Diarrhoeal risk for the three pilot districts shows a concentration within the densely populated areas, the well drained and the coastal areas. In Bongo, diarrhoeal risk was mainly concentrated in the central, southern and western parts of the district whereas the northern and eastern parts showed relatively lower risks

Diarrhoea risk distribution in the Gomoa West Municipality of the Central region showed the densely populated settlements along the coast, the east and mid north with the western parts showing relatively lower risk.

In the Keta Municipality, diarrhoea risk distribution shows the densely populated coastal areas as well as the northern part as having the highest risk. Areas to the west and middle of the municipality showed relatively lower risk.

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� Figure 10. bongo District cSM risk map

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� Figure 11. bongo District malaria risk map

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� Figure 11. bongo District malaria risk map � Figure 12. bongo District diarrhoea risk map

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� Figure 13. Gomoa West Municipal diarrhoea risk map

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� Figure 13. Gomoa West Municipal diarrhoea risk map � Figure 14. Gomoa West Municipal malaria risk map

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� Figure 15. Keta Municipal malaria risk map

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� Figure 16. Keta Municipal diarrhoea risk map

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4.7 ChallengesMajor challenges in developing the risk maps had to do with a variety of factors as discussed below:

� the level of spatial aggregation of disease incidence data: the total number of cases reported for the diseases of interest obtained from the GHS was aggregated at the facility/sub-district level. This presented challenges in the fact that the ideal level of aggregation for mapping the spatial incidence of the respective diseases is at the community level where the predisposing factors are deemed to have acted. Mapping at the health facility level introduces some generalization into the data and does not allow for the spatial differentiation of disease incidence to the communities.

� coarseness of climatic data: the low density of network of the meteorological stations operated by the Ghana Meteorological Agency also presents another challenge in the fact that climate data is highly generalized without any significant sub-district variations.

� 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.

� Lack of spatial data on some confounding/non-environmental data: data on some non climatic but important confounding factors important for health risk mapping such as inoculation rates, larviciding, ITN use, indoor spraying as well personal hygiene and sanitation factors in addition to economic and physical vulnerabilities at the sub district level also limits the wholeness of the models developed.

� Additionally, most of the data on climatic factors and reported disease burdens for the different pilot regions required geo-rectification prior to analysis and model development.

� Other challenges for the projects had to do with data requirements for the risk mapping which were indicated in the inception report and were to be facilitated by the client proved a challenge as these data were held by third parties who were oblivious to the time-bounded nature of the study. Thus in all, these served to delay the output of the consultant unduly.

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5.0 Summary overview and status of the deliverables

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

� table 13: Status of project deliverables

Deliverable No. Deliverable title Status

1Project inception report describing the methodology and approach

Completed

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

Completed

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

Completed

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

Completed

5 Draft technical report Completed

6 Final technical report Completed

7 Policy Brief based on the technical report, Completed

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

Completed

5.1 ConclusionHealth risk mapping makes it possible for public administrators to discover the origin or source and spread of epidemic outbreaks while ensuring that we are prepared for risks changing over time when planning resource allocation, building infrastructure and ensuring that surveillance systems are able to detect changing patterns of disease. But more importantly, it may spatially identify the high-risk areas, which can be targeted for environmental hazards and public health prevention.

Climate sensitive health risk models and maps for three pilot districts namely; Bongo in the Upper East, Keta in the Volta and Gomoa West in the Central regions of Ghana have been

produced using disease-specific climate data sets, epidemiologic data as well as related human environmental data.

Capacity building activities involving the training of beneficiary district and regional health officers in health risk mapping were also undertaken and this would go a long way to improving our capacity to prepare and respond to climate-sensitive diseases and will also allow us to be better positioned to address the challenges that climate change will bring. Long-term climate projections will be increasingly important to reducing vulnerabilities and increasing resilience which will help populations cope with the health effects of climate change.

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5.2 Recommendations � Disease incidence data for risk modelling

should be disaggregated at the level of the community from which the patients hail.

� Climate data sets should be a lot more localised rather than the highly generalised data derived from the rather sparse meteorological network.

� Environmental data sets should be mapped a much larger scale than the 1:50,000 for sub-district representation at least 1:10,000.

� Community level data relating to non-environmental/confounding factors such as economic vulnerability, control activities like larviciding, ITN use, indoor spraying and personal/environmental hygiene will help improve the modelling results.

references Atherbolt, T.B., LeChevallier, M.W., Norton, W.D. and Rosen, J.S.(1998) Effect of rainfall on Giardia and Cryptosporidium. Journal of the American Water Works Association 90, 66–80

Briggs, D. (2000). Environmental health hazard mapping for Africa.

Campanella, N. (1999) Infectious diseases and natural disasters: the effects of Hurricane Mitch over Villanueva municipal area, Nicar-agua. Public Health Reviews 27, 311–319

Colwell, R.R. (1996) Global climate change and infectious disease: the cholera paradigm. Science 274, 2025–2031.

Croner, C.M., J.S.Sperling, and F.R.Broome. 1996. Geographic information systems (GIS): New

perspectives in understanding human health and environmental relationships. Statistics in Medicine15(17—18):1961—1977.

Greenland, S. and H.Morgenstern. 1989. Ecological bias, confounding, and effect modification.

International Journal of Epidemiology 18(1):269—274.

Guerrant RL, Kosek M, Moore S, Lorntz B, Brantley R, Lima AA. Magnitude and impact of diarrheal diseases. Arch Med Res 2002;33: 351-5

Hunter P.R. Climate change and water-borne and vector-borne diseases. Journal of Applied Microbiology 2003;94:37-46

Islam, M.S., Draser, B.S. and Bradley, D.J. (1990) Long persistence of toxigenic Vibrio cholerae O1 in the mucilage sheath of a blue-green alga, Anabaena variabilis. Journal of Tropical Medicine and Hygiene 93, 133–139.

J. P. Besancenot, M. Boko, P.C. Oke, 1997, weather conditions and cerebrospinal meningitis in Benin (Gulf of Guinea, West Africa). European Journal of Epidemiology 13: 807–815

McCarthy, M.C., He, J., Hyams, K.C., el-Tigani, A., Khalid, I.O. and Carl, M. (1994) Acute hepatitis E infection during the 1988 floods in Khartoum, Sudan. Transactions of the Royal Society of Tropical Medicine & Hygiene 88, 177

Miossec, L., Le Guyader, F., Haugarreau, L. and Pommepuy, M. (2000) Magnitude of rainfall on viral contamination of the marine environment during gastroenteritis epidemics in human coastal population. Revue d Epidemiologie et de Sante Publique 48(suppl 2), 2S62–2S71

Nuckols, J.R., J.K.Berry, and L.Stallones. 1994. Defining populations potentially exposed to chemical waste mixtures using computer-aided mapping analysis. In Toxicology of Chemical

Mixtures,R.S.H.Yang (Ed.), pp. 473—504. Orlando, FL: Academic Press.

Rosenstein, N. E., B. A. Perkins, D. S. Stephens, T. Popovic, and J. M. Hughes. 2001. Meningococcal Disease. New England Journal of Medicine 344:1378-1388.

Singh RBK, Hales S, Wet N, Rishi R, Hearnden M, Weinstein P. The Influence of Climate Variation and Change on Diarrheal Disease in the Pacific Islands. Environmental Health Perspectives 2001; 109:2

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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

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Policy BriefSeptember, 2012


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Executive SummaryThe 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 both communicable and non communicable diseases. Malaria, cerebrospinal meningitis (CSM) and diarrhea are known to be among the top climate sensitive communicable diseases in Ghana. They are known to be responsible for almost half the total disease burden.

Malaria is a vector borne disease. It is transmitted by the female anopheles mosquito. The life processes and activities of this vector are affected by climatic conditions. Temperature, rainfall and humidity are factors that affect the reproductive patterns of the mosquito hence affect the transmission of the disease. During the rainy season there is provision of ideal breeding grounds for the vector therefore malaria is more prevalent.

CSM is caused by bacteria. It is transmitted through respiratory and throat secretions. It is known to be prevalent in the Northern parts of Ghana. It is linked to the dry, hot and dusty climatic conditions that prevail there. The incidence reduces drastically with the onset of rain. Diarrhoea is a food borne disease common in areas with unhygienic sanitary conditions. During the rainy season,

incidence of diarrhea is high as compared to the dry season.

Public health interventions that address these diseases particularly through improved risk mapping as a case in point, are therefore viewed as very essential to realizing existing health goals.

As a nation, we do have strong systems to monitor and respond to situations which will occur as a result of climate change. Currently, there are no health risk maps for the climate sensitive diseases and there is limited capacity for developing risk maps at all levels of the health sector. These hinder the capacity to forecast risks and respond to those risks associated with climate change. There is therefore the need to provide a spatial representation of the risk and warning signs which would be regularly updated to enhance quick and efficient response to these climate sensitive diseases.

The aim of this assignment was to develop climate change health risk maps at sub – national scales (three pilot districts namely Keta, Gomoa West and Bongo) showing current and future severity and incidence of climate - sensitive diseases.

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MethodologyBongo 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: � Meningitis risk in the Bongo district was

high within the district capital where there was high population density and low in the rural and sparsely populated communities.

� Generally malaria risk was seen fairly distributed within the districts studied but was high within the densely populated communities. Diarrheal risk was also high along the coast.

� There were adequate information on the three diseases at regional and district level, however the data is not disaggregated at the lower levels such as the communities and this is a challenge.

� Climate datasets are not localized but are rather highly generalized data derived from the rather sparse meteorological network. There are no well documented data on climatic patterns and its impact on disease burdens in the districts.

� There is lack of spatial data on some non-climatic but important confounding factors such as ITN use indoor spraying as well as personal in addition to economic and physical vulnerabilities at the sub district level and community levels.

� There are no systems in place that detect high risk communities.

� Policy makers, administrators, health workers are not well trained and equipped to draw and efficiently analyze these risk maps even if it exits.

There is therefore a lack of clear policy direction and there is generally no response measures in place to handle surge of climate sensitive diseases in case there is an unexpected variation in the climatic condition.

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Key Policy options:Health risk mapping makes it possible for policy makers have a clearer understanding of disease risks based on the relevant factors and therefore enable them prepare for risks changing over time when planning, allocating resources, building infrastructure and ensuring that surveillance systems are able to detect changing patterns of disease.

� Morbidity and mortality data should be disaggregated at the community level (where the patients hail from) to enhance proper risk modeling.

� AdvantagesIt allows for determination of risk profile at the community level and this helps to target interventions.

� DisadvantagesCostly in terms of human resources and logistics.

� Climate data sets (rainfall, temperature, humidity etc) should be more localized rather than the highly generalized data derived from the rather sparse meteorological network to allow for sub-district or lower level analysis.

� AdvantagesThis allows for development of localized climate change health risk maps.

� DisadvantagesCostly in terms of human resources and logistics such as rain gauges and setting up climate stations.

� We should develop long term climate projections

� Advantages y It will help reduce vulnerabilities

and increase resilience which will y help populations cope with the

health effects of climate change. y It will improve our capacity to

prepare and respond to climate sensitive diseases. Risk mapping will allow us to be better

y positioned to address the challenges that climate change will bring.

It is therefore extremely important to design risk maps for climate sensitive diseases maps for high risk areas and the country as a whole.

Policy recommendations

GhanaHealthService � Morbidity and mortality data should be

disaggregated at the community level (where the patients hail from) to enhance proper risk modeling.

� Health professional should be trained in the interpretation and use of health risk maps

Ghana Meteorological Agency � To collect climate data sets at a

more localized rather than the highly generalized data derived from the rather sparse meteorological network to allow for sub-district or lower level analysis

Sources:1. Ghana Meteorological Agency data

2. GHS data, Ghana Health Service (GHS) facility data.

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REPUBLIC OF GHANAMINISTRY OF HEALTH


www.climatehealthghana.org

Documents

Climate Change Health Risk Mapping - UNDP · 2020-05-05 · Climate Change Health Risk Mapping 1 Sub-National Climate Risk Maps for Ghana Executive summary 3 1.0 Introduction 5 1.1