This study develops an integrated innovation for malaria early warning systems (MEWS), based on vulnerability monitoring, seasonal climate variability data, and epidemiologic surveillance. The main aim of the study is to examine the relationship between intra-annual climate variability and malaria transmission in Nigeria. For this study, climatic conditions considered suitable for the development of the malaria parasite and its transmission through the mosquito stage of its life cycle are temperatures within the range from 18°C to 32°C. Below 18°C the parasite development decreases significantly, while above 32°C the survival of the mosquito is compromised. Relative humidity greater than 60% is also considered a requirement for the mosquito to survive long enough for the parasite to develop sufficiently to be transmitted to its human host stage. The research findings show that seasonality of climate greatly influences the seasonality of malaria transmission. Specifically, rainfall plays an important role in the distribution and maintenance of breeding sites for the mosquito vector. Rainfall and surface water is required for the egg laying and larval stages of the mosquito life cycle and monthly rainfall above 80 mm is considered a requirement. Also, it is temperature that regulates the development rate of both the mosquito larvae and the malaria parasite (Plasmodium species) within the mosquito host. Relative humidity and temperature play an important role in the survival and longevity of the mosquito vector. This study is in conformity with the findings of the IPCC (2001) that malaria is caused by four distinct species of the Plasmodium parasite, transmitted by mosquitoes of the genus Anopheles, which are most abundant in tropical/subtropical regions, although they are also found in limited numbers in temperate climates.
Abstract. This paper analysed the factors responsible for the re-emergence of cholera and predicted the future occurrence of Cholera in Lagos State, Nigeria using factor analysis, multiple linear regression analysis and a cellular automata model for the prediction. The study revealed six Local Government Areas (LGAs) under very high threat, nine under low threat, and Surulere and some parts of Amuwo Odofin under medium threat in the near future. These areas have an average population of 200,000 people each with the total tending towards millions of people, all under threat of cholera occurring and re-emerging in their communities. The factors relating to the re-emergence of the disease were discovered to be environmental (rainfall, R
This study is about the distribution of health care facilities in Kogi State within the context of the geography and politics of the state. Hence, the study analyses the spatial patterns of health care facilities among the three senatorial districts (which corresponds to the division along major ethnic lines) in the state. Also, the ownership structure of facilities and the relationship between population and distribution of health care facilities in the state are analysed. The list of health care facilities and ownership in Nigeria obtained from the Department of Health Planning and Research, Federal Ministry of Health served as database for the analysis of the spatial patterns of distribution and ownership of health care facilities in Kogi State. Also, the National Population Commission's census figures provided information on the population of the State. Kogi State was stratified into the three existing senatorial districts-Kogi Central, Kogi East, and Kogi West. The total number of health care facilities and their ownership in each stratum were determined and used for the analyses. Data show that there exist inequalities in the distribution of HCFs among the various senatorial districts in the state. Kogi east senatorial district recorded the highest concentration of HCFs having 66.3% of all HCFs in the state, followed by Kogi west (19.6%) and Kogi central (14.1%). It is observed that the facility-population ratios for both PHC and SHC (1:2575 and 1:29024 respectively) are high. These proportions vary among the various senatorial districts; for example PHC-population ratios were 1:6850, 1:2746 and 1:1689 for Kogi central, west and east respectively; the ratios for SHC were 1:41,859, 1:27804 and 1:23736 for Kogi central, west and east respectively. Although, the government dominates the ownership of health care facilities in the state, her impact is heaviest in Kogi east where she owns 93% of HCFs as opposed to 70% in Kogi central where the impact is least. Kogi east which has produced the civilian Chief Executives of the state since its creation in 1991 ranks far ahead of the two other districts in the distribution of HCFs. Appropriate authorities should endeavour to achieve a more equitable distribution of health care facilities in the state, so as to engender equity and social justice.
This study analysed the spatial patterns and characteristics of healthcare facilities and HIV/AIDS response sites; and the relationship between the distribution of population and healthcare facilities/HIV/AIDS response sites in Benue State, the State with the highest record of HIV/AIDS in Nigeria. Primary and secondary data were used for the study. GPS receiver was used to obtain the geographic coordinates of healthcare facilities and HIV/AIDS response sites; and questionnaire to acquire attribute data of the sites. The secondary data used included the list of all healthcare facilities at community and LGA levels, maps, and the population of the state. The spatial analyses of the phenomena of interest were done based on the LGAs. All the 1243 healthcare facilities in the 23 LGAs of the state were captured in the study. Four key HIV/AIDS services (VCT, PMTCT, ART and HBC) were purposively selected for the study. The analogue map of Benue State was processed and used for various GIS analyses and cartographic enhancement for the purpose of report presentation. The study identified three categories of Healthcare Facilities (primary, secondary and tertiary) in the state. There existed spatial variation in the distribution of the various healthcare facilities in the state. The PHCs were observed to be more widely distributed in the state (93.4%) than the SHCs (6.3%) and THCs (0.2%) which were observed to be largely concentrated in the urban LGAs. Also, specialised HIV/AIDS services like PMTCT and ART were observed to be concentrated in the urban LGAs. The population/Facility ratio for PHCF, SHCF and THCF were 2,371:1; 34,413:1; and 1,376,539:1 respectively. There existed a direct relationship between both population and distribution of healthcare facilities (r = 0.694, p > 0.5); and population and the distribution of HIV/AIDS response sites (r = 0.664, p > 0.5) in the state. The study concluded that the problem of HIV/AIDS in Benue State is more engendered by the paucity of information about the availability of response sites than their inadequacy; and recommends that a robust database for healthcare facilities and HIV/AIDS response sites be developed at all levels in order to enhance information flow to policy formulators and by extension people who require healthcare and HIV/AIDS services.
The study examined the distribution patterns and developed a model for determining the optimum location of healthcare facilities in Osun State, Nigeria. These were with a view to improving the spatial distribution of and equitable access to healthcare facilities in the State. Primary and secondary data were used in the study. The primary data comprised the geographic coordinates of all the healthcare facilities in the State, while the secondary data included the list of all the healthcare facilities in the State. The cartographic model for determining the optimum location of healthcare facilities was developed. The data were analysed using percentage and Geographical Information System (GIS) analysis tools such as nearest neigbour ratio (NNR), buffering, overlay and query. The study identified 919 healthcare facilities of four categories, namely, primary (603, 65.6%), private (262, 28.5%), secondary (51, 5.6%) and tertiary (3, 0.3%) in the State. Primary and private healthcare facilities depicted clustered patterns of distribution; while secondary and tertiary depicted random distribution patterns. Using the secondary healthcare facilities as a case study, the developed cartographic method revealed both the optimum number and locations of additional facilities required, at 10km buffer distance to meet the set standards. The study identified that additional seven secondary healthcare facilities are required in four of the 30 LGAs of the State. The model also holds for primary, private and tertiary healthcare facilities by simply varying the buffer distance at 5km, 5km and 20km respectively. The study concluded that there were inequalities in the spatial distribution of healthcare facilities in the study area.
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