Spatial distribution of malaria in Warri metropolis

Efe, S. I.; Ojoh, C. O.

doi:10.4236/ojepi.2013.33018

Cited by 11 publications

(7 citation statements)

References 23 publications

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“…The climate data which contains the mean annual temperatures and rainfall amount were used. Where there are missing data, extrapolation and interpolation methods adopted by [6] [19] [20] was employed. Statistical diagram, polynomial and linear trend analysis and multiple regression analysis were used for the study.…”

Section: Methods Of Data Collectionmentioning

confidence: 99%

Climate and Epidemiology of Malaria in Port Harcourt Region, Nigeria

Weli¹,

Efe²

2015

AJCC

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The study examines the effect of climate on the occurrence of malaria in Port Harcourt. To achieve this, the study adopted the survey designs which involved the use of archival data of climate parameters and the survey of hospitals for epidemiological data of malaria cases reports within Port Harcourt and environs. The archival weather data on temperature and rainfall were collected from the Nigeria Meteorological Service Agency (NIMET) for a period of sixty-five (65) years (1950-2014), and those for medical records of malaria cases were extracted from the University of Port Harcourt Teaching Hospitals (UPTH), Braithwaite Memorial Hospital (BMH) and some renowned private hospitals in Port Harcourt and environs for a period of sixty-five (65) years. The multiple regression analysis was used to determine the relative effect of rainfall and temperature on malaria incidence. The polynomial and linear trend analysis was used for the trend pattern of malaria cases, rainfall and temperature. The results revealed an annual rainfall of 2375 mm, with an increase of 1581 mm of rainfall from 1950 till date. It also showed an urban warming of 3˚C from 1950 to 2014, and a mean of 27.2˚C in temperature in Port Harcourt. The results of the multiple regression reveal that the prevalence of malaria is significantly dependent on the increase in rainfall and temperature in Port Harcourt. There are double maxima of malaria cases with 1006 cases in July and 1540 cases in September, which coincide with the months with double rain maxima. On this premise and health care planning and development, it is recommended that regular clearing of drains and the surrounding environment on monthly basis should be intensified, as well as the distribution of mosquito's insecticide net by the three tier of government.

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Section: Methods Of Data Collectionmentioning

confidence: 99%

Climate and Epidemiology of Malaria in Port Harcourt Region, Nigeria

Weli¹,

Efe²

2015

AJCC

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“…A challenge in studying malaria risk in Nigeria is the heterogeneity of the prevalence, which is attributed to high variability in climate conditions as well as the landscape [2,16]. Few published studies in Nigeria have linked malaria prevalence to several influencing factors, including climate and environmental conditions [2,10,11,13,15,[17][18][19][20], socioeconomic factors [15,[21][22][23], geographical factors [10,[13][14][15]18,24], and control strategies as well as prevalence of other febrile illnesses [22,[25][26][27]. Additionally, several authors in other malaria endemic countries have investigated the correlation between malaria and important meteorological variables as observed in Venezuela [6][7][8][9], in Zimbabwe [28], in Zambia [29,30], in Côte D'Ivoire [31], in Ghana [32], in Burundi [33], in Ethiopia [34,35] and many more.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Effects of Climate and Environmental Factors on Under-5 Children Malaria Spatial Distribution Using Generalized Additive Models (GAMs)

Ugwu¹,

Zewotir²

2020

JEGH

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Although malaria burden has declined globally following scale up of intervention, the disease has remained a leading cause of hospitalization and deaths among children aged under-5 years in Nigeria. Malaria is known to be related to climate and environmental conditions. Previous research has usually studied the effects of these factors, neglecting possible correlation between them, high correlation among variables is a source of multicollinearity that induces overfitting in regression modelling. In this paper, a factor analysis was first introduced to circumvent the issue of multicollinearity and a Generalized Additive Model (GAM) was subsequently explored to identify the important risk factors that might influence the prevalence of childhood malaria in Nigeria. The GAM incorporated the complexity of the survey data, while simultaneously modelling the nonlinear and spatial random effects to allow a more precise identification of the major malaria risk factors that influence the geographical distribution of the disease. From our findings, the three latent factor components (constituted by humidity, precipitation, potential evapotranspiration, and wet days/maximum and minimum temperature/proximity to permanent waters, respectively) were significantly associated with malaria prevalence. Our analysis also detected statistically significant and nonlinear effect of altitude: the risk of malaria increased with lower values but declined sharply with higher values. A significant spatial variability in under-5 malaria prevalence across the survey clusters was also observed; malaria burden was higher in the northern part of Nigeria. Investigating the impact of important risk factors and geographical location on childhood malaria is of high relevance for the sustainable development goals (SDGs) 2015-2030 Agenda on malaria eradication, and we believe that the information obtained from this study and the generated risk maps can be useful to effectively target intervention efforts to high-risk areas based on climate and environmental context.

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“…Warri metropolis is one of the rapidly growing cities in Nigeria, with a population rising rapidly from 19,526 in 1933, 55,256 in 1963, 280,000 in 1980, 500,000 in 1991 to 536,023 in 2006 [5]. The population growth will lead to an increase in the amount of solid waste generated which will potentially escalate Methane emission from solid waste and further leads to urban warming…”

Section: Study Areamentioning

confidence: 99%

“…Warri has grown to cover the surrounding towns of Effurun, Ekpan, Enerhen, Edjeba, Ogunu, Jakpa, Ovwian-Aladja, Udu Road, etc. with an areal expansion of over 100 sqr.km [5]. This areal expansion has led to increase in temperature and precipitation in the area [6].…”

Section: Study Areamentioning

confidence: 99%

Effects of Solid Waste on Urban Warming in Warri Metropolis, Nigeria

Efe¹,

Cheke²,

Ojoh³

2013

ACS

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This study examined the effects of solid waste on urban warming in Warri metropolis. To achieve this, the study generated data from field measurements of volume of waste generated and greenhouse gases. The data were collected from five (5) major waste dump sites in Warri metropolis. The data generated were analysed using bivariate correlation and multiple regression. Results obtained revealed that 25.1% of waste generated in Warri metropolis was found in the commercial areas of the metropolis while 4.3% of the waste generated was recorded in the low density Areas, 19.7% of the waste generated was found in the high density area (Okumagba, Ugborikoko, Enerhen village, Airport road and upper/lower Eredjuwa) and the remaining 51.9% was generated in the industrial, traditional and fringe zones of Warri metropolis. Warri metropolis has an urban bias of 4.16˚C. There is a significant relationship between temperature and volume of waste generated at R value of 0.722. The temperature in Warri metropolis is significantly dependent on greenhouse gases emitted at P < 0.05. It is therefore, recommended that programmes to sensitize and sustain the waste management concepts among communities through media be established.

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Spatial distribution of malaria in Warri metropolis

Cited by 11 publications

References 23 publications

Climate and Epidemiology of Malaria in Port Harcourt Region, Nigeria

Climate and Epidemiology of Malaria in Port Harcourt Region, Nigeria

Evaluating the Effects of Climate and Environmental Factors on Under-5 Children Malaria Spatial Distribution Using Generalized Additive Models (GAMs)

Effects of Solid Waste on Urban Warming in Warri Metropolis, Nigeria

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