Mapping malaria risk using geographic information systems and remote sensing: The case of Bahir Dar City, Ethiopia

Minale, Amare Sewnet; Alemu, Kalkidan

doi:10.4081/gh.2018.660

Cited by 12 publications

(10 citation statements)

References 18 publications

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“…The malaria cases were clustered along the foothills and increased during the rainy season in these townships. The higher rainfall, temperature and elevation are known to be associated with malaria [23]. However, in the current study, cases were clustered along the foothills compared to elevated areas.…”

Section: Discussioncontrasting

confidence: 60%

Malaria hot spot along the foothills of Rakhine state, Myanmar: geospatial distribution of malaria cases in townships targeted for malaria elimination

Khine

Kyaw²,

Thekkur

et al. 2019

Trop Med Health

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BackgroundMyanmar has targeted elimination of malaria by 2030. In three targeted townships of Rakhine state of Myanmar, a project is being piloted to eliminate malaria by 2025. The comprehensive case investigation (CCI) and geotagging of cases by health workers is a core activity under the project. However, the CCI data is not analyzed for obtaining information on geospatial distribution of cases and timeliness of diagnosis. In this regard, we aimed to depict geospatial distribution and assess the proportion with delayed diagnosis among diagnosed malaria cases residing in three targeted townships during April 2018 to March 2019.MethodsThis was a cross sectional analysis of CCI data routinely collected by national malaria control programme. The geocode (latitude and longitude) of the address was analysed using Quantum Geographic Information System software to deduce spot maps and hotspots of cases. The EpiData analysis software was used to summarize the proportion with delay in diagnosis (diagnosed ≥24 hours after the fever onset).ResultsOf the 171 malaria cases diagnosed during study period, the CCI was conducted in 157 (92%) cases. Of them, 127 (81%) cases reported delay in diagnosis, 138 (88%) cases were indigenous who got infection within the township and 13 (8%) were imported from outside the township. Malaria hotspots were found along the foothills with increase in cases during the rainy season. The indigenous cases were concentrated over the foothills in the northern and southern borders of Toungup township.ConclusionIn the targeted townships for malaria elimination, the high proportion of the cases was indigenous and clustered at the foothill areas during rainy season. The programme should strengthen case surveillance and healthcare services in the areas with aggregation of cases to eliminate the malaria in the township. As high majority of patients have delayed diagnosis, the reasons for delay has to be explored and corrective measures needs to be taken.

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Section: Discussioncontrasting

confidence: 60%

Malaria hot spot along the foothills of Rakhine state, Myanmar: geospatial distribution of malaria cases in townships targeted for malaria elimination

Khine

Kyaw²,

Thekkur

et al. 2019

Trop Med Health

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“…An even more recent study conducted by Minale and Alemu (2018), focuses on creating a malaria risk map for Bahir Dar City, Amhara Province in northeast Ethiopia. Their risk map was derived from "temperature, rainfall, terrain (altitude and slope), proximity-related factors (distance from lake, river, and health facilities) and LULC" (Minale and Alemu, 2018, 159).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Scholarly research has documented that mosquito breeding is significantly affected by elevation. In Sub-Saharan Africa, research documents limited mosquito breeding occurs in elevations greater than 1500 meters and low to no mosquito breeding occurs at elevation greater than 2500 meters (Minale and Alemu, 2018). The study area elevations were acquired directly from the SRTM DEM.…”

Section: ° -28° 41%mentioning

confidence: 99%

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Response of Malaria Hazard and Risk to a Hydroelectric Dam in Ethiopia Using GIS and Remote Sensing

Smith¹,

Sim²

2020

Preprint

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Malaria is a disease spread by female mosquitos of the Anopheles genus. It is acutely prevalent in Sub-Saharan Africa, where 90% of malaria deaths occur annually. One Sub-Saharan African country historically impacted by malaria is Ethiopia. In the past twenty years, malaria prevalence has decreased throughout Sub-Saharan Africa; yet, anthropogenic environmental changes are changing the landscape of malaria. Scholarly literature has cited a positive relationship between hydroelectric dams and malaria in Sub-Saharan Africa. Ethiopia is currently expanding their hydroelectric infrastructure. The Gilgel Gibe III Dam is located on the Omo River in Southwestern Ethiopia. It began generating electricity in 2015 and its reservoir has a capacity of 14,700 million m3 of water. This research utilized Geographic Information Systems and Remote Sensing to identify populations at an increased risk of malaria due to Gilgel Gibe III Dam. Two different techniques were employed: the proximity approach and the remote sensing approach. The proximity approach was based on distance from the reservoir. It identified all populations living within three kilometers of the reservoir as being at an increased risk. The remote sensing approach evaluated the slope, elevation, water content, and land surface temperature of the study area to create a mosquito breeding habitat risk map. Then, populations living within three kilometers of the two main High-Risk areas were identified. This study suggests that mosquito breeding habitat risk is not equally distributed throughout the Gilgel Gibe III Reservoir. This causes certain populations to be at a heightened risk of malaria.

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“…Identification of specific epidemiological patterns helps to implement preventive strategies, which are of special importance in cities where air pollution has a direct impact on RSDs [10,13]. Geographical information systems (GIS) makes it possible to identify spatiotemporal relationships between disease and mortality by combining spatial and temporal data [32], and a few epidemiological studies related to RSDs have been carried out in Iran [33][34][35]. For example, Sharifi et al [36] studied risk factors for COPD applying a stratified cluster sampling method in five provinces of Iran, including Tehran.…”

Section: Introductionmentioning

confidence: 99%

Mortality rates due to respiratory tract diseases in Tehran, Iran during 2008–2018: a spatiotemporal, cross-sectional study

et al. 2020

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Background Tehran, the 22nd most populous city in the world, has the highest mortality rate due to respiratory system diseases (RSDs) in Iran. This study aimed to investigate spatiotemporal patterns of mortality due to these diseases in Tehran between 2008 and 2018. Methods We used a dataset available from Tehran Municipality including all cases deceased due RSDs in this city between 2008 and 2018. Global Moran’s I was performed to test whether the age-adjusted mortality rates were randomly distributed or had a spatial pattern. Furthermore, Anselin Local Moran’s I was conducted to identify potential clusters and outliers. Results During the 10-year study, 519,312 people died in Tehran, 43,177 because of RSDs, which corresponds to 831.1 per 10,000 deaths and 5.0 per 10,000 population. The death rate was much higher in men (56.8%) than in women (43.2%) and the highest occurred in the > 65 age group (71.2%). Overall, three diseases dominated the mortality data: respiratory failure (44.2%), pneumonia (15.9%) and lung cancer (10.2%). The rates were significantly higher in the central and southeastern parts of the city and lower in the western areas. It increased during the period 2008–2018 and showed a clustered spatial pattern between 2008 and 2013 but presented a random geographical pattern afterwards. Conclusions This study provides a first report of the spatial distribution of mortality due to RSDs in Tehran and shows a significant increase in respiratory disease mortality in the last ten years. Effective control of the excess fatality rates would warrant a combination of urban prevention and treatment strategies including environmental health plans.

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Mapping malaria risk using geographic information systems and remote sensing: The case of Bahir Dar City, Ethiopia

Cited by 12 publications

References 18 publications

Malaria hot spot along the foothills of Rakhine state, Myanmar: geospatial distribution of malaria cases in townships targeted for malaria elimination

Malaria hot spot along the foothills of Rakhine state, Myanmar: geospatial distribution of malaria cases in townships targeted for malaria elimination

Response of Malaria Hazard and Risk to a Hydroelectric Dam in Ethiopia Using GIS and Remote Sensing

Mortality rates due to respiratory tract diseases in Tehran, Iran during 2008–2018: a spatiotemporal, cross-sectional study

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