Estimating hospital catchments from in-patient admission records: a spatial statistical approach applied to malaria
Admission records are seldom used in sub-Saharan Africa to delineate hospital catchments for the spatial description of hospitalised disease events. We set out to investigate spatial hospital accessibility for severe malarial anaemia (SMA) and cerebral malaria (CM). Malaria admissions for children b...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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Springer Nature
2020
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| _version_ | 1797074836404568064 |
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| author | Alegana, VA Khazenzi, C Akech, SO Snow, RW |
| author_facet | Alegana, VA Khazenzi, C Akech, SO Snow, RW |
| author_sort | Alegana, VA |
| collection | OXFORD |
| description | Admission records are seldom used in sub-Saharan Africa to delineate hospital catchments for the spatial description of hospitalised disease events. We set out to investigate spatial hospital accessibility for severe malarial anaemia (SMA) and cerebral malaria (CM). Malaria admissions for children between 1 month and 14 years old were identified from prospective clinical surveillance data recorded routinely at four referral hospitals covering two complete years between December 2015 to November 2016 and November 2017 to October 2018. These were linked to census enumeration areas (EAs) with an age-structured population. A novel mathematical-statistical framework that included EAs with zero observations was used to predict hospital catchment for malaria admissions adjusting for spatial distance. From 5766 malaria admissions, 5486 (95.14%) were linked to specific EA address, of which 272 (5%) were classified as cerebral malaria while 1001 (10%) were severe malaria anaemia. Further, results suggest a marked geographic catchment of malaria admission around the four sentinel hospitals although the extent varied. The relative rate-ratio of hospitalisation was highest at <1-hour travel time for SMA and CM although this was lower outside the predicted hospital catchments. Delineation of catchments is important for planning emergency care delivery and in the use of hospital data to define epidemiological disease burdens. Further hospital and community-based studies on treatment-seeking pathways to hospitals for severe disease would improve our understanding of catchments. |
| first_indexed | 2024-03-06T23:41:59Z |
| format | Journal article |
| id | oxford-uuid:6fa03f2b-0eaa-45c9-bce1-627b2396345f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:41:59Z |
| publishDate | 2020 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:6fa03f2b-0eaa-45c9-bce1-627b2396345f2022-03-26T19:31:51ZEstimating hospital catchments from in-patient admission records: a spatial statistical approach applied to malariaJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:6fa03f2b-0eaa-45c9-bce1-627b2396345fEnglishSymplectic ElementsSpringer Nature2020Alegana, VAKhazenzi, CAkech, SOSnow, RWAdmission records are seldom used in sub-Saharan Africa to delineate hospital catchments for the spatial description of hospitalised disease events. We set out to investigate spatial hospital accessibility for severe malarial anaemia (SMA) and cerebral malaria (CM). Malaria admissions for children between 1 month and 14 years old were identified from prospective clinical surveillance data recorded routinely at four referral hospitals covering two complete years between December 2015 to November 2016 and November 2017 to October 2018. These were linked to census enumeration areas (EAs) with an age-structured population. A novel mathematical-statistical framework that included EAs with zero observations was used to predict hospital catchment for malaria admissions adjusting for spatial distance. From 5766 malaria admissions, 5486 (95.14%) were linked to specific EA address, of which 272 (5%) were classified as cerebral malaria while 1001 (10%) were severe malaria anaemia. Further, results suggest a marked geographic catchment of malaria admission around the four sentinel hospitals although the extent varied. The relative rate-ratio of hospitalisation was highest at <1-hour travel time for SMA and CM although this was lower outside the predicted hospital catchments. Delineation of catchments is important for planning emergency care delivery and in the use of hospital data to define epidemiological disease burdens. Further hospital and community-based studies on treatment-seeking pathways to hospitals for severe disease would improve our understanding of catchments. |
| spellingShingle | Alegana, VA Khazenzi, C Akech, SO Snow, RW Estimating hospital catchments from in-patient admission records: a spatial statistical approach applied to malaria |
| title | Estimating hospital catchments from in-patient admission records: a spatial statistical approach applied to malaria |
| title_full | Estimating hospital catchments from in-patient admission records: a spatial statistical approach applied to malaria |
| title_fullStr | Estimating hospital catchments from in-patient admission records: a spatial statistical approach applied to malaria |
| title_full_unstemmed | Estimating hospital catchments from in-patient admission records: a spatial statistical approach applied to malaria |
| title_short | Estimating hospital catchments from in-patient admission records: a spatial statistical approach applied to malaria |
| title_sort | estimating hospital catchments from in patient admission records a spatial statistical approach applied to malaria |
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