1
|
Thawer SG, Golumbeanu M, Lazaro S, Chacky F, Munisi K, Aaron S, Molteni F, Lengeler C, Pothin E, Snow RW, Alegana VA. Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania. Sci Rep 2023; 13:10600. [PMID: 37391538 PMCID: PMC10313820 DOI: 10.1038/s41598-023-37669-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 06/26/2023] [Indexed: 07/02/2023] Open
Abstract
As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017-2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation.
Collapse
Affiliation(s)
- Sumaiyya G Thawer
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland.
- University of Basel, Basel, Switzerland.
| | - Monica Golumbeanu
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Samwel Lazaro
- Ministry of Health, Dodoma, Tanzania
- National Malaria Control Programme, Dodoma, Tanzania
| | - Frank Chacky
- Ministry of Health, Dodoma, Tanzania
- National Malaria Control Programme, Dodoma, Tanzania
| | - Khalifa Munisi
- Ministry of Health, Dodoma, Tanzania
- National Malaria Control Programme, Dodoma, Tanzania
| | - Sijenunu Aaron
- Ministry of Health, Dodoma, Tanzania
- National Malaria Control Programme, Dodoma, Tanzania
| | - Fabrizio Molteni
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- National Malaria Control Programme, Dodoma, Tanzania
| | - Christian Lengeler
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Emilie Pothin
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
- Clinton Health Access Initiative, New York, USA
| | - Robert W Snow
- Population Health Unit, KEMRI-Welcome Trust Research Programme, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Victor A Alegana
- World Health Organization, Regional Office for Africa, Brazzaville, Republic of Congo
| |
Collapse
|
2
|
Kamau A, Musau M, Mtanje G, Mataza C, Bejon P, Snow RW. OUP accepted manuscript. Trans R Soc Trop Med Hyg 2022; 116:966-970. [PMID: 35415749 PMCID: PMC9526839 DOI: 10.1093/trstmh/trac029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 01/25/2022] [Accepted: 03/18/2022] [Indexed: 11/14/2022] Open
Affiliation(s)
- Alice Kamau
- Corresponding author: Tel: +254-722 203417; E-mail:
| | - Moses Musau
- KEMRI-Wellcome Trust Research Programme, P.O. Box 43640-00100, Nairobi, Kenya
| | - Grace Mtanje
- KEMRI-Wellcome Trust Research Programme, P.O. Box 43640-00100, Nairobi, Kenya
| | - Christine Mataza
- KEMRI-Wellcome Trust Research Programme, P.O. Box 43640-00100, Nairobi, Kenya
- Ministry of Health, Kilifi County Government, P.O. Box 519-80108, Kilifi, Kenya
| | - Philip Bejon
- KEMRI-Wellcome Trust Research Programme, P.O. Box 43640-00100, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, New Richards Building, Old Road Campus, Roosevelt Drive, OX3 7LG, Oxford, UK
| | - Robert W Snow
- KEMRI-Wellcome Trust Research Programme, P.O. Box 43640-00100, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, New Richards Building, Old Road Campus, Roosevelt Drive, OX3 7LG, Oxford, UK
| |
Collapse
|
3
|
Alegana VA, Macharia PM, Muchiri S, Mumo E, Oyugi E, Kamau A, Chacky F, Thawer S, Molteni F, Rutazanna D, Maiteki-Sebuguzi C, Gonahasa S, Noor AM, Snow RW. Plasmodium falciparum parasite prevalence in East Africa: Updating data for malaria stratification. PLOS GLOBAL PUBLIC HEALTH 2021; 1:e0000014. [PMID: 35211700 PMCID: PMC7612417 DOI: 10.1371/journal.pgph.0000014] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 11/15/2021] [Indexed: 11/18/2022]
Abstract
The High Burden High Impact (HBHI) strategy for malaria encourages countries to use multiple sources of available data to define the sub-national vulnerabilities to malaria risk, including parasite prevalence. Here, a modelled estimate of Plasmodium falciparum from an updated assembly of community parasite survey data in Kenya, mainland Tanzania, and Uganda is presented and used to provide a more contemporary understanding of the sub-national malaria prevalence stratification across the sub-region for 2019. Malaria prevalence data from surveys undertaken between January 2010 and June 2020 were assembled form each of the three countries. Bayesian spatiotemporal model-based approaches were used to interpolate space-time data at fine spatial resolution adjusting for population, environmental and ecological covariates across the three countries. A total of 18,940 time-space age-standardised and microscopy-converted surveys were assembled of which 14,170 (74.8%) were identified after 2017. The estimated national population-adjusted posterior mean parasite prevalence was 4.7% (95% Bayesian Credible Interval 2.6-36.9) in Kenya, 10.6% (3.4-39.2) in mainland Tanzania, and 9.5% (4.0-48.3) in Uganda. In 2019, more than 12.7 million people resided in communities where parasite prevalence was predicted ≥ 30%, including 6.4%, 12.1% and 6.3% of Kenya, mainland Tanzania and Uganda populations, respectively. Conversely, areas that supported very low parasite prevalence (<1%) were inhabited by approximately 46.2 million people across the sub-region, or 52.2%, 26.7% and 10.4% of Kenya, mainland Tanzania and Uganda populations, respectively. In conclusion, parasite prevalence represents one of several data metrics for disease stratification at national and sub-national levels. To increase the use of this metric for decision making, there is a need to integrate other data layers on mortality related to malaria, malaria vector composition, insecticide resistance and bionomic, malaria care-seeking behaviour and current levels of unmet need of malaria interventions.
Collapse
Affiliation(s)
- Victor A. Alegana
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
- Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
| | - Peter M. Macharia
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
- Centre for Health Informatics, Computing, and Statistics, Lancaster Medical School, Lancaster University, Lancaster, United Kingdom
| | - Samuel Muchiri
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Eda Mumo
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Elvis Oyugi
- Division of National Malaria Programme, Ministry of Health, Nairobi, Kenya
| | - Alice Kamau
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Frank Chacky
- National Malaria Control Programme, Ministry of Health, Community Development, Gender, Elderly and Children, Dodoma, Tanzania
| | - Sumaiyya Thawer
- National Malaria Control Programme, Ministry of Health, Community Development, Gender, Elderly and Children, Dodoma, Tanzania
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Fabrizio Molteni
- National Malaria Control Programme, Ministry of Health, Community Development, Gender, Elderly and Children, Dodoma, Tanzania
- Swiss Tropical and Public Health Institute, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Damian Rutazanna
- National Malaria Control Division, Ministry of Health, Kampala, Uganda
| | - Catherine Maiteki-Sebuguzi
- National Malaria Control Division, Ministry of Health, Kampala, Uganda
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | | | - Abdisalan M. Noor
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Robert W. Snow
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
4
|
King M, George AE, Cisteró P, Tarr-Attia CK, Arregui B, Omeonga S, Chen H, Meyer García-Sípido A, Sarukhan A, Bassat Q, Lansana DP, Mayor A. No evidence of false-negative Plasmodium falciparum rapid diagnostic results in Monrovia, Liberia. Malar J 2021; 20:238. [PMID: 34039355 PMCID: PMC8157453 DOI: 10.1186/s12936-021-03774-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 05/16/2021] [Indexed: 11/25/2022] Open
Abstract
Background Malaria diagnosis in many malaria-endemic countries relies mainly on the use of rapid diagnostic tests (RDTs). The majority of commercial RDTs used in Africa detect the Plasmodium falciparum histidine-rich protein 2 (PfHRP2). pfhrp2/3 gene deletions can therefore lead to false-negative RDT results. This study aimed to evaluate the frequency of PCR-confirmed, false-negative P. falciparum RDT results in Monrovia, Liberia. Methods PfHRP2-based RDT (Paracheck Pf®) and microscopy results from 1038 individuals with fever or history of fever (n = 951) and pregnant women at first antenatal care (ANC) visit (n = 87) enrolled in the Saint Joseph’s Catholic Hospital (Monrovia) from March to July 2019 were used to assess the frequency of false-negative RDT results. True–false negatives were confirmed by detecting the presence of P. falciparum DNA by quantitative PCR in samples from individuals with discrepant RDT and microscopy results. Samples that were positive by 18S rRNA qPCR but negative by PfHRP2-RDT were subjected to multiplex qPCR assay for detection of pfhrp2 and pfhrp3. Results One-hundred and eighty-six (19.6%) and 200 (21.0%) of the 951 febrile participants had a P. falciparum-positive result by RDT and microscopy, respectively. Positivity rate increased with age and the reporting of joint pain, chills and shivers, vomiting and weakness, and decreased with the presence of coughs and nausea. The positivity rate at first ANC visit was 5.7% (n = 5) and 8% (n = 7) by RDT and microscopy, respectively. Out of 207 Plasmodium infections detected by microscopy, 22 (11%) were negative by RDT. qPCR confirmed absence of P. falciparum DNA in the 16 RDT-negative but microscopy-positive samples which were available for molecular testing. Among the 14 samples that were positive by qPCR but negative by RDT and microscopy, 3 only amplified pfldh, and among these 3 all were positive for pfhrp2 and pfhrp3. Conclusion There is no qPCR-confirmed evidence of false-negative RDT results due to pfhrp2/pfhrp3 deletions in this study conducted in Monrovia (Liberia). This indicates that these deletions are not expected to affect the performance of PfHRP2-based RDTs for the diagnosis of malaria in Liberia. Nevertheless, active surveillance for the emergence of PfHRP2 deletions is required.
Collapse
Affiliation(s)
- Mandella King
- Saint Joseph's Catholic Hospital, Tubman Boulevard, Oldest Congo, Town, PO Box 10512, 1100, Monrovia, Liberia
| | - Alexander E George
- Liberia Medicines & Health Products Regulatory Authority, VP Road, Old Road, Sinkor , PO Box 1994, Monrovia, Liberia
| | - Pau Cisteró
- ISGlobal, Barcelona Institute for Global Health, Hospital Clínic - Universitat de Barcelona, Carrer Rosselló 153 (CEK Building), 08036, Barcelona, Spain
| | - Christine K Tarr-Attia
- Saint Joseph's Catholic Hospital, Tubman Boulevard, Oldest Congo, Town, PO Box 10512, 1100, Monrovia, Liberia
| | - Beatriz Arregui
- ISGlobal, Barcelona Institute for Global Health, Hospital Clínic - Universitat de Barcelona, Carrer Rosselló 153 (CEK Building), 08036, Barcelona, Spain
| | - Senga Omeonga
- Saint Joseph's Catholic Hospital, Tubman Boulevard, Oldest Congo, Town, PO Box 10512, 1100, Monrovia, Liberia
| | - Haily Chen
- ISGlobal, Barcelona Institute for Global Health, Hospital Clínic - Universitat de Barcelona, Carrer Rosselló 153 (CEK Building), 08036, Barcelona, Spain
| | | | - Adelaida Sarukhan
- ISGlobal, Barcelona Institute for Global Health, Hospital Clínic - Universitat de Barcelona, Carrer Rosselló 153 (CEK Building), 08036, Barcelona, Spain
| | - Quique Bassat
- ISGlobal, Barcelona Institute for Global Health, Hospital Clínic - Universitat de Barcelona, Carrer Rosselló 153 (CEK Building), 08036, Barcelona, Spain.,Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique.,Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.,ICREA, Pg. Lluís Companys 23, 08010, Barcelona, Spain.,Pediatrics Department, Hospital Sant Joan de Déu, Universitat de Barcelona, Esplugues,, Barcelona, Spain
| | - Dawoh Peter Lansana
- Saint Joseph's Catholic Hospital, Tubman Boulevard, Oldest Congo, Town, PO Box 10512, 1100, Monrovia, Liberia
| | - Alfredo Mayor
- ISGlobal, Barcelona Institute for Global Health, Hospital Clínic - Universitat de Barcelona, Carrer Rosselló 153 (CEK Building), 08036, Barcelona, Spain. .,Centro de Investigação em Saúde de Manhiça (CISM), Maputo, Mozambique. .,Consorcio de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
| |
Collapse
|
5
|
Kamau A, Mtanje G, Mataza C, Bejon P, Snow RW. Spatial-temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast. Malar J 2021; 20:227. [PMID: 34016100 PMCID: PMC8138976 DOI: 10.1186/s12936-021-03758-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/09/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The over-distributed pattern of malaria transmission has led to attempts to define malaria "hotspots" that could be targeted for purposes of malaria control in Africa. However, few studies have investigated the use of routine health facility data in the more stable, endemic areas of Africa as a low-cost strategy to identify hotspots. Here the objective was to explore the spatial and temporal dynamics of fever positive rapid diagnostic test (RDT) malaria cases routinely collected along the Kenyan Coast. METHODS Data on fever positive RDT cases between March 2018 and February 2019 were obtained from patients presenting to six out-patients health-facilities in a rural area of Kilifi County on the Kenyan Coast. To quantify spatial clustering, homestead level geocoded addresses were used as well as aggregated homesteads level data at enumeration zone. Data were sub-divided into quarterly intervals. Kulldorff's spatial scan statistics using Bernoulli probability model was used to detect hotspots of fever positive RDTs across all ages, where cases were febrile individuals with a positive test and controls were individuals with a negative test. RESULTS Across 12 months of surveillance, there were nine significant clusters that were identified using the spatial scan statistics among RDT positive fevers. These clusters included 52% of all fever positive RDT cases detected in 29% of the geocoded homesteads in the study area. When the resolution of the data was aggregated at enumeration zone (village) level the hotspots identified were located in the same areas. Only two of the nine hotspots were temporally stable accounting for 2.7% of the homesteads and included 10.8% of all fever positive RDT cases detected. CONCLUSION Taking together the temporal instability of spatial hotspots and the relatively modest fraction of the malaria cases that they account for; it would seem inadvisable to re-design the sub-county control strategies around targeting hotspots.
Collapse
Affiliation(s)
- Alice Kamau
- KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya. .,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.
| | - Grace Mtanje
- KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya
| | - Christine Mataza
- KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.,Ministry of Health, Kilifi County Government, Kilifi, Kenya
| | - Philip Bejon
- KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| | - Robert W Snow
- KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
| |
Collapse
|
6
|
Alegana VA, Suiyanka L, Macharia PM, Ikahu-Muchangi G, Snow RW. Malaria micro-stratification using routine surveillance data in Western Kenya. Malar J 2021; 20:22. [PMID: 33413385 PMCID: PMC7788718 DOI: 10.1186/s12936-020-03529-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 11/27/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND There is an increasing need for finer spatial resolution data on malaria risk to provide micro-stratification to guide sub-national strategic plans. Here, spatial-statistical techniques are used to exploit routine data to depict sub-national heterogeneities in test positivity rate (TPR) for malaria among patients attending health facilities in Kenya. METHODS Routine data from health facilities (n = 1804) representing all ages over 24 months (2018-2019) were assembled across 8 counties (62 sub-counties) in Western Kenya. Statistical model-based approaches were used to quantify heterogeneities in TPR and uncertainty at fine spatial resolution adjusting for missingness, population distribution, spatial data structure, month, and type of health facility. RESULTS The overall monthly reporting rate was 78.7% (IQR 75.0-100.0) and public-based health facilities were more likely than private facilities to report ≥ 12 months (OR 5.7, 95% CI 4.3-7.5). There was marked heterogeneity in population-weighted TPR with sub-counties in the north of the lake-endemic region exhibiting the highest rates (exceedance probability > 70% with 90% certainty) where approximately 2.7 million (28.5%) people reside. At micro-level the lowest rates were in 14 sub-counties (exceedance probability < 30% with 90% certainty) where approximately 2.2 million (23.1%) people lived and indoor residual spraying had been conducted since 2017. CONCLUSION The value of routine health data on TPR can be enhanced when adjusting for underlying population and spatial structures of the data, highlighting small-scale heterogeneities in malaria risk often masked in broad national stratifications. Future research should aim at relating these heterogeneities in TPR with traditional community-level prevalence to improve tailoring malaria control activities at sub-national levels.
Collapse
Affiliation(s)
- Victor A Alegana
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, P.O. Box 43640-00100, Nairobi, Kenya. .,Geography and Environmental Science, University of Southampton, Southampton, SO17 1BJ, UK. .,Faculty of Science and Technology, Lancaster University, Lancaster, LAI 4YW, UK.
| | - Laurissa Suiyanka
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, P.O. Box 43640-00100, Nairobi, Kenya
| | - Peter M Macharia
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, P.O. Box 43640-00100, Nairobi, Kenya
| | - Grace Ikahu-Muchangi
- National Malaria Control Programme, Ministry of Health, P.O Box 30016-00100, Nairobi, Kenya
| | - Robert W Snow
- Population Health Unit, Kenya Medical Research Institute-Wellcome Trust Research Programme, P.O. Box 43640-00100, Nairobi, Kenya.,Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, OX3 7LJ, UK
| |
Collapse
|
7
|
Mpimbaza A, Sserwanga A, Rutazaana D, Kapisi J, Walemwa R, Suiyanka L, Kyalo D, Kamya M, Opigo J, Snow RW. Changing malaria fever test positivity among paediatric admissions to Tororo district hospital, Uganda 2012-2019. Malar J 2020; 19:416. [PMID: 33213469 PMCID: PMC7678291 DOI: 10.1186/s12936-020-03490-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 11/09/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The World Health Organization (WHO) promotes long-lasting insecticidal nets (LLIN) and indoor residual house-spraying (IRS) for malaria control in endemic countries. However, long-term impact data of vector control interventions is rarely measured empirically. METHODS Surveillance data was collected from paediatric admissions at Tororo district hospital for the period January 2012 to December 2019, during which LLIN and IRS campaigns were implemented in the district. Malaria test positivity rate (TPR) among febrile admissions aged 1 month to 14 years was aggregated at baseline and three intervention periods (first LLIN campaign; Bendiocarb IRS; and Actellic IRS + second LLIN campaign) and compared using before-and-after analysis. Interrupted time-series analysis (ITSA) was used to determine the effect of IRS (Bendiocarb + Actellic) with the second LLIN campaign on monthly TPR compared to the combined baseline and first LLIN campaign periods controlling for age, rainfall, type of malaria test performed. The mean and median ages were examined between intervention intervals and as trend since January 2012. RESULTS Among 28,049 febrile admissions between January 2012 and December 2019, TPR decreased from 60% at baseline (January 2012-October 2013) to 31% during the final period of Actellic IRS and LLIN (June 2016-December 2019). Comparing intervention intervals to the baseline TPR (60.3%), TPR was higher during the first LLIN period (67.3%, difference 7.0%; 95% CI 5.2%, 8.8%, p < 0.001), and lower during the Bendiocarb IRS (43.5%, difference - 16.8%; 95% CI - 18.7%, - 14.9%) and Actellic IRS (31.3%, difference - 29.0%; 95% CI - 30.3%, - 27.6%, p < 0.001) periods. ITSA confirmed a significant decrease in the level and trend of TPR during the IRS (Bendicarb + Actellic) with the second LLIN period compared to the pre-IRS (baseline + first LLIN) period. The age of children with positive test results significantly increased with time from a mean of 24 months at baseline to 39 months during the final IRS and LLIN period. CONCLUSION IRS can have a dramatic impact on hospital paediatric admissions harbouring malaria infection. The sustained expansion of effective vector control leads to an increase in the age of malaria positive febrile paediatric admissions. However, despite large reductions, malaria test-positive admissions continued to be concentrated in children aged under five years. Despite high coverage of IRS and LLIN, these vector control measures failed to interrupt transmission in Tororo district. Using simple, cost-effective hospital surveillance, it is possible to monitor the public health impacts of IRS in combination with LLIN.
Collapse
Affiliation(s)
- Arthur Mpimbaza
- Child Health and Development Centre, Makerere University, College of Health Sciences, Kampala, Uganda.
- Infectious Diseases Research Collaboration, Kampala, Uganda.
| | | | - Damian Rutazaana
- National Malaria Control Division, Ministry of Health, Kampala, Uganda
| | - James Kapisi
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Richard Walemwa
- Department of Prevention, Care and Treatment, Infectious Diseases Institute, Kampala, Uganda
| | - Laurissa Suiyanka
- Population Health Unit, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya
| | - David Kyalo
- Population Health Unit, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya
| | - Moses Kamya
- Infectious Diseases Research Collaboration, Kampala, Uganda
| | - Jimmy Opigo
- National Malaria Control Division, Ministry of Health, Kampala, Uganda
| | - Robert W Snow
- Population Health Unit, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| |
Collapse
|