Impact of seasonal malaria chemoprevention: a plausibility evaluation of routine data from health facilities in three implementing states in Nigeria Article Swipe
Ebenezer C. Ikechukwu
,
Ekechi Okereke
,
Olabisi Ogunmola
,
Jennifer Chukwumerije
,
Daniel Emeto
,
Emmanuel Salifu
,
Ayodeji Balogun
,
Chibuzo Oguoma
,
Emmanuel Shekarau
,
Nnenna Ogbulafor
,
Eoin Cassidy
,
Christian Rassi
,
Olusola Oresanya
,
Chukwudi A Nnaji
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6413508/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-6413508/v1
Background Seasonal malaria chemoprevention (SMC) has been recommended by the World Health Organization since 2012 for children aged 3–59 months in areas where malaria transmission is highly seasonal. By 2024, SMC had been successfully implemented in all 21 eligible states in Nigeria. Given this widespread implementation, there has been increasing interest in understanding the impact of the intervention under programmatic conditions. This study assessed changes in malaria incidence and related epidemiological outcomes among the target population of children in three SMC implementing states in Nigeria. Methods A pre-post study plausibility evaluation design was used for this study. Data from routine health management information systems were extracted from selected health facilities to compare the incidence of parasitologically-confirmed uncomplicated malaria cases and secondary outcomes among children aged 3–59 months within the catchment populations of those health facilities. Mixed-effects, multilevel, negative binomial regression models were employed to estimate the impact of SMC on outcomes of interest between the pre-SMC period (2021) and SMC period (2022). Results Data were collected in 36 health facilities: 12 each in Kogi state, Oyo state, and the Federal Capital Territory. The mean incidence of uncomplicated malaria was 20 cases per 1000 children aged 3–59 months in 2021, and 9 cases per 1000 children in 2022. After accounting for potential confounders, malaria incidence was 50% (95% confidence interval [CI]: 39–60) lower in the SMC period compared with the pre-SMC period (adjusted incidence rate ratio (IRR): 0.50, 95% CI: 0.40–0.61, p < 0.001), with notable variations in the level of reduction across the three study locations. Incidence of all-cause fever per 1000 children was 29% (95% CI: 14–41) lower in 2022 compared with 2021 (adjusted IRR: 0.71, 95% CI: 0.59–0.86, p < 0.001). Observed levels of severe malaria and attributable deaths were too low to measure the impact of SMC on those outcomes. Conclusion The study found significantly lower levels of incidence of uncomplicated malaria following the introduction of SMC. It thus provides evidence on the potential impact of the intervention in real-world settings while underscoring the need for further improvement to and utilisation of routine data to monitor impact in eligible settings.
Related Topics
Concepts
Malaria
Environmental health
Health facility
Medicine
Geography
Business
Health services
Population
Immunology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-6413508/v1
- https://www.researchsquare.com/article/rs-6413508/latest.pdf
- OA Status
- gold
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409558956
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4409558956Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-6413508/v1Digital Object Identifier
- Title
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Impact of seasonal malaria chemoprevention: a plausibility evaluation of routine data from health facilities in three implementing states in NigeriaWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-04-17Full publication date if available
- Authors
-
Ebenezer C. Ikechukwu, Ekechi Okereke, Olabisi Ogunmola, Jennifer Chukwumerije, Daniel Emeto, Emmanuel Salifu, Ayodeji Balogun, Chibuzo Oguoma, Emmanuel Shekarau, Nnenna Ogbulafor, Eoin Cassidy, Christian Rassi, Olusola Oresanya, Chukwudi A NnajiList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-6413508/v1Publisher landing page
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https://www.researchsquare.com/article/rs-6413508/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-6413508/latest.pdfDirect OA link when available
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Malaria, Environmental health, Health facility, Medicine, Geography, Business, Health services, Population, ImmunologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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25Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.IRR: | 276 |
| abstract_inverted_index.Kogi | 174 |
| abstract_inverted_index.SMC. | 320 |
| abstract_inverted_index.This | 62 |
| abstract_inverted_index.aged | 18, 125, 195 |
| abstract_inverted_index.been | 7, 33, 49 |
| abstract_inverted_index.data | 347 |
| abstract_inverted_index.each | 172 |
| abstract_inverted_index.from | 99, 107 |
| abstract_inverted_index.mean | 184 |
| abstract_inverted_index.need | 338 |
| abstract_inverted_index.rate | 234 |
| abstract_inverted_index.this | 44, 96 |
| abstract_inverted_index.thus | 322 |
| abstract_inverted_index.used | 94 |
| abstract_inverted_index.were | 105, 142, 165, 292 |
| abstract_inverted_index.with | 228, 244, 273 |
| abstract_inverted_index.(SMC) | 5 |
| abstract_inverted_index.0.50, | 237 |
| abstract_inverted_index.0.71, | 277 |
| abstract_inverted_index.2021, | 199 |
| abstract_inverted_index.2022. | 207 |
| abstract_inverted_index.2024, | 30 |
| abstract_inverted_index.After | 208 |
| abstract_inverted_index.Given | 43 |
| abstract_inverted_index.World | 11 |
| abstract_inverted_index.[CI]: | 220 |
| abstract_inverted_index.among | 73, 123 |
| abstract_inverted_index.areas | 22 |
| abstract_inverted_index.cases | 119, 191, 202 |
| abstract_inverted_index.fever | 260 |
| abstract_inverted_index.found | 307 |
| abstract_inverted_index.level | 249 |
| abstract_inverted_index.lower | 222, 269, 309 |
| abstract_inverted_index.ratio | 235 |
| abstract_inverted_index.since | 14 |
| abstract_inverted_index.study | 63, 89, 255, 306 |
| abstract_inverted_index.there | 47 |
| abstract_inverted_index.those | 133, 302 |
| abstract_inverted_index.three | 80, 254 |
| abstract_inverted_index.under | 59 |
| abstract_inverted_index.where | 23 |
| abstract_inverted_index.while | 335 |
| abstract_inverted_index.(2021) | 158 |
| abstract_inverted_index.(IRR): | 236 |
| abstract_inverted_index.3–59 | 19, 126, 196 |
| abstract_inverted_index.Health | 12 |
| abstract_inverted_index.across | 252 |
| abstract_inverted_index.deaths | 291 |
| abstract_inverted_index.design | 92 |
| abstract_inverted_index.health | 101, 109, 134, 169 |
| abstract_inverted_index.highly | 27 |
| abstract_inverted_index.impact | 55, 147, 298, 328, 350 |
| abstract_inverted_index.levels | 285, 310 |
| abstract_inverted_index.models | 141 |
| abstract_inverted_index.months | 20, 127, 197 |
| abstract_inverted_index.period | 157, 161, 226, 231 |
| abstract_inverted_index.severe | 287 |
| abstract_inverted_index.state, | 175, 177 |
| abstract_inverted_index.states | 40, 83 |
| abstract_inverted_index.study. | 97 |
| abstract_inverted_index.target | 75 |
| abstract_inverted_index.within | 128 |
| abstract_inverted_index.(2022). | 162 |
| abstract_inverted_index.0.001), | 243 |
| abstract_inverted_index.0.001). | 283 |
| abstract_inverted_index.Capital | 181 |
| abstract_inverted_index.Federal | 180 |
| abstract_inverted_index.Methods | 86 |
| abstract_inverted_index.Results | 163 |
| abstract_inverted_index.between | 154 |
| abstract_inverted_index.changes | 65 |
| abstract_inverted_index.compare | 112 |
| abstract_inverted_index.further | 340 |
| abstract_inverted_index.malaria | 3, 24, 67, 118, 188, 213, 288, 315 |
| abstract_inverted_index.measure | 296 |
| abstract_inverted_index.monitor | 349 |
| abstract_inverted_index.notable | 245 |
| abstract_inverted_index.pre-SMC | 156, 230 |
| abstract_inverted_index.related | 70 |
| abstract_inverted_index.routine | 100, 346 |
| abstract_inverted_index.systems | 104 |
| abstract_inverted_index.14–41) | 268 |
| abstract_inverted_index.39–60) | 221 |
| abstract_inverted_index.Nigeria. | 42, 85 |
| abstract_inverted_index.Observed | 284 |
| abstract_inverted_index.Seasonal | 2 |
| abstract_inverted_index.assessed | 64 |
| abstract_inverted_index.binomial | 139 |
| abstract_inverted_index.children | 17, 78, 124, 194, 205, 263 |
| abstract_inverted_index.compared | 227, 272 |
| abstract_inverted_index.eligible | 39, 352 |
| abstract_inverted_index.employed | 143 |
| abstract_inverted_index.estimate | 145 |
| abstract_inverted_index.evidence | 324 |
| abstract_inverted_index.interest | 51, 153 |
| abstract_inverted_index.interval | 219 |
| abstract_inverted_index.negative | 138 |
| abstract_inverted_index.outcomes | 72, 122, 151 |
| abstract_inverted_index.pre-post | 88 |
| abstract_inverted_index.provides | 323 |
| abstract_inverted_index.selected | 108 |
| abstract_inverted_index.settings | 334 |
| abstract_inverted_index.(adjusted | 232, 275 |
| abstract_inverted_index.Incidence | 257 |
| abstract_inverted_index.all-cause | 259 |
| abstract_inverted_index.catchment | 130 |
| abstract_inverted_index.collected | 166 |
| abstract_inverted_index.extracted | 106 |
| abstract_inverted_index.following | 316 |
| abstract_inverted_index.incidence | 68, 114, 185, 214, 233, 312 |
| abstract_inverted_index.outcomes. | 303 |
| abstract_inverted_index.potential | 211, 327 |
| abstract_inverted_index.reduction | 251 |
| abstract_inverted_index.seasonal. | 28 |
| abstract_inverted_index.secondary | 121 |
| abstract_inverted_index.settings. | 353 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Conclusion | 304 |
| abstract_inverted_index.Territory. | 182 |
| abstract_inverted_index.accounting | 209 |
| abstract_inverted_index.confidence | 218 |
| abstract_inverted_index.evaluation | 91 |
| abstract_inverted_index.facilities | 110 |
| abstract_inverted_index.increasing | 50 |
| abstract_inverted_index.locations. | 256 |
| abstract_inverted_index.management | 102 |
| abstract_inverted_index.population | 76 |
| abstract_inverted_index.real-world | 333 |
| abstract_inverted_index.regression | 140 |
| abstract_inverted_index.variations | 246 |
| abstract_inverted_index.widespread | 45 |
| abstract_inverted_index.conditions. | 61 |
| abstract_inverted_index.facilities. | 135 |
| abstract_inverted_index.facilities: | 170 |
| abstract_inverted_index.implemented | 35 |
| abstract_inverted_index.improvement | 341 |
| abstract_inverted_index.information | 103 |
| abstract_inverted_index.multilevel, | 137 |
| abstract_inverted_index.populations | 131 |
| abstract_inverted_index.recommended | 8 |
| abstract_inverted_index.utilisation | 344 |
| abstract_inverted_index.0.40–0.61, | 240 |
| abstract_inverted_index.0.59–0.86, | 280 |
| abstract_inverted_index.Organization | 13 |
| abstract_inverted_index.attributable | 290 |
| abstract_inverted_index.confounders, | 212 |
| abstract_inverted_index.implementing | 82 |
| abstract_inverted_index.intervention | 58, 331 |
| abstract_inverted_index.introduction | 318 |
| abstract_inverted_index.plausibility | 90 |
| abstract_inverted_index.programmatic | 60 |
| abstract_inverted_index.successfully | 34 |
| abstract_inverted_index.transmission | 25 |
| abstract_inverted_index.underscoring | 336 |
| abstract_inverted_index.significantly | 308 |
| abstract_inverted_index.uncomplicated | 117, 187, 314 |
| abstract_inverted_index.understanding | 53 |
| abstract_inverted_index.Mixed-effects, | 136 |
| abstract_inverted_index.chemoprevention | 4 |
| abstract_inverted_index.epidemiological | 71 |
| abstract_inverted_index.implementation, | 46 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| abstract_inverted_index.parasitologically-confirmed | 116 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 14 |
| citation_normalized_percentile.value | 0.17178257 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |