Models for malaria control optimization-a systematic review.
Limited resource setting
Malaria
Optimization
Resource allocation
Journal
Malaria journal
ISSN: 1475-2875
Titre abrégé: Malar J
Pays: England
ID NLM: 101139802
Informations de publication
Date de publication:
03 Oct 2024
03 Oct 2024
Historique:
received:
03
07
2024
accepted:
21
09
2024
medline:
4
10
2024
pubmed:
4
10
2024
entrez:
3
10
2024
Statut:
epublish
Résumé
Despite advances made in curbing the global malaria burden since the 2000s, progress has stalled, in part due to a plateauing of the financing available to implement needed interventions. In 2020, approximately 3.3 billion USD was invested globally for malaria interventions, falling short of the targeted 6.8 billion USD set by the GTS, increasing the financial gap between desirable and actual investment. Models for malaria control optimization are used to disentangle the most efficient interventions or packages of interventions for inherently constrained budgets. This systematic review aimed to identify and characterise models for malaria control optimization for resource allocation in limited resource settings and assess their strengths and limitations. Following the Prospective Register of Systematic Reviews and Preferred reporting Items for Systematic Reviews and Meta-Analysis guidelines, a comprehensive search across PubMed and Embase databases was performed of peer-reviewed literature published from inception until June 2024. The following keywords were used: optimization model; malaria; control interventions; elimination interventions. Editorials, commentaries, opinion papers, conference abstracts, media reports, letters, bulletins, pre-prints, grey literature, non-English language studies, systematic reviews and meta-analyses were excluded from the search. The search yielded 2950 records, of which 15 met the inclusion criteria. The studies were carried out mainly in countries in Africa (53.3%), such as Ghana, Nigeria, Tanzania, Uganda, and countries in Asia (26.7%), such as Thailand and Myanmar. The most used interventions for analyses were insecticide-treated bed nets (93.3%), IRS (80.0%), Seasonal Malaria Chemoprevention (33.3%) and Case management (33.3%). The methods used for estimating health benefits were compartmental models (40.0%), individual-based models (40.0%), static models (13.0%) and linear regression model (7%). Data used in the analysis were validated country-specific data (60.0%) or non-country-specific data (40.0%) and were analysed at national only (40.0%), national and subnational levels (46.7%), or subnational only levels (13.3%). This review identified available optimization models for malaria resource allocation. The findings highlighted the need for country-specific analysis for malaria control optimization, the use of country-specific epidemiological and cost data in performing modelling analyses, performing cost sensitivity analyses and defining the perspective for the analysis, with an emphasis on subnational tailoring for data collection and analysis for more accurate and good quality results. It is critical that the future modelling efforts account for fairness and target at risk malaria populations that are hard-to-reach to maximize impact. PROSPERO Registration number: CRD42023436966.
Sections du résumé
BACKGROUND
BACKGROUND
Despite advances made in curbing the global malaria burden since the 2000s, progress has stalled, in part due to a plateauing of the financing available to implement needed interventions. In 2020, approximately 3.3 billion USD was invested globally for malaria interventions, falling short of the targeted 6.8 billion USD set by the GTS, increasing the financial gap between desirable and actual investment. Models for malaria control optimization are used to disentangle the most efficient interventions or packages of interventions for inherently constrained budgets. This systematic review aimed to identify and characterise models for malaria control optimization for resource allocation in limited resource settings and assess their strengths and limitations.
METHODS
METHODS
Following the Prospective Register of Systematic Reviews and Preferred reporting Items for Systematic Reviews and Meta-Analysis guidelines, a comprehensive search across PubMed and Embase databases was performed of peer-reviewed literature published from inception until June 2024. The following keywords were used: optimization model; malaria; control interventions; elimination interventions. Editorials, commentaries, opinion papers, conference abstracts, media reports, letters, bulletins, pre-prints, grey literature, non-English language studies, systematic reviews and meta-analyses were excluded from the search.
RESULTS
RESULTS
The search yielded 2950 records, of which 15 met the inclusion criteria. The studies were carried out mainly in countries in Africa (53.3%), such as Ghana, Nigeria, Tanzania, Uganda, and countries in Asia (26.7%), such as Thailand and Myanmar. The most used interventions for analyses were insecticide-treated bed nets (93.3%), IRS (80.0%), Seasonal Malaria Chemoprevention (33.3%) and Case management (33.3%). The methods used for estimating health benefits were compartmental models (40.0%), individual-based models (40.0%), static models (13.0%) and linear regression model (7%). Data used in the analysis were validated country-specific data (60.0%) or non-country-specific data (40.0%) and were analysed at national only (40.0%), national and subnational levels (46.7%), or subnational only levels (13.3%).
CONCLUSION
CONCLUSIONS
This review identified available optimization models for malaria resource allocation. The findings highlighted the need for country-specific analysis for malaria control optimization, the use of country-specific epidemiological and cost data in performing modelling analyses, performing cost sensitivity analyses and defining the perspective for the analysis, with an emphasis on subnational tailoring for data collection and analysis for more accurate and good quality results. It is critical that the future modelling efforts account for fairness and target at risk malaria populations that are hard-to-reach to maximize impact.
TRIAL REGISTRATION
BACKGROUND
PROSPERO Registration number: CRD42023436966.
Identifiants
pubmed: 39363178
doi: 10.1186/s12936-024-05118-3
pii: 10.1186/s12936-024-05118-3
doi:
Types de publication
Systematic Review
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
295Subventions
Organisme : Bill and Melinda Gates Foundation
ID : INV047-048
Informations de copyright
© 2024. The Author(s).
Références
WHO. World Malaria Report 2023. Geneva: World Health Organization; 2023.
WHO. High burden to high impact: a targeted malaria response. Geneva: World Health Organization; 2019.
WHO. Global technical strategy for malaria 2016–2030 2021 update. Geneva: World Health Organization; 2021.
WHO. WHO Guidelines for Malaria, 16 October 2023. Geneva: World Health Organization; 2023.
WHO. Seasonal malaria chemoprevention with sulfadoxine-pyrimethamine plus amodiaquine in children: a field guide. Geneva: World Health Organization; 2013.
NMCP. Cameroon malaria strategic plan 2019–2023. Yaounde, Cameroon: Cameroon National Malaria Control Programme; 2019.
WHO. A framework for malaria elimination. Geneva: World Health Organization; 2017.
RBM. Action and investment to deft malaria 2016–2030 for a Malaria-Free World. Geneva: World Health Organization on behalf of the Roll Back Malaria Partnership Secretariat; 2015.
Mills A, Lubell Y, Hanson K. Malaria eradication: the economic, financial and institutional challenge. Malar J. 2008;7:11.
doi: 10.1186/1475-2875-7-S1-S11
Drake TL, Lubell Y, Kyaw SS, Devine A, Kyaw MP, Day NPJ, et al. Geographic resource allocation based on cost effectiveness: an application to malaria policy. Appl Health Econ Health Policy. 2017;15:299–306.
pubmed: 28185133
pmcid: 5427090
doi: 10.1007/s40258-017-0305-2
Drake TL, Kyaw SS, Kyaw MP, Smithuis FM, Day NP, White LJ, et al. Cost effectiveness and resource allocation of Plasmodium falciparum malaria control in Myanmar: a modelling analysis of bed nets and community health workers. Malar J. 2015;14:376.
pubmed: 26416075
pmcid: 4587798
doi: 10.1186/s12936-015-0886-x
Conteh L, Shuford K, Agboraw E, Kont M, Kolaczinski J, Patouillard E. Costs and cost-effectiveness of malaria control interventions: a systematic literature review. Value Health. 2021;24:1213–22.
pubmed: 34372987
pmcid: 8324482
doi: 10.1016/j.jval.2021.01.013
Avancena ALV, Hutton DW. Optimization models for HIV/AIDS resource allocation: a systematic review. Value Health. 2020;23(11):1509–21.
pubmed: 33127022
doi: 10.1016/j.jval.2020.08.001
Smith DL, Battle KE, Hay SI, Barker CM, Scott TW, McKenzie FE. Ross, macdonald, and a theory for the dynamics and control of mosquito-transmitted pathogens. PLoS Pathog. 2012;8: e1002588.
pubmed: 22496640
pmcid: 3320609
doi: 10.1371/journal.ppat.1002588
Hamilton M, Mahiane G, Werst E, Sanders R, Briët O, Smith T, et al. Spectrum-Malaria: a user-friendly projection tool for health impact assessment and strategic planning by malaria control programmes in sub-Saharan Africa. Malar J. 2017;16:68.
pubmed: 28183343
pmcid: 5301449
doi: 10.1186/s12936-017-1705-3
Scott N, Hussain SA, Martin-Hughes R, Fowkes FJI, Kerr CC, Pearson R, et al. Maximizing the impact of malaria funding through allocative efficiency: using the right interventions in the right locations. Malar J. 2017;16:368.
pubmed: 28899373
pmcid: 5596957
doi: 10.1186/s12936-017-2019-1
Sherrard-Smith E, Winskill P, Hamlet A, Ngufor C, N’Guessan R, Guelbeogo MW, et al. Optimising the deployment of vector control tools against malaria: a data-informed modelling study. Lancet Planet Health. 2022;6:e100–9.
pubmed: 35065707
doi: 10.1016/S2542-5196(21)00296-5
Walker PG, Griffin JT, Ferguson NM, Ghani AC. Estimating the most efficient allocation of interventions to achieve reductions in Plasmodium falciparum malaria burden and transmission in Africa: a modelling study. Lancet Glob Health. 2016;4:e474–84.
pubmed: 27269393
doi: 10.1016/S2214-109X(16)30073-0
Caro JJ, Briggs AH, Siebert U, Kuntz KM, Force I-SMGRPT. Modeling good research practices–overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–1. Value Health. 2012;15:796–803.
pubmed: 22999128
doi: 10.1016/j.jval.2012.06.012
Brady OJ, Slater HC, Pemberton-Ross P, Wenger E, Maude RJ, Ghani AC, et al. Role of mass drug administration in elimination of Plasmodium falciparum malaria: a consensus modelling study. Lancet Glob Health. 2017;5:e680–7.
pubmed: 28566213
pmcid: 5469936
doi: 10.1016/S2214-109X(17)30220-6
Silal SP, Little F, Barnes KI, White LJ. Towards malaria elimination in Mpumalanga, South Africa: a population-level mathematical modelling approach. Malar J. 2014;13:297.
pubmed: 25086861
pmcid: 4127654
doi: 10.1186/1475-2875-13-297
Okello G, Jones C, Bonareri M, Ndegwa SN, Mcharo C, Kengo J, et al. Challenges for consent and community engagement in the conduct of cluster randomized trial among school children in low income settings: experiences from Kenya. Trials. 2013;14:142.
pubmed: 23680181
pmcid: 3661351
doi: 10.1186/1745-6215-14-142
Minakawa N, Dida GO, Sonye GO, Futami K, Kaneko S. Unforeseen misuses of bed nets in fishing villages along Lake Victoria. Malar J. 2008;7:165.
pubmed: 18752662
pmcid: 2532690
doi: 10.1186/1475-2875-7-165
Caminade C, McIntyre KM, Jones AE. Impact of recent and future climate change on vector-borne diseases. Ann N Y Acad Sci. 2019;1436:157–73.
pubmed: 30120891
doi: 10.1111/nyas.13950
Winskill P, Walker PG, Cibulskis RE, Ghani AC. Prioritizing the scale-up of interventions for malaria control and elimination. Malar J. 2019;18:122.
pubmed: 30961603
pmcid: 6454681
doi: 10.1186/s12936-019-2755-5
Walker PGT, Griffin JT, Ferguson NM, Ghani AC. Estimating the most efficient allocation of interventions to achieve reductions in Plasmodium falciparum malaria burden and transmission in Africa: a modelling study. Lancet Global Health. 2016;4:e474–84.
pubmed: 27269393
doi: 10.1016/S2214-109X(16)30073-0
Sherrard-Smith E, Winskill P, Hamlet A, Ngufor C, N’Guessan R, Guelbeogo MW, et al. Optimising the deployment of vector control tools against malaria: a data-informed modelling study. Lancet Planetary Health. 2022;6:e100–9.
pubmed: 35065707
doi: 10.1016/S2542-5196(21)00296-5
Shretta R, Silal SP, Malm K, Mohammed W, Narh J, Piccinini D, et al. Estimating the risk of declining funding for malaria in Ghana: the case for continued investment in the malaria response. Malar J. 2020;19:196.
pubmed: 32487148
pmcid: 7268595
doi: 10.1186/s12936-020-03267-9
Shretta R, Silal SP, Celhay OJ, Gran Mercado CE, Kyaw SS, Avancena A, et al. Malaria elimination transmission and costing in the Asia-Pacific: Developing an investment case. Wellcome Open Res. 2019;4:60.
pubmed: 32025571
doi: 10.12688/wellcomeopenres.14769.1
Faye S, Cico A, Gueye AB, Baruwa E, Johns B, Ndiop M, et al. Scaling up malaria intervention “packages” in Senegal: using cost effectiveness data for improving allocative efficiency and programmatic decision-making. Malar J. 2018;17:159.
pubmed: 29636051
pmcid: 5894199
doi: 10.1186/s12936-018-2305-6
Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan — a web and mobile app for systematic reviews. Syst Rev. 2016;5:210.
pubmed: 27919275
pmcid: 5139140
doi: 10.1186/s13643-016-0384-4
Njau J, Silal SP, Kollipara A, Fox K, Balawanth R, Yuen A, et al. Investment case for malaria elimination in South Africa: a financing model for resource mobilization to accelerate regional malaria elimination. Malar J. 2021;20:344.
pubmed: 34399767
pmcid: 8365569
doi: 10.1186/s12936-021-03875-z
Winskill P, Walker PG, Griffin JT, Ghani AC. Modelling the cost-effectiveness of introducing the RTS, S malaria vaccine relative to scaling up other malaria interventions in sub-Saharan Africa. BMJ Glob Health. 2017;2: e000090.
pubmed: 28588994
pmcid: 5321383
doi: 10.1136/bmjgh-2016-000090
Stuckey EM, Stevenson J, Galactionova K, Baidjoe AY, Bousema T, Odongo W, et al. Modeling the cost effectiveness of malaria control interventions in the highlands of western Kenya. PLoS ONE. 2014;9: e107700.
pubmed: 25290939
pmcid: 4188563
doi: 10.1371/journal.pone.0107700
Winskill P, Slater HC, Griffin JT, Ghani AC, Walker PGT. The US President’s Malaria Initiative, Plasmodium falciparum transmission and mortality: a modelling study. PLoS Med. 2017;14: e1002448.
pubmed: 29161259
pmcid: 5697814
doi: 10.1371/journal.pmed.1002448
Sudathip P, Kongkasuriyachai D, Stelmach R, Bisanzio D, Sine J, Sawang S, et al. The investment case for malaria elimination in Thailand: a cost-benefit analysis. Am J Trop Med Hyg. 2019;100:1445–53.
pubmed: 30994098
pmcid: 6553898
doi: 10.4269/ajtmh.18-0897
Patouillard E, Griffin J, Bhatt S, Ghani A, Cibulskis R. Global investment targets for malaria control and elimination between 2016 and 2030. BMJ Glob Health. 2017;2: e000176.
pubmed: 29242750
pmcid: 5584487
doi: 10.1136/bmjgh-2016-000176
Dudley HJ, Goenka A, Orellana CJ, Martonosi SE. Multi-year optimization of malaria intervention: a mathematical model. Malar J. 2016;15:133.
pubmed: 26931111
pmcid: 4774123
doi: 10.1186/s12936-016-1182-0
Diallo OO, Diallo A, Toh KB, Diakité N, Dioubaté M, Runge M, et al. Subnational tailoring of malaria interventions to prioritize the malaria response in Guinea. Medrxiv. 2024. https://doi.org/10.1101/2024.06.26.24309532v1 .
doi: 10.1101/2024.06.26.24309532v1
pubmed: 39072042
pmcid: 11275686
Ozodiegwu ID, Ambrose M, Galatas B, Runge M, Nandi A, Okuneye K, et al. Application of mathematical modelling to inform national malaria intervention planning in Nigeria. Malar J. 2023;22:137.
pubmed: 37101146
pmcid: 10130303
doi: 10.1186/s12936-023-04563-w
Awine T, Silal SP. Assessing the effectiveness of malaria interventions at the regional level in Ghana using a mathematical modelling application. PLoS Glob Public Health. 2022;2: e0000474.
pubmed: 36962718
pmcid: 10021332
doi: 10.1371/journal.pgph.0000474
Runge M, Snow RW, Molteni F, Thawer S, Mohamed A, Mandike R, et al. Simulating the council-specific impact of anti-malaria interventions: a tool to support malaria strategic planning in Tanzania. PLoS ONE. 2020;15: e0228469.
pubmed: 32074112
pmcid: 7029840
doi: 10.1371/journal.pone.0228469
Runge M, Molteni F, Mandike R, Snow RW, Lengeler C, Mohamed A, et al. Applied mathematical modelling to inform national malaria policies, strategies and operations in Tanzania. Malar J. 2020;19:101.
pubmed: 32122342
pmcid: 7053121
doi: 10.1186/s12936-020-03173-0
Landier J, Parker DM, Thu AM, Lwin KM, Delmas G, Nosten FH, et al. Effect of generalised access to early diagnosis and treatment and targeted mass drug administration on Plasmodium falciparum malaria in Eastern Myanmar: an observational study of a regional elimination programme. Lancet. 2018;391:1916–26.
pubmed: 29703425
pmcid: 5946089
doi: 10.1016/S0140-6736(18)30792-X