Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
Department of Biology, University of Oxford, Oxford, United Kingdom.
Malar J. 2024 Oct 3;23(1):295. doi: 10.1186/s12936-024-05118-3.
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.
自 21 世纪以来，尽管在遏制全球疟疾负担方面取得了进展，但进展已经停滞，部分原因是用于实施必要干预措施的资金供应已达到上限。2020 年，全球用于疟疾干预措施的投资约为 33 亿美元，低于全球疫苗和免疫联盟设定的 68 亿美元目标，这增加了理想投资和实际投资之间的资金缺口。疟疾控制优化模型用于理清最有效的干预措施或一揽子干预措施，以满足固有限制预算的需求。本系统评价旨在确定和描述资源有限环境下疟疾控制优化的模型，以进行资源分配，并评估其优缺点。
按照系统评价前瞻性登记和系统评价与荟萃分析首选报告项目的指南，在 PubMed 和 Embase 数据库中全面检索了自成立以来至 2024 年 6 月发表的同行评议文献。使用了以下关键词：优化模型；疟疾；控制干预措施；消除干预措施。从检索中排除社论、评论、意见论文、会议摘要、媒体报道、信件、公告、预印本、灰色文献、非英语语言研究、系统评价和荟萃分析。
检索结果得到 2950 条记录，其中 15 条符合纳入标准。这些研究主要在非洲国家（53.3%）进行，如加纳、尼日利亚、坦桑尼亚、乌干达和亚洲国家（26.7%），如泰国和缅甸。分析中最常用的干预措施包括经杀虫剂处理的蚊帐（93.3%）、室内滞留喷洒（80.0%）、季节性疟疾化学预防（33.3%）和病例管理（33.3%）。用于估计健康效益的方法包括房室模型（40.0%）、个体基础模型（40.0%）、静态模型（13.0%）和线性回归模型（7.0%）。分析中使用的数据是经过验证的特定国家的数据（60.0%）或非特定国家的数据（40.0%），仅在国家层面（40.0%）、国家和次国家层面（46.7%）或次国家层面（13.3%）进行分析。
本综述确定了疟疾资源分配的现有优化模型。研究结果强调了疟疾控制优化的国家具体分析的必要性，在进行建模分析时使用国家特定的流行病学和成本数据，进行成本敏感性分析，并确定分析的视角，强调针对数据收集和分析的次国家定制，以获得更准确和高质量的结果。未来的建模工作必须考虑公平性，并以难以到达的高危疟疾人群为目标，以最大限度地发挥影响。
PROSPERO 注册号：CRD42023436966。
