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Planning human resources and facilities to achieve Sustainable Development Goals: a decision-analytical modelling approach to predict cancer control requirements in Indonesia

OBJECTIVES: Indonesia aims to achieve universal health coverage (UHC) and Sustainable Development Goals (SDGs), including SDG 3 target 4, which focuses on cancer control, by 2030. This study aimed to forecast the human resources for health (HRH) and facilities required for cancer control in Indonesi...

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Detalles Bibliográficos
Autores principales: Melyda, Gondhowiardjo, Soehartati, Jackson, Louise J, Oppong, Raymond
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9086632/
https://www.ncbi.nlm.nih.gov/pubmed/35534085
http://dx.doi.org/10.1136/bmjopen-2021-059555
Descripción
Sumario:OBJECTIVES: Indonesia aims to achieve universal health coverage (UHC) and Sustainable Development Goals (SDGs), including SDG 3 target 4, which focuses on cancer control, by 2030. This study aimed to forecast the human resources for health (HRH) and facilities required for cancer control in Indonesia over an 11-year period to support these goals. DESIGN: A two-stage Markov model was developed to forecast the demand side of facilities and HRH requirements for cancer control in Indonesia over an 11-year period. SETTING: Data sources used include the Indonesia Health Profile Report (2019), the Indonesian Radiation Oncology Society Database and National Cancer Control Committee documents (2019). METHODS: The study involved modelling the current availability of HRH and healthcare facilities in Indonesia and predicting future requirements. The gap between the current and the required HRH and facilities related to oncology, and the costs associated with meeting these requirements, were analysed. RESULTS: Results indicate the need to increase the number of healthcare facilities and HRH to achieve SDG targets. However, UHC for cancer care still may not be achieved, as eastern Indonesia is predicted to have no tertiary hospital until 2030. The forecast shows that Indonesia had a median of only 39% of the HRH requirements in 2019. Closing the HRH gap requires around a 47.6% increase in salary expenditure. CONCLUSION: This study demonstrates the application of decision-analytical modelling approach to planning HRH and facilities in the context of a low-to-middle-income country. Scaling up oncology services in Indonesia to attain the SDG targets will require expansion of the number and capability of healthcare facilities and HRH. This work allows an in-depth understanding of the resources needed to achieve UHC and SDGs and could be utilised in other disease areas and contexts.