BackgroundThis study is a systematic literature review of HIV, nutrition, and primary care activity-based costing (ABC) studies conducted in low-and middle-income countries. ABC studies are critical for understanding the quantities and unit costs of the activities and resources for specific cost functions. The results of ABC studies enable governments, funders, and policymakers to utilize costing results to make efficient, cost-effective decisions on how to allocate scarce resources. MethodsWe followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology for systematic literature reviews. Key search terms included: (1) activity-based costing and time-driven activity-based costing, (2) cost of services, (3) HIV interventions OR (4) primary health care. Terms were searched within article titles and abstracts in PubMed, EconLit, and Scopus. Results1,884 abstracts were screened and reduced to 57 articles using exclusion criteria. After a full text review, 16 articles were included in the final data synthesis. Findings were used to classify costs into relevant and common inputs for activity-based costing. All costs were converted to unit cost (cost per patient) and inflated to January 2020 USD. The largest unit cost across nutrition services was training (US$194.16 per patient, 34.6% of total unit cost). The largest unit cost for HIV was antiretroviral therapy (ART)

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Background With rising cost pressures on health care systems, machine-learning (ML)-based algorithms are increasingly used to predict health care costs. Despite their potential advantages, the successful implementation of these methods could be undermined by biases introduced in the design, conduct, or analysis of studies seeking to develop and/or validate ML models. The utility of such models may also be negatively affected by poor reporting of these studies. In this systematic review, we aim to evaluate the reporting quality, methodological characteristics, and risk of bias of ML-based prediction models for individual-level health care spending. Methods We will systematically search PubMed and Embase to identify studies developing, updating, or validating ML-based models to predict an individual’s health care spending for any medical condition, over any time period, and in any setting. We will exclude prediction models of aggregate-level health care spending, models used to infer causality, models using radiomics or speech parameters, models of non-clinically validated predictors (e.g., genomics), and cost-effectiveness analyses without predicting individual-level health care spending. We will extract data based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS), previously published research, and relevant recommendations. We will assess the adherence of ML-based studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and examine the inclusion of transparency and reproducibility indicators (e.g. statements on data sharing). To assess the risk of bias, we will apply the Prediction model Risk Of Bias Assessment Tool (PROBAST). Findings will be stratified by study design, ML methods used, population characteristics, and medical field. Discussion Our systematic review will appraise the quality, reporting, and risk of bias of ML-based models for individualized health care cost prediction. This review will provide an overview of the available models and give insights into the strengths and limitations of using ML methods for the prediction of health spending.

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