Moving Forward with Taking a Societal Perspective: A Themed Issue on Productivity Costs, Consumption Costs and Informal Care Costs

Brouwer, Werner; Baal, Pieter van

doi:10.1007/s40273-023-01307-1

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…This, in turn, will impact the incremental cost of an intervention, and therefore the results of the cost-effectiveness analysis [ 22 ]. While an increasing number of economic evaluations are including productivity losses, a consensus has yet to be reached on best practices for how to estimate these costs when included [ 23 ]. A recent review provided a comprehensive list of instruments that can be used to measure time losses owing to illness across all settings; however, it did not describe the valuation method chosen, nor the method for assigning a monetary value [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Methods for Estimating Productivity Losses due to Illness or Caregiving in Low- and Middle-Income Countries

Setiawan,

Cassidy-Seyoum,

Thriemer

et al. 2024

PharmacoEconomics

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Background Productivity losses are often included in costing studies and economic evaluations to provide a comprehensive understanding of the economic burden of disease. Global guidance on estimating productivity losses is sparse, especially for low-and middle-income countries (LMICs) where informal and unpaid work remains dominant. This study aims to describe current practices for valuing productivity losses in LMICs. Methods We performed a systematic review of studies published before April 2022 using three databases, including PubMed, Cochrane Library and Web of Science Core Collection. We included any costing or economic evaluation study conducted in a LMIC that provided methodological details on how the monetary value for productivity losses was estimated. Two reviewers independently screened articles for inclusion, extracted data and assessed the quality of the studies. Results A total of 281 articles were included. While most studies did not specify the overall approach used to measure and value productivity losses (58%), the human capital approach was the most frequently used approach to measure productivity losses when this was clearly stated (39%). The most common methods to estimate a monetary value for productivity losses were market wages (51%), self-reported wages (28%) and macroeconomic measures (15%). Conclusion Reporting standards for productivity losses in LMIC settings have room for improvement. While market wages were the most frequently used method to estimate the monetary value of productivity losses, this relies on context-specific data availability. Until a consensus is reached on if, when and how to include productivity losses in costing and economic evaluation studies, future studies could include a sensitivity analysis to explore the impact of different methods for estimating the monetary value of productivity losses. Supplementary Information The online version contains supplementary material available at 10.1007/s40273-024-01402-x.

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Section: Introductionmentioning

confidence: 99%

A Systematic Review of Methods for Estimating Productivity Losses due to Illness or Caregiving in Low- and Middle-Income Countries

Setiawan,

Cassidy-Seyoum,

Thriemer

et al. 2024

PharmacoEconomics

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Assessing the Health and Welfare Benefits of Interventions Using the Wider Societal Impacts Framework

Premji,

Griffin

2024

Value in Health

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Combating Antimicrobial Resistance Through a Data-Driven Approach to Optimize Antibiotic Use and Improve Patient Outcomes: Protocol for a Mixed Methods Study

Mayito,

Tumwine,

Galiwango

et al. 2024

JMIR Res Protoc

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Background It is projected that drug-resistant infections will lead to 10 million deaths annually by 2050 if left unabated. Despite this threat, surveillance data from resource-limited settings are scarce and often lack antimicrobial resistance (AMR)–related clinical outcomes and economic burden. We aim to build an AMR and antimicrobial use (AMU) data warehouse, describe the trends of resistance and antibiotic use, determine the economic burden of AMR in Uganda, and develop a machine learning algorithm to predict AMR-related clinical outcomes. Objective The overall objective of the study is to use data-driven approaches to optimize antibiotic use and combat antimicrobial-resistant infections in Uganda. We aim to (1) build a dynamic AMR and antimicrobial use and consumption (AMUC) data warehouse to support research in AMR and AMUC to inform AMR-related interventions and public health policy, (2) evaluate the trends in AMR and antibiotic use based on annual antibiotic and point prevalence survey data collected at 9 regional referral hospitals over a 5-year period, (3) develop a machine learning model to predict the clinical outcomes of patients with bacterial infectious syndromes due to drug-resistant pathogens, and (4) estimate the annual economic burden of AMR in Uganda using the cost-of-illness approach. Methods We will conduct a study involving data curation, machine learning–based modeling, and cost-of-illness analysis using AMR and AMU data abstracted from procurement, human resources, and clinical records of patients with bacterial infectious syndromes at 9 regional referral hospitals in Uganda collected between 2018 and 2026. We will use data curation procedures, FLAIR (Findable, Linkable, Accessible, Interactable and Repeatable) principles, and role-based access control to build a robust and dynamic AMR and AMU data warehouse. We will also apply machine learning algorithms to model AMR-related clinical outcomes, advanced statistical analysis to study AMR and AMU trends, and cost-of-illness analysis to determine the AMR-related economic burden. Results The study received funding from the Wellcome Trust through the Centers for Antimicrobial Optimisation Network (CAMO-Net) in April 2023. As of October 28, 2024, we completed data warehouse development, which is now under testing; completed data curation of the historical Fleming Fund surveillance data (2020-2023); and collected retrospective AMR records for 599 patients that contained clinical outcomes and cost-of-illness economic burden data across 9 surveillance sites for objectives 3 and 4, respectively. Conclusions The data warehouse will promote access to rich and interlinked AMR and AMU data sets to answer AMR program and research questions using a wide evidence base. The AMR-related clinical outcomes model and cost data will facilitate improvement in the clinical management of AMR patients and guide resource allocation to support AMR surveillance and interventions. International Registered Report Identifier (IRRID) PRR1-10.2196/58116

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Moving Forward with Taking a Societal Perspective: A Themed Issue on Productivity Costs, Consumption Costs and Informal Care Costs

Cited by 4 publications

References 31 publications

A Systematic Review of Methods for Estimating Productivity Losses due to Illness or Caregiving in Low- and Middle-Income Countries

A Systematic Review of Methods for Estimating Productivity Losses due to Illness or Caregiving in Low- and Middle-Income Countries

Assessing the Health and Welfare Benefits of Interventions Using the Wider Societal Impacts Framework

Combating Antimicrobial Resistance Through a Data-Driven Approach to Optimize Antibiotic Use and Improve Patient Outcomes: Protocol for a Mixed Methods Study

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