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Record W4416910737 · doi:10.1177/0272989x251395096

When Do Published Cost-Effectiveness Analyses Include Societal Costs? An Empirical Analysis, 2013–2023

2025· article· en· W4416910737 on OpenAlexaboutno aff
Deepti Patil, Bengt Liljas, Peter J. Neumann, Meng Li

Bibliographic record

VenueMedical Decision Making · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
FundersAstraZeneca United States
KeywordsOddsOdds ratioInclusion (mineral)Health careCost–benefit analysisProductivityLogistic regressionEconomic impact analysisMultivariate analysis

Abstract

fetched live from OpenAlex

ObjectiveTo examine trends in the inclusion of societal costs in published cost-effectiveness analyses (CEAs), factors associated with their inclusion, and the impact of societal costs on incremental costs and incremental cost-effectiveness ratios (ICERs).MethodsWe analyzed 7,800 CEAs from 2013 to 2023 using the Tufts Medical Center CEA registry. The inclusion of societal costs in CEAs was evaluated across study characteristics. Associations between study characteristics and the inclusion of societal costs were analyzed using multivariate logistic regression. For studies reporting health care and societal perspectives, we assessed the impact of including societal costs on incremental costs and ICERs.ResultsFrom 2013 to 2023, CEAs including societal costs increased from 19% to 28%. Productivity was the most frequently reported component (12%), followed by transportation (8%), caregiver time (6%), patient time (5%), and consumption costs (1%). Compared with US-based analyses, studies from Scandinavian countries (adjusted odds ratio [OR]: 3.6) and the Netherlands (5.6) had higher odds of including societal costs, whereas studies from Canada (0.7), Australia (0.6), and the United Kingdom (0.4) had lower odds. Studies on mental health disorders (6.2) and immunization (4.1) had the highest odds of including societal costs. Compared with CEAs focused on adults, CEAs targeting pediatric populations had higher odds (OR: 1.6), while those targeting the elderly had lower odds (OR: 0.7). Upon inclusion of societal costs, incremental costs decreased in 72% and increased in 28% of studies; the ICER decreased in 74% and increased in 26% of studies.ConclusionDespite the increase in recent years, societal costs are infrequently included in CEAs, with substantial variation by country, disease, and population. Including societal costs can meaningfully improve value assessments and should be guided by relevance, evidence, and decision context.HighlightsBuilding on prior work by Kim et al. (2020), which analyzed approximately 6,900 cost-effectiveness analyses (CEAs), this study examined a larger and more recent sample of 7,800 CEAs from 2013 to 2023. In addition to updating the evidence base, we conducted new analyses to assess trends, associated factors, and the effect of including societal costs on incremental cost-effectiveness ratios (ICERs), thus providing insights that were not explored in prior work and addressing a key evidence gap in health economics.The inclusion of societal costs in CEAs rose modestly from 19% to 28% from 2013 to 2023, with substantial variation across countries, diseases, and intervention types. In some cases, the inclusion of societal costs affected incremental costs and ICERs enough to cross commonly used cost-effectiveness thresholds.The inclusion of societal costs can help improve value assessments in health care interventions, but it should be guided by relevance, available evidence, and the potential to influence decision making. Identifying when and where societal costs meaningfully affect outcomes can support more consistent and appropriate use.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.741
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.433
GPT teacher head0.573
Teacher spread0.140 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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