Systematic review of the impact of health care expenditure on health outcome measures: implications for cost-effectiveness thresholds

ABSTRACT

Objective

Empirical estimates of the impact of healthcare expenditure on health outcome measures may inform the cost-effectiveness threshold (CET) for guiding funding decisions. This study aims to systematically review studies that estimated this, summarize and compare the estimates by country income level.

Methods

We searched PubMed, Scopus, York Research database, and [anonymized] for Reviews and Dissemination database from inception to 1 August 2023. For inclusion, a study had to be an original article, estimating the impact of healthcare expenditure on health outcome measures at a country level, and presented estimates, in terms of cost per quality-adjusted life year (QALY) or disability-adjusted life year (DALY).

Results

We included 18 studies with 385 estimates. The median (range) estimates were PPP$ 11,224 (PPP$ 223 – PPP$ 288,816) per QALY gained and PPP$ 5,963 (PPP$ 71 – PPP$ 165,629) per DALY averted. As ratios of Gross Domestic Product per capita (GDPPC), these estimates were 0.376 (0.041–182.840) and 0.318 (0.004–37.315) times of GDPPC, respectively.

Conclusions

The commonly used CET of GDPPC seems to be too high for all countries, but especially low-to-middle-income countries where the potential health losses from misallocation of the same money are greater.

Registration

The review protocol was published and registered in PROSPERO (CRD42020147276).

KEYWORDS:

• Systematic review
• cost-effectiveness threshold
• supply-side method
• opportunity cost
• threshold

Article highlights

• A cost-effectiveness threshold represents the maximum acceptable monetary amount per unit of health outcome for adopting a health technology. One method to empirically estimate CET is through a supply-side approach, informed by the impact of healthcare expenditure on health outcome measures. Under this method, the marginal cost per unit of health outcome produced (e.g. QALY, DALY) by the healthcare system is estimated to determine the health opportunity cost and serve as reference value to guide funding and reimbursement decisions, especially when there are fixed budgets for healthcare.

• According to this systematic review, the median of existing empirical estimates of health opportunity costs where health outcome is measured using QALYs were 0.400 GDPPC, 0.320 GDPPC, 0.285 GDPPC, and 0.344 GDPPC per QALY gained for high-income, upper-middle-income, lower-middle-income, and low-income countries, respectively. Where health outcome is measured using DALYs, the median estimates of health opportunity costs were 1.035 GDPPC, 0.406 GDPPC, 0.168 GDPPC, and 0.104 GDPPC per DALY averted for high-income, upper-middle-income, lower-middle-income, and low-income countries, respectively.

• The results suggested that the commonly used CET range of one to three times GDPPC does not reflect the available evidence on health opportunity costs and is likely to lead to the adoption of relatively cost-ineffective health technologies with consequential reductions in population health, and particularly important implications for low-to-middle-income countries where the potential health losses from the misallocation of the same money may be greater.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Availability of data and material

The datasets during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Author contributions

All authors participated sufficiently in the work to take public responsibility for it. All made substantial contributions to the intellectual content of the paper. Particularly, M A Junio Gloria, M Thavorncharoensap, U Chaikledkaew, S Youngkong, and A Thakkinstian contributed to the: concept and design, acquisition of data, analysis and interpretation of data, drafting of the manuscript and critical revision of paper for important intellectual content. J Ochalek, A J Culyer and N Chaiyakunapruk contributed to the: analysis and interpretation of data, drafting of the manuscript and critical revision of paper for important intellectual content. All authors read and approved the final manuscript.

Acknowledgments

This work had been part of the training in the Mahidol University Health Technology Assessment (MUHTA) program. Whereas the scholarship had been provided by the Mahidol University and the International Decision Support Initiative (iDSI). This work was produced as part of the International Decision Support Initiative (www.idsihealth.org) which supports countries to get the best value for money from health spending. iDSI receives funding support from the Bill & Melinda Gates Foundation, the UK Department for International Development, and the Rockefeller Foundation. The findings, interpretations and conclusions expressed in this article do not necessarily reflect the views of the aforementioned funding agencies.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/14737167.2023.2296562.

Additional information

Funding

This paper was not funded.