Equity weights for socioeconomic position: two methods—survey of stated preferences and epidemiological data

Anita Lal, Mohammadreza Mohebi, Rohan Sweeney, Marjory Moodie, Anna Peeters, Rob Carter

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Background: There is an implicit equity approach in cost-effectiveness analysis that values health gains of socioeconomic position groups equally. An alternative approach is to integrate equity by weighting quality-adjusted life-years according to the socioeconomic position group. Objectives: To use two approaches to derive equity weights for use in cost-effectiveness analysis in Australia, in contexts in which the use of the traditional nonweighted quality-adjusted life-years could increase health inequalities between already disadvantaged groups. Methods: Equity weights derived using epidemiological data used burden of disease and mortality data by Socio-Economic Indexes for Areas quintiles from the Australian Institute of Health and Welfare. Two ratios were calculated comparing quintile 1 (lowest) to the total Australian population, and comparing quintile 1 to quintile 5 (highest). Preference-based weights were derived using a discrete choice experiment survey (n = 710). Respondents chose between two programs, with varying gains in life expectancy going to a low- or a high-income group. A probit model incorporating nominal values of the difference in life expectancy was estimated to calculate the equity weights. Results: The epidemiological weights ranged from 1.2 to 1.5, with larger weights when quintile 5 was the denominator. The preference-based weights ranged from 1.3 (95% confidence interval 1.2–1.4) to 1.8 (95% confidence interval 1.6–2.0), with a tendency for increasing weights as the gains to the low-income group increased. Conclusions: Both methods derived plausible and consistent weights. Using weights of different magnitudes in sensitivity analysis would allow the appropriate weight to be considered by decision makers and stakeholders to reflect policy objectives.

Original languageEnglish
Number of pages7
JournalValue in Health
DOIs
Publication statusAccepted/In press - 2018

Keywords

  • cost-effectiveness analysis
  • equity
  • equity weighting
  • socioeconomic position

Cite this

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title = "Equity weights for socioeconomic position: two methods—survey of stated preferences and epidemiological data",
abstract = "Background: There is an implicit equity approach in cost-effectiveness analysis that values health gains of socioeconomic position groups equally. An alternative approach is to integrate equity by weighting quality-adjusted life-years according to the socioeconomic position group. Objectives: To use two approaches to derive equity weights for use in cost-effectiveness analysis in Australia, in contexts in which the use of the traditional nonweighted quality-adjusted life-years could increase health inequalities between already disadvantaged groups. Methods: Equity weights derived using epidemiological data used burden of disease and mortality data by Socio-Economic Indexes for Areas quintiles from the Australian Institute of Health and Welfare. Two ratios were calculated comparing quintile 1 (lowest) to the total Australian population, and comparing quintile 1 to quintile 5 (highest). Preference-based weights were derived using a discrete choice experiment survey (n = 710). Respondents chose between two programs, with varying gains in life expectancy going to a low- or a high-income group. A probit model incorporating nominal values of the difference in life expectancy was estimated to calculate the equity weights. Results: The epidemiological weights ranged from 1.2 to 1.5, with larger weights when quintile 5 was the denominator. The preference-based weights ranged from 1.3 (95{\%} confidence interval 1.2–1.4) to 1.8 (95{\%} confidence interval 1.6–2.0), with a tendency for increasing weights as the gains to the low-income group increased. Conclusions: Both methods derived plausible and consistent weights. Using weights of different magnitudes in sensitivity analysis would allow the appropriate weight to be considered by decision makers and stakeholders to reflect policy objectives.",
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Equity weights for socioeconomic position : two methods—survey of stated preferences and epidemiological data. / Lal, Anita; Mohebi, Mohammadreza; Sweeney, Rohan; Moodie, Marjory; Peeters, Anna; Carter, Rob.

In: Value in Health, 2018.

Research output: Contribution to journalArticleResearchpeer-review

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T1 - Equity weights for socioeconomic position

T2 - two methods—survey of stated preferences and epidemiological data

AU - Lal, Anita

AU - Mohebi, Mohammadreza

AU - Sweeney, Rohan

AU - Moodie, Marjory

AU - Peeters, Anna

AU - Carter, Rob

PY - 2018

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N2 - Background: There is an implicit equity approach in cost-effectiveness analysis that values health gains of socioeconomic position groups equally. An alternative approach is to integrate equity by weighting quality-adjusted life-years according to the socioeconomic position group. Objectives: To use two approaches to derive equity weights for use in cost-effectiveness analysis in Australia, in contexts in which the use of the traditional nonweighted quality-adjusted life-years could increase health inequalities between already disadvantaged groups. Methods: Equity weights derived using epidemiological data used burden of disease and mortality data by Socio-Economic Indexes for Areas quintiles from the Australian Institute of Health and Welfare. Two ratios were calculated comparing quintile 1 (lowest) to the total Australian population, and comparing quintile 1 to quintile 5 (highest). Preference-based weights were derived using a discrete choice experiment survey (n = 710). Respondents chose between two programs, with varying gains in life expectancy going to a low- or a high-income group. A probit model incorporating nominal values of the difference in life expectancy was estimated to calculate the equity weights. Results: The epidemiological weights ranged from 1.2 to 1.5, with larger weights when quintile 5 was the denominator. The preference-based weights ranged from 1.3 (95% confidence interval 1.2–1.4) to 1.8 (95% confidence interval 1.6–2.0), with a tendency for increasing weights as the gains to the low-income group increased. Conclusions: Both methods derived plausible and consistent weights. Using weights of different magnitudes in sensitivity analysis would allow the appropriate weight to be considered by decision makers and stakeholders to reflect policy objectives.

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