Equity in HTA : what doesn’t get measured, gets marginalised

When making recommendations about the public funding of new health technologies, policy makers typically pay close attention to quantitative evidence about the comparative effectiveness, cost effectiveness and budget impact of those technologies – what we might call “efficiency” criteria. Less attention is paid, however, to quantitative evidence about who gains and who loses from these public expenditure decisions, and whether those who gain are better or worse off than the rest of the population in terms of their health – what we might call “equity” criteria. Two studies recently published in this journal by Shmueli and colleagues suggest that this efficiency-oriented imbalance in the use of quantitative evidence may have unfortunate consequences – as the old adage goes: “what gets measured, gets done”. The first study, by Shmueli, Golan, Paolucci and Mentzakis, found that health policy makers in Israel think equity considerations are just as important as efficiency considerations – at least when it comes to making hypothetical technology funding decisions in a survey. By contrast, the second study – by Shmueli alone – found that efficiency rules the roost when it comes to making real decisions about health technology funding in Israel. Both studies have limitations and potential biases, and more research is needed using qualitative methods and more nuanced survey designs to determine precisely which kinds of equity consideration decision makers think are most important and why these considerations do not appear to be given much weight in decision making. However, the basic overall finding from the two studies seems plausible and important. It suggests that health technology funding bodies need to pay closer attention to equity considerations, and to start making equity a quantitative endpoint of health technology assessment using the methods of equity-informative economic evaluation that are now available

THE ECONOMICS OF NON-COMMUNICABLE DISEASES IN RURAL BANGLADESH

Background: In Matlab, Bangladesh, a rural sub-district with ongoing demographic surveillance, an epidemiological transition is well under way with an emerging burden of disease attributed to non-communicable diseases (NCD). In this setting there is a need to understand NCDs in terms of socioeconomic determinants and economic impacts to individuals and households, which helps inform the decision to develop NCD-related public policies. This work addresses these issues by characterizing the education gradient in mortality over a period of 24 years and by evaluating the household-level economic impacts after an adult NCD death and subsequent coping strategies. Methods: Paper #1 uses data from the routine Matlab surveillance system for the populations in 1982, 1996 and 2005, looking prospectively at both NCDs and infectious disease mortality over a five year follow-up period. Cox proportional hazard models are used for multivariate analysis to assess the education gradient in mortality for each broad cause of death category and to what extent components of wealth, occupation and marital status contribute to this gradient. In papers #2 and #3, all of the adult NCD deaths in 2010 in Matlab were identified and directly matched to a comparison group of households with no deaths. A regression standardization approach is used in Paper #2 to obtain a marginal estimate of the relative risk of a household being poor after an NCD death in terms of an asset-based wealth index, self-rated economic condition and land ownership. Paper #3 examines the coping strategies that households use after an NCD death. Logistic regression is then used to look at household and individual-level characteristics related to coping and an econometric difference-in-difference (DiD) approach is used to examine changes in household composition. Results: Paper #1 finds a larger education gradient for females for both NCD and infectious disease mortality when the data is pooled for all three time periods. For both males and females, a larger gradient is also found for infectious diseases. Marital status of an individual explains more of the education gradient in mortality than occupational status or household wealth. Paper #2 shows that there is a 14-19% increased risk of a household being poor two years after an adult NCD death, depending on which measure of economic status is used. Individual characteristics of a male death, prime age death or death of a married household member leads to a higher risk of a household being poor. In Paper #3, the results for coping enriches this picture further. The most common coping strategy among households after the death was the reduction of spending on basic household items. A prime age death is positively associated with the most number of coping strategies, four, and there is evidence that poorer households have more limited coping options. The DiD results for household composition show that households moderately replace human capital in terms of recruiting new adults to the household and that households are more likely to recruit adult females after a prime age death. Conclusion: The rising NCD burden in low income countries means that more understanding of the economic impacts of these diseases is needed. Using census data in the demographic surveillance system in rural Matlab, Bangladesh, this dissertation explores the individuals and household economic impacts associated with NCD deaths. An NCD death has the potential to impose severe economic consequences for households, impacting household wealth and propagating a poverty trap where poor household are not able to make gains in terms of economic mobility. Health shocks from NCDs lead to coping strategies that may have long term negative consequences for households. This appears to be especially true when the death is to a prime, working-age household member. Policy Recommendation: This work emphasizes the need for more intense prevention efforts for NCDs in rural Bangladesh. In Matlab, where this study is based, there have been intensive efforts to reduce maternal and childhood mortality that has been documented as a global success story. This work shows that there are important distributional and efficiency concerns related to NCD health that should motivate more public intervention. In terms of equity, there are higher rates of mortality among the least educated and health gains can be made with continuing progress in rural education and access to social psychological resources. As well, there may be longer term costs to household members, in terms of a poverty trap, when there is an NCD death. Better access to financial protection resources and preventive care is needed. This is especially true for households that are at risk of having a premature adult death. The review of the economic impacts from NCDs that are provided from this work provide an argument for developing more NCD-related policies in rural Bangladesh

Who benefits from health insurance? : Uncovering heterogeneous policy impacts using causal machine learning.

To be able to target health policies more efficiently, policymakers require knowledge about which individuals benefit most from a particular programme. While traditional approaches for subgroup analyses are constrained only to consider a small number of arbitrarily set, pre-defined subgroups, recently proposed causal machine learning (CML) approaches help explore treatment-effect heterogeneity in a more flexible yet principled way. This paper illustrates one such approach – ‘causal forests’ – in evaluating the effect of mothers’ health insurance enrolment in Indonesia. Contrasting two health insurance schemes (subsidised and contributory) to no insurance, we find beneficial average impacts of enrolment in contributory health insurance on maternal health care utilisation and infant mortality. For subsidised health insurance, however, both effects were smaller and not statistically significant. The causal forest algorithm identified significant heterogeneity in the impacts of the contributory insurance scheme: disadvantaged mothers (i.e. with lower wealth quintiles, lower educated, or in rural areas) benefit the most in terms of increased health care utilisation. No significant heterogeneity was found for the subsidised scheme, even though this programme targeted vulnerable populations. Our study demonstrates the power of CML approaches to uncover the heterogeneity in programme impacts, hence providing policymakers with valuable information for programme design

The relationship between non-communicable disease occurrence and poverty—evidence from demographic surveillance in Matlab, Bangladesh

In low-income countries, a growing proportion of the disease burden is attributable to non- communicable diseases (NCDs). There is little knowledge, however, of their impact on wealth, human capital, economic growth or household poverty. This article estimates the risk of being poor after an NCD death in the rural, low-income area of Matlab, Bangladesh. In a matched cohort study, we estimated the 2-year relative risk (RR) of being poor in Matlab households with an NCD death in 2010. Three separate measures of household economic status were used as outcomes: an asset-based index, self-rated household economic condition and total household landholding. Several estimation methods were used including contingency tables, log-binomial regression and regression standardization and machine learning. Households with an NCD death had a large and significant risk of being poor. The unadjusted RR of being poor after death was 1.19, 1.14 and 1.10 for the asset quintile, self-rated condition and landholding outcomes. Adjusting for household and individual level independent variables with log-binomial regression gave RRs of 1.19 [standard error (SE) 0.09], 1.16 (SE 0.07) and 1.14 (SE 0.06), which were found to be exactly the same using regression standardization (SE: 0.09, 0.05, 0.03). Machine learning-based standardization produced slightly smaller RRs though still in the same order of magnitude. The findings show that efforts to address the burden of NCD may also combat household poverty and provide a return beyond improved health. Future work should attempt to disentangle the mechanisms through which economic impacts from an NCD death occur

Effects of a sugar-sweetened beverage tax on prices and affordability of soft drinks in Chile : A time series analysis

Chile is one of several countries that recently implemented a fiscal policy to reduce soft drink (SD) intake and obesity. In 2014 the government increased the existing ad-valorem tax on high-sugar SD by 5% and decreased by 3% the tax on low-sugar SD, based on a 6.25gr/100 ml sugar threshold. This study aims to evaluate the tax modification passed-on to consumers through prices, and to calculate changes in affordability of SDs. We analysed nationally representative consumer price index data of 41 soft drinks within 6 beverage categories between 2009 and 2016. Price change post-tax implementation was estimated for different categories (carbonates, juices, concentrates, waters and energy-sport drinks), using time-series analyses. In addition, changes in affordability were evaluated by estimating the changes in prices relative to wages. The price of carbonates increased by 5.60% (CI 95% 3.18–8.03%) immediately after the tax was implemented. A sustained increase in the prices of concentrates was observed after the implementation. Unexpectedly, a smaller increase was also seen for the price of bottled water – a category that saw no tax change. There were no effects for juices and energy-sports drinks. There was a reduction in affordability for carbonates, concentrates and waters. Overall, the fiscal policy was effective in increasing prices and there are some signs of reduced affordability. Results varied substantially among categories directly affected by the tax policy. While for carbonates the price increase exceeded the tax change (‘over-shifting’), in other categories subject to a tax cut, a price reduction was expected but the opposite occurred. As the effect of the tax on prices differed between categories, the effects of the tax policy on consumption patterns are likely to be mixed. Our findings underline the need to better understand and anticipate price setting behaviour of firms in response to a tax

Adverse Selection in Community Based Health Insurance among Informal Workers in Bangladesh: An EQ-5D Assessment

Community-based Health Insurance (CBHI) schemes are recommended for providing financial risk protection to low-income informal workers in Bangladesh. We assessed the problem of adverse selection in a pilot CBHI scheme in this context. In total, 1292 (646 insured and 646 uninsured) respondents were surveyed using the Bengali version of the EuroQuol-5 dimensions (EQ-5D) questionnaire for assessing their health status. The EQ-5D scores were estimated using available regional tariffs. Multiple logistic regression was applied for predicting the association between health status and CBHI scheme enrolment. A higher number of insured reported problems in mobility (7.3%; p = 0.002); self-care (7.1%; p = 0.000) and pain and discomfort (7.7%; p = 0.005) than uninsured. The average EQ-5D score was significantly lower among the insured (0.704) compared to the uninsured (0.749). The regression analysis showed that those who had a problem in mobility (m 1.25–2.17); self-care (OR = 2.29; 95% CI: 1.62–3.25) and pain and discomfort (OR = 1.43; 95% CI: 1.13–1.81) were more likely to join the scheme. Individuals with higher EQ-5D scores (OR = 0.46; 95% CI: 0.31–0.69) were less likely to enroll in the scheme. Given that adverse selection was evident in the pilot CBHI scheme, there should be consideration of this problem when planning scale-up of these kind of schemes

Fairer decisions, better health for all : Health equity and cost-effectiveness analysis

This report provides a non-technical introduction to practical methods for using cost-effectiveness analysis to address health equity concerns, with applications to low-, middle- and high-income countries. These methods can provide information about the likely impacts of alternative health policy decisions on inequalities in health, financial risk protection and other health-related outcomes that may be considered unfair, allowing for the distribution of costs as well as benefits. They can also provide information about the trade-offs that sometimes arise between improving total health and reducing health inequalities of different kinds. We distinguish three general ways of using cost-effectiveness analysis to address health equity concerns: (1) equity impact analysis, which quantifies the distribution of costs and effects across a population by equity-relevant variables such as socioeconomic status, ethnicity, location, gender, age and severity of illness; (2) equity constraint analysis, which counts the cost of choosing fairer but less cost-effective options; and (3) equity weighting analysis, which uses equity weights or parameters to explore how much concern for equity is required to choose fairer but less cost-effective options. We hope this report will raise awareness of the practical tools of cost-effectiveness analysis that are now available to help give health care and public health policy makers a better understanding of who gains and who loses from their priority setting decisions

Digital mobility sub-study in the Parkinson's Progressive Marker Initiative (PPMI) study

This poster details the gait sub-study project in PPMI. The sub-study aims to test the feasibility and validity of digital mobility data for enrichment of the prodromal screening and to assess the sensitivity of these measures to early phase progression in prodromal and recently diagnosed patients with Parkinson’s disease. The poster details the protocol and the initial preliminary results of the first 21 participants. The post5er was presented in the Movement Disorders Society Conference held in Copenhagen, Denmark in Sept 2023