Assessing risk of bias:a proposal for a unified framework for observational studies and randomized trials

BACKGROUND: Evidence based medicine aims to integrate scientific evidence, clinical experience, and patient values and preferences. Individual health care professionals need to appraise the evidence from randomized trials and observational studies when guidelines are not yet available. To date, tools for assessment of bias and terminologies for bias are specific for each study design. Moreover, most tools appeal only to methodological knowledge to detect bias, not to subject matter knowledge, i.e. in-depth medical knowledge about a topic. We propose a unified framework that enables the coherent assessment of bias across designs. METHODS: Epidemiologists traditionally distinguish between three types of bias in observational studies: confounding, information bias, and selection bias. These biases result from a common cause, systematic error in the measurement or common effect of the intervention and outcome respectively. We applied this conceptual framework to randomized trials and show how it can be used to identify bias. The three sources of bias were illustrated with graphs that visually represent researchers' assumptions about the relationships between the investigated variables (causal diagrams). RESULTS: Critical appraisal of evidence started with the definition of the research question in terms of the population of interest, the compared interventions and the main outcome. Next, we used causal diagrams to illustrate how each source of bias can lead to over- or underestimated treatment effects. Then, we discussed how randomization, blinded outcome measurement and intention-to-treat analysis minimize bias in trials. Finally, we identified study aspects that can only be appraised with subject matter knowledge, irrespective of study design. CONCLUSIONS: The unified framework encompassed the three main sources of bias for the effect of an assigned intervention on an outcome. It facilitated the integration of methodological and subject matter knowledge in the assessment of bias. We hope that graphical diagrams will help clarify debate among professionals by reducing misunderstandings based on different terminology for bias

The Effect of Growth and Inequality in Incomes on Health Inequality: Theory and Empirical Evidence from the European Panel

Europe aims at combining income growth with improvements in social cohesion as measured by income and health inequalities. We show that, theoretically, both aims can be reconciled only under very specific conditions concerning the type of growth and the income responsiveness of health. We investigate whether these conditions held in Europe in the nineties using panel data from the European Community Household Panel surveys. We use pooled interval regressions and inequality decompositions to demonstrate that (i) in all countries except Austria, the income elasticity of health is positive and increases with income, and (ii) that income growth was not pro-rich in most EU countries, resulting in little or no reductions in income inequality and modest increases in income-related health inequality in the majority of countries

Decomposing cross-country differences in quality adjusted life expectancy: the impact of value sets

Background: The validity, reliability and cross-country comparability of summary measures of population health (SMPH) have been persistently debated. In this debate, the measurement and valuation of nonfatal health outcomes have been defined as key issues. Our goal was to quantify and decompose international differences in health expectancy based on health-related quality of life (HRQoL). We focused on the impact of value set choice on cross-country variation. Methods: We calculated Quality Adjusted Life Expectancy (QALE) at age 20 for 15 countries in which EQ-5D population surveys had been conducted. We applied the Sullivan approach to combine the EQ 5D based HRQoL data with life tables from the Human Mortality Database. Mean HRQoL by country gender-age was estimated using a parametric model. We used nonparametric bootstrap techniques to compute confidence intervals. QALE was then compared across the six country-specific time trade-off value sets that were available. Finally, three counterfactual estimates were generated in order to assess the contribution of mortality, health states and healthstate values to cross-country differences in QALE. Results: QALE at age 20 ranged from 33 years in Armenia to almost 61 years in Japan, using the UK value set. The value sets of the other five countries generated different estimates, up to seven years higher. The relative impact of choosing a different value set differed across country-gender strata between 2% and 20%. In 50% of the countrygender strata the ranking changed by two or more positions across value sets. The decomposition demonstrated a varying impact of health states, health-state values, and mortality on QALE differences across countries. Conclusions: The choice of the value set in SMPH may seriously affect cross country comparisons of health expectancy, even across populations of similar levels of wealth and education. In our opinion, it is essential to get more insight into the drivers of differences in health-state values across populations. This will enhance the usefulness of health-expectancy measures.Technology, Policy and Managemen

Socio-economic status and self-reported tuberculosis: A multilevel analysis in a low-income township in the eastern cape, South Africa

Few studies have investigated the interplay of multiple factors affecting the prevalence of tuberculosis in developing countries. The compositional and contextual factors that affect health and disease patterns must be fully understood to successfully control tuberculosis. Experience with tuberculosis in South Africa was examined at the household level (overcrowding, a leaky roof, social capital, unemployment, income) and at the neighbourhood level (Gini coefficient of inequality, unemployment rate, headcount poverty rate). A hierarchical random-effects model was used to assess household-level and neighbourhoodlevel effects on self-reported tuberculosis experience. Every tenth household in each of the 20 Rhini neighbourhoods was selected for inclusion in the sample. Eligible respondents were at least 18 years of age and had been residents of Rhini for at least six months of the previous year. A Kish grid was used to select one respondent from each targeted household, to ensure that all eligible persons in the household stood an equal chance of being included in the survey. We included 1,020 households within 20 neighbourhoods of Rhini, a suburb of Grahamstown in the Eastern Cape, South Africa. About one-third of respondents (n=329; 32%) reported that there had been a tuberculosis case within the household. Analyses revealed that overcrowding (P≤0.05) and roof leakage (P≤0.05) contributed significantly to the probability of a household tuberculosis experience experience, whereas higher social capital (P≤0.01) significantly reduced this probability. Overcrowding, roof leakage and the social environment affected tuberculosis prevalence in this economically disadvantaged community. Policy makers should consider the possible benefits of programs that deal with housing and social environments when addressing the spread of tuberculosis in economically poor districts

Dear Policymaker: Have you made up your mind?

Objectives: To get insight in what criteria as presented in Health Technology Assessment (HTA) studies are important for decision makers in health care priority setting. Methods: We performed a discrete choice experiment (DCE) among Dutch health care professionals (policymakers, HTA experts, advanced HTA students). In 27 choice sets, we asked respondents to elect reimbursement of one of two different health care interventions, which represented unlabeled, curative treatments. Both treatments were incrementally compared to usual care. The results of the interventions were normal outputs of HTA studies with a societal perspective. Results were analysed using a multinomial logistic regression model. Upon completion of the questionnaire we discussed the exercise with policymakers. Results: Severity of disease, costs per QALY gained, individual health gain, and the budget impact were the most decisive decision criteria. A program targeting more severe diseases increased the probability of reimbursement dramatically. Uncertainty related to cost-effectiveness was also important. Respondents preferred health gains that include quality of life improvements over extension of life without improved quality of life. Savings in productivity costs were not crucial in decision making, although these are to be included in Dutch reimbursement dossiers for new drugs. Regarding subgroups, we found that policymakers attached relatively more weight to disease severity than others but less to uncertainty. Conclusions: Dutch policymakers and other health care professionals seem to have reasonably well articulated preferences: six of seven attributes were significant. Disease severity, budget impact, and cost-effectiveness were very important. The results are comparable to international studies, but reveal a larger set of important decision criteria