The equity dimension in evaluations of the quality and outcomes framework: A systematic review

<p>Abstract</p> <p>Background</p> <p>Pay-for-performance systems raise concerns regarding inequity in health care because providers might select patients for whom targets can easily be reached. This paper aims to describe the evolution of pre-existing (in)equity in health care in the period after the introduction of the Quality and Outcomes Framework (QOF) in the UK and to describe (in)equities in exception reporting. In this evaluation, a theory-based framework conceptualising equity in terms of equal access, equal treatment and equal treatment outcomes for people in equal need is used to guide the work.</p> <p>Methods</p> <p>A systematic MEDLINE and Econlit search identified 317 studies. Of these, 290 were excluded because they were not related to the evaluation of QOF, they lacked an equity dimension in the evaluation, their qualitative research focused on experiences or on the nature of the consultation, or unsuitable methodology was used to pronounce upon equity after the introduction of QOF.</p> <p>Results</p> <p>None of the publications (n = 27) assessed equity in access to health care. Concerning equity in treatment and (intermediate) treatment outcomes, overall quality scores generally improved. For the majority of the observed indicators, all citizens benefit from this improvement, yet the extent to which different patient groups benefit tends to vary and to be highly dependent on the type and complexity of the indicator(s) under study, the observed patient group(s) and the characteristics of the study. In general, the introduction of QOF was favourable for the aged and for males. Total QOF scores did not seem to vary according to ethnicity. For deprivation, small but significant residual differences were observed after the introduction of QOF favouring less deprived groups. These differences are mainly due to differences at the practice level. The variance in exception reporting according to gender and socio-economic position is low.</p> <p>Conclusions</p> <p>Although QOF seems not to be socially selective at first glance, this does not mean QOF does not contribute to the inverse care law. Introducing different targets for specific patient groups and including appropriate, non-disease specific and patient-centred indicators that grasp the complexity of primary care might refine the equity dimension of the evaluation of QOF. Also, information on the actual uptake of care, information at the patient level and monitoring of individuals' health care utilisation tracks could make large contributions to an in-depth evaluation. Finally, evaluating pay-for-quality initiatives in a broader health systems impact assessment strategy with equity as a full assessment criterion is of utmost importance.</p

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease