Overcoming health inequalities: A participative evaluation framework fit for the task

Aims: Healthcare providers are confronted with the claim that the distribution of health and healthcare provision is inherently unfair. There is also a growing awareness that the tools and methodologies applied in tackling health inequalities require further development. Evaluations as well as interventions usually focus on population-based indicators, but do not always provide guidance for frontline service evaluation and delivery. That is why the evaluation framework presented here focuses on facilitating local service development, service provider and user involvement, and the adequate representation of different population groups. Methods: A participative evaluation framework was constructed by drawing on six common success characteristics extrapolated from the published literature and policies on health inequalities. This framework was then applied to an intervention addressing women’s psychosocial health needs in order to demonstrate its utility in practice. Results: The framework provides healthcare professionals with an evidence-based tool for evaluating projects or programmes targeting health inequalities in ways that are responsive to local contexts and stakeholders. Conclusion: This participative evaluation framework supports the identification of meaningful psychosocial and contextual indicators for assessing the diverse health and social needs of service users. It uses multi-dimensional indicators to assess health and social care needs, to inform local service development, and to facilitate the exchange of knowledge between researchers, service providers, and service users. The inherent responsiveness enables rigorous yet flexible action on local health inequalities.

Socio-economic inequalities in cerebral palsy prevalence in the United Kingdom: a register-based study

Evidence is unclear as to whether there is a socio-economic gradient in cerebral palsy (CP) prevalence beyond what would be expected from the socio-economic gradient for low birthweight, a strong risk factor for CP. We conducted a population-based study in five regions of the UK with CP registers, to investigate the relationship between CP prevalence and socio-economic deprivation, and how it varies by region, by birthweight and by severity and type of CP. The total study population was 1 657 569 livebirths, born between 1984 and 1997. Wards of residence were classified into five quintiles according to a census-based deprivation index, from Q1 (least deprived) to Q5 (most deprived). Socio-economic gradients were modelled by Poisson regression, and region-specific estimates combined by meta-analysis.

The prevalence of postneonatally acquired CP was 0.14 per 1000 livebirths overall. The mean deprivation gradient, expressed as the relative risk in the most deprived vs. the least deprived quintile, was 1.86 (95% confidence interval [95% CI 1.19, 2.88]). The prevalence of non-acquired CP was 2.22 per 1000 livebirths. For non-acquired CP the gradient was 1.16 [95% CI 1.00, 1.35]. Evidence for a socio-economic gradient was strongest for spastic bilateral cases (1.32 [95% CI 1.09, 1.59]) and cases with severe intellectual impairment (1.59 [95% CI 1.06, 2.39]). There was evidence for differences in gradient between regions. The gradient of risk of CP among normal birthweight births was not statistically significant overall (1.21 [95% CI 0.95, 1.54]), but was significant in two regions. There was non-significant evidence of a reduction in gradients over time.

The reduction of the higher rates of postneonatally acquired CP in the more socioeconomically deprived areas is a clear goal for prevention. While we found evidence for a socio-economic gradient for non-acquired CP of antenatal or perinatal origin, the picture was not consistent across regions, and there was some evidence of a decline in inequalities over time. The steeper gradients in some regions for normal birthweight cases and cases with severe intellectual impairment require further investigation.

Increasing the accuracy of Nitrogen Dioxide (NO2) pollution mapping using geographically weighted regression (GWR) and geostatistics.

Nitrogen Dioxide (NO2) is known to act as an environmental trigger for many respiratory illnesses. As a pollutant it is difficult to map accurately, as concentrations can vary greatly over small distances. In this study three geostatistical techniques were compared, producing maps of NO2 concentrations in the United Kingdom (UK). The primary data source for each technique was NO2 point data, generated from background automatic monitoring and background diffusion tubes, which are analysed by different laboratories on behalf of local councils and authorities in the UK. The techniques used were simple kriging (SK), ordinary kriging (OK) and simple kriging with a locally varying mean (SKlm). SK and OK make use of the primary variable only. SKlm differs in that it utilises additional data to inform prediction, and hence potentially reduces uncertainty. The secondary data source was Oxides of Nitrogen (NOx) derived from dispersion modelling outputs, at 1km x 1km resolution for the UK. These data were used to define the locally varying mean in SKlm, using two regression approaches: (i) global regression (GR) and (ii) geographically weighted regression (GWR). Based upon summary statistics and cross-validation prediction errors, SKlm using GWR derived local means produced the most accurate predictions. Therefore, using GWR to inform SKlm was beneficial in this study.