Socioeconomic deprivation is inversely associated with measles incidence: a longitudinal small-area analysis, Germany, 2001 to 2017

Background Although measles is endemic throughout the World Health Organization European Region, few studies have analysed socioeconomic inequalities and spatiotemporal variations in the disease’s incidence. Aim To study the association between socioeconomic deprivation and measles incidence in Germany, while considering relevant demographic, spatial and temporal factors. Methods We conducted a longitudinal small-area analysis using nationally representative linked data in 401 districts (2001–2017). We used spatiotemporal Bayesian regression models to assess the potential effect of area deprivation on measles incidence, adjusted for demographic and geographical factors, as well as spatial and temporal effects. We estimated risk ratios (RR) for deprivation quintiles (Q1–Q5), and district-specific adjusted relative risks (ARR) to assess the area-level risk profile of measles in Germany. Results The risk of measles incidence in areas with lowest deprivation quintile (Q1) was 1.58 times higher (95% credible interval (CrI): 1.32–2.00) than in those with highest deprivation (Q5). Areas with medium-low (Q2), medium (Q3) and medium-high deprivation (Q4) had higher adjusted risks of measles relative to areas with highest deprivation (Q5) (RR: 1.23, 95%CrI: 0.99–1.51; 1.05, 95%CrI: 0.87–1.26 and 1.23, 95%CrI: 1.05–1.43, respectively). We identified 54 districts at medium-high risk for measles (ARR > 2) in Germany, of which 22 were at high risk (ARR > 3). Conclusion Socioeconomic deprivation in Germany, one of Europe’s most populated countries, is inversely associated with measles incidence. This association persists after demographic and spatiotemporal factors are considered. The social, spatial and temporal patterns of elevated risk require targeted public health action and policy to address the complexity underlying measles epidemiology.

Evaluating equality in prescribing Novel Oral Anticoagulants (NOACs) in England: The protocol of a Bayesian small area analysis

Background Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting about 1.6% of the population in England. Novel oral anticoagulants (NOACs) are approved AF treatments that reduce stroke risk. In this study, we estimate the equality in individual NOAC prescriptions with high spatial resolution in Clinical Commissioning Groups (CCGs) across England from 2014 to 2019. Methods A Bayesian spatio-temporal model will be used to estimate and predict the individual NOAC prescription trend on ‘prescription data’ as an indicator of health services utilisation, using a small area analysis methodology. The main dataset in this study is the “Practice Level Prescribing in England,” which contains four individual NOACs prescribed by all registered GP practices in England. We will use the defined daily dose (DDD) equivalent methodology, as recommended by the World Health Organization (WHO), to compare across space and time. Four licensed NOACs datasets will be summed per 1,000 patients at the CCG-level over time. We will also adjust for CCG-level covariates, such as demographic data, Multiple Deprivation Index, and rural-urban classification. We aim to employ the extended BYM2 model (space-time model) using the RStan package. Discussion This study suggests a new statistical modelling approach to link prescription and socioeconomic data to model pharmacoepidemiologic data. Quantifying space and time differences will allow for the evaluation of inequalities in the prescription of NOACs. The methodology will help develop geographically targeted public health interventions, campaigns, audits, or guidelines to improve areas of low prescription. This approach can be used for other medications, especially those used for chronic diseases that must be monitored over time.

Monitoring the spatiotemporal epidemiology of Covid-19 incidence and mortality: a small-area analysis in Germany

Background: As response to the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), countries worldwide have implemented mitigation and control measures at national and subnational level. Timely monitoring of risks of SARS-CoV-2 incidence and associated deaths at small-area level is essential to inform local response strategies. However, the potentials of spatial epidemiology to contribute to this aim are yet untapped in most countries. Using the example of Germany, we analysed the spatiotemporal epidemiology of SARS-CoV-2 incidence and associated deaths at district level to develop a tool for monitoring incidence and mortality rates and to estimate district-specific risks of disease incidence. Methods: We conducted a longitudinal small-area analysis for 401 districts to assess the district-specific risks of SARS-CoV-2 incidence by using nationally representative data from the national surveillance system in Germany on a daily basis (January 28 th to May 4 th 2020). We used a Bayesian spatiotemporal model to estimate the district-specific risk ratios (RR) of SARS-CoV-2 incidence and the posterior exceedance probability for RR thresholds greater than 1, 2 or 3, respectively. We further calculated standardised incidence (SIR) and mortality ratios (SMR) stratified by sex and age groups to assess the spatial distribution of SARS-CoV-2 incidence and deaths. Results: A total of 85 districts (21 % of all districts) showed a RR greater than 3, and 63 districts (16 % of all districts) exceed the RR threshold with a probability of greater than 80 %. Median RR was 1.19 (range 0-523.08), and the median SIR and SMR were 0.34 (range 0-423.94) and 0 (range 0-343.39), respectively. Elevated RR, and correspondingly high SIR and SMR, were observed in at-risk districts (identified by the spatiotemporal model) in southern and western districts of Germany. Daily updates of district-specific risk, SIR and SMR are implemented in a web-based platform. Conclusions: Our approach provides an informative and timely tool to monitor the district-specific risks of SARS-CoV-2 incidence and associated deaths. This approach can be used to inform local authorities for decision-making and strategy planning on containing the SARS-CoV-2 pandemic.