Background We investigated associations of area-level deprivation with obstetric and perinatal outcomes in a large population-based routine dataset. Methods We used the data of n = 827,105 deliveries ...who were born in hospitals between 2009 to 2016 in Bavaria, Germany. The Bavarian Index of Multiple Deprivation (BIMD) on district level was assigned to each mother by the zip code of her residential address. We calculated odds ratios (ORs) with 95% confidence intervals (CIs) for preterm deliveries, Caesarian sections (CS), stillbirths, small for gestational age (SGA) births and low 5-minute Apgar scores by BIMD quintiles with and without adjustment for potential confounders. Results We observed a significantly increased risk for preterm deliveries in mothers from the most deprived compared to the least deprived districts (e.g. OR 95% CI for highest compared to lowest deprivation quintile: 1.06 1.03, 1.09) in adjusted analyses. Increased deprivation was also associated with higher SGA and secondary CS rates, but with lower proportions of stillbirths, primary CS and low Apgar scores. When one large clinic with an unusually high stillbirth rate was excluded, the association of BIMD with stillbirths was attenuated and almost disappeared. Conclusions We found that area-level deprivation in Bavaria was positively associated with preterm and SGA births, confirming previous studies. In contrast, the finding of an inverse association between deprivation and both stillbirth rates and low Apgar score came somewhat surprising. However, we conclude that the stillbirths finding is spurious and reflects regional bias due to a clinic which seems to specialize in termination of pregnancies.
Abstract
Background
Early appropriate diagnosis and treatment of interstitial lung diseases (ILD) is crucial to slow disease progression and improve survival. Yet it is unknown whether initial ...management in an expert centre is associated with improved outcomes. Therefore, we assessed mortality, hospitalisations and health care costs of ILD patients initially diagnosed and managed in specialised ILD centres versus non-specialised centres and explored differences in pharmaceutical treatment patterns.
Methods
An epidemiological claims data analysis was performed, including patients with different ILD subtypes in Germany between 2013 and 2018. Classification of specialised centres was based on the number of ILD patients managed and procedures performed, as defined by the European Network on Rare Lung Diseases. Inverse probability of treatment weighting was used to adjust for covariates. Mortality and hospitalisations were examined via weighted Cox models, cost differences by weighted gamma regression models and differences in treatment patterns with weighted logistic regressions.
Results
We compared 2022 patients managed in seven specialised ILD centres with 28,771 patients managed in 1156 non-specialised centres. Specialised ILD centre management was associated with lower mortality (HR: 0.87, 95% CI 0.78; 0.96), lower all-cause hospitalisation (HR: 0.93, 95% CI 0.87; 0.98) and higher respiratory-related costs (€669, 95% CI €219; €1156). Although risk of respiratory-related hospitalisations (HR: 1.00, 95% CI 0.92; 1.10) and overall costs (€− 872, 95% CI €− 75; €1817) did not differ significantly, differences in treatment patterns were observed.
Conclusion
Initial management in specialised ILD centres is associated with improved mortality and lower all-cause hospitalisations, potentially due to more differentiated diagnostic approaches linked with more appropriate ILD subtype-adjusted therapy.
ObjectivesThis study aimed to assess the impact of using different weighting procedures for the German Index of Multiple Deprivation (GIMD) investigating their link to mortality rates.Design and ...settingIn addition to the original (normative) weighting of the GIMD domains, four alternative weighting approaches were applied: equal weighting, linear regression, maximization algorithm and factor analysis. Correlation analyses to quantify the association between the differently weighted GIMD versions and mortality based on district-level official data from Germany in 2010 were applied (n=412 districts).Outcome measuresTotal mortality (all age groups) and premature mortality (<65 years).ResultsAll correlations of the GIMD versions with both total and premature mortality were highly significant (p<0.001). The comparison of these associations using Williams’s t-test for paired correlations showed significant differences, which proved to be small in respect to absolute values of Spearman’s rho (total mortality: between 0.535 and 0.615; premature mortality: between 0.699 and 0.832).ConclusionsThe association between area deprivation and mortality proved to be stable, regardless of different weighting of the GIMD domains. The theory-based weighting of the GIMD should be maintained, due to the stability of the GIMD scores and the relationship to mortality.
Overweight and obesity are severe public health problems worldwide. Obesity can lead to chronic diseases such as type 2 diabetes mellitus. Environmental factors may affect lifestyle aspects and are ...therefore expected to influence people's weight status. To assess environmental risks, several methods have been tested using geographic information systems. Freely available data from online geocoding services such as OpenStreetMap (OSM) can be used to determine the spatial distribution of these obesogenic factors. The aim of our study was to develop and test a spatial obesity risk score (SORS) based on data from OSM and using kernel density estimation (KDE).
Obesity-related factors were downloaded from OSM for two municipalities in Bavaria, Germany. We visualized obesogenic and protective risk factors on maps and tested the spatial heterogeneity via Ripley's K function. Subsequently, we developed the SORS based on positive and negative KDE surfaces. Risk score values were estimated at 50 random spatial data points. We examined the bandwidth, edge correction, weighting, interpolation method, and numbers of grid points. To account for uncertainty, a spatial bootstrap (1000 samples) was integrated, which was used to evaluate the parameter selection via the ANOVA F statistic.
We found significantly clustered patterns of the obesogenic and protective environmental factors according to Ripley's K function. Separate density maps enabled ex ante visualization of the positive and negative density layers. Furthermore, visual inspection of the final risk score values made it possible to identify overall high- and low-risk areas within our two study areas. Parameter choice for the bandwidth and the edge correction had the highest impact on the SORS results.
The SORS made it possible to visualize risk patterns across our study areas. Our score and parameter testing approach has been proven to be geographically scalable and can be applied to other geographic areas and in other contexts. Parameter choice played a major role in the score results and therefore needs careful consideration in future applications.
Socioeconomic inequalities in cancer survival have been reported in various countries but it is uncertain to what extent they persist in countries with relatively comprehensive health insurance ...coverage such as Germany. We investigated the association between area‐based socioeconomic deprivation on municipality level and cancer survival for 25 cancer sites in Germany. We used data from seven population‐based cancer registries (covering 32 million inhabitants). Patients diagnosed in 1998 to 2014 with one of 25 most common cancer sites were included. Area‐based socioeconomic deprivation was assessed using the categorized German Index of Multiple Deprivation (GIMD) on municipality level. We estimated 3‐month, 1‐year, 5‐year and 5‐year conditional on 1‐year age‐standardized relative survival using period approach for 2012 to 2014. Trend analyses were conducted for periods between 2003‐2005 and 2012‐2014. Model‐based period analysis was used to calculate relative excess risks (RER) adjusted for age and stage. In total, 2 333 547 cases were included. For all cancers combined, 5‐year survival rates by GIMD quintile were 61.6% in Q1 (least deprived), 61.2% in Q2, 60.4% in Q3, 59.9% in Q4 and 59.0% in Q5 (most deprived). For most cancer sites, the most deprived quintile had lower 5‐year survival compared to the least deprived quintile even after adjusting for stage (all cancer sites combined, RER 1.16, 95% confidence interval 1.14‐1.19). For some cancer sites, this association was stronger during short‐term follow‐up. Trend analyses showed improved survival from earlier to recent periods but persisting deprivation differences. The underlying reasons for these persisting survival inequalities and strategies to overcome them should be further investigated.
What's new?
Socioeconomic inequality is known to affect cancer survival rates, even in countries with universal health‐care coverage. This large German study analyzed smaller‐scale population areas (~1200 residents each), and found that survival rates for most common cancers were lower among patients from lower‐income areas than among those from more affluent areas. The underlying causes of this association between socioeconomic deprivation and decreased cancer survival should be further investigated, as should strategies to correct these causal factors.
There is increasing evidence that prevention programmes for type 2 diabetes mellitus (T2DM) and obesity need to consider individual and regional risk factors. Our objective is to assess the ...independent association of area level deprivation with T2DM and obesity controlling for individual risk factors in a large study covering the whole of Germany.
We combined data from two consecutive waves of the national health interview survey 'GEDA' conducted by the Robert Koch Institute in 2009 and 2010. Data collection was based on computer-assisted telephone interviews. After exclusion of participants <30 years of age and those with missing responses, we included n=33,690 participants in our analyses. The outcome variables were the 12-month prevalence of known T2DM and the prevalence of obesity (BMI ≥ 30 kg/m(2)). We also controlled for age, sex, BMI, smoking, sport, living with a partner and education. Area level deprivation of the districts was defined by the German Index of Multiple Deprivation. Logistic multilevel regression models were performed using the software SAS 9.2.
Of all men and women living in the most deprived areas, 8.6% had T2DM and 16.9% were obese (least deprived areas: 5.8% for T2DM and 13.7% for obesity). For women, higher area level deprivation and lower educational level were both independently associated with higher T2DM and obesity prevalence highest area level deprivation: OR 1.28 (95% CI: 1.05-1.55) for T2DM and OR 1.28 (95% CI: 1.10-1.49) for obesity. For men, a similar association was only found for obesity OR 1.20 (95% CI: 1.02-1.41), but not for T2DM.
Area level deprivation is an independent, important determinant of T2DM and obesity prevalence in Germany. Identifying and targeting specific area-based risk factors should be considered an essential public health issue relevant to increasing the effectiveness of diabetes and obesity prevention.
Although socioeconomic inequalities in cancer survival have been demonstrated both within and between countries, evidence on the variation of the inequalities over time past diagnosis is sparse. ...Furthermore, no comprehensive analysis of socioeconomic differences in cancer survival in Germany has been conducted. Therefore, we analyzed variations in cancer survival for patients diagnosed with one of the 25 most common cancer sites in 1997–2006 in ten population‐based cancer registries in Germany (covering 32 million inhabitants). Patients were assigned a socioeconomic status according to the district of residence at diagnosis. Period analysis was used to derive 3‐month, 5‐year and conditional 1‐year and 5‐year age‐standardized relative survival for 2002–2006 for each deprivation quintile in Germany. Relative survival of patients living in the most deprived district was compared to survival of patients living in all other districts by model‐based period analysis. For 21 of 25 cancer sites, 5‐year relative survival was lower in the most deprived districts than in all other districts combined. The median relative excess risk of death over the 25 cancer sites decreased from 1.24 in the first 3 months to 1.16 in the following 9 months to 1.08 in the following 4 years. Inequalities persisted after adjustment for stage. These major regional socioeconomic inequalities indicate a potential for improving cancer care and survival in Germany. Studies on individual‐level patient data with access to treatment information should be conducted to examine the reasons for these socioeconomic inequalities in cancer survival in more detail.
What's new?
Around the world, affluent patients survive cancer better than impoverished ones, regardless of access to health insurance. In this study, the authors provide the first detailed analysis of how socio‐economic condition correlates with cancer survival in Germany, where all have health insurance. Using population‐based registries, they compared survival rates for 25 cancer sites. After adjusting for stage, they still found that patients in the poorest district had a lower five‐year survival rate than those in all other districts combined. The inequality was worst in the first three months, but persisted throughout the five years.
To strengthen the coordinating function of general practitioners (GPs) in the German healthcare system, a copayment of €10 was introduced in 2004. Due to a perceived lack of efficacy and a high ...administrative burden, it was abolished in 2012. The present cohort study investigates characteristics and differences of GP-coordinated and uncoordinated patients in Bavaria, Germany, concerning morbidity and ambulatory specialist costs and whether these differences have changed after the abolition of the copayment. We performed a retrospective routine data analysis, using claims data of the Bavarian Association of the Statutory Health Insurance Physicians during the period 2011–2012 (with copayment) and 2013–2016 (without copayment), covering 24 quarters. Coordinated care was defined as specialist contact only with referral. Multinomial regression modelling, including inverse probability of treatment weighting, was used for the cohort analysis of 500 000 randomly selected patients. Longitudinal regression models were calculated for cost estimation. Coordination of care decreased substantially after the abolition of the copayment, accompanied by increasing proportions of patients with chronic and mental diseases in the uncoordinated group, and a corresponding decrease in the coordinated group. In the presence of the copayment, uncoordinated patients had €21.78 higher specialist costs than coordinated patients, increasing to €24.94 after its abolition. The results indicate that patients incur higher healthcare costs for specialist ambulatory care when their care is uncoordinated. This effect slightly increased after abolition of the copayment. Beyond that, the abolition of the copayment led to a substantial reduction in primary care coordination, particularly affecting vulnerable patients. Therefore, coordination of care in the ambulatory setting should be strengthened.
Because it is impossible to know which statistical learning algorithm performs best on a prediction task, it is common to use stacking methods to ensemble individual learners into a more powerful ...single learner. Stacking algorithms are usually based on linear models, which may run into problems, especially when predictions are highly correlated. In this study, we develop a greedy algorithm for model stacking that overcomes this issue while still being very fast and easy to interpret. We evaluate our greedy algorithm on 7 different data sets from various biomedical disciplines and compare it to linear stacking, genetic algorithm stacking and a brute force approach in different prediction settings. We further apply this algorithm on a task to optimize the weighting of the single domains (e.g., income, education) that build the German Index of Multiple Deprivation (GIMD) to be highly correlated with mortality.
The greedy stacking algorithm provides good ensemble weights and outperforms the linear stacker in many tasks. Still, the brute force approach is slightly superior, but is computationally expensive. The greedy weighting algorithm has a variety of possible applications and is fast and efficient. A python implementation is provided.
Background
The choice of a hospital should be based on individual need and accessibility. For maternity hospitals, this includes known or expected risk factors, the geographic accessibility and level ...of care provided by the hospital. This study aims to identify factors influencing hospital choice with the aim to analyze if and how many deliveries are conducted in a risk-appropriate and accessible setting in Bavaria, Germany.
Methods
This is a cross-sectional secondary data analysis based on all first births in Bavaria (2015-18) provided by the Bavarian Quality Assurance Institute for Medical Care. Information on the mother and on the hospital were included. The Bavarian Index of Multiple Deprivation 2010 was used to account for area-level socioeconomic differences. Multiple logistic regression models were used to estimate the strength of association of the predicting factors and to adjust for confounding.
Results
We included 195,087 births. Distances to perinatal centers were longer than to other hospitals (16 km vs. 12 km). 10% of women with documented risk pregnancies did not deliver in a perinatal center. Regressions showed that higher age (OR 1.03; 1.02–1.03 95%-CI) and risk pregnancy (OR 1.44; 1.41–1.47 95%-CI) were associated with choosing a perinatal center. The distances travelled show high regional variation with a strong urban-rural divide.
Conclusion
In a health system with free choice of hospitals, many women chose a hospital close to home and/or according to their risks. However, this is not the case for 10% of mothers, a group that would benefit from more coordinated care.
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