Do regional characteristics predict developmental delay? Analyses of German school entry examination

Abstract Background Children's health and development are strongly linked to their living situation, including their family's socioeconomic position (SEP) and living region. However, research on the impact of the living region on children's development beyond family SEP is scarce. This study evaluated whether rurality and regional socioeconomic deprivation (DEP) are associated with children's development independently of family SEP. Methods The study used population-based data of 5-6.5 years old children (n = 22,801) from mandatory school entry examinations (SEE) in the German federal state of Brandenburg, which were examined in 2018/2019. The SEE data have been linked with data on i. rurality that was defined by an inverted population density and ii. regional DEP that were provided by the German Index of Socioeconomic Deprivation. By binary multilevel models, the predictive values of rurality and regional DEP for global developmental delay (GDD) were evaluated, while adjusting for family SEP. Results Children with high family SEP showed reduced odds for GDD compared to medium family SEP (female: OR = 4.26, CI95=3.14-5.79, male: OR = 3.46, CI95=2.83-4.22) and low family SEP (female: OR = 16.58, CI95=11.90-23.09, male: OR = 12.79, CI95=10.13-16.16). Regional DEP additionally predicted GDD, with higher odds for children from more deprived regions (female: OR = 1.35, CI95=1.13-1.62, male: OR = 1.20, CI95=1.05-1.39). Rurality did not predict GDD beyond family SEP and regional DEP. Conclusions In addition to family SEP, the regional DEP has an effect on children's developmental delay. Hence, Public Health should take into account regional socioeconomic conditions as determinant of health over the life course in addition to family SEP. Key messages • Regional socioeconomic deprivation contributes to inequalities in children's development and health. • Besides family SEP, regional socioeconomic circumstances are of particular interest to promote health over the life course.


Background:
Employment and income are important determinants of mental health (MH), but the extent to which unemployment effects are mediated by reduced income is unclear. We estimated the total effect (TE) of unemployment on MH and the controlled direct effect (CDE) not acting via income.

Methods:
We studied adults 25-64y from nine waves of the representative UK Household Longitudinal Study (n = 45,497/ obs = 202,297). Unemployment was defined as not being in paid employment; common mental disorder (CMD) was defined as a General Health Questionnaire-12 score !4. We conducted causal mediation analysis using inverse probability of treatment weights to estimate odds ratios (OR) and absolute differences for the effects of unemployment on CMD as measured in the same sweep, before (TE) and after (CDE) blocking the income pathway. The percentage mediated by income was 100 Ã (TE-CDE)/TE, with standard errors calculated via bootstrapping. Multiple imputation addressed missingness.

Conclusions:
In the UK, a high proportion of the short-term effect of unemployment on MH is not explained by income, particularly for those who are younger or already living in poverty. Population attributable fractions suggested 16.5% of CMD burden was due to unemployment, with 13.9% directly attributable to job loss rather than resultant income changes. Further research is needed across different European countries to determine how different welfare regimes might moderate these effects, and to investigate longer-term effects.

Key messages:
Unemployment has a clear detrimental effect on MH in the short-term.
Only a small proportion of this effect appears to be mediated by income.

Background:
Children's health and development are strongly linked to their living situation, including their family's socioeconomic position (SEP) and living region. However, research on the impact of the living region on children's development beyond family SEP is scarce. This study evaluated whether rurality and regional socioeconomic deprivation (DEP) are associated with children's development independently of family SEP.

Methods:
The study used population-based data of 5-6.5 years old children (n = 22,801) from mandatory school entry examinations (SEE) in the German federal state of Brandenburg, which were examined in 2018/2019. The SEE data have been linked with data on i. rurality that was defined by an inverted population density and ii. regional DEP that were provided by the German Index of Socioeconomic Deprivation. By binary multilevel models, the predictive values of rurality and regional DEP for global developmental delay (GDD) were evaluated, while adjusting for family SEP.

Conclusions:
In addition to family SEP, the regional DEP has an effect on children's developmental delay. Hence, Public Health should take into account regional socioeconomic conditions as determinant of health over the life course in addition to family SEP.
Key messages: Regional socioeconomic deprivation contributes to inequalities in children's development and health. Besides family SEP, regional socioeconomic circumstances are of particular interest to promote health over the life course.

Background:
Purchasing power parities (PPPs) are indicators of price level differences for all goods and services across countries. Their calculation follows the É KS method (https://ec.europa.eu/ eurostat/cache/metadata/en/prc_ppp_esms.htm). Prices of pharmaceuticals are collected by national statistical offices using different methods, from asking prices in pharmacies to retrieving scanner data. The sample is limited to 150 topselling medicines, which are not available in all countries. Consequently, the PLIs (price level indices) and PPPs derived are sometimes based on only 50 pharmaceuticals. In a cooperation between Eurostat and EURIPID the PPP calculation was alternatively done with the EURIPID database for 28 countries.

Methods:
The study compared the PLIs/PPPs derived from the E20-2 ''Furniture and health'' survey (aka CGS) with those from EURIPID for 2018-2020. The main challenge was to identify comparable products from the 224,448 products in EURIPID. For this we grouped those that shared the same 1) ATC, 2) active substance(s) & strength(s), 3) pack size group and 4) dosage form group (e.g., oromucosal) resulting in 157,186 distinctive products compared to 1,928 included in CGS. We used the Gross Retail price (GRP) as defined in the Eurostat PPP manual.

Results:
The ranking of PPPs was similar in both approaches, with Switzerland and Iceland in the lead and Poland and Hungary in the end. Only for some countries, e.g. the Netherlands deviations were identified. The Pearson correlation between the 2020 PLI for the Euripid subsample using the asterisk method with all products marked as representative and the CGS results was 0,946.

Conclusions:
Results from EURIPID show the same trend as the CGS. It is possible to replace the national data collection by a central source. This would reduce the data collection burden on the statistical offices and allows a closer monitoring of the evolution of pharmaceutical prices (bi-annual PPP publication instead of current 3-year interval) Key messages: To monitor affordability of medicines for all citizens it is important to compare prices on a regular basis with a simple tool as e.g. some products prices differed by 1000-times across countries. The EURIPID database (www.euripid.eu) allows detailed analysis of pharmaceutical prices in Europe and is available for free to non-commerical researchers.