Regional differences in healthcare costs further explained: The contribution of health, lifestyle, loneliness and mastery.

Healthcare costs in the Netherlands are rising and vary considerably among regions. Explaining regional differences in healthcare costs can help policymakers in targeting appropriate interventions in order to restrain costs. Factors usually taken into account when analyzing regional differences in healthcare costs are demographic structure and socioeconomic status (SES). However, health, lifestyle, loneliness and mastery have also been linked to healthcare costs. Therefore, this study analyzes the contribution of health, lifestyle factors (BMI, alcohol consumption, smoking and physical activity), loneliness, and mastery to regional differences in healthcare costs. Analyses are performed in a linked dataset (n = 334,721) from the Dutch Public Health Services, Statistics Netherlands, the National Institute for Public Health and the Environment (year 2016), and the healthcare claims database Vektis (year 2017) with Poisson and zero-inflated binomial regressions. Regional differences in general practitioner consult costs remain significant even after taking into account health, lifestyle, loneliness, and mastery. Regional differences in costs for mental, pharmaceutical, and specialized care are less pronounced and can be explained to a large extent. For total healthcare costs, regional differences are mostly explained through the factors included in this study. Hence, addressing lifestyle factors, loneliness and mastery can help policymakers in restraining healthcare costs. In this study, the region of Zuid-Limburg represents the reference region. Use compare regions for health and healthcare costs (Regiovergelijker gezondheid en zorgkosten) in order to select all other Dutch regions as reference region. Supplementary Information: The online version of this article (10.1007/s12508-022-00369-4) contains supplementary material, which is available to authorized users.

The Role of Cohesion Policy Funds in Decreasing the Health Gaps Measured by the EURO-HEALTHY Population Health Index.

Social, economic, and environmental differences across the European Union significantly affect opportunities to move forward in achieving greater equity in health. Cohesion Policy (CP) funds can contribute positively through investments in the main determinants of health. The aim of this study is to analyze to what extent the planned investments for 2014-2020 are addressing the regional health gaps, in light of the population health index (PHI), a multidimensional measure developed by the EURO-HEALTHY project. The operational programs of all regions were analyzed, namely, the CP planned investments by field of intervention. Analysis of variance was performed to examine whether the regional scores in the PHI dimensions were statistically different across regions with different levels of development (measured by gross domestic product (GDP)). Results show that 98% of regions with worse performances on the PHI are less developed regions. Overall, all regions present planned investments in intervention fields linked to dimensions appraised within the PHI (e.g., employment, income, education, pollution). Yet, more needs to be done to focus regional investments in health determinants where regions still lag behind. The PHI has the potential to inform future CP restructuring, providing evidence to extend the current eligibility criteria to other dimensions beyond the GDP.

The Strategies to Address Regional Health Inequalities in Gyeongsangnam-Do: Health Plus Happiness Plus Projects / 農村醫學 地域保健

OBJECTIVES: This study was conducted to implement Health Plus Happiness Plus projects in Gyeongsangnam-Do and assess the policy implications of initiatives to address regional health inequalities. METHODS: Health Plus Happiness Plus projects were started as strategies to address regional health inequalities in Gyeongsangnam-Do. The principles of these projects are taken from the Health Action Zones initiatives in England: participation, partnership, resource concentration in project areas. The time period for these projects is from 2010 to 2017, and the total budget is 5.6 billion won. In 2010, a 6.8 hundred million won total budget was invested in 17 project areas. Such investments fell into four broad categories: establishment of the means and local framework; survey development to analyze the health determinants; development of an education and training center; and establishment of a technical support center. RESULTS: Education and training programs for practitioners and coordinators were provided, and project teams and project promotion committees were established in project areas. Health survey result briefing meetings were held, and 17 health committees were established in project areas. CONCLUSIONS: Health Plus Happiness Plus projects have some problems in relation to participation and partnerships, however, if these principled projects are performed continuously, they will contribute to a reduction of standardized mortality rate and regional health inequalities in Gyeongsangnam-Do and the improvement of residents' well-being in project areas.