Subscribe to RSS
DOI: 10.1055/a-2326-6768
Spatial and Socioeconomic Patterns of Mental Health and Healthcare Utilization in Cologne, Germany
Article in several languages: English | deutsch Funding Information We acknowledge support for the Article Processing Charge from the DFG (German Research Foundation, 491454339). — http://dx.doi.org/10.13039/501100001659; 491454339Abstract
Background Children and adolescents are significantly tied to their family's socioeconomic position and living environment. Neighbourhood and the living environment have been identified as potential risk factors for mental disorders in this age group.
Aim of the Study The aim of the study was to investigate the distribution of mental and behavioural disorders (prevalence) and the provision of mental health services for children and adolescents aged 0–19 years in the city of Cologne. In particular, the study aimed to examine the association of these factors with area deprivation and the availability of mental health services covered by statutory health insurance. Finally, possible spatial variations in these aspects were analysed.
Method Claims data of children and adolescents aged 0 to 19 years included in four statutory health insurance of the year 2021 were analysed. A deprivation index using data on the level of the ZIP code area was calculated. Analyses were carried out descriptively, using ordinary least squares (OLS) and geographically weighted regression (GWR).
Results The prevalence of mental and behavioural disorders in children and adolescents varied across ZIP code areas, with higher rates in the northern, southern, and eastern parts of the city. The results indicated that the use of services by male children and adolescents with a prevalent diagnosis of mental and behavioural disorders was higher in areas with a higher density of healthcare providers. However, prevalence was on the whole lower in areas with a higher density of healthcare providers. In addition, the density of health care providers was higher in the city centre with comparatively lower deprivation.
Conclusion These results indicate inadequate access to care for children and young people outside the city centre. However, due to the heterogeneity of the population in these areas, this study provides only preliminary insights. Data with a finer geographic resolution are needed for further research in order to analyse the association further.
Publication History
Article published online:
19 August 2024
© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Georg Thieme Verlag
Rüdigerstraße 14, 70469 Stuttgart, Germany
-
References
- 1 World Health Organisation, UN-HABITAT. Hidden cities: unmasking and overcoming health inequities in urban settings.; 2010
- 2 Dahlgren G, Whitehead M. The Dahlgren-Whitehead model of health determinants: 30 years on and still chasing rainbows. Public Health 2021; 199: 20-24
- 3 Adjaye-Gbewonyo K, Kawachi I. Use of the Yitzhaki Index as a test of relative deprivation for health outcomes: a review of recent literature. Soc Sci Med 2012; 75: 129-137
- 4 Hoffmann S, Tschorn M, Michalski N. et al. Association of regional socioeconomic deprivation and rurality with global developmental delay in early childhood: Data from mandatory school entry examinations in Germany. Health Place 2022; 75: 102794
- 5 Matthews H, Limb M. Defining an agenda for the geography of children: review and prospect. Progress in Human Geography 1999; 23: 61-90
- 6 Visser K, Bolt G, Finkenauer C. et al. Neighbourhood deprivation effects on young people's mental health and well-being: A systematic review of the literature. Soc Sci Med 2021; 270: 113542
- 7 Bronfenbrenner U. The Ecology of Human Development. Experiments by nature and design. Cambridge, Massachusetts, and London. Harvard University Press; 1979
- 8 Townsend P. Poverty in the United Kingdom. A survey of household resources and standards of living. London: Lane; 1979
- 9 Karmann A, Weinhold I, Wende D. Area Deprivation and its Impact on Population Health: Conceptual Aspects, Measurement and Evidence from Germany. Review of Economics 2019; 70: 69-98
- 10 Buka SL, Monuteaux M, Earlsi F. The Epidemiology of Child and Adolescent Mental Disorders. In: Tsuang MT, Tohen M, Hrsg. Textbook in psychiatric epidemiology. 2. Aufl. Hoboken, N.J: Wiley-Liss; 2010: 629-655
- 11 Ma L, Huang Y, Liu T. Unequal impact of the COVID-19 pandemic on mental health: Role of the neighborhood environment. Sustain Cities Soc 2022; 87: 104162
- 12 Ravens-Sieberer U, Kaman A, Otto C. et al. Psychische Gesundheit und Lebensqualität von Kindern und Jugendlichen während der COVID-19-Pandemie – Ergebnisse der COPSY-Studie. 2020;
- 13 Lemkow-Tovías G, Lemkow L, Cash-Gibson L. et al. Impact of COVID-19 inequalities on children: An intersectional analysis. Sociology of Health & Illness 2023; 45: 145-162
- 14 Poß-Doering R, Hegelow M, Borchers M. et al. Evaluating the structural reform of outpatient psychotherapy in Germany (ES-RiP trial) – a qualitative study of provider perspectives. BMC Health Serv Res 2021; 21: 1204
- 15 Rabe-Menssen C, Ruh M, Dazer A. Die Versorgungssituation seit der Reform der Psychotherapie-Richtlinie 2017: Ergebnisse der DPtV-Onlineumfragen 2017 und 2018 zu Wartezeiten. Psychotherapie Aktuell 2019; 25-34
- 16 Ravens-Sieberer U, Kaman A, Otto C. et al. Mental Health and Quality of Life in Children and Adolescents During the COVID-19 Pandemic-Results of the Copsy Study. Dtsch Arztebl Int 2020; 117: 828-829
- 17 KVB. Die Bedarfsplanung. Grundlagen, Instrumente und Umsetzung; 2020
- 18 Fülöp G, Kopetsch T, Schöpe P. Bedarfsgerechte Versorgungsplanung. Gesundheits- und Sozialpolitik 2007; 57-63
- 19 Schillen P, In der Schmitten J, Danielzik K. et al. Primärärztliche Versorgungsungleichheiten zu Ungunsten der Bevölkerung sozial benachteiligter Stadtgebiete – eine Fallanalyse am Beispiel der Stadt Essen. Gesundheitswesen 2023; 85: 1131-1139
- 20 Kistemann T, Schröer M-A. Kleinräumige kassenärztliche Versorgung und subjektives Standortwahlverhalten von Vertragsärzten in einem überversorgten Planungsgebiet. Gesundheitswesen 2007; 69: 593-600
- 21 Strumann C, Emcke T, Flägel K. et al. Regionale Unterschiede zwischen Fachärztinnen und Fachärzten für Allgemeinmedizin und hausärztlich tätigen Internistinnen und Internisten in der hausärztlichen Versorgung. Z Evid Fortbild Qual Gesundhwes 2020; 150-152: 88-95
- 22 Karbach U, Ansmann L, Scholten N. et al. Bericht aus einem laufenden Forschungsprojekt: CoRe-Net, das Kölner Kompetenznetzwerk aus Versorgungspraxis und Versorgungsforschung, und der Value-based Healthcare-Ansatz. Z Evid Fortbild Qual Gesundhwes 2018; 130: 21-26
- 23 Schubert I, Köster I, Ihle P. Verwendung von GKV-Diagnosen in der Sekundärdatenforschung. In: Swart E, Ihle P, Hrsg. Routinedaten im Gesundheitswesen. Handbuch Sekundärdatenanalyse: Grundlagen, Methoden und Perspektiven. Bern: Verlag Hans Huber; 2005
- 24 Michalski N, Reis M, Tetzlaff F. et al. D-German Index of Socioeconomic Deprivation (GISD): Revision. Aktualisierung und Anwendungsbeispiele 2022;
- 25 Creditreform. Schuldneratlas Metropolregion Köln/Bonn. Detailanalyse nach Postleitzahlen, Gemeinden und Stadtteilen; 2023
- 26 Kauhl B, Schweikart J, Krafft T. et al. Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression. Int J Health Geogr 2016; 15: 38
- 27 Kauhl B, Maier W, Schweikart J. et al. Exploring the small-scale spatial distribution of hypertension and its association to area deprivation based on health insurance claims in Northeastern Germany. BMC Public Health 2018; 18: 121
- 28 Matthews SA, Yang T-C. Mapping the results of local statistics: Using geographically weighted regression. Demogr Res 2012; 26: 151-166
- 29 Wheeler DC, Páez A. Geographically Weighted Regression. In: Fischer MM, Getis A, Hrsg. Handbook of Applied Spatial Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010: 461-486
- 30 Swart E, Gothe H, Geyer S. et al. Gute Praxis Sekundärdatenanalyse (GPS): Leitlinien und Empfehlungen. Gesundheitswesen 2015; 77: 120-126
- 31 Stadt Köln. Postleitzahlgebiete Köln (12.01.2023). Online: https:// www.offenedaten-koeln.de/dataset/postleitzahlgebiete-k %C3 %B6ln letzter Zugriff: 26.12.2023
- 32 Poppe A, Butz C, van de Sand H. et al. Mentale Gesundheit von Kindern und Jugendlichen in den Coronajahren 2020 / 2021 im Vergleich zum Vorzeitraum – Ein CoRe-Net Versorgungsbericht; 2023
- 33 BVÖGD. Schuleingangsuntersuchungen – Schuluntersuchungen fallen wegen Corona aus – soziale Benachteiligung nimmt zu. Gesundheitsökonomie & Qualitätsmanagement 2021; 26: 193
- 34 Statista Research Department. Anzahl der Mitglieder und Versicherten der gesetzlichen und privaten Krankenversicherung in den Jahren 2017 bis 2023. (in Millionen) (20.09.2023). Im Internet: https://de.statista.com/statistik/daten/studie/155823/umfrage/gkv-pkv-mitglieder-und-versichertenzahl-im-vergleich/ Stand: 09.12.2023