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Abstract

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.

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2021-04-29
2024-11-07
http://instance.metastore.ingenta.com/content/10.2807/1560-7917.ES.2021.26.17.1900755
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References

  1. Dabbagh A, Laws RL, Steulet C, Dumolard L, Mulders MN, Kretsinger K, et al. Progress Toward Regional Measles Elimination - Worldwide, 2000-2017. MMWR Morb Mortal Wkly Rep. 2018;67(47):1323-9.  https://doi.org/10.15585/mmwr.mm6747a6  PMID: 30496160 
  2. Moss WJ. Measles. Lancet. 2017;390(10111):2490-502.  https://doi.org/10.1016/S0140-6736(17)31463-0  PMID: 28673424 
  3. World Health Organization (WHO). 2018 Assessment report of the Global Vaccine Action Plan. Strategic Advisory Group of Experts on Immunization. Geneva: WHO; 2018.Available from: https://apps.who.int/iris/bitstream/handle/10665/276967/WHO-IVB-18.11-eng.pdf?sequence=1&isAllowed=y
  4. World Health Organization (WHO). Immunization coverage 2019. Geneva: WHO; 2020. Available from: https://www.who.int/en/news-room/fact-sheets/detail/immunization-coverage
  5. World Health Organization (WHO). Measles – European: Region Disease outbreak news - update 6 May 2019 2019. Geneva: WHO; 2019. Available from: https://www.who.int/csr/don/06-may-2019-measles-euro/en/
  6. World Health Organization (WHO). New measles surveillance data for 2019. Geneva: WHO; 2019. Available from: https://www.who.int/immunization/newsroom/measles-data-2019/en/
  7. World Health Organization Regional Office for Europe (WHO/Europe). European Vaccine Action Plan 2015-2020. Copenhagen: WHO/Europe; 2014. Available from: https://www.euro.who.int/__data/assets/pdf_file/0007/255679/WHO_EVAP_UK_v30_WEBx.pdf
  8. Schneider MC, Machado G. Environmental and socioeconomic drivers in infectious disease. Lancet Planet Health. 2018;2(5):e198-9.  https://doi.org/10.1016/S2542-5196(18)30069-X  PMID: 29709281 
  9. Robert-Koch-Institut (RKI). Impfquoten bei Erwachsenen in Deutschland. Aktuelles aus der KV-Impfsurveillance und der Onlinebefragung von Krankenhauspersonal. [Vaccination rates among adults in Germany. The latest from Association of Statutory Health Insurance Physicians vaccination surveillance and the online survey of hospital staff]. Epidemiolgisches Bulletin.2019;44:457-72.German.
  10. Takla A, Wichmann O, Rieck T, Matysiak-Klose D. Measles incidence and reporting trends in Germany, 2007-2011. Bull World Health Organ. 2014;92(10):742-9.  https://doi.org/10.2471/BLT.13.135145  PMID: 25378728 
  11. Bocquier A, Ward J, Raude J, Peretti-Watel P, Verger P. Socioeconomic differences in childhood vaccination in developed countries: a systematic review of quantitative studies. Expert Rev Vaccines. 2017;16(11):1107-18.  https://doi.org/10.1080/14760584.2017.1381020  PMID: 28914112 
  12. Fielding JE, Bolam B, Danchin MH. Immunisation coverage and socioeconomic status - questioning inequity in the ‘No Jab, No Pay’ policy. Aust N Z J Public Health. 2017;41(5):455-7.  https://doi.org/10.1111/1753-6405.12676  PMID: 28664595 
  13. Poethko-Müller C, Kuhnert R, Gillesberg Lassen S, Siedler A. [Vaccination coverage of children and adolescents in Germany: New data from KiGGS Wave 2 and trends from the KiGGS study]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2019;62(4):410-21. PMID: 30788538 
  14. Arat A, Burström B, Östberg V, Hjern A. Social inequities in vaccination coverage among infants and pre-school children in Europe and Australia - a systematic review. BMC Public Health. 2019;19(1):290.  https://doi.org/10.1186/s12889-019-6597-4  PMID: 30866881 
  15. Hungerford D, Macpherson P, Farmer S, Ghebrehewet S, Seddon D, Vivancos R, et al. Effect of socioeconomic deprivation on uptake of measles, mumps and rubella vaccination in Liverpool, UK over 16 years: a longitudinal ecological study. Epidemiol Infect. 2016;144(6):1201-11.  https://doi.org/10.1017/S0950268815002599  PMID: 26542197 
  16. Andrianou XD, Del Manso M, Bella A, Vescio MF, Baggieri M, Rota MC, et al. Spatiotemporal distribution and determinants of measles incidence during a large outbreak, Italy, September 2016 to July 2018. Euro Surveill. 2019;24(17):13-24.  https://doi.org/10.2807/1560-7917.ES.2019.24.17.1800679  PMID: 31039836 
  17. Hughes GJ, Gorton R. Inequalities in the incidence of infectious disease in the North East of England: a population-based study. Epidemiol Infect. 2015;143(1):189-201.  https://doi.org/10.1017/S0950268814000533  PMID: 24642034 
  18. Yang W, Wen L, Li SL, Chen K, Zhang WY, Shaman J. Geospatial characteristics of measles transmission in China during 2005-2014. PLOS Comput Biol. 2017;13(4):e1005474.  https://doi.org/10.1371/journal.pcbi.1005474  PMID: 28376097 
  19. Robert Koch Institute (RKI). SURVSTAT@RKI 2.0. Web-based query on data reported under the German 'Protection against Infection Act'. Berlin: RKI; 2019. Available from: https://survstat.rki.de/Default.aspx
  20. Kroll LE, Schumann M, Hoebel J, Lampert T. German Index of Socioeconomic Deprivation (GISD) Version 1.0: Robert Koch Institute; 2017. Available from: https://datorium.gesis.org/xmlui/handle/10.7802/1460
  21. Statistische Ämter des Bundes und der Länder. Regionaldatenbank Deutschland. Düsseldorf: Information und Technik Nordrhein-Westfalen (IT.NRW). Regional database Germany; 2019. German. Available from: https://www.regionalstatistik.de/genesis/online/logon
  22. Bundesinstitut für Kartographie und Geodäsie (BKG). Verwaltungsgebiete 1:1.000 000. [Administrative areas 1: 1,000,000]. Frankfurt am Main: BKG; 2018. German. Available from: http://www.geodatenzentrum.de/geodaten/gdz_rahmen.gdz_div?gdz_spr=deu&gdz_akt_zeile=5&gdz_anz_zeile=1&gdz_unt_zeile=16&gdz_user_id=0
  23. Martins TG, Simpson D, Lindgren F, Rue H. Bayesian computing with INLA: New features. Comput Stat Data Anal. 2013;67:68-83.  https://doi.org/10.1016/j.csda.2013.04.014 
  24. Rue H, Martino S, Chopin N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximation. J R Stat Soc B. 2009;71(2):319-92.  https://doi.org/10.1111/j.1467-9868.2008.00700.x 
  25. Blangiardo M, Cameletti M. Spatial and Spatio-temporal bayesian Models with R-INLA. New Jersey: John Wiley & Sons, Ltd; 2015. Available from: https://www.wiley.com/en-us/Spatial+and+Spatio+temporal+Bayesian+Models+with+R+INLA-p-9781118326558
  26. Knorr-Held L. Bayesian modelling of inseparable space-time variation in disease risk. Stat Med. 2000;19(17-18):2555-67.  https://doi.org/10.1002/1097-0258(20000915/30)19:17/18<2555::AID-SIM587>3.0.CO;2-#  PMID: 10960871 
  27. Richardson S, Thomson A, Best N, Elliott P. Interpreting posterior relative risk estimates in disease-mapping studies. Environ Health Perspect. 2004;112(9):1016-25.  https://doi.org/10.1289/ehp.6740  PMID: 15198922 
  28. Goffrier B, Schulz M. Bätzing-Feigenbaum J. Analyse des räumlichen Zusammenhangs zwischen den Impfquoten der Masern- und Meningokokken-C-Impfungen. [Analysis of the spatial relationship between measles and meningococcal C vaccination rates]. Berlin: Zentralinstitut für die kassenärztliche Versorgung in Deutschland (Zi); 2017.German. Available from: https://www.versorgungsatlas.de/fileadmin/ziva_docs/84/VA-84-KorrelationMasernMenC-Bericht-V1_2.pdf
  29. Gallup. Wellcome Global Monitor – First Wave Findings. London: Wellcome; 2019. Available from: https://wellcome.org/sites/default/files/wellcome-global-monitor-2018.pdf
  30. Sathyanarayana Rao TS, Andrade C. The MMR vaccine and autism: Sensation, refutation, retraction, and fraud. Indian J Psychiatry. 2011;53(2):95-6.  https://doi.org/10.4103/0019-5545.82529  PMID: 21772639 
  31. Busse R, Blümel M. Germany: Health System Review. In: Busse R, Figueras J, McKee M, Mossialos E, Thomson S, van Ginneken E, editors. Health Systems in Transition Series (HiTs). Copenhagen: European Observatory on Health Systems and Policies; 2014.
  32. Paul KT, Loer K. Contemporary vaccination policy in the European Union: tensions and dilemmas. J Public Health Policy. 2019;40(2):166-79.  https://doi.org/10.1057/s41271-019-00163-8  PMID: 30894672 
  33. Ozegowski S, Sundmacher L. Wie „bedarfsgerecht“ ist die Bedarfsplanung? Eine Analyse der regionalen Verteilung der vertragsärztlichen Versorgung. [Is the needs-based planning mechanism effectively needs-based? An analysis of the regional distribution of outpatient care providers]. Gesundheitswesen. 2012;74(10):618-26. PMID: 22886336 
  34. Bundesministerium für Gesundheit (BMG). Impfpflicht soll Kinder vor Masern schützen. [Compulsory vaccination to protect children against measles.]. Bonn: BMG; 2019. Available from: https://www.bundesgesundheitsministerium.de/impfpflicht.html
  35. Scheidt-Nave C, Kamtsiuris P, Gößwald A, Hölling H, Lange M, Busch MA, et al. German health interview and examination survey for adults (DEGS) - design, objectives and implementation of the first data collection wave. BMC Public Health. 2012;12(1):730.  https://doi.org/10.1186/1471-2458-12-730  PMID: 22938722 
  36. Offe J, Dieterich A, Bozorgmehr K, Trabert G. Parallel report to the CESCR on the right to health for non-nationals in Germany. Berlin: Ärzte der Welt; 2018.
  37. Bozorgmehr K, Nöst S, Thaiss HM, Razum O. Die gesundheitliche Versorgungssituation von Asylsuchenden : Bundesweite Bestandsaufnahme über die Gesundheitsämter. [Health care provisions for asylum-seekers: A nationwide survey of public health authorities in Germany]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz. 2016;59(5):545-55.  https://doi.org/10.1007/s00103-016-2329-4  PMID: 27072501 
  38. Bozorgmehr K, Wahedi K, Noest S, Szecsenyi J, Razum O. Infectious disease screening in asylum seekers: range, coverage and economic evaluation in Germany, 2015. Euro Surveill. 2017;22(40).  https://doi.org/10.2807/1560-7917.ES.2017.22.40.16-00677  PMID: 29019315 
  39. Graham M, Winter AK, Ferrari M, Grenfell B, Moss WJ, Azman AS, et al. Measles and the canonical path to elimination. Science. 2019;364(6440):584-7.  https://doi.org/10.1126/science.aau6299  PMID: 31073065 
  40. Gibney KB, Cheng AC, Hall R, Leder K. Sociodemographic and geographical inequalities in notifiable infectious diseases in Australia: a retrospective analysis of 21 years of national disease surveillance data. Lancet Infect Dis. 2017;17(1):86-97.  https://doi.org/10.1016/S1473-3099(16)30309-7  PMID: 27789179 
  41. Pini A, Stenbeck M, Galanis I, Kallberg H, Danis K, Tegnell A, et al. Socioeconomic disparities associated with 29 common infectious diseases in Sweden, 2005-14: an individually matched case-control study. Lancet Infect Dis. 2019;19(2):165-76.  https://doi.org/10.1016/S1473-3099(18)30485-7  PMID: 30558995 
  42. Gibney KB, Leder K. Socioeconomic disparities and infection: it’s complicated. Lancet Infect Dis. 2019;19(2):116-7.  https://doi.org/10.1016/S1473-3099(18)30511-5  PMID: 30558993 
  43. Kroll LE, Schumann M, Hoebel J, Lampert T. Regional health differences – developing a socioeconomic deprivation index for Germany. Journal of Health Monitoring.2017;2(2):103-20.  https://doi.org/10.17886/RKI-GBE-2017-048.2 
  44. Noble M, Wright G, Smith G, Dibben C. Measuring Multiple Deprivation at the Small-Area Level. Environment and Planning A: Economy and Space.2016;38(1):169-85.  https://doi.org/10.1068/a37168 
Socioeconomic deprivation is inversely associated with measles incidence: a longitudinal small-area analysis, Germany, 2001 to 2017
<p class="citation"> <span>Citation style for this article:</span> <span class="meta-value authors"> <a href="/search?value1=Sven+Rohleder&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Rohleder Sven</a>, <a href="/search?value1=Christian+Stock&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Stock Christian</a>, <a href="/search?value1=Kayvan+Bozorgmehr&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Bozorgmehr Kayvan</a></span>. Socioeconomic deprivation is inversely associated with measles incidence: a longitudinal small-area analysis, Germany, 2001 to 2017. <a href="/content/ecdc">Euro Surveill.</a> 2021;26(17):pii=1900755. <a href="https://doi.org/10.2807/1560-7917.ES.2021.26.17.1900755" title="doi link" target="_blank">https://doi.org/10.2807/1560-7917.ES.2021.26.17.1900755</a> <span class="ES_Article_citation"> <span class="generated">Received</span>: 17 Dec 2019;&nbsp;&nbsp; <span class="generated">Accepted</span>: 06 Oct 2020 </span> </p>
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