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Does the Neighborhood Area of Residence Influence Non-Attendance in an Urban Mammography Screening Program? A Multilevel Study in a Swedish City

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  • Magdalena Lagerlund
  • Juan Merlo
  • Raquel Pérez Vicente
  • Sophia Zackrisson

Abstract

Background and aim: The public health impact of population-based mammography screening programs depends on high participation rates. Thus, monitoring participation rates, as well as understanding and considering the factors influencing attendance, is important. With the goal to acquire information on the appropriate level of intervention for increasing screening participation our study aimed to (1) examine whether, over and above individual factors, the neighborhood of residence influences a woman’s mammography non-attendance, and (2) evaluate, whether knowing a woman’s neighborhood of residence would be sufficient to predict non-attendance. Methods: We analyze all women invited to mammography screening in 2005–09, residing in the city of Malmö, Sweden. Information regarding mammography screening attendance was linked to data on area of residence, demographic and socioeconomic characteristics available from Statistics Sweden. The influence of individual and neighborhood factors was assessed by multilevel logistic regression analysis with 29,901 women nested within 212 neighborhoods. Results: The prevalence of non-attendance among women was 18.3%. After adjusting for individual characteristics, the prevalence in the 212 neighborhoods was 3.6%. Neighborhood of residence had little influence on non-attendance. The multilevel analysis indicates that 8.4% of the total individual differences in the propensity of non-attendance were at the neighborhood level. However, when adjusting for specific individual characteristics this general contextual effect decreased to 1.8%. This minor effect was explained by the sociodemographic characteristic of the neighborhoods. The discriminatory accuracy of classifying women according to their non-attendance was 0.747 when considering only individual level variables, and 0.760 after including neighborhood level as a random effect. Conclusion: Our results suggest that neighborhoods of residence in Malmö, Sweden (as defined by small-area market statistics (SAMS) areas) do not condition women’s participation in population based mammography screening. Thus, interventions should be directed to the whole city and target women with a higher risk of non-attendance.

Suggested Citation

  • Magdalena Lagerlund & Juan Merlo & Raquel Pérez Vicente & Sophia Zackrisson, 2015. "Does the Neighborhood Area of Residence Influence Non-Attendance in an Urban Mammography Screening Program? A Multilevel Study in a Swedish City," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-21, October.
    • Handle: RePEc:plo:pone00:0140244
    DOI: 10.1371/journal.pone.0140244
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    1. Ansgar Wübker, 2014. "Explaining variations in breast cancer screening across European countries," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(5), pages 497-514, June.
    2. Klaus Larsen & Jørgen Holm Petersen & Esben Budtz-Jørgensen & Lars Endahl, 2000. "Interpreting Parameters in the Logistic Regression Model with Random Effects," Biometrics, The International Biometric Society, vol. 56(3), pages 909-914, September.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Margaret Pepe & Holly Janes & Gary Longton & Wendy Leisenring & Polly Newcomb, 2004. "Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic or Prognostic Marker," UW Biostatistics Working Paper Series 1035, Berkeley Electronic Press.
    5. Islam, M. Kamrul & Merlo, Juan & Kawachi, Ichiro & Lindström, Martin & Burström, Kristina & Gerdtham, Ulf-G., 2006. "Does it really matter where you live? A panel data multilevel analysis of Swedish municipality-level social capital on individual health-related quality of life," Health Economics, Policy and Law, Cambridge University Press, vol. 1(3), pages 209-235, July.
    6. Gunnar Olsson, 2000. "From a = b to a = a," Environment and Planning A, , vol. 32(7), pages 1235-1244, July.
    7. Karel G M Moons & Joris A H de Groot & Walter Bouwmeester & Yvonne Vergouwe & Susan Mallett & Douglas G Altman & Johannes B Reitsma & Gary S Collins, 2014. "Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist," PLOS Medicine, Public Library of Science, vol. 11(10), pages 1-12, October.
    8. Lindström, Martin & Merlo, Juan & Östergren, Per-Olof, 2002. "Individual and neighbourhood determinants of social participation and social capital: a multilevel analysis of the city of Malmö, Sweden," Social Science & Medicine, Elsevier, vol. 54(12), pages 1779-1791, June.
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