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Original research
Characterising variability and predictors of infant mortality in urban settings: findings from 286 Latin American cities
  1. Ana F Ortigoza1,
  2. José A Tapia Granados2,
  3. J Jaime Miranda3,
  4. Marcio Alazraqui4,
  5. Diana Higuera5,
  6. Georgina Villamonte3,
  7. Amélia Augusta de Lima Friche6,
  8. Tonatiuh Barrientos Gutierrez7,
  9. Ana V Diez Roux1
  1. 1 Urban Health Collaborative, Drexel University, Philadelphia, Pennsylvania, USA
  2. 2 History and Political Science, Drexel University, Philadelphia, Pennsylvania, USA
  3. 3 CRONICAS Centre of Excellence in Chronic Diseases, Universidad Peruana Cayetano Heredia, Lima, Peru
  4. 4 Instituto de Salud Colectiva, Universidad Nacional de Lanus, Lanus, Argentina
  5. 5 Escuela de Medicina, Universidad de Los Andes, Bogota, Colombia
  6. 6 School of Medicine, Universidade Federal de Minas Gerais Faculdade de Medicina, Belo Horizonte, Brazil
  7. 7 Instituto Nacional de Salud Publica, Mexico DF, Mexico
  1. Correspondence to Ana F Ortigoza, Urban Health Collaborative, Drexel University, 3600 Market Street, Room 717E, Philadelphia, PA 19104, USA; afo25{at}drexel.edu

Abstract

Background Urbanisation in Latin America (LA) is heterogeneous and could have varying implications for infant mortality (IM). Identifying city factors related to IM can help design policies that promote infant health in cities.

Methods We quantified variability in infant mortality rates (IMR) across cities and examined associations between urban characteristics and IMR in a cross-sectional design. We estimated IMR for the period 2014–2016 using vital registration for 286 cities above 100 000 people in eight countries. Using national censuses, we calculated population size, growth and three socioeconomic scores reflecting living conditions, service provision and population educational attainment. We included mass transit availability of bus rapid transit and subway. Using Poisson multilevel regression, we estimated the per cent difference in IMR for a one SD (1SD) difference in city-level predictors.

Results Of the 286 cities, 130 had <250 000 inhabitants and 5 had >5 million. Overall IMR was 11.2 deaths/1000 live births. 57% of the total IMR variability across cities was within countries. Higher population growth, better living conditions, better service provision and mass transit availability were associated with 6.0% (95% CI −8.3 to 3.7%), 14.1% (95% CI −18.6 to −9.2), 11.4% (95% CI −16.1 to −6.4) and 6.6% (95% CI −9.2 to −3.9) lower IMR, respectively. Greater population size was associated with higher IMR. No association was observed for population-level educational attainment in the overall sample.

Conclusion Improving living conditions, service provision and public transportation in cities may have a positive impact on reducing IMR in LA cities.

  • Infant mortality
  • Urbanisation
  • Social inequalities
  • Public health policy
https://creativecommons.org/licenses/by/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

https://doi.org/10.1136/jech-2020-215137

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    Footnotes

    • Twitter Ana F Ortigoza @AnaOrtigoza14.

    • Contributors AFO and AVDR conceived the study. AFO did the statistical analyses. AFO and AVDR drafted the first version of the manuscript. MA, DH, GV, AAdLF and TBG participated in or supported data collection. JTG and JJM participated in the edition of the manuscript providing critical inputs. All authors participated in the interpretation of the results and approved the final version of the manuscript.

    • Funding This work was supported by the Wellcome Trust initiative ‘Our Planet, Our Health’ (grant 205177/Z/16/Z). The study funder had no role in study design, data collection, data analysis, data interpretation or writing of this study. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data availability statement Data are available upon reasonable request.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.