Cited by 431COVID-19 epidemiological studiesPublic Health and Social InequalitiesCOVID-19 Clinical Research Studies

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Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile

Open access

Published version of a preprint

Abstract

Urban socioeconomics and mortality: Santiago, Chile, is a highly segregated city with distinct zones of affluence and deprivation. This setting offers a window on how social factors propel the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in an economically vulnerable society with high levels of income inequality. Mena et al. analyzed incidence and mortality attributed to SARS-CoV-2 to understand spatial variations in disease burden. Infection fatality rates were higher in lower-income municipalities because of comorbidities and lack of access to health care. Disparities between municipalities in the quality of their health care delivery system became apparent in testing delays and capacity. These indicators explain a large part of the variation in COVID-19 underreporting and deaths and show that these inequalities disproportionately affected younger people. Science , abg5298, this issue p. eabg5298

Overview

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Written from the paper’s full text.

TL;DR

In Greater Santiago, Chile, COVID-19 incidence, mortality, testing delays, and infection fatality rates were strongly patterned by municipal socioeconomic status, with lower-SES areas experiencing higher burden and younger-age mortality. The authors link these disparities to mobility constraints, early underreporting, and unequal health care capacity.

Key findings

  • SES ranged from Vitacura (93.7) to La Pintana (17.0). Maximum weekly incidence was 22.6 cases per 10,000 in Vitacura in mid-May versus 76.4 in La Pintana in the first week of June; maximum weekly deaths were 4.4 per 10,000 in San Ramón (SES 19.7) versus 1.6 in Vitacura in June.
  • During full lockdown, the two highest-SES municipalities reduced mobility by up to 61%, whereas lowest-SES municipalities reduced mobility on average to 40%.
  • Observed deaths in May–July 2020 were more than 1.73 times expected (CI 1.68–1.79), peaking above 2110 deaths in epidemiological week 24 versus an expected 802 deaths and an average of 798 deaths between 2015 and 2019. Age-specific observed deaths were nearly twofold higher for people younger than 40, 2.8 times higher for ages 40–60, 3.2 times higher for ages 60–80, and 2.4 times higher for ages 80+.
  • Low- versus high-SES IFR ratios were 1.4 for ages 60–80, 1.7 for ages 40–60, and 3.1 for ages 0–40. Positivity was negatively associated with SES, timeliness was associated with SES, and tests per death were positively associated with SES; South and West zones had four times fewer beds per 10,000 people and four times lower private health system enrollment than the East zone, and more than 90% of COVID-19–attributed deaths in South and West zones occurred outside health care facilities versus 55% in the East zone.

Methods

  • Ecological analysis of 34 municipalities in Greater Santiago, home to almost 7 million people, using SES defined as 100 − the 2019 social priority index (SPI), COVID-19 data from the EPIVIGILA platform, vital statistics mortality data, and Facebook Data for Good mobility data.
  • RmMAP, a Poisson deconvolution model with a quadratic penalty, back-calculated infections from COVID-19–attributed deaths and an onset-to-death distribution; Gaussian process regression estimated excess deaths using 2000–2019 mortality data; a hierarchical Bayesian model inferred CFR/IFR by age and municipality from cases, positivity, deaths, and SES.
  • Analyses related SES to incidence, age-adjusted death rates, mobility, testing positivity, timeliness, and tests per death, and compared IFR ratios across low, mid-low, mid-high, and high SES categories.

Limitations

  • The paper states that mobile phone mobility data are likely biased because of differential mobile phone ownership across demographic groups, though results were consistent with other Santiago studies using different measurements.
  • The paper states that RmMAP estimates rely on the infection-to-death distribution and assume IFRs do not change over time; excess mortality estimates depend on kernel choice; and IFR estimates rely on Bayesian assumptions about reporting rates and age distribution of infections, with sensitivity analyses suggesting stability.
  • Evident from the text: the study is observational and ecological, using municipality-level SES and aggregated data, so it cannot establish causality; age information of reported cases was not available at the municipality level.

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Integrity signals

Registry

Crossref · 10.1126/science.abg5298

Cites

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Statistics

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Figures

Effect of socioeconomic inequalities on COVID-19 outcomes. The map on the left shows the municipalities that were included in this study, colored by their socioeconomic status score. For the comparison between COVID-19 deaths and excess deaths (top right), COVID-19–confirmed deaths are shown in light green and COVID-19–attributed deaths in dark green. Excess deaths, shown in gray, correspond to the difference between observed and predicted deaths. Predicted deaths were estimated using a Gaussian process model. The shading indicates 95% credible intervals for the excess deaths. The infection fatality rates (bottom right) were inferred by implementing a hierarchical Bayesian model, with vertical lines representing credible intervals by age and socioeconomic status.
Effect of socioeconomic inequalities on COVID-19 outcomes. The map on the left shows the municipalities that were included in this study, colored by their socioeconomic status score. For the comparison between COVID-19 deaths and excess deaths (top right), COVID-19–confirmed deaths are shown in light green and COVID-19–attributed deaths in dark green. Excess deaths, shown in gray, correspond to the difference between observed and predicted deaths. Predicted deaths were estimated using a Gaussian process model. The shading indicates 95% credible intervals for the excess deaths. The infection fatality rates (bottom right) were inferred by implementing a hierarchical Bayesian model, with vertical lines representing credible intervals by age and socioeconomic status.CC BY · PubMed Central
Fig. 1 SES, COVID-19 cases and deaths, and mobility data in Greater Santiago. (A) Municipalities that are part of Greater Santiago are colored according to their SES, where a lower score is indicative of a lower SES. (B) COVID-19 cases normalized by population size per municipality. Municipalities are sorted by SES, starting with the one that has the highest SES at the top. (C) COVID-19–attributed deaths normalized by population size per municipality. (D) Age-adjusted COVID-19–attributed death rate and its association with SES. The dots and the whiskers represent the median and the 95% confidence intervals, respectively, reflecting uncertainty on the standard population used for weighting. R2, coefficient of determination. (E) Daily reduction in mobility by municipality colored by its SES value. (F) Average reduction in mobility during the full lockdown period and its association with SES. The urban and the business centers, Santiago and Providencia, respectively, experienced a greater reduction in mobility than expected based just on their socioeconomic profile. In (D) and (F), the shaded area indicates 95% regression confidence interval.
Fig. 1 SES, COVID-19 cases and deaths, and mobility data in Greater Santiago. (A) Municipalities that are part of Greater Santiago are colored according to their SES, where a lower score is indicative of a lower SES. (B) COVID-19 cases normalized by population size per municipality. Municipalities are sorted by SES, starting with the one that has the highest SES at the top. (C) COVID-19–attributed deaths normalized by population size per municipality. (D) Age-adjusted COVID-19–attributed death rate and its association with SES. The dots and the whiskers represent the median and the 95% confidence intervals, respectively, reflecting uncertainty on the standard population used for weighting. R2, coefficient of determination. (E) Daily reduction in mobility by municipality colored by its SES value. (F) Average reduction in mobility during the full lockdown period and its association with SES. The urban and the business centers, Santiago and Providencia, respectively, experienced a greater reduction in mobility than expected based just on their socioeconomic profile. In (D) and (F), the shaded area indicates 95% regression confidence interval.CC BY · PubMed Central
Fig. 2 Inferred cases and reported tests conducted for the Greater Santiago area. (A) Inferred and reported cases over time. For our RmMAP reconstructions, we considered the log-normal onset-to-death distribution described in (38) and two age-stratified IFR estimates, one from the Diamond Princess cruise ship (39) and another from a seroprevalence study in Spain (40). For comparison, we also present reconstructions based on the Covidestim method (41) and by the rescaling of case counts by the underreporting of estimates obtained with the method of Russell et al. (42). (B) Association between average daily tests and SES during the early peak. The early peak is defined as those cases reported between 8 March and 4 April. The shaded area indicates 95% regression confidence interval. (C) Reported cases per 10,000 by municipality during the early peak. (D) Tests per 10,000 by municipality during the early peak. (E) Inferred cases obtained from the RmMAP-Spain model per 10,000 by municipality during the early peak. For (C) to (E), the record of at least one COVID-19–confirmed or COVID-19–attributed death for that particular week is highlighted with solid or dashed boxes, respectively.
Fig. 2 Inferred cases and reported tests conducted for the Greater Santiago area. (A) Inferred and reported cases over time. For our RmMAP reconstructions, we considered the log-normal onset-to-death distribution described in (38) and two age-stratified IFR estimates, one from the Diamond Princess cruise ship (39) and another from a seroprevalence study in Spain (40). For comparison, we also present reconstructions based on the Covidestim method (41) and by the rescaling of case counts by the underreporting of estimates obtained with the method of Russell et al. (42). (B) Association between average daily tests and SES during the early peak. The early peak is defined as those cases reported between 8 March and 4 April. The shaded area indicates 95% regression confidence interval. (C) Reported cases per 10,000 by municipality during the early peak. (D) Tests per 10,000 by municipality during the early peak. (E) Inferred cases obtained from the RmMAP-Spain model per 10,000 by municipality during the early peak. For (C) to (E), the record of at least one COVID-19–confirmed or COVID-19–attributed death for that particular week is highlighted with solid or dashed boxes, respectively.CC BY · PubMed Central
Fig. 3 Excess deaths and its association with demographic and socioeconomic factors. (A) Observed deaths (solid dark blue line) in Greater Santiago compared with predicted deaths for 2020 (solid light blue line and its confidence intervals shaded in lighter color), using a Gaussian process regression model built with historical mortality data from 2000 to 2019 (dashed blue lines). The values contain all the possible causes of deaths. (B) Age-specific trends of the observed deaths compared with the predicted deaths for 2020. (C) COVID-19 deaths versus excess deaths. COVID-19–confirmed deaths are shown in light green, whereas COVID-19–attributed deaths are shown in dark green. Excess deaths correspond to the difference between observed and predicted deaths. (D) Comparison of excess deaths and COVID-19–attributed deaths per municipality colored by SES and normalized by population size. (E) Monthly average of z scores of observed deaths between April and July by age group. The z scores correspond to the standard deviations over expected values. (F) Historical deaths due to influenza and pneumonia (teal dashed lines) and cancer (pink dashed lines) compared with the observed deaths during 2020 (solid lines). In (B), (C), and (E), the shaded region indicates 95% regression confidence interval.
Fig. 3 Excess deaths and its association with demographic and socioeconomic factors. (A) Observed deaths (solid dark blue line) in Greater Santiago compared with predicted deaths for 2020 (solid light blue line and its confidence intervals shaded in lighter color), using a Gaussian process regression model built with historical mortality data from 2000 to 2019 (dashed blue lines). The values contain all the possible causes of deaths. (B) Age-specific trends of the observed deaths compared with the predicted deaths for 2020. (C) COVID-19 deaths versus excess deaths. COVID-19–confirmed deaths are shown in light green, whereas COVID-19–attributed deaths are shown in dark green. Excess deaths correspond to the difference between observed and predicted deaths. (D) Comparison of excess deaths and COVID-19–attributed deaths per municipality colored by SES and normalized by population size. (E) Monthly average of z scores of observed deaths between April and July by age group. The z scores correspond to the standard deviations over expected values. (F) Historical deaths due to influenza and pneumonia (teal dashed lines) and cancer (pink dashed lines) compared with the observed deaths during 2020 (solid lines). In (B), (C), and (E), the shaded region indicates 95% regression confidence interval.CC BY · PubMed Central
Fig. 4 Testing capacity and waiting times. (A) Positivity over time. Positivity is defined as the proportion of PCR tests that are positive in a given week. (B) Association between average positivity and SES. (C) Association between positivity and weekly number of cases per 10,000. (D) Association between the overall age-adjusted number of deaths per 10,000 and the average positivity over the same period. (E) Association between average daily tests per 10,000 and SES. (F) Association between tests per 10,000 and deaths per 10,000. (G) Timeliness over time. Timeliness is defined as the proportion of PCR tests that appear in the public records within 1 week from the onset of symptoms. Two weeks in June (shaded in gray) were excluded from the analysis because of inconsistencies in data, leading to unreliable delay estimates. (H) Association between average timeliness and SES. (I) Association between timeliness and weekly number of cases per 10,000. (J) Association between the overall age-adjusted number of deaths per 10,000 and the average timeliness. (K) Association between timeliness and positivity. Dots are representative of weekly data per municipality. (L) Association between tests per death (age-adjusted) and SES. Figures with different dot colors illustrate the SES value according to the reference presented in (A). In (B) to (F) and (H) to (L), the shaded region indicates 95% regression confidence interval.
Fig. 4 Testing capacity and waiting times. (A) Positivity over time. Positivity is defined as the proportion of PCR tests that are positive in a given week. (B) Association between average positivity and SES. (C) Association between positivity and weekly number of cases per 10,000. (D) Association between the overall age-adjusted number of deaths per 10,000 and the average positivity over the same period. (E) Association between average daily tests per 10,000 and SES. (F) Association between tests per 10,000 and deaths per 10,000. (G) Timeliness over time. Timeliness is defined as the proportion of PCR tests that appear in the public records within 1 week from the onset of symptoms. Two weeks in June (shaded in gray) were excluded from the analysis because of inconsistencies in data, leading to unreliable delay estimates. (H) Association between average timeliness and SES. (I) Association between timeliness and weekly number of cases per 10,000. (J) Association between the overall age-adjusted number of deaths per 10,000 and the average timeliness. (K) Association between timeliness and positivity. Dots are representative of weekly data per municipality. (L) Association between tests per death (age-adjusted) and SES. Figures with different dot colors illustrate the SES value according to the reference presented in (A). In (B) to (F) and (H) to (L), the shaded region indicates 95% regression confidence interval.CC BY · PubMed Central
Fig. 5 Inference of CFRs and IFRs by age and SES. (A) Estimates of CFR by age and SES based on a simple assignment of cases to age groups. Our estimates of CFR have been validated by the official ICOVID platform (www.icovidchile.cl/), which confirmed that 119 out of the 136 observed CFRs fall within our confidence intervals. Confidence intervals are derived from a bootstrap procedure described in the supplementary materials. (B) Inferred IFR by age and SES using our ensemble of hierarchical Bayesian models, along with associated credible intervals. (C) IFR ratio between the low- and high-SES categories by age group. Four socioeconomic categories were defined based on SES quantiles: low, mid-low, mid-high, and high.
Fig. 5 Inference of CFRs and IFRs by age and SES. (A) Estimates of CFR by age and SES based on a simple assignment of cases to age groups. Our estimates of CFR have been validated by the official ICOVID platform (www.icovidchile.cl/), which confirmed that 119 out of the 136 observed CFRs fall within our confidence intervals. Confidence intervals are derived from a bootstrap procedure described in the supplementary materials. (B) Inferred IFR by age and SES using our ensemble of hierarchical Bayesian models, along with associated credible intervals. (C) IFR ratio between the low- and high-SES categories by age group. Four socioeconomic categories were defined based on SES quantiles: low, mid-low, mid-high, and high.CC BY · PubMed Central

Verification

Claims the paper makes

40 Own result · 16 Not checked yet · 16 Own interpretation · 7 Own method · 6 No basis given

Verify all 30 references — free account
  • It may state two separate claims as one. 36
  • Probably not a substantial claim. 12
47 of 47
  • The paper relies on this without support.

    The characterization of the COVID-19 pandemic in South America has received less attention than in North America, Europe, and parts of Asia.

    “the characterization of the pandemic in South America has received less attention, despite the severe impact in many countries during the Southern Hemisphere winter season.”
  • The paper relies on this without support.

    Chile set up a notably thorough reporting system and made many key datasets publicly available.

    “Unlike many other countries, Chile set up a notably thorough reporting system and made many key datasets publicly available.”
  • The paper relies on this without support.

    The pathways modulating differential COVID-19 outcomes by socioeconomic status are not well understood.

    “Because the pathways modulating these differential outcomes are not well understood, comprehensive accounts are urgently needed (28)”
  • The paper relies on this without support.

    Human mobility indicators inferred from anonymized mobile phone data are a valid proxy for physical distancing.

    “Changes in human mobility—a proxy for physical distancing—during lockdown periods follow a similar trend.”
  • The paper relies on this without support.

    The virus was introduced in Chile by travelers from affluent municipalities.

    “the month in which the virus was introduced in Chile by travelers from affluent municipalities.”
  • The authors state this as an assumption.

    The infection fatality rates (IFRs) do not change over time.

    “assume that the IFRs do not change over time”
  • The authors state this as an assumption.

    There exists an underlying municipality-specific proportion infected pm such that within each age group the number of infected people is on average pm × Pm,a.

    “An implicit assumption in Eq. 8 is the existence of an underlying municipality-specific proportion infected pm so that on each age group, the number of infected people is (on average) pm×Pm,a.”
  • The authors state this as an assumption.

    The logit of the municipality-specific proportion infected pm follows logit(pm) = p0 + μm.

    “We also assumed the following relation for pm:logitpm=p0+μm(9)”
8 of 8

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Citations

Cites

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Research works printed but not linked: 6

  1. C. K. Williams C. E. Rasmussen Gaussian Processes for Machine Learning (MIT Press 2006) vol. 2.
  2. P. Maas et al . in Proceedings of the 16th International Conference on Information Systems for Crisis Response and Management València Spain May 19-22 2019 (ISCRAM Association 2019) vol. 19 p. 3173.
  3. R. L. White in Instrumentation in Astronomy VIII D. L. Crawford E. R. Craine Eds. (International Society for Optics and Photonics 1994) vol. 2198 pp. 1342–1348.
  4. M. Titsias in Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics D. van Dyk M. Welling Eds. vol. 5 of Proceedings of Machine Learning Research (PMLR 2009) pp. 567–574.
  5. M. Plummer et al . in Proceedings of the 3rd International Workshop on Distributed Statistical Computing (Vienna Austria 2003) vol. 124 pp. 1–10.
  6. A. Wilson R. Adams in Proceedings of the 30th International Conference on Machine Learning (PMLR 2013) pp. 1067–1075.

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Metadata

Authors
Mena, G. E., Martinez, P. P., Mahmud, A. S., Marquet, P. A., Buckee, C. O., & Santillana, M.
Affiliations
  • University of Oxford (GB)
  • Harvard University (US)
  • Santa Fe Institute (US)
  • Boston Children's Hospital (US)
Corresponding author
  • Mena, Gonzalo E.
  • Martinez, Pamela P.
  • Buckee, Caroline O.
  • Santillana, Mauricio
Venue
Science
Published
2021-05-28
Volume
372
Issue
6545
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W3157070895
Cited by
431
References
63
Citations by year
2026: 19 · 2025: 40 · 2024: 82 · 2023: 110 · 2022: 116 · 2021: 63
FWCI
35.86
Type
journal article
Language
en
Open access
Locations
  • Science (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (U54GM088558)
  • National Institute of General Medical Sciences (R01 GM130668)
  • Teikyo University School of Medicine (U54GM088558, R01GM130668)
  • Harvard Data Science Initiative
  • Schmidt futures award
Topics
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Social Sciences › Health › Public Health and Social Inequalities
  • Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
Keywords
COVID-19 pandemic, socioeconomic status, socioeconomic inequalities in health, COVID-19 mortality, excess mortality, infection fatality rate, COVID-19 incidence, public health capacity, youth, Santiago de Chile
MeSH terms
Physical Distancing, COVID-19, COVID-19 Nucleic Acid Testing, Adult, Age Factors, Aged, Chile, Cities, Humans, Middle Aged, Mortality, Poverty, Social Class, Socioeconomic Factors, Urban Health, Incidence

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