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

In the paper

“A strong negative association between positivity and SES (Fig. 4B) further denotes difficulties in access to health care that is even more pronounced in lower-SES municipalities.”

In the paper

“Although this urban center accounts for 36% of the country’s population, it has reported 55% of the confirmed COVID-19 cases and 65% of the COVID-19–attributed deaths before epidemiological week 36 (end of August 2020).”

In the paper

“As shown in Fig. 3A, the number of deaths observed between May and July 2020 is more than 1.73 (confidence interval 1.68 to 1.79) times the expected value, with a peak surpassing 2110 death counts in epidemiological week 24 (first week of June 2020) compared with an expected value of 802 deaths and an average number of deaths of 798 between 2015 and 2019.”

In the paper

“COVID-19–confirmed deaths—those with a polymerase chain reaction (PCR)–confirmed SARS-CoV-2 test—follow a similar temporal pattern, and the difference between confirmed and COVID-19–attributed deaths gets smaller toward the end of August, indicative of an improved testing capacity.”

In the paper

“The maximum incidence in Vitacura was 22.6 weekly cases per 10,000 individuals during the middle of May, whereas La Pintana reported a maximum of 76.4 weekly cases per 10,000 individuals during the first week of June (Fig. 1B).”

In the paper

“Socioeconomic status (SES) in the municipalities varies widely, with Vitacura having the highest value (SES = 93.7) and La Pintana the lowest (SES = 17.0; Fig. 1A)”

In the paper

“For instance, the highest rate of 4.4 weekly deaths per 10,000 individuals is observed in San Ramon, a municipality with a SES of 19.7, whereas Vitacura reported a maximum of 1.6 weekly deaths per 10,000 in June.”

In the paper

“the group that contains people between 60 and 80 years old displays the highest weekly count (936 during epidemiological week 24), the biggest deviation from the predicted values, and the highest values of excess deaths (645 more deaths than expected; Fig. 3B).”

In the paper

“Timeliness also negatively correlates with the number of cases (Fig. 4I), the total number of deaths (Fig. 4J), and positivity (Fig. 4K).”

In the paper

“Despite changes in positivity rates over time, this testing metric also significantly correlated with the number of cases (Fig. 4C) and number of deaths (Fig. 4D).”

In the paper

“Many months into the epidemic, the early positive association between tests per capita and SES (Fig. 2B) reversed (Fig. 4E), indicative of an improvement in testing capacity over time, so that more tests were performed in the most affected areas.”

In the paper

“When looking at tests per death, a metric that can be used as a faithful proxy of testing capacity (22), we observe a positive correlation with SES (Fig. 4L), indicating that testing disparities persisted during the epidemic, with low-SES areas being affected the most.”

What the sources say

• Not checked yetcites [22] D. K. Kennedy, A. Seale, D. Bausch, “How experts use data to identify emerging COVID-19 success stories,” Our World in Data, 30 June 2020; https://ourworldindata.org/identify-covid-exemplars.

In the paper

“Although people younger than 40 years old have an overall lower mortality rate than those from older age groups as expected, they still exhibit a nearly twofold increase in the total deaths, with a peak in the observed deaths occurring 2 weeks earlier than that for those older than 60 years of age (Fig. 3B).”

In the paper

“In addition, an early peak of reported cases was only observed in high-SES municipalities during the middle of March (Fig. 2C), even though several COVID-19 deaths, which are lagged with respect to infection by up to several weeks, were reported in low-SES municipalities during the same period.”

In the paper

“For the age groups 40 to 60, 60 to 80, and older than 80, the observed deaths are 2.8, 3.2, and 2.4 times higher than expected, respectively.”

In the paper

“Similarly, our resulting IFR estimates once corrected for underascertainment display a similar pattern (Fig. 5B) but on an order of magnitude lower than the CFR estimates.”

In the paper

“Similarly, the number of tests started to positively correlate with deaths (Fig. 4F), suggesting that the number of tests are strong predictors of mortality.”

In the paper

“The age groups 60 to 80 and 40 to 60 exhibit IFRs that are 1.4 and 1.7 times higher, respectively, in the low-SES category compared with the high-SES one.”

In the paper

“The anomalies in the observed versus predicted deaths for 2020 across different age groups also display a significant negative association with SES, except for the 80+ group (Fig. 3E), suggesting a higher death burden in lower-SES municipalities, independent of their age composition.”

In the paper

“COVID-19–attributed deaths for the entire Greater Santiago area fall withing the credible intervals of excess deaths until late June, when the attributed deaths increase to rates that are even higher than the excess deaths, suggesting that underreporting in COVID-19–attributed deaths is unlikely (Fig. 3C).”

In the paper

“Our results show that the positivity signal tracked the course of the epidemic, peaking at times of highest incidence between May and July and suggesting a highly saturated health care system during this period across the entire city (Fig. 4A).”

In the paper

“First, our analysis of human mobility indicates that municipalities with lower SES were less compliant with stay-at-home orders, possibly because people from lower-SES areas are unable to work from home, which leaves them at a higher disease risk.”

In the paper

“Third, anomalies in the overall excess deaths are higher in low-SES areas, particularly in people younger than 80 years old, suggesting that more vulnerable municipalities were hit the hardest.”

In the paper

“People in low-SES municipalities are more likely to be overweight and to live in overcrowded conditions (supplementary materials), factors that ultimately can put these populations at higher disease risk.”

In the paper

“We also show in the supplementary materials that the South and West zones (based on health coverage division) have four times fewer beds per 10,000 people and four times lower proportion enrolled in the private health system than the East zone, which contains all the municipalities with an SES of 60 or higher.”

In the paper

“Notably, more than 90% of the COVID-19–attributed deaths in the South and West zones occurred in places other than health care facilities, compared with 55% in the East zone.”

In the paper

“Santiago, Chile, is a highly segregated city with distinct zones of affluence and deprivation.”

In the paper

“A decrease in the number of cancer-attributed deaths can be explained by mortality displacement (19, 20)”

What the sources say

• Not checked yetcites [19] A. Banerjee, L. Pasea, S. Harris, A. Gonzalez-Izquierdo, A. Torralbo, L. Shallcross, M. Noursadeghi, D. Pillay, N. Sebire, C. Holmes, C. Pagel, W. K. Wong, C. Langenberg, B. Williams, S. Denaxas, H. Hemingway, Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: A population-based cohort study. Lancet 395, 1715– 1725 (2020). 10.1016/S0140-6736(20)30854-0 32405103 PMC7217641 source
• Not checked yetcites [20] J. M. Aburto, R. Kashyap, J. Schöley, C. Angus, J. Ermisch, M. C. Mills, J. B. Dowd, Estimating the burden of the COVID-19 pandemic on mortality, life expectancy and lifespan inequality in England and Wales: a population-level analysis. J. Epidemiol. Community Health 10.1136/jech-2020-215505 (2021). 10.1136/jech-2020-215505 PMC7818788 33468602 source

In the paper

“Infection fatality rates were higher in lower-income municipalities because of comorbidities and lack of access to health care.”

In the paper

“These findings suggest that an early first wave of infections occurred during March and quickly spread through the rest of the city without being captured by the official counts.”

In the paper

“Our results align with the recent literature on uneven health risks globally, which has highlighted how socially and economically deprived populations are more vulnerable to the burden of epidemics (24, 25).”

What the sources say

• Not checked yetcites [24] O. P. Ottersen, J. Dasgupta, C. Blouin, P. Buss, V. Chongsuvivatwong, J. Frenk, S. Fukuda-Parr, B. P. Gawanas, R. Giacaman, J. Gyapong, J. Leaning, M. Marmot, D. McNeill, G. I. Mongella, N. Moyo, S. Møgedal, A. Ntsaluba, G. Ooms, E. Bjertness, A. L. Lie, S. Moon, S. Roalkvam, K. I. Sandberg, I. B. Scheel, The political origins of health inequity: Prospects for change. Lancet 383, 630– 667 (2014). 10.1016/S0140-6736(13)62407-1 24524782 source
• Not checked yetcites [25] E. R. Meara, S. Richards, D. M. Cutler, The gap gets bigger: Changes in mortality and life expectancy, by education, 1981–2000. Health Aff. (Millwood) 27, 350– 360 (2008). 10.1377/hlthaff.27.2.350 18332489 PMC2366041 source

In the paper

“The lack of association between IFR and SES in the oldest age group can be attributed to a lower life expectancy (23), which is factored into the estimation of SES (see methods for details), and the fact that elderly people might be, in general, healthier enough to survive until that age.”

What the sources say

• Not checked yetcites [23] U. Bilal, M. Alazraqui, W. T. Caiaffa, N. Lopez-Olmedo, K. Martinez-Folgar, J. J. Miranda, D. A. Rodriguez, A. Vives, A. V. Diez-Roux, Inequalities in life expectancy in six large Latin American cities from the SALURBAL study: An ecological analysis. Lancet Planet. Health 3, e503– e510 (2019). 10.1016/S2542-5196(19)30235-9 31836433 PMC6926471 source

In the paper

“Our result is consistent with analyses of New York City neighborhoods (11) and with findings from other studies conducted in Santiago that used different socioeconomic and mobility metrics (12–14).”

What the sources say

• Not checked yetcites [11] S. M. Kissler, N. Kishore, M. Prabhu, D. Goffman, Y. Beilin, R. Landau, C. Gyamfi-Bannerman, B. T. Bateman, J. Snyder, A. S. Razavi, D. Katz, J. Gal, A. Bianco, J. Stone, D. Larremore, C. O. Buckee, Y. H. Grad, Reductions in commuting mobility correlate with geographic differences in SARS-CoV-2 prevalence in New York City. Nat. Commun. 11, 4674 (2020). 10.1038/s41467-020-18271-5 32938924 PMC7494926 source
• Not checked yetcites [12] A. Carranza, M. Goic, E. Lara, M. Olivares, G. Y. Weintraub, J. Covarrubia, C. Escobedo, N. Jara, L. J. Basso,The social divide of social distancing: Shelter-in-place behavior in Santiago during the COVID-19 pandemic. SSRN 3691373 [Preprint]. 12 September 2020;. 10.2139/ssrn.3691373 source
• Not checked yetcites [13] M. Bennett, All things equal? Heterogeneity in policy effectiveness against COVID-19 spread in chile. World Dev. 137, 105208 (2021). 10.1016/j.worlddev.2020.105208 32994662 PMC7513907 source
• Not checked yetcites [14] N. Gozzi, M. Tizzoni, M. Chinazzi, L. Ferres, A. Vespignani, N. Perra, Estimating the effect of social inequalities in the mitigation of COVID-19 across communities in Santiago de Chile. Nat. Commun. 12, 2429 (2021). 10.1038/s41467-021-22601-6 33893279 PMC8065143 source

In the paper

“These experiments confirm that RmMAP is capable of recovering this bimodal phenomena, whereas other methods fail to do so; they oversmooth the true signal, and the earlier peak is typically not recovered.”

In the paper

“Age-specific death data may also help explain the heterogeneity in COVID-19 burden and COVID-19–attributable deaths in different countries (9).”

What the sources say

• Not checked yetcites [9] M. O’Driscoll, G. Ribeiro Dos Santos, L. Wang, D. A. T. Cummings, A. S. Azman, J. Paireau, A. Fontanet, S. Cauchemez, H. Salje, Age-specific mortality and immunity patterns of SARS-CoV-2. Nature 590, 140– 145 (2021). 10.1038/s41586-020-2918-0 33137809 source

In the paper

“The mortality database is built with the death certificates, which are subjected to a rigorous validation process, to guarantee the reliability and validity of the information.”

In the paper

“Mobility data from mobile phones are likely to be biased because of differential mobile phone ownership in different demographic groups.”

In the paper

“Since March 2020, the DHSI has implemented the recommendations of the World Health Organization for coding the deaths resulting from COVID-19.”

In the paper

“First, a higher IFR may reflect limited access to health services during the pandemic, and the strong association between the number of tests per death and SES supports this claim.”

In the paper

“Our IFR estimates are derived from a complex Bayesian model and are based on assumptions regarding reporting rates and age distribution of infections.”

In the paper

“Mounting evidence suggests that such differences have also manifested in the context of the COVID-19 pandemic (26, 27).”

What the sources say

• Not checked yetcites [26] B. Burström, W. Tao, Social determinants of health and inequalities in COVID-19. Eur. J. Public Health 30, 617– 618 (2020). 10.1093/eurpub/ckaa095 32638998 PMC7454505 source
• Not checked yetcites [27] R. Laxminarayan, B. Wahl, S. R. Dudala, K. Gopal, C. Mohan B, S. Neelima, K. S. Jawahar Reddy, J. Radhakrishnan, J. A. Lewnard, Epidemiology and transmission dynamics of COVID-19 in two Indian states. Science 370, 691– 697 (2020). 10.1126/science.abd7672 33154136 PMC7857399 source

In the paper

“Because public health measures were taken in response to nominal case counts, these places were underprepared, with a poor health care response that resulted in higher death counts.”

In the paper

“The main appeal of this framework is that although most of the components are not identifiable (e.g., if reporting rates and true cases are both unknown, the same observed case counts can be achieved by multiplying both by the same factor) (37), we can borrow from better-known quantities (e.g., rough estimates of prevalence, reporting, etc.) to enhance identification while propagating the appropri”

What the sources say

• Not checked yetcites [37] G. Papadopoulos, J. Santos Silva, Identification issues in some double-index models for non-negative data. Econ. Lett. 117, 365– 367 (2012). 10.1016/j.econlet.2012.06.001 source

In the paper

“Our results show a strong link between socioeconomic and demographic factors with COVID-19 outcomes and testing capacity of COVID-19 in Santiago.”

In the paper

“In Chile, recent studies have suggested a link between SES and the effectiveness of nonpharmaceutical interventions such as stay-at-home orders (12, 13, 29).”

What the sources say

• Not checked yetcites [12] A. Carranza, M. Goic, E. Lara, M. Olivares, G. Y. Weintraub, J. Covarrubia, C. Escobedo, N. Jara, L. J. Basso,The social divide of social distancing: Shelter-in-place behavior in Santiago during the COVID-19 pandemic. SSRN 3691373 [Preprint]. 12 September 2020;. 10.2139/ssrn.3691373 source
• Not checked yetcites [13] M. Bennett, All things equal? Heterogeneity in policy effectiveness against COVID-19 spread in chile. World Dev. 137, 105208 (2021). 10.1016/j.worlddev.2020.105208 32994662 PMC7513907 source
• Not checked yetcites [29] Y. Li, E. A. Undurraga, J. R. Zubizarreta, Effectiveness of localized lockdowns in the COVID-19 pandemic. medRxiv 2020.08.25.20182071 [Preprint]. 30 March 2021; 10.1101/2020.08.25.20182071 PMC8807239 35029649 source

In the paper

“Our Bayesian method for joint inference of IFRs and underreporting is a new contribution in this field.”

In the paper

“RmMAP overcomes this issue by adding a quadratic penalty to the log-likelihood.”