Inequity in Mortality Rates and Potential Years of Life Lost caused by COVID-19 in the Greater Santiago, Chile

doi:10.21203/rs.3.rs-2451409/v1

Background

Several studies have shown that in Chile income inequality is relevant in explaining inequities in health outcomes. The COVID-19 pandemic has also had a non-neutral impact, with higher mortality rates in municipalities with lower socioeconomic status.

Methods

We study inequity in mortality based on Potential Years of Life Lost (PYLL) in 34 urban municipalities of the Metropolitan Region (Greater Santiago) and analyze its evolution between 2018 and 2021 and by COVID-19 waves. In order to compare the results obtained for PYLL, we also computed the mortality rates adjusted by direct standardization. In addition, we used the concentration index to measure the health inequalities between municipalities.

Results

In the first year of the pandemic, the absolute PYLL and the crude mortality rate for all causes of death showed an increase of 13.6% and 19.8%, respectively. Moreover, 409,086 years of life were prematurely lost in 2020, one-fifth of them due to COVID-19. The concentration indices show signs of inequality in both mortality rates and PYLL, where it is more pronounced when calculating the latter measure.

Conclusions

Results show that the excess deaths due to the COVID-19 pandemic affected the most economically disadvantaged municipalities, and even more so the young people in those places.

COVID-19

PYLL

Mortality

Health inequity

Since its arrival, Chile has been dealing with COVID-19. After two years of stringent restrictions, the country has moved into a situation of “new normal”, in which many restrictions have been removed [1,2]. Although today the situation looks better than two years ago, COVID-19 is an ongoing crisis. By November 27, 2022, the country registered 4.9 million accumulated cases and 62.377 COVID-related deaths [3].

Many public policies have been implemented during these years, mostly focused on reducing the number of cases and deaths, including mobility restrictions and a large-scale vaccine rollout [1,4]. These actions have reduced the number of deaths and other health indicators, such as hospital bed occupancy rates [3]. Even though these are good news—reducing the overall impact of the pandemic—, fewer analyses are available on the distributional effects of COVID-19 in Chile.

Measuring distributional effects is relevant, particularly for a country like Chile, in which income inequality has been documented as relevant to explain health outcomes inequities [5]; COVID-19 impact has also shown a non-neutral pattern, with worse outcomes registered in places with lower socioeconomic status [6–8].

In this context, thinking about inequality and inequity in Chile is crucial for several reasons. First, highlighting the unequal effect of the pandemic but also the potential impacts of the policies implemented to address COVID-19 is relevant for designing new strategies, particularly in this period of “new normal” in the country. Second, a broader discussion has been carried out in the past years, mostly motivated by the existence of unjust and avoidable inequalities, including those in the health system, that ended up in the social outbreak of October 2019, a new coalition in power, a process to change the constitution and, today, a debate of the reform of the health system [9–11].

The aim of this article is to measure the impact of COVID-19 on mortality inequity—over a period of four years— in the Greater Santiago area, comprising 34 urban municipalities of the Metropolitan Region, Chile. The Metropolitan Region is an interesting case of study, since it concentrates more than 40% of the country’s population—35.9% of the country’s population living in the Greater Santiago area—, with an important socioeconomic and demographic heterogeneity at the municipality level [12].

To measure the mortality inequity, the adjusted-mortality rates and potential years of life lost (PYLL) were used in this work. The PYLL differs from adjusted-mortality rates in that they consider the age of the people who died in their calculation, giving higher values if more young people have died in a given municipality. Although there are studies that have examined inequity in mortality in the Metropolitan Region [6,7], none of them have focused on analyzing PYLL. PYLL has been used to study mortality inequity by COVID-19 in other countries [13–15] and for other diseases [16–18].

Understanding these distributional effects of the disease can help advocate for the implementation of policies targeted to those in more need, and emphasize the urgency of addressing the structural inequities in the country and the Chilean health system.

Methodology

The study utilizes data from the 34 urban municipalities of the Metropolitan Region, known as Greater Santiago. For each municipality, two mortality measures were calculated: adjusted-mortality rates and potential years of life lost (PYLL). Mortality rates are adjusted by direct standardization, i.e, they are calculated considering the same age structure per administrative unit. Thus, possible confounding effects of the composition of each population are eliminated, allowing comparison between municipalities. For the PYLL, the OECD 2022 methodology was adopted, which also standardizes by age and considers a threshold of 75 years, below which the years lost prematurely are counted [19].

The expressions for the standardized-mortality rate (SMR) and potential years of life lost (PYLL) are as follows:

$$SM{R}_{it}={\sum }_{a=0}^{K}({d}_{at}/{P}_{at})*{Pr}_{a}*\text{1,000}$$

1

$${PYLL}_{it}={\sum }_{a=0}^{L-1}(L-a)({d}_{at}/{P}_{at})*{Pr}_{a}*\text{1,000}$$

2

$SM{R}_{it}$ : Standardized mortality rate per 1,000 people using the direct method in municipality “i” in period “t”.
$K$ : Maximum age reached at the time of death in the municipality “i” in period “t”.
${d}_{at}$ : Number of deaths at age “a” in the municipality “i” in period “t”.
${P}_{at}$ : Number of persons aged “a” in the municipality “i” in period “t”.
${Pr}_{a}$ : Proportion of persons aged “a” in the 2015 OECD population (reference population [20]).
${PYLL}_{it}$ : Potential Years of Life Lost in the municipality “i” in period “t”.
L: Upper age limit established for the calculation of the measure (75 years according to the OECD).

The weighting of deaths at early ages can be observed in the term $(L-a)$ in Eq. (2). Thus, a person who dies at 15 years of age has more weight in the PYLL index than one who dies at 70 years of age, because the first contributes to 60 years of life lost prematurely while the second has only 5 years of life lost prematurely.

Additionally, the crude mortality rate and the absolute potential years of life lost are reported, which are calculated at the regional level, and are shown below:

$$CM{R}_{jt}=\frac{{d}_{jt}}{{P}_{N}}*1.000$$

3

$${ABS\_PYLL}_{jt}={\sum }_{a=0}^{L-1}(L-a){d}_{at}$$

4

$CM{R}_{jt}$ : Crude mortality rate per 1,000 people for the cause of death "j" in period "t".
${d}_{jt}$ : Number of deaths by the cause of death "j" in period "t".
${P}_{N}$ : Total number of people in the study population.
${ABS\_PYLL}_{jt}$ : Absolute Potential Years of Life Lost for the cause of death "j" in period "t".
L: Upper age limit established for the calculation of the measure (75 years).
${d}_{at}$ : Number of deaths at age “a” in period “t”.

To study the evolution of the indicators, two approaches were followed. First, yearly data were used, considering four different periods: two before the onset of the pandemic (2018 and 2019) and two to capture mortality patterns during the pandemic (2020 and 2021). Second, indicators were calculated for each COVID-19 wave. For this purpose, we used a previously defined way to identify COVID-19 waves, based on the scale (case rate) and dynamics (changes in the number of cases) of COVID-19 incident rates [21]. Thus, for the Metropolitan Region, four study periods are identified, corresponding to four fully completed waves: a first wave beginning on May 3, 2020, and ending on July 26, 2020; the second wave between February 21, 2021, and July 11, 2021; the third wave that lasted from October 17, 2021, to November 21, 2021, and; the last wave covering the period between January 2, 2022 and April 24, 2022. In order to better capture the impact of COVID-19 deaths, due to the time lag between cases and deaths, two additional weeks were considered after the end of each wave.

For the analysis of health inequalities in the Greater Santiago area, the concentration index is computed. This index corresponds to a measure of economic inequality that has been adapted to measure health inequality. It is conceptually similarly to the Gini index, but it results from a bivariate distribution of health and social group ranking, and thus is not a measure of total inequality but measures the relationship between socioeconomic ranking and health [22–25]. It is defined according to the concentration curve and summarizes its main finding. The concentration curve is constructed by plotting the cumulative percentage of a health variable (y-axis) against the cumulative percentage of the population, ordered according to a socioeconomic variable (x-axis). In the hypothetical case that the health variable is evenly distributed in the population, a 45° line called “line of equality” is drawn. Whereas if it is not evenly distributed, the curve will lie above/below the line of equality if the burden of disease is concentrated in the most economically disadvantaged/advantaged population, where a greater distance between the curve and the line means greater inequality in health. In this context, the concentration index is calculated as twice the area between the curve and the line of equality, whereby means of its sign and magnitude it is possible to summarize all the scenarios just mentioned. An index equal to 0 indicates equity in the health variable and an index with a negative/positive sign indicates that the burden of the disease is concentrated in the most economically disadvantaged/advantaged population, respectively. In addition, since this index fluctuates between − 1 and 1, the closer to these limits the greater this inequity in health will be.

Analyzing inequalities by year and wave is useful to compare periods before and after the arrival of COVID-19 in Chile (years) as well as to analyze how these inequalities vary during the pandemic (waves). It should be noted that, for a complete analysis, not only the cause of death by confirmed and suspected COVID-19 was analyzed, but also the total causes of death. This is important to study how the COVID-19 pandemic containment measures and the way deaths were classified impacted the rates of the other causes.

Data sources

The number of deaths by COVID-19, was obtained from public data provided weekly by the Department of Health Statistics and Information of the Chilean Ministry of Health [26]. This database records all deaths that have occurred in Chile since 2016 and classifies them according to the International Classification of Diseases (ICD10), where relevant information such as age, sex and the municipality of residence of the deceased is presented. On the other hand, the population estimated for each municipality by age stratum was obtained from the National Institute of Statistics of Chile, which projects the population for the last census carried out in 2017 [27]. The calculation of the municipality average income per household was obtained from the 2017 National Socioeconomic Characterization Survey (CASEN) [28]. Finally, the confirmed cases of COVID-19 came from the databases of the Chilean Ministry of Health available in a public repository of the Chilean Ministry of Science, Technology, Knowledge and Innovation [29].

Table 1 shows the mortality indicators for the Metropolitan Region. An increase in absolute PYLL for all causes in the last 4 years is observed, with the largest change occurring with the advent of COVID-19, growing 13.6% between 2019 and 2020; this rise is 4.5 times greater than the observed in the previous year. In fact, during the first pandemic year there were 409,086 PYLL of which 90,734 were due to confirmed or suspected COVID-19, i.e, one-fifth of all absolute PYLL (22.2%). Similarly, to what was observed for the PYLL, the crude mortality for all causes experienced an increase during the four years: the rate in 2020 (6.3 deaths per 1,000 inhabitants) represents an increase of 19.8% with respect to the previous year (5.3 deaths per 1,000 inhabitants). It should be noted that despite the decrease in total crude mortality in 2021, the values of the mortality measures studied are still higher than pre-pandemic levels. Continuing with the annual analysis, a decrease in both absolute PYLL and crude mortality can be observed when all causes of death excluding COVID-19 are considered. This does not necessarily mean an improvement in the other causes of death, as it is reasonable to consider other hypotheses, such as that excess deaths from COVID-19 would produce deaths in the population that would be expected in the short term, reducing the number of deaths in other causes.

To compare the different waves of COVID-19, the mortality measures were divided by weeks of duration of each wave, which are presented in parentheses in Table 1. Considering the weekly values of absolute PYLL for all causes, the first two waves are the most severe with 11,081.5 and 9,103.6 absolute PYLL per week, for which two-fifths (43.6%) and one-third (33.9%) are explained by COVID-19. It should be noted that with the evolution of the pandemic in Chile, the contribution of PYLL by COVID-19 in each wave is, in general, decreasing.

The advantage of analyzing both mortality rates and PYLL after the arrival of COVID-19 is that by considering the last one it is possible to differentiate whether different age groups were affected differently by the disease. Thus, due to the marked increase observed in both mortality measures in 2020, it is possible to mention that the population exhibited a higher number of deaths, and a large part of them correspond to young people (behavior similar to the first waves of COVID-19).

Table 1: Summary of mortality indicators by periods in the Metropolitan Region.

Considering the previous analysis, it is interesting to observe the behavior of these variables at the municipal level. Figure 1 shows the standardized PYLL and mortality rates for COVID-19 confirmed in the first year of the pandemic (2020). As can be seen, the burden of the disease is unequal between municipalities, with the greatest differences for PYLL. Although the causes of inequity are multiple, we observe that municipalities with higher incomes—such as Vitacura, Lo Barnechea, and Las Condes—have lower values in mortality measures. On the other hand, the municipalities with the worst health outcomes are those with lower average income per household.

Specifically, the confirmed COVID-19 standardized PYLL rate ranged from 0.166 in the least impacted municipality (Vitacura) to 2.222 in the most affected (San Ramón), which represents a ratio of 13.4. On the other hand, the standardized mortality rate ranged from 0.070 (Vitacura) to 0.313 (La Pintana), which represents a ratio of 4.5. This behavior is also observed considering all causes of death (see Appendix 1).

Figure 1: PYLL and mortality rate by COVID-19 confirmed (2020) in the municipalities of the Greater Santiago.

The spatial relationship between PYLL and income is presented in Fig. 2. The figure on the left shows the spatial distribution of the PYLL rate for the first year of the confirmed COVID-19 pandemic, while the figure on the right shows the average income per household for each municipality. From them, it can be noted that there is an inverse relationship between years of life prematurely lost and income (this relationship can also be observed in Appendix 2).

Figure 2: Spatial distribution of confirmed COVID-19 PYLL (2020) and average income per household.

To quantify the inequality visualized in Figs. 1 and 2, the concentration index is calculated for all causes of death and, specifically, for COVID-19. The results are shown in Table 2. It is noteworthy that all values are negative, indicating that the burden of the rates is concentrated in the most economically disadvantaged municipalities. In addition, it should be noted that the inequities shown are more pronounced when estimating the burden of disease using PYLL, suggesting greater inequality in younger age groups.

As can be observed, with the arrival of COVID-19 the PYLL and death rates indexes show an increase in inequality compared to the previous two years. It should be noted that whether or not we consider COVID-19 as the cause of death in 2020, it is possible to observe this rise. Where, the PYLL concentration index goes from − 0.112 (2019) to -0.149 (2020) if all causes of death are considered or to -0.138 without considering COVID-19 deaths. Comparing the concentration indexes in the first year of the pandemic, more inequity is detected in COVID-19 deaths than in the remaining causes. On the other hand, in the second year of the pandemic a decrease in inequity is observed compared to the first year for all causes of death, returning to levels closer to those seen in pre-pandemic years; this decrease in inequity is also observed for COVID-19 deaths.

Table 2: Concentration indexes for mortalities and PYLL according to cause of death and period.

This study aims to characterize the burden of COVID-19 in terms of age-adjusted PYLL and mortality rates in the municipalities of the Greater Santiago, estimating its effect in different periods of the pandemic and analyzing potential inequities in the rates according to the municipality’s average household income. The results show that the first year of the pandemic (2020) is a break point in the evolution of mortality indicators in the study population, with absolute PYLL and crude mortality rate for all causes of death showing an increase of 13.6% and 19.8% over the previous year, respectively. Regarding deaths associated with COVID-19 it is possible to see that in both 2020 and 2021 one fifth of the total PYLL were contributed by this cause of death (22.2% and 21.6%). However, when examining the pandemic period by waves it is observed that the contribution of COVID-19 is, in general, decreasing.

When ordering PYLL higher differences in this indicator can be observed between municipalities than those observed when using standardized mortality. Moreover, these differences are correlated with the average household income in the municipalities. The concentration index computed in this work was higher for PYLL than for mortality, i.e., PYLL reflects greater inequality in mortality due to the average household income of the municipalities. The reason is that PYLL differentiates deaths by the age at which they occur.

In addition, from the concentration index, a stabilization in inequality can be observed as the pandemic progresses in Chile (waves). For example, the first wave exhibits the greatest inequities for PYLL and mortality rates associated with confirmed COVID-19, and as new outbreaks emerge, the inequity assessed in each wave tends to attenuate (indexes closer to 0). One possible explanation is that the municipalities with lower average income were more affected in the initial stages of COVID-19, but through the strategies implemented to combat the advance of the epidemic —such as the massive vaccination plan that did not discriminate by income [30,31]—this inequity was reduced to some extent. Other possible explanations are that the economic aid policies through vouchers allowed-in a certain way-a better compliance with quarantines of those who needed to make a living, or the possibility of a greater harvest effect in more deprived areas, where there is usually a higher density of disease and severe disease. Finally, it should be noted that despite this reduction in recent periods, these avoidable differences in the PYLL and mortality rates studied are far from being eradicated.

The study has some limitations that should be considered during the analysis. First, there are temporal differences between the data used: while information related to deaths allows us to identify the impact on a certain municipality in a specific period, information related to population projections and average income per household are calculated based on information from 2017. Thus, both standardized mortality rates and concentration indexes, which use these data in their formulas, may present slight differences. It should be noted that population projections may be the most affected due to the significant increase in the migrant population in Chile in recent years [32], however, there is no other data source with more updated representative information.

Secondly, it should be noted that the results show in a descriptive manner the evolution of health inequalities through the years and waves of COVID-19, not being able to identify causality effects. Thus, although it can be seen that health inequality in PYLL/mortality rate due to COVID-19 attenuates as the pandemic passes, it is not possible —with the data available— to differentiate whether it is the effect of the vaccination plan, economic vouchers, all of these measures, or any other.

Finally, it should be noted that this study focuses on the 34 urban municipalities of the Metropolitan Region due to the large percentage of the Chilean population concentrated there and the significant socioeconomic and demographic heterogeneity it possesses. However, it is important to recognize that both the rural municipalities and the other regions not considered could present a different behavior from the one presented in this work, and it would be of interest to replicate the analysis in these geographic units. In this same sense, it is also possible to extend the analysis beyond the critical moments of the pandemic (waves) by studying what happened between outbreaks of COVID-19, thus being able to have an even more complete understanding of the health inequalities presented in the pandemic period.

This analysis shows the existence of health inequities, using the concentration index, in both standardized mortality rates and years of life potentially lost by COVID-19, when comparing the urban municipalities of the Metropolitan Region according to average household income. It highlights that such inequity is more pronounced when estimating the communal burden of disease using PYLL, suggesting that the most economically disadvantaged communes were not only the most affected in terms of excess death from COVID-19, but were more unequally affected in deaths of younger groups.

We hope that this work will be useful to highlight the importance of revealing the distributive health effects present in an unequal country such as Chile. It will be useful both for decision makers in the country and policy makers around the world in the evaluation of future prevention and management strategies. Thus, the next generation of COVID-19 -or other emerging disease- policies should, for example, provide targeted support to more disadvantaged municipalities/groups. Along the same lines, the results may be relevant in the reformulation of the current health system.

Finally, we hope that this work will encourage the study of health inequalities based on other types of characteristics, beyond the economic, and in other contexts that help to minimize the negative effects caused by the pandemic.

PYLL

Potential Years of Life Lost.

Ethics approval and consent to participate

Ethics approval: The study uses data obtained from open databases from various public institutions. Thus, the data have the characteristic of being anonymous, secondary and aggregated. In the Methods section we mention the different sources of information from which the data were obtained (26-29). Thus, due to the characteristics already mentioned, this study was not submitted to an Ethics Committee.
Consent to participate: “Not applicable”, all the data are obtained from open databases provided by different public institutions. Thus, the data have the characteristics of being anonymous, secondary and aggregated.

Consent for publication:

“Not applicable”. The manuscript does not contain data from any individual person.

Availability of data and materials:

The COVID-related deaths data “Defunciones por Causa (actualización semanal)” that support the findings of this study are available from the open data of the Department of Health Statistics and Information [https://deis.minsal.cl/#datos abiertos]. The municipal projections of the population data “Estimaciones y proyecciones 2002-2035, comunas” are available from the open data of the National Institute of Statistics [https://www.ine.cl/estadisticas/sociales/demografia-y-vitales/proye
cciones-de-poblacion]. The average income per household data “Base de Datos Casen 2017” are available in the social observatory section of the ministry of social development and family website [http://observatorio.ministeriodesarrollosocial.gob.cl/encuesta-casen-2017]. Finally, the confirmed cases of COVID-19 data “Covid-19.csv” are available from the public repository of the Chilean Ministry of Science, Technology, Knowledge and Innovation [https://github.com/MinCien
cia/Datos-COVID19/tree/master/output/producto1].

Competing interests:

“Not applicable”. The authors declare that they have no competing interests.

Funding

Source of funding: DICYT Asociative Project 022191MH DAS, Universidad de Santiago de Chile.

The funding source was not involved in the study design, data collection, analysis and interpretation. It only finances the project in which we participate.

Authors' contributions

Conceptualization and methodology, CV, AA; validation, AA, CV, FE, PVD, MM; formal analysis, CV, AA; data curation, AA; writing—original draft preparation, AA, CV, FE, PVD, MM; writing—review and editing, AA, FE, PVD; visualization, AA; supervision, PVD, FE, CV, MM; funding acquisition, PVD, FE, CV, MM. All authors have read and agreed to the published version of the manuscript.

Acknowledgements

The authors thank the support of DICYT Asociativo Grant 022191MH DAS to carry out the project “Understanding causes and consequences of COVID-19 and other diseases on population health: multicausality, time, and space for decision-making”.

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del Castillo B, Aravena C, Latorre R. Cómo se fraguó el engranaje logístico que permitió vacunar a cinco millones de personas en 41 días. 2021. https://www.latercera.com/la-tercera-pm/noticia/como-se-fraguo-el-engranaje-logistico-que-permitio-vacunar-a-cinco-millones-de-personas-en-41-dias/SNQWQVSIPRCHPGENMI4MMI5RJA/. Accessed 19 Nov 2022.
Instituto Nacional de Estadísticas (INE). Población extranjera residente en Chile llegó a 1.482.390 personas en 2021, un 1,5% más que en 2020. https://www.ine.gob.cl/prensa/detalle-prensa/2022/10/12/población-extranjera-residente-en-chile-llegó-a-1.482.390-personas-en-2021-un-1-5-más-que-en-2020#:~:text=las%20Conferencias%20Ciudadanas!-,Población%20extranjera%20residente%20en%20Chile%20llegó%20a%201.482.3
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mbia%20y%20Bolivia. Accessed 19 Nov 2022.

Table 1

Summary of mortality indicators by periods in the Metropolitan Region.
Measure	Period
Measure	2018	2019	2020	2021	Wave 1	Wave 2	Wave 3	Wave 4
Weeks (duration)	52	52	52	52	14	22	7	18
Crude mortality all causes (Mortality/weeks)	5.187 (0.099)	5.289 (0.102)	6.337 (0.122)	6.247 (0.120)	2.646 (0.189)	3.014 (0.137)	0.77 (0.110)	2.07 (0.115)
Stand. Mortality all causes (Stand. Mortality/weeks)	0.7035 (0.0135)	0.7147 (0.0137)	0.8504 (0.0164)	0.8231 (0.0158)	0.3564 (0.0255)	0.3949 (0.0180)	0.1017 (0.0145)	0.2683 (0.0149)
Crude mortality all except COVID-19 (Mortality/weeks)	-	-	4.621 (0.089)	4.805 (0.092)	1.389 (0.099)	1.980 (0.090)	0.703 (0.100)	1.763 (0.098)
Stand. Mortality all causes except COVID-19 (Stand. Mortality/weeks)	-	-	0.6204 (0.0119)	0.6353 (0.0122)	0.1881 (0.0134)	0.2623 (0.0119)	0.0928 (0.0133)	0.2280 (0.0127)
Crude mortality total COVID-19 (Mortality/weeks)	-	-	1.715 (0.033)	1.441 (0.028)	1.260 (0.090)	1.034 (0.047)	0.063 (0.009)	0.306 (0.017)
Crude mortality confirmed COVID-19 (Mortality/weeks)	-	-	1.339 (0.026)	1.101 (0.021)	0.966 (0.069)	0.770 (0.035)	0.063 (0.009)	0.27 (0.015)
Stand. Mortality total COVID-19 (Stand. Mortality/weeks)	-	-	0.230 (0.0044)	0.1878 (0.0036)	0.1683 (0.0120)	0.1327 (0.0060)	0.0089 (0.0013)	0.0402 (0.0022)
Abs. PYLL all causes (Abs. PYLL/weeks)	349,372 (6,718.7)	359,908 (6,921.3)	409,086 (7,845.5)	412,769 (7,937.9)	155,141 (11081.5)	200,279 (9,103.6)	50,737 (7,248.1)	130,395 (7,244.2)
Stand. PYLL all causes (Stand. PYLL/weeks)	4.9564 (0.0953)	4.9598 (0.0954)	5.5223 (0.1062)	5.4623 (0.1050)	2.1181 (0.1513)	2.6600 (0.1209)	0.6668 (0.0953)	1.6945 (0.0941)
Abs. PYLL all except COVID-19 (Abs. PYLL/weeks)	-	-	318,352 (6,105.4)	323,730 (6,225.6)	87,554 (6,253.9)	132,353 (6,016.0)	47,533 (6,790.4)	119,461 (6,636.7)
Stand. PYLL all except COVID-19 (Stand. PYLL/weeks)	-	-	4.2559 (0.0818)	4.2605 (0.0819)	1.1750 (0.0839)	1.7455 (0.079.)	0.6236 (0.0890)	1.5470 (0.0859)
Abs. PYLL total COVID-19 (Abs. PYLL/weeks)	-	-	90,734 (1,740.1)	89,039 (1,712.3)	67,587 (4,827.6)	67,926 (3,087.5)	3,204 (457.7)	10,934 (607.4)
Abs. PYLL confirmed COVID-19 (Abs. PYLL/weeks)	-	-	71,107 (1,363.6)	67,713 (1,302.1)	52,098 (3,721.3)	51,234 (2,328.8)	2,809 (401.3)	9,873 (548.5)
Stand. PYLL total COVID-19 (Stand. PYLL/weeks)	-	-	1.2664 (0.0244)	1.2018 (0.0231)	0.9430 (0.0674)	0.9145 (0.0416)	0.0432 (0.0062)	0.1475 (0.0082)
Fraction PYLL total COVID-19	-	-	22.2%	21.6%	43.6%	33.9%	6.3%	8.4%

Table 2

Concentration indexes for mortalities and PYLL according to cause of death and period.
Measure	Diagnostic group	Concentration index
Measure	Diagnostic group	2018	2019	2020	2021	Wave 1	Wave 2	Wave 3	Wave 4
Standardized mortality rates	All causes of death	-0.059	-0.058	-0.074	-0.064	-0.091	-0.079	-0.050	-0.055
	All except COVID-19	-	-	-0.059	-0.055	-0.067	-0.059	-0.045	-0.050
	COVID-19 confirmed and suspected	-	-	-0.112	-0.103	-0.116	-0.117	-0.095	-0.085
	COVID-19 confirmed	-	-	-0.115	-0.098	-0.119	-0.111	-0.090	-0.091
PYLL	All causes of death	-0.117	-0.112	-0.149	-0.122	-0.182	-0.126	-0.119	-0.129
	All except COVID-19	-	-	-0.138	-0.114	-0.168	-0.112	-0.117	-0.129
	COVID-19 confirmed and suspected	-	-	-0.186	-0.150	-0.200	-0.152	-0.137	-0.125
	COVID-19 confirmed	-	-	-0.187	-0.147	-0.202	-0.150	-0.110	-0.135

No competing interests reported.

Figure3appendix1.docx
Appendix 1: Gradients of PYLL and communal mortality rate by all causes of death (2020).
Figure4appendix2.docx
Appendix 2: Confirmed COVID-19 PYLL per 1,000 inhabitants according to average income per household (2020).

Inequity in Mortality Rates and Potential Years of Life Lost caused by COVID-19 in the Greater Santiago, Chile

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Abstract

Figures

Background

Methods

Methodology

Data sources

Results

Discussion

Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

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