COVID-19 vaccination coverage of school-aged children in Santiago, Chile, correlates with socioeconomic status: Longitudinal observational study

doi:10.21203/rs.3.rs-4547811/v1

Download PDF

Article

COVID-19 vaccination coverage of school-aged children in Santiago, Chile, correlates with socioeconomic status: Longitudinal observational study

https://doi.org/10.21203/rs.3.rs-4547811/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

The burden of COVID-19 was heterogeneous, indicating that the effects of this disease are synergistic with both other non-communicable diseases and socioeconomic status (SES), highlighting its syndemic character. While the appearance of vaccines moderated the pandemic effects, their coverage was heterogeneous too, both when comparing different countries, and when comparing different populations within countries. Of note, once again SES appears to be a correlated factor.

We analyzed publicly available data detailing the percentage of school-aged, vaccinated children in different municipalities belonging to the Metropolitan Area (MA) of Santiago, Chile. Vaccination data was compiled per school type, either public, state-subsidized, or private, at three different dates during the COVID-19 pandemic to cover the dispersion of Delta, Omicron, and its subvariants BA.4 and BA.5. We computed the median vaccination ratio for each municipality and school type and calculated their Spearman’s rank correlation coefficient with each one of nine SES indices.

The percentage of school-age children who received vaccinations against COVID-19 correlates with SES. This strong correlation is observed in public and state-subsidized schools, but not in private schools. Although inequity in vaccination coverage decreased over time, it remained higher among students enrolled either in public or state-subsidized schools compared to those of private schools.

Although available data was insufficient to explore plausible causes behind lower vaccination coverage, it is likely that a combination of factors including the lack of proper information about the importance of vaccination, the lack of incentives for children’s vaccination, low trust in the government, and limited access to vaccines for lower-income people, may all have contributed. These findings raise the need to design better strategies to overcome shortcomings in vaccination campaigns to confront future pandemics.

Trial Registration: The present work does not involve clinical trials.

Health sciences/Health care/Public health/Epidemiology

Health sciences/Health care/Health policy

Vaccination

COVID-19

Inequality

Children vaccination

As of October 2022, the pandemic caused by the novel coronavirus, SARS-CoV-2 has caused more than 6 million deaths worldwide [1]. After more than three years since the detection of the first case in China [2], COVID-19 continues to pose challenges: from the impact of anti-vaxxers movements [3, 4] to the appearance of new variants such as Delta and Omicron [5]. In an unprecedented biotechnological response, vaccine manufacturers developed, and National Regulatory Authorities approved in record time, several vaccines that were proven safe and effective in diminishing infection, hospital admissions, and deaths rates [6–8]. However, unequal access to vaccines creates enormous risks not only for the population of developing countries but also for the rest of the world. In fact, the Gamma, Delta, and Omicron variants of SARS-CoV-2, the etiological agent of COVID-19, emerged from high prevalence conditions in Brazil, India, and the African continent, respectively, spreading rapidly throughout the world [5]. Additionally, due to the influence of anti-vaxxer movements driven by the abundance of fake news, anti-science, and the promotion of highly individualistic behaviors, assessing and comprehending the willingness to receive COVID-19 vaccination is essential for the development of more effective public policies aimed at promoting vaccination [9–11], while keeping a balance with strict restrictions and possible socioeconomic impact [12].

Over the course of three years, COVID-19 spread across the globe. However, its impact varied considerably depending on the socioeconomic status (SES) of the affected population. Several studies have reported associations between SES and COVID-19 incidence, mortality, and vaccination coverage [13–15]. Locally, a seminal study showed that during the first stages of the pandemic, mortality attributed to COVID-19 was higher in places with lower SES in the Metropolitan Area of Santiago, Chile [13]. Data exhibited by this study corroborated that COVID-19 is, in fact, a syndemic disease: an epidemiological condition whose burden among the population is synergistic with both non-communicable diseases and SES [16, 17]. Thus, wealthier municipalities—usually more affluent and therefore healthier—were found to be less vulnerable to COVID-19. A similar correlation has been reported for other countries [18]. For instance, in the USA, localities with lower levels of education and a higher proportion of African American population–both factors usually associated but not equivalent to lower SES–are linked with a higher number of COVID-19 cases and fatalities, together with a higher proportion of long-term consequences of the infection [19–21]. Similarly, in Sweden, a higher number of COVID-19-related deaths, occurred in areas of lower SES [22], while in India, population density and literacy have been found to be positively and negatively correlated with COVID-19 infection rates, respectively, highlighting the syndemic nature of this disease [23].

In addition to the correlation between socioeconomic status (SES) and both the incidence and severity of COVID-19, vaccination rates have also been linked to SES at the country level. For example, some countries, such as Israel and Chile, accessed vaccines early in 2021, achieving coverage rates exceeding 90% of their populations [24, 25]. In contrast, less affluent nations like Haiti have struggled to vaccinate even 5% of their population [23, 26]. Thus, a lower proportion of the population receiving the COVID-19 vaccine has been associated with lower SES, a situation compounded by factors including unchecked immigration, political instability, and diminished trust in government [9–11, 14, 27]. Irregular immigrants often face limited access to healthcare services and typically reside in lower-income areas. Moreover, regions experiencing political unrest or where public trust in the government is low have difficulties in executing effective vaccination campaigns and, to an even greater extent, in persuading the population to adhere to mandates and guidelines.

In addition to its successful COVID-19 vaccination campaign aimed at the general population [28], the Chilean government conducted an extensive vaccination program for children. By October 2022, over 90% of children aged 6 to 17 were fully vaccinated, having received at least two doses of the vaccine [29]. Following the government's authorization of COVID-19 vaccines for very young children, the Ministry of Education developed a 'safe back-to-school plan.' This plan included the lifting of seating capacity limitations in classrooms where vaccination rates exceeded 80% [30]. Notably, even before the completion of the vaccination campaign for children under 12 years old was achieved, class attendance restrictions were lifted, eroding the availability of incentives for children to be vaccinated. In contrast, all individuals above 12 years old were compelled by the Ministry of Health’s to have a Sanitary Pass certifying that the person was up to date with the vaccination timetable before attending cultural, shopping, and eating venues [31].

Although the level of vaccination for each school was closely monitored by the Chilean government, to the best of our knowledge, the relationship between SES, vaccination coverage and the nature of the school, was not assessed. In this study, we determined the SES of the 32 municipalities located in the Metropolitan Area of Santiago, the capital of Chile [32]. For each municipality we computed the median school vaccination coverage per school type, at three different dates distributed along the pandemic so to cover the spread of SARS-CoV-2 variants Delta and Omicron BA.4 and BA.5: November 15, 2021; March 1st, 2022; and May 26, 2022, respectively.

Our results indicate a strong correlation between SES indices and the median school vaccination coverage. We further explored differences between school types, evaluating private, subsidized, and public schools' data separately. As expected, vulnerable municipalities with low SES exhibit lower levels of vaccination coverage. Surprisingly, while a strong correlation between vaccination coverage and SES is present in both public and state-subsidized schools, the correlation is meaningless for private schools. Therefore, in the latter, vaccination coverage seems to be independent of the SES of the municipality.

Exploring the correlation between SES indices: all indices are correlated with SPI.

Since the SPI index is only available for the city of Santiago, we evaluated the correlation between other SES indices and the SPI to determine whether other SES indices may be used as proxies to access the socioeconomic status of all municipalities in the Metropolitan Area of Santiago, Chile. Our results indicate that all the evaluated SES indices do correlate with SPI (Supplementary Fig. 1), with CDI being the one with the highest correlation strength (|ρ| >= 0.90)

Vaccination coverage is correlated with SES in schools belonging to Santiago, Chile.

In this study we evaluated the vaccination and SES data of students enrolled in the 1,667 schools belonging to the 34 municipalities of the Metropolitan Area of Santiago, Chile. We divided the population according to the school type: public, state-subsidized, and private schools. Their enrollments as of March 2021 were: 297,928 (29%), 575,426 (55%), 169,898 (16%), respectively. The population estimate of Santiago is 6,075,403 (30.9% of the country’s population). As in any other large city around the world, SES indices vary widely. For instance, the municipality of Providencia (Fig. 1A; CDI rank 1) has the lowest rate of Multidimensional Poverty (0.034), whilst Lo Espejo (Fig. 1A; CDI rank 33), has the highest rate of Multidimensional Poverty (0.375): more than 10 times higher than that of Providencia. As expected, the three municipalities with the highest CDI are also the ones with the highest level of vaccination coverage in schools (Table 1 and Fig. 1A). In contrast, the four municipalities with the lowest CDI are those with the lowest vaccination coverage, except for Pedro Aguirre Cerda, a municipality exhibiting lower vaccination coverage and ranking 26th in CDI (Table 1 and Fig. 1A). Notably, our study also shows that there are no municipalities with either high or medium vaccination coverage and low CDI could be found. Consequently, no municipalities with low vaccination coverage and high CDI can be identified (Table 1 and Fig. 1A).

Table 1

SES indices and vaccination coverage for each municipality.
Ranking	Municipality	Vaccine %	CDI	Poverty	Multidimensional poverty	PHLBS	Health and social welfare	Economy and resources	Education	SVI	SPI
1	Providencia	91,0	0,876	0,004	0,034	0,026	0,985	0,776	0,878	0,693	0,266
2	Las Condes	92,0	0,875	0,002	0,042	0,013	0,993	0,780	0,866	0,702	0,117
3	Santiago	89,0	0,778	0,041	0,096	0,173	0,895	0,635	0,828	0,738	0,571
4	Vitacura	91,0	0,764	0,001	0,035	0,005	0,995	0,498	0,900	0,588	0,063
5	Lo Barnechea	90,0	0,677	0,028	0,172	0,068	0,945	0,403	0,816	0,863	0,320
6	Ñuñoa	90,0	0,660	0,009	0,058	0,055	0,968	0,360	0,825	0,748	0,374
7	San Miguel	88,0	0,608	0,049	0,173	0,057	0,945	0,306	0,777	0,740	0,533
8	La Reina	89,0	0,607	0,010	0,069	0,051	0,970	0,282	0,819	0,756	0,325
9	Maipú	87,0	0,597	0,026	0,132	0,027	0,970	0,282	0,779	0,766	0,584
10	La Florida	86,0	0,589	0,045	0,190	0,037	0,954	0,284	0,755	0,766	0,593
11	Quilicura	86,0	0,577	0,057	0,179	0,025	0,949	0,267	0,757	0,787	0,560
12	Huechuraba	88,5	0,563	0,056	0,288	0,076	0,929	0,267	0,720	0,904	0,581
13	Pudahuel	84,0	0,552	0,083	0,225	0,075	0,909	0,250	0,740	0,857	0,651
14	La Cisterna	85,0	0,551	0,066	0,178	0,188	0,878	0,244	0,780	0,804	0,669
15	Macul	86,0	0,545	0,075	0,135	0,070	0,925	0,237	0,737	0,753	0,550
16	Independencia	86,0	0,540	0,085	0,209	0,056	0,915	0,231	0,747	0,752	0,714
17	Puente Alto	85,0	0,539	0,073	0,233	0,019	0,946	0,218	0,759	0,825	0,643
18	Recoleta	85,0	0,538	0,069	0,225	0,123	0,902	0,231	0,746	0,805	0,738
19	Quinta Normal	84,0	0,538	0,037	0,235	0,092	0,938	0,246	0,673	0,795	0,702
20	Peñalolén	87,0	0,530	0,044	0,263	0,089	0,930	0,216	0,742	0,872	0,641
21	Estación Central	84,0	0,530	0,058	0,235	0,139	0,907	0,228	0,720	0,766	0,699
22	San Bernardo	85,0	0,520	0,094	0,261	0,055	0,914	0,213	0,723	0,855	0,751
23	Cerrillos	82,5	0,489	0,065	0,274	0,060	0,933	0,192	0,653	0,827	0,644
24	Renca	82,0	0,478	0,037	0,245	0,080	0,940	0,173	0,673	0,875	0,685
25	San Joaquín	81,0	0,472	0,052	0,211	0,122	0,915	0,185	0,620	0,815	0,752
26	Pedro Aguirre Cerda	78,0	0,469	0,062	0,268	0,101	0,921	0,175	0,639	0,869	0,695
27	Conchalí	82,0	0,466	0,074	0,294	0,085	0,917	0,173	0,639	0,812	0,771
28	El Bosque	84,0	0,454	0,096	0,227	0,052	0,916	0,141	0,723	0,878	0,746
29	San Ramón	81,0	0,439	0,046	0,279	0,078	0,935	0,139	0,651	0,880	0,803
30	La Granja	83,0	0,427	0,048	0,219	0,080	0,936	0,128	0,646	0,896	0,701
31	Lo Prado	80,5	0,406	0,058	0,245	0,124	0,910	0,118	0,623	0,844	0,755
32	Lo Espejo	73,5	0,391	0,067	0,375	0,096	0,917	0,109	0,600	0,908	0,818
33	Cerro Navia	78,0	0,373	0,076	0,346	0,127	0,900	0,094	0,614	0,895	0,810
34	La Pintana	78,0	0,360	0,141	0,327	0,048	0,892	0,084	0,623	0,934	0,830

When analyzing the data gathered on May 26, 2022, three months after the peak of the Omicron spreading (around February 14), the correlation between each SES index and the median school vaccination for each municipality ranged from a very strong correlation, with a |ρ| >= 0.90 for the case of Economy and Resources, Education, CDI, and SPI indices; to a strong correlation, with a |ρ| >= 0.70 for the case of Multidimensional Poverty, and SVI; and to a moderate correlation, with |ρ| > 0.40 for the case of Poverty, PHLBS and Health and Social Welfare. All correlations were statistically significant (p < 0.01) (Fig. 1B). Of note, similar trends with different correlation values between SES indices and vaccination coverage were found on November 15, 2021, three days after the peak of the Delta variant spreading, and on March 5, 2022, in the middle of the second wave of Omicron infection produced by the dispersion of the BA.4/BA.5 subvariant (Supplementary Figs. 2 and 3).

We further explored whether the correlation between the vulnerability of different municipalities and the vaccination coverage may be accounted for in the three main types of schools existing in Chile: private, state-subsidized, and public schools. While parents and tutors enrolling their school-aged children in private schools must pay a full tuition fee, in the case of state-subsidized schools, the tuition fee is importantly reduced by the state subsidy. In the case of public schools, the state covers the full tuition fee. Our analysis reveals that a similar correlation, as previously mentioned, exists within both state-subsidized and public schools, albeit with variations in the correlation values (Fig. 2A-D). Unexpectedly, our analysis reveals no observable correlation between the vulnerability of municipalities and vaccination coverage within the population attending private schools. (Fig. 2E-F).

Progression of inequity in school vaccination coverage over time.

With the aim to assess the inequity of the vaccination coverage, we calculated the p90/p40 ratio in three dates, covering the spread of different SARS-CoV-2 variants. We found that overall, inequity decreases in all school types over time along the COVID-19 pandemic (Fig. 3). Importantly, the largest reduction in the inequity of the COVID-19 vaccination coverage occurs between November 15, 2021, and March 01, 2022, after the peak of the Omicron variant dispersion. In all time periods analyzed, inequity in vaccination coverage persists at higher levels in public schools compared to private and state-subsidized schools. This occurs despite the existence of a significant correlation between the vulnerability of municipalities and vaccination coverage, indicating that lower SES is associated with reduced vaccination rates. Notably, this disparity in vaccination coverage is more pronounced in public schools. In contrast, both private and state-subsidized schools demonstrate greater equity in vaccination coverage, regardless of the SES of the municipality in which they are situated.

This study revealed a correlation between 9 SES indices and the COVID-19 vaccination coverage in schools belonging to 34 municipalities of the Metropolitan Area of Santiago, Chile; one of the countries with the highest rate of vaccination in the world [28]. After analyzing the Spearman’s rank correlation between socioeconomic indices and COVID-19 vaccination coverage, we found a statistically significant correlation between lower SES and lower vaccination coverage. Even though inequity in vaccination coverage diminished over time along the pandemic, this inequity remains higher in public schools compared to that of private and state-subsidized schools, for all municipalities (Fig. 3). In general terms, our results are consistent with previous literature, indicating that vaccine coverage is lower in vulnerable populations living in poverty, having lower literacy and with a lesser educational level [14, 27, 33]. A potential rationale for these observations may be rooted in the varied personal beliefs across different SES groups. Specifically, one study noted a discernible negative correlation between SES and skepticism towards the COVID-19 vaccine’s secondary effects and conspiracy theories, offering an insightful perspective into our findings [34].

In addition to the statistically significant correlation between the vulnerability of different municipalities and the COVID-19 vaccination coverage–i.e. municipalities with lower SES exhibit lower coverage–, different school types exhibit distinct vaccination coverage. Thus, both public and state-subsidized schools show lower vaccination coverage than that of private schools. Although the strength of these correlations varies depending on the municipality (Fig. 2), this trend is maintained along the pandemic (Fig. 3). However, an important difference in vaccination coverage between school types, remains. Furthermore, analysis of vaccination coverage, in terms of percentages as reported by the Chilean government, uncovers significant differences across school types. However, a more alarming situation emerges when examining the raw numbers of unvaccinated children, particularly those attending schools with the lowest vaccination rates. The total number of children enrolled in public and state-subsidized schools, which demonstrate significantly lower vaccination coverage, is more than five times greater than that in private schools, with the respective figures being 873,354 and 169,898. This discrepancy highlights the critical issue of lower vaccination rates primarily among students in public and state-subsidized schools. Collectively, these two sectors account for approximately 84% of all student enrollments in the Metropolitan Area of Santiago, Chile. Considering that municipalities with lower SES are the ones having the highest population density [35], a lower vaccination coverage together with higher population density, are both key factors to consider when dealing with the higher burden of COVID-19 on these populations. Although the disparity in vaccination coverage diminishes over time, research has demonstrated that delays in vaccination schedules are associated with heightened adverse effects [36].

Available literature points to a combination of factors that may produce this uneven vaccination coverage. Among others, the lack of proper information about the importance and safety of vaccination, insufficient incentives for vaccination, religious beliefs, the negative view of pharmaceutical companies, and low trust in the government, may all have played a role in this uneven vaccination rate [9–11, 14, 27, 37]. Research indicates that the impact of incentives on vaccination uptake varies across populations. Notably, monetary incentives have been found to be more effective than either encouraging messages or the promise of increased freedoms [38, 39]. A critical consideration is that children typically do not independently decide to receive vaccinations; instead, these decisions are made by their parents or caregivers. A key factor influencing vaccination decisions is the perceived risk associated with the vaccine [36]. Although the correlations discussed herein do not show significance when considering the adult population, it is plausible that concerns regarding vaccine risk may be amplified in the context of vaccinating children. Specifically, adults might be willing to accept the perceived risk of vaccination for themselves but hesitate to subject their children to the same perceived risks. Beyond the willingness to vaccinate, logistical barriers play a crucial role in determining vaccination uptake differences. These include the availability of vaccine doses and the accessibility of vaccination centers, which is influenced by their location, transportation options, queue lengths, and operating hours. Such factors are particularly relevant for children, who require accompaniment by an adult to vaccination sites. This necessity poses additional challenges for households where both parents work and lack childcare support, potentially impacting vaccination rates. To enhance vaccination campaigns and public policies, a more intricate examination of these contributing factors is necessary. The reliance on broad data may risk simplifying the complexities of pandemics, underlining the importance of detailed, granular data in formulating responses to not only COVID-19 but future public health crises as well.

One of the limitations of our study is that the SES indices correspond to the municipal averages, so we cannot perform analyses nor extract conclusions at a granular level. In other words, assuming that an averaging SES index of the municipality would be an adequate descriptor for the whole spectrum of wealth exhibited on the municipality could be considered as a naive approach. This limitation becomes evident when trying to explain the differences in vaccination coverage between school types; we can only assume that the sub-populations that are enrolled in private schools (16%) are those that can afford the high tuition costs corresponding to the least vulnerable families in each municipality. Conversely, students enrolled in state-subsidized or in public schools are considered most vulnerable to the burden of COVID-19 (84%). Anyhow, without further socioeconomic details at an individual student level, we can only speculate about the causal roots of this inequity in vaccination coverage. Considering that COVID-19 vaccination in Chile is free of charge and governmentally orchestrated, determining the causal roots behind the lower vaccination levels noted here is crucial to develop better public policies. For example, survey studies should include questions about willingness to vaccinate children and elderly dependents of the respondent and detailed socioeconomic and educational background, vaccination throughput of each vaccination center should be analyzed in detail and correlation analysis should be performed with more detailed, granular data.

Even though we are unable to identify the causes of lower vaccination ratios among school-aged children in municipalities of lower, it is imperative that public policies counteract this behavior. Otherwise, the inequity in vaccination coverage will be perpetuated in future pandemics, exacerbating their impact on the more vulnerable population.

Data sources

Socioeconomic status

To generate a socioeconomic profile of the municipalities in Santiago, Chile, we used nine different SES indices so to obtain a broader multi-factorial socioeconomic description. It should be noted that the reliability of all indices referenced in this study is high, as they have been sourced directly from the Government of Chile or from previously published materials. While these indices represent the most current data publicly available, it is important to acknowledge the potential for changes in the socioeconomic indices subsequent to the time of data analysis.

The Social Priority Index (SPI) was published in 2019 by the Regional Ministerial Secretariat of Social Development and Family. It is a synthetic index that integrates relevant aspects of communal social development, including income, education, and health [40].

The School Vulnerability Index (SVI), published in 2021 by the National Board of School Aid and Scholarships (JUNAEB), is the ratio of the sum of the students in the first, second, and third priority ranking compared to the total school enrollment. Students are classified in priorities 1, 2, and 3 according to poverty conditions and risk of school failure. Thus, students living in lower socioeconomic conditions (Priority 1) tend to exhibit a higher risk of school failure, and thus receive the highest priority from the Board [41].

The Community Development Index (CDI), published in 2020, is a comprehensive index that integrates three socioeconomic dimensions: Health and Social Welfare; Economy and Resources; and Education. The development of the CDI involves a meticulous process of selecting a variety of standardized and comparable indicators, aims to provide a reliable instrument for monitoring and quantifying the impact of development processes from a territorial perspective. The CDI, as well as its individual dimensions, is available for each municipality in the country [42], offering a tool for local management and decision-making. In this work, we have chosen to consider the CDI and its individual components separately. This decision stems from our aim to leverage the distinct yet complementary insights that the aggregate index and its underlying dimensions offer. The CDI provides a holistic view of community development across municipalities, facilitating broad comparisons and trend analysis. At the same time, analyzing the individual dimensions enables a deeper dive into specific areas of socioeconomic development, allowing for a nuanced understanding of strengths and challenges within each municipality. This dual approach enriches our analysis, contributing to a more comprehensive socioeconomic profile that can better inform targeted strategies for community development.

The following indices are calculated as the percentage of people belonging to each category respectively for each municipality. Poverty is determined by a threshold defined as a function of income level and household size. Multidimensional Poverty is defined as being part of a household that cannot achieve adequate living conditions in a set of five relevant dimensions of well-being: (1) Education; (2) Health; (3) Labor and Social Security; (4) Housing and Environment; and (5) Networks and Social Cohesion. These conditions are measured through a weighted set of 15 indicators (three for each dimension). Households that accumulate deficiencies of 22.5% or more are classified as being in a situation of multidimensional poverty. The Poverty and Multidimensional Poverty indices were extracted from the National Socioeconomic Characterization Survey (CASEN) 2017, while the PHLBS, which is the percentage of People in Households Lacking Basic Services, was extracted from the Social Registry of Households 2020. Both instruments are used by the Ministry of Social Development to focus on social assistance among the population. [43].

The decision to evaluate these SES indices individually, rather than consolidating them into a single composite SES index, was driven by two primary considerations. First, analyzing the indices separately facilitates a nuanced understanding of the various dimensions of SES and their distinct impacts on vaccination coverage among school-aged children. Second, due to the complex and multifaceted nature of SES, a composite index could potentially obscure significant disparities and trends that are only discernible when these dimensions are examined on their own. This methodological approach aligns with our primary objective: to investigate the relationship between different aspects of SES and vaccination coverage in children, ensuring a more accurate and comprehensive analysis.

Pandemic Data

Data about new cases, deaths and other aspects was published regularly by the Ministry of Science, Technology and Innovation on their freely available repository [44]. Specifically, we used the Daily Total National Cases to visualize the changing trends following the arrival of the different variants and sub-variants.

Vaccinations

Even though in Chile the COVID-19 vaccination campaign is orchestrated and conducted by the Ministry of Health, the Ministry of Education publishes vaccination coverage per grade and school on its website [29]. To accurately assess the extent of vaccine coverage in each school and grade, the Ministry of Education combines its own database of school enrollments with the vaccination records from the Ministry of Health. This integration is accomplished by using the national identity number (RUT) of each student, which is the unique personal identifier in Chile. By using the RUT, they can precisely link each student’s school records with their vaccination status, ensuring a detailed and accurate understanding of the vaccination coverage across different educational institutions. To study whether the spread of different SARS-CoV-2 variants among the population might influence the vaccination process among school-age students in different municipalities in Santiago, Chile, we downloaded data published on November 15, 2021; March 1st, 2022; and May 26, 2022. By selecting these dates, we covered the spread of different SARS-CoV-2 variants: Delta in November, Omicron in March and Omicron subvariants BA.4-BA.5 in May [44]. The data shows the proportion of students in each school and grade who are fully vaccinated, partially vaccinated, or not vaccinated at all. ‘Fully vaccinated’ refers to students who have received all required doses of the COVID-19 vaccine, ‘partially vaccinated’ indicates those who have only received some but not all of the necessary doses, and ‘not vaccinated’ denotes students who have not received any doses of the vaccine.

School metadata

The Chilean Ministry of Education provides easily accessible metadata for each educational institution on their public repository [45]. This metadata was used to identify public, state-subsidized and private schools in every municipality.

Statistical Analysis

All statistical analyses were performed on the downloaded data by programming in Python v3.1 using the SciPy v1.7.1 package [46]. To measure inequity, we employed the percentile ratio p90/p40, inspired by The Palma ratio [47]. In this context, "p90" refers to the 90th percentile, representing the mean vaccination coverage among the top 10% of municipalities with the highest vaccination rates. Conversely, "p40" denotes the 40th percentile, representing the mean vaccination coverage among the bottom 40% of municipalities with the lowest vaccination rates. To compute the ratio, we divided the mean vaccination coverage of the municipalities at the 90th percentile by the mean vaccination coverage of municipalities at the 40th percentile. The essence of using the p90/p40 ratio is that it highlights disparities in vaccination coverage, with maximum equity being indicated when the ratio equals 1.

The calculation of the mean vaccination coverage for the highest 10% of municipalities (p90) involves aggregating the vaccination rates of municipalities that fall into the 90th percentile and then calculating the average. This figure represents the benchmark for high vaccination coverage within the sample and enables a comparison with lower-performing municipalities to assess equity in vaccination distribution.

Spearman’s rank-order correlation was computed using Statsmodels v0.13.1. Plots were made with Seaborn v0.11.2.

Ethical considerations

All data used in this research was sourced in an anonymous form.

CDI

Community development index

Metropolitan area

PHLBS

People in households lacking basic services

SES

Socioeconomic status

SPI

Social priority index

SVI

School vulnerability index

Funding

This material is based upon work supported by the U.S. Air Force Office of Scientific Research under award number FA9550-20-1-0196. Financial support is also acknowledged to Centro Ciencia & Vida, FB210008, Financiamiento Basal para Centros Científicos y Tecnológicos de Excelencia de ANID. Funding sources had no role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

AI use Disclosure

Generative AI tools (ChatGPT using GPT-4) were used only to check style and grammar in some paragraphs.

Data Availability

The whole dataset as well as the python code used to perform the statistical analyses presented here are available on GitHub in the following repository.

https://github.com/DLab/SantiagoSchoolVax

Conflicts of Interest

None declared.

Abbreviations

CDI: Community development index

MA: Metropolitan area

PHLBS: People in households lacking basic services.

SES: Socioeconomic status

SPI: Social priority index

SVI: School vulnerability index

Author contributions

Enzo Guerrero-Araya: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Visualization, Writing.

Cesar Ravello: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing.

Mario Rosemblatt: Resources, Supervision, Project Administration, Writing

Tomás Pérez-Acle: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing.

Enzo Guerrero-Araya and Cesar Ravello share the first authorship.

Ritchie, H. et al. Coronavirus (COVID-19) Deaths Web Page. 2022. https://ourworldindata.org/covid-deaths.
WHO. Listings of WHO’s response to COVID-19 Web Page. 2022. https://www.who.int/news/item/29-06-2020-covidtimeline.
Burgess, R. A. et al. The COVID-19 vaccines rush: participatory community engagement matters more than ever. Lancet 397, 8–10. issn: 1474-547X 0140–6736. (2021). PMID: 33308484
Burki, T. The online anti-vaccine movement in the age of COVID-19. The Lancet Digital Health 2, e504–e505 (2020). PMID: 32984795
Aleem, A., Akbar Samad, A. B. & Slenker, A. K. Emerging Variants of SARS-CoV-2 And Novel Therapeutics Against Coronavirus (COVID-19) in StatPearls (StatPearls Publishing, 2022). PMID: 34033342
Baden, L. R. et al. Efficacy and Safety of the mRNA-1273 SARS-CoV-2 Vaccine. N Engl J Med 384, 403–416. issn: 1533–4406 0028-4793. (2021). PMID: 33378609
Polack, F. P. et al. Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine. N Engl J Med 383. issn: 1533–4406 0028-4793. (2020). PMID: 33301246
Jara, A. et al. Effectiveness of an Inactivated SARS-CoV-2 Vaccine in Chile. N Engl J Med 385, 875–884. issn: 1533–4406 0028-4793. (2021). PMID: 34233097
Weitzer, J. et al. Willingness to receive an annual COVID-19 booster vaccine in the Germanspeaking DA-CH region in Europe: A cross-sectional study. The Lancet Regional Health-Europe 18, 100414 (2022). PMID: 35651957
De Giorgio, A. et al. Willingness to Receive COVID-19 Booster Vaccine: Associations between Green-Pass, Social Media Information, Anti-Vax Beliefs, and Emotional Balance. Vaccines 10. issn: 2076-393X. https://www.mdpi.com/2076-393X/10/3/481 (2022). PMID: 35335113
Lee, S. K., Sun, J., Jang, S. & Connelly, S. Misinformation of COVID-19 vaccines and vaccine hesitancy. Scientific reports 12, 1–11 (2022). PMID: 35953500
Coccia M. Improving preparedness for next pandemics: Max level of COVID-19 vaccinations without social impositions to design effective health policy and avoid flawed democracies. Environ Res. 2022;213:113566. doi: 10.1016/j.envres.2022.113566. Epub 2022 May 31. PMID: 35660409; PMCID: PMC9155186.
Mena, G. E. et al. Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile. Science 372. issn: 1095–9203 0036-8075. (2021). PMID: 33906968
Caspi, G. et al. Socioeconomic disparities and COVID-19 vaccination acceptance: a nationwide ecologic study. Clin Microbiol Infect 27, 1502–1506. issn: 1469 – 0691 1198-743X. (2021). PMID: 34111591
Saban, M., Myers, V., Ben-Shetrit, S. & Wilf-Miron, R. Socioeconomic gradient in COVID-19 vaccination: evidence from Israel. Int J Equity Health 20, 242. issn: 1475–9276 14759276. (2021). PMID: 34749718
Horton, R. Offline: COVID-19 is not a pandemic. The lancet 396, 874 (2020). PMID: 32979964
Vineis, P. COVID-19 as a syndemic: from inequalities to biological embodiment. European Journal of Public Health 31, 164–004 (2021). PMCID: PMC8574607
Wachtler, B. et al. Socioeconomic inequalities and COVID-19 - A review of the current international literature. J Health Monit 5, 3–17. issn: 2511–2708 2511–2708. (2020). PMID: 35146298
Hawkins, R. B., Charles, E. J. & Mehaffey, J. H. Socio-economic status and COVID-19-related cases and fatalities. Public Health 189, 129–134. issn: 1476–5616 0033-3506. (2020). PMID: 33227595
Gaglioti, A. H. et al. Population-Level Disparities in COVID-19: Measuring the Independent Association of the Proportion of Black Population on COVID-19 Cases and Deaths in US Counties. J Public Health Manag Pract 27, 268–277. issn: 1550–5022 1078–4659. (2021). PMID: 33762542
Berger, Z., Altiery, D. E. J. V., Assoumou, S. A. & Greenhalgh, T. Long COVID and Health Inequities: The Role of Primary Care. Milbank Q 99, 519–541. issn: 1468-0009 0887378X. (2021). PMID: 33783907
Strang, P., Furst, P. & Schultz, T. Excess deaths from COVID-19 correlate with age and socioeconomic status. A database study in the Stockholm region. Ups J Med Sci 125, 297–304. (2020). PMID: 33100083
Tamrakar, V. et al. District level correlates of COVID-19 pandemic in India during March-October 2020. PLoS One 16, e0257533. issn: 1932–6203 1932–6203. (2021). PMID: 34591892
Mathieu, E. et al. A global database of COVID-19 vaccinations. Nat Hum Behav 5, 947–953.issn: 2397–3374 2397–3374. (2021). PMID: 33972767
Balicer, R. D. & Ohana, R. Israel’s COVID-19 endgame 2021.
WHO COVID-19 Dashboard. Geneva: World Health Organization, 2020. https://covid19.who.int/region/amro/country/ht.
Débarre, F., Lecoeur, E., Guimier, L., Jauffret-Roustide, M. & Jannot, A.-S. The French Covid-19 vaccination policy did not solve vaccination inequities: a nationwide study on 64.5 million people. The European Journal of Public Health 32, 825–830. issn: 1101–1262 (2022). PMID: 36102834
Castillo, C., Dintrans, P. V. & Maddaleno, M. The successful COVID-19 vaccine rollout in Chile: Factors and challenges. Vaccine X 9, 100114 (2021). PMID: 34518818
Ministerio de Educación, C. Avance de la vacunación contra el COVID-19 por colegio Web Page. Jan. 2022. https://vacunacionescolar.mineduc.cl/.
Ministerio de Educación, C. Imparte lineamientos generales para la Planificacion del año escolar 2022 Online Document. 2021. https://www.mineduc.cl/wp-content/uploads/sites/19/ 2021/11/LineamientosEscolar2022.pdf.
Ministerio de Salud, C. Pase de Movilidad Online Document. 2022. https://www.chileatiende.gob.cl/fichas/107011/2/pdf.
Banzhaf, E., Reyes-Paecke, S., Muller, A. & Kindler, A. Do demographic and land-use changes contrast urban and suburban dynamics? A sophisticated reflection on Santiago de Chile. Habitat International 39, 179–191. issn: 0197–3975. https://www.sciencedirect.com/science/article/pii/S0197397512000860 (2013).
Ministerio de Ciencia Tecnología Conocimiento e Innovación, C. Totales Nacionales Diarios Online Database. 2021. https://github.com/MinCiencia/Datos-COVID19/tree/master/output/producto5
Schober, P., Boer, C. & Schwarte, L. A. Correlation Coefficients: Appropriate Use and Interpretation. Anesth Analg 126, 1763–1768. issn: 1526–7598 0003-2999. (2018). PMID: 29481436
Barry, V. et al. Patterns in COVID-19 Vaccination Coverage, by Social Vulnerability and Urbanicity - United States, December 14, 2020-May 1, 2021. MMWR Morb Mortal Wkly Rep 70. 1545-861x, 818–824 (2021). PMID: 34081685
Salazar-Fernández C, Baeza-Rivera MJ, Villanueva M, Bautista JAP, Navarro RM, Pino M. Predictors of COVID-19 Vaccine Intention: Evidence from Chile, Mexico, and Colombia. Vaccines. 2022; 10(7):1129. https://doi.org/10.3390/vaccines10071129
Livert Aquino, F. & Gainza, X. Understanding density in an uneven city, Santiago de Chile: implications for social and environmental sustainability. Sustainability 6, 5876–5897 (2014).
Coccia, M., Optimal levels of vaccination to reduce COVID-19 infected individuals and deaths: A global analysis, Environmental Research, Volume 204, Part C, 2022, 112314, ISSN 0013-9351, https://doi.org/10.1016/j.envres.2021.112314.
Lazarus, J. V. et al. Revisiting COVID-19 vaccine hesitancy around the world using data from 23 countries in 2021. Nat Commun 13, 3801 (2022). PMID: 35778396
Mardi P, Djalalinia S, Kargar R, Jamee M, Esmaeili Abdar Z, Qorbani M. Impact of incentives on COVID-19 vaccination; A systematic review. Frontiers in medicine. 2022;9:810323.
Klüver H, Hartmann F, Humphreys M, Geissler F, Giesecke J. Incentives can spur COVID-19 vaccination uptake. Proceedings of the National Academy of Sciences. 2021;118(36):e2109543118.
Secretaría Regional Ministerial de Desarrollo Social y Familia, C. Indice De Prioridad Social De Comunas 2019 Online Document. 2019. http://www.desarrollosocialyfamilia.gob.cl/storage/docs/INDICE.%5C_DE%5C_PRIORIDAD%5C_SOCIAL%5C_2019.pdf%7D.
JUNAEB. Indice de Vulnerabilidad Escolar Web Page. 2022. https://www.junaeb.cl/ive.
Hernández Bonivento, J. et al. Indice de Desarrollo Comunal. Chile 2020. Aplica (2020). doi:https://doi.org/10.32457/ISBN9789568454944962020-ED1
Ministerio de Desarrollo Social y Familia, C. Encuesta Casen en Pandemia Web Page. 2020. http://observatorio.ministeriodesarrollosocial.gob.cl/encuesta-casen-en-pandemia2020.
Ministerio de Educación, C. Directorio de Establecimientos Educacionales Web Page. Jan. 2022. ttps://datosabiertos.mineduc.cl/directorio-de-%20establecimientos-educacionales/.
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 17, 261–272. issn: 1548–7105 1548–7091. (2020). PMID: 32015543
Palma, J. G. Homogeneous Middles vs. Heterogeneous Tails, and the End of the ‘Inverted-U’: It’s All About the Share of the Rich. Development and Change 42, 87–153. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-7660.2011.01694.x. (2011).

No competing interests reported.

COVID19vaccinationcoverageofschoolagedchildreninSantiagoChilecorrelateswithSESSUPP.docx

Download PDF

Editorial decision: Revision requested
18 Jul, 2024
Reviews received at journal
16 Jul, 2024
Reviews received at journal
03 Jul, 2024
Reviewers agreed at journal
01 Jul, 2024
Reviewers agreed at journal
24 Jun, 2024
Reviewers invited by journal
24 Jun, 2024
Editor assigned by journal
24 Jun, 2024
Editor invited by journal
10 Jun, 2024
Submission checks completed at journal
08 Jun, 2024
First submitted to journal
07 Jun, 2024

You are reading this latest preprint version

COVID-19 vaccination coverage of school-aged children in Santiago, Chile, correlates with socioeconomic status: Longitudinal observational study

Status:

Version 1

Abstract

Figures

Introduction

Results

Discussion

Methods

Data sources

Socioeconomic status

Pandemic Data

Vaccinations

School metadata

Statistical Analysis

Ethical considerations

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1