# data[].attributes.body
Autor: Zhaohui

Fecha: noviembre 2022

Enlaces:

¿Cómo evitar futuras preguntas sobre los "orígenes del Covid-19"? - CienciaDirecta

# data[].attributes.body
Autores: (P. Rodríguez Ledo)

Fecha: 22 de marzo de 2021

Enlaces: https://mgyf.org/descripcion-de-201-sintomas-de-afectacion-multiorganica-producida-en-pacientes-afectados-por-covid-19-persistente/

# data[].attributes.body
Autor: Alberto Palacios

Fecha: febrero 2021

Enlace:

Noticias falsas sobre el covid | Reumatología Global

# data[].attributes.attachment_link.description
The mist peels back its pestilent curtain amidst barking and motorcycle exhaust. It’s a cool morning of another year ruled by the death toll. As soon as he silences the alarm clock with a flick of his hand, Diego reaches for his cell phone and prepares himself to get updated. Over two million people have died from the pandemic and with the English variant (cryptically named B.1.1.7), business opportunities are plentiful.

# data[].attributes.headline
¿Cuántas muertes hubo en el brote del covid-19?

# data[].attributes.subject
¿Cuántas muertes hubo en el brote del covid-19?

# data[].attributes.body
Autor: Serin Kuriakose

Fecha: junio 2022

Enlace:

Revista de Medicina Familiar y Atención Primaria

# data[].attributes.attachment_link.title
COVID-19: Situation analysis in the district of Ernakulam : Journal of Family Medicine and Primary Care

# data[].attributes.attachment_link.description
ted in the other states started coming down, the Ernakulam district continued to record a large number of cases. Aims: To analyse the situation of the COVID-19 pandemic in the district of Ernakulam, Kerala. Material and Methods: The authors were part of the COVID-19 surveillance unit of Ernakulam district, and hence, had access to the data collected. The available data were analysed in the following phases of the pandemic: First phase: From the reporting of the first case in Kerala in January to the reporting of the first case in the Ernakulam district. Second phase: Cases reported mostly in those with a travel history and their contacts to the period of community spread. Third phase: From the start of community spread. Results and Discussion: As of July 5, 2021, the Ernakulam district reported 3,60,345 cases of the COVID-19 infection with 1,317 deaths and the recovery rate being 96.45%. Despite factors like high human development index (HDI), access to the Internet and social media, access to affordable healthcare, etc., factors like high population density, airports, seaports, railway stations, container terminals, IT parks, major highways, tourist spots, beaches, large shopping malls, large floating population, a huge number of migrant labourers, a large proportion of the elderly population, high prevalence of non-communicable diseases, etc., are the some of the major challenges. The preparedness of the fight against COVID-19 included the training of all healthcare workers, ward level rapid response teams (RRT), upgradation of health facilities, district-level patient management system, provisions to manage biomedical waste, etc., The containment zone strategy is currently based on the local self-government area-wise weekly test positivity rate (TPR). The cluster containment is focused on the early identification of clusters. Currently, the Ernakulam district reports one of the highest numbers of COVID-19 cases in India. This is mainly because of the high number of tests (five to six times to national average) and targeted testing strategy. This is scientifically proven by the very low case fatality rate (0.35%), low-bed occupancy rate of the COVID treatment facilities and the latest seroprevalence study by Indian Council for Medical Research (ICMR). Conclusions: So far, the Ernakulam district could excel in its efforts to fight against COVID-19. But even now, when we are moving forward with the immunisation of the healthcare workers, front-line workers, elderly population, our main strategies to prevent COVID-19 remain the same—proper social distancing, hand hygiene, use of masks, avoiding unnecessary travels and gathering, early identification of cases and treatment.…

# data[].attributes.body
Autores: Flor María Paguay Hidalgo

Mayra Alexandra Sánchez Zurita

fecha: 10 de diciembre de 2022

Enlace: https://sct.ageditor.ar/index.php/sct/article/view/119

# data[].attributes.body
Autores: Glenn G. Fort, MD, MPH

Fecha: 2023

Enlace: https://www.clinicalkey.es/#!/content/book/3-s2.0-B9788413823034002401?scrollTo=%23hl0000631

# data[].attributes.body
Autores: Isaías G. Noguez-Tinoco

Fecha: 01.03.2021

Enlace: https://revistafiguras.acatlan.unam.mx/index.php/figuras/article/view/154

# data[].attributes.headline
Diferentes tipos de vacunas contra la COVID-19: cómo funcionan

# data[].attributes.subject
Diferentes tipos de vacunas contra la COVID-19: cómo funcionan

# data[].attributes.body
Enlace:

https://www.mayoclinic.org/es/diseases-conditions/coronavirus/in-depth/different-types-of-covid-19-vaccines/art-20506465

# data[].attributes.headline
EL COVID 19 ¿como afecto a los estudiantes?

# data[].attributes.subject
EL COVID 19 ¿como afecto a los estudiantes?

# data[].attributes.body
Enlace:

https://www.unesco.org/es/articles/el-impacto-de-la-pandemia-en-los-aprendizajes-de-los-estudiantes-de-america-latina-y-el-caribe

# data[].attributes.headline
Trastornos gastrointestinales de nueva aparición en pacientes con COVID-19

# data[].attributes.subject
Trastornos gastrointestinales de nueva aparición en pacientes con COVID-19

# data[].attributes.body
Autores:

Sagar Changela ,

Samad Ashraf ,

Justin Y. Lu ,

Kevin E. Duong ,

Sonia Henry ,

Stephen H. Wang y

Tim Q. Duong

Fecha:

30 de diciembre de 2024

Enlace :

https://www.nature.com/articles/s41598-024-83232-7

# data[].attributes.attachment_link.readable_text
Abstract

This study examined the incidence, characteristics, and risk factors of new gastrointestinal disorders (GID) associated with SARS-CoV-2 infection up to 3.5 years post-infection. This retrospective study included 35,102 COVID-19 patients and 682,594 contemporary non-COVID-19 patients without past medical history of GID (controls) from the Montefiore Health System in the Bronx (3/1/2020 to 7/31/2023). Comparisons were made with unmatched and propensity-matched (1:2) controls. The primary outcome was new GID which included peptic ulcer, inflammatory bowel disease, irritable bowel syndrome, diverticulosis, diverticulitis, and biliary disease. Multivariate Cox proportional hazards model analysis was performed with adjustment for covariates. There were 2,228 (6.34%) COVID-19 positive patients who developed new GID compared to 38,928 (5.70%) controls. COVID-19 patients had an elevated risk of developing new GID (adjusted HR = 1.18 (95% CI 1.12-1.25) compared to propensity-matched controls, after adjusting for confounders that included smoking, obesity, diabetes, hypertension. These findings underscore the need for additional research and follow-up of at-risk individuals for developing GID post infection.

Similar content being viewed by others

Article Open access

Article Open access

Article Open access

Introduction

SARS-CoV-2 infection, although primarily affects the respiratory system1,2,3, is associated with a range of acute gastrointestinal (GI) symptoms4,5,6,7. These acute GI symptoms include diarrhea, nausea, vomiting, abdominal pain, and loss of appetite, and are reported in a significant number of COVID-19 patients4,5,6,7. There is evidence that SARS-CoV-2 virus enters epithelial cells in the GI tract via the ACE2 receptor and replicate in the GI tract8,9,10. This is not surprising as the influenza virus (a coronavirus) also has GI manifestations and is known to replicate in human intestinal tissue11,12,13. Viral ribonucleic acid (RNA) has also been detected in the stools of patients with confirmed influenza14,15.

Although GI symptoms have been reported during acute SARS-CoV-2 infection4,5,6,7, whether these symptoms persist post COVID-19 and whether SARS-CoV-2 infection results in increased incidence of new GI disorders (GID) post infection is mostly unknown. These GID could include inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), diverticulitis, gastritis, pancreatitis, peptic ulcer, Crohn's disease, ulcerative colitis etc5,16,17,18,19. The relative risk of SARS-CoV-2 infection compared to known risks of GID are unknown. Known risks include demographic factors (i.e., old age, female sex, race, ethnicity) and pre-existing conditions (smoking, diabetes, hypertension, chronic kidney disease (CKD), asthma, and obesity). It is unclear whether SARS-CoV-2 infection exerts differential effects on GID post infection in different racial or ethnic subgroups as well as individuals with pre-existing medical conditions. Exploring these associations could provide critical insights into the longer-term GI effects of COVID-19.

The goal of this study was to investigate the incidence of new-onset GID in patients post COVID-19 compared to matched controls without COVID-19 up to 3.5 years post COVID-19. We also identified risk factors for developing new-onset GID. Data came from the Montefiore Health System in the Bronx and its environs which serves a diverse population, and which was one of the epicenters of early COVID-19 pandemic and its subsequent waves.

Method

This retrospective cohort study with waived informed consent was approved by the Institutional Review Board (2021-13658) of the Albert Einstein College of Medicine-Montefiore Health System in accordance with all relevant guidelines and regulations of the Albert Einstein College of Medicine. Informed consent was waived for this study by the Institutional Review Board (2021-13658) of the Albert Einstein College of Medicine. Data (March 2020 to July 2023) came from the Montefiore Health System which consisted of several hospitals and outpatient clinics in the Bronx and its environs that a diverse population. Data were extracted as described previously20,21. To ensure data accuracy, we performed extensive cross-validation of all major variables by manual chart review of subsets of patients. Studies using an earlier version of this large database to address different questions have been published20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39.

COVID-19 patients were those with a positive polymerase-chain-reaction (PCR) test for COVID-19. Controls were patients without any history of a positive COVID-19 test or documentation of COVID-19 in our health system. The index date was defined as the date of the PCR test for the COVID-19 cohort or the first healthcare encounter after March 1, 2020, for the control cohort. Patients who did not return to our health system 14 days post index date or later were excluded. Propensity score matching (1:2 experimental: control group) was performed for patients who returned to the health system at least 14 days after the index date, using age (within ± 5 years), sex, race, and ethnicity.

Demographic data, such as age, sex, race, and ethnicity, as well as pre-existing comorbidities (diabetes, hypertension, COPD, CKD, cardiovascular disease, asthma, and obesity) were extracted. The independent variable was COVID-19 status. The dependent variable was GI conditions, which included esophagitis, gastritis, peptic ulcer, ulcerative colitis, irritable bowel syndrome, irritable bowel disease, diverticulosis, diverticulitis biliary disease, etc. (Supplemental Table 1).

The primary outcome was new-onset GI conditions. Sociodemographic factors were compared between COVID-19 and controls. Cumulative incidence curves for outcomes of the two groups were constructed with accounts for competing risk (death). Univariate and multivariate Cox-proportional hazard analyses for new onset GI conditions were performed for unmatched and matched data.

Statistics

All the statistical analysis was performed on SAS system version 9.4 (SAS Institute Inc., Cary, NC) and R version 4.3.2. Analysis of group differences employed chi-square tests for categorical variables and unpaired t-tests for continuous variables. When comparing differences of categorical variables across three groups, chi-square tests were used to identify group differences and ad hoc pairwise tests were used to identify the specific pairwise group difference. Statistics were not adjusted for multiple comparison due to the exploratory nature of the study. All statistical tests were two-sided, and P values less than 0.05 were considered statistically significant.

Results

Figure 1 shows the patient selection flowchart. The study consisted of 1,187,102 individuals from Montefiore health system, of which 56,745 were COVID-19 positive and 1,130,357 were COVID-19 negative. After excluding individuals with prior GID, there were 41,046 COVID-19 positive patients and 822,305 COVID-19 negative patients, of which 35,102 COVID-19 positive patients and 682,594 COVID-19 negative patients return to our health system post index date.

Flowchart of cohort selection. After determining which patients were positive for COVID-19 versus COVID-19 negative, patients were excluded based on prior gastrointestinal history. Additionally, COVID-19 negative patients were identified after the index date determined in our study as March 2020.

Table 1 summarizes the distribution of new-onset GID cases, demographics and comorbidities of COVID-19 positive and negative cohort (not matched data). There were 2,228 (6.34%) COVID-19 positive and 38,928 (5.7%) COVID-19 negative who had new GI. COVID-19 positive patients were older (41.79 vs. 39.76, p < 0.001) more likely active smokers (14.51% vs. 13.13%, p < 0.001) and had high prevalence of all major comorbidities ( p  < 0.001) compared to COVID-19 negative patients. Both the cohort has higher proportions of female (56.18% and 55.77%, respectively) and non-Hispanic patients (59.05% and 65.11%, respectively).

Table 1 Profiles of all patients with and without COVID-19 (unmatched cohorts).

COVID-19 negative patients were matched with COVID-19 positive patients by age, sex, race and ethnicity (1:2 match) using propensity score matching. Figure 2 represents the cumulative incidence curve of developing new GI disorders stratified by COVID-19 status with accounting for competing risk (death) for the matched cohorts. COVID-19 cohort showed higher new incident GI compared to the non-COVID-19 cohort.

Cumulative incidence curve of new onset of gastrointestinal conditions stratified by patients with COVID-19 to non-COVID-19 patients (matched cohort). Cohort data were matched by age, sex and ethnicity.

Table 2 shows the results of Cox proportional hazard model for matched data. Crude model illustrates that hazard of having new GI was 1.37 (95% CI 1.30-1.44) time higher among COVID-19 positive compared to COVID-19 negative patients. Smoking, diabetes, hypertension, CKD, asthma and obesity were significantly associated with developing new GID in the multivariate adjusted model. After accounting for these covariates, the adjusted HR for new GID for COVID-19 status was 1.18 (95% CI 1.12-1.25). Results of the Cox-proportional hazard model of the unmatched data, shown in Supplementary Table 2, were similar to those of the matched data.

Table 2 Crude and multivariable Cox-proportional hazard ratios of matched cohorts.

To investigate the potential effects of different viral strains on outcomes, incidences of new GID for different predominant strains with one-year observation post infection were tabulated (Supplemental Table 3). The highest GID incidence was observed in the Alpha strain period (3.91%), while the lowest was during the Delta strain period (2.48%). Incidences of new GID among infections by the original, Alpha, Delta and Omicron strain were similar, except that of Delta was lower than those of others ( p  < 0.05, Chi square).

Discussion

This study investigated the incidence of new GID and identified risk factors contributing to the development of these disorders up to 3.5 years post-infection. We found that 6.34% (2,228) of COVID-19-positive patients developed new GI disorders, compared to 5.70% (38,928) of controls, with COVID-19 patients being more likely to develop new GI disorders than matched controls (adjusted HR = 1.18, 95% CI [1.12-1.25]).

Acute GI manifestations

Many studies have documented GI symptoms during acute COVID-19. A systematic review of 16 studies including a total of 3646 patients found that the most commonly presenting acute GI symptom in patients with COVID-19 were diarrhea (47% of patients), followed by anorexia (19%)4. Other common acute symptoms included nausea, vomiting and abdominal pain. Another systematic review by Kariyawasam et al. reported that the most common acute GI symptoms were anorexia, diarrhea, nausea, vomiting and abdominal pain/discomfort5. These acute GI manifestations were reported in 11.4-61.1% of individuals with COVID-19. A study conducted by Han et al., 2020 investigated the digestive symptoms within 206 patients with low-severity COVID-19 and found that 67 patients presented with diarrhea which lasted from 1 to 14 days7. This study also tested stool RNA within 22 COVID-19 infected patients and determined that 12 (54.5%) patients tested positive for viral RNA in stool.

Post-acute GI manifestations

By contrast, studies of persistent GI symptoms and new incident GID post infection are sparse. Cheung et al. found 10-20% of COVID-19 patients experienced persistent GI symptoms after the resolution of respiratory manifestations40. Austhof et al. found that of the 1475 participants in the Arizona Cohort, 33.8% ( n = 499) had acute GI symptoms. The odds of developing persisting GI symptoms > 45 days post infection in patients with acute GI symptoms was 4 times higher than those without acute GI symptoms (OR = 4.29. 05% CI 2.45-7.53)16. Hawkings et al. performed a systematic review of literature from Dec 2019 to Jul 2023 and reported a prevalence for persistent GI symptoms of 10.8% in COVID-19 patients compared with 4.9% in healthy controls41. Golla et al. compared presence of GI symptoms in a 6-month follow-up period in 320 patients with COVID-19 to 320 matched healthy spouses/family member controls and 280 healthy COVID-19 negative controls19. Of the 320 COVID-19 cases, 36 (11.3%) developed GI at 1 month follow-up with persistent symptoms in 27 (9.4%) patients at 3 months and 21 (6.6%) patients at 6 months. Notably, none of the healthy controls (320 healthy spouses/family members and 280 healthy COVID-19 negative controls) had GI symptoms up to 6 months of follow-up ( p < 0.01). Karime et al. reported that patients with diverticulitis at the time of COVID-19 diagnosis had higher complication rates of intestinal perforation (55.9% vs. 27.7%, p = 0.01), peritonitis (29.4% vs. 4.3%, p < 0.01), and the need for emergent surgical intervention (38.2% vs. 10.6%, p < 0.01) within 30 days of COVID-19 diagnosis ( N = 47) between 2020 and 2022 from Mayo Clinic18. There are only a few reports on new GID post infection. One such report is by Xu et al. who studied the VA population of 154,068 people with COVID-19, 5,638,795 contemporary controls, and 5,859,621 historical controls, and found that people with COVID-19 showed increased risks of GID (dyspepsia, gastroesophageal reflux disease, peptic ulcer disease, functional intestinal disorders, acute pancreatitis, hepatic and biliary disease), with a hazard ratio of any new incidence of 1.37 (95% CI 1.33,1.41) 1-year post infection17.

Although patients infected by the original SARS-CoV-2 strain were more likely to have acute severe COVID-19 outcomes compared to other strains42, the original strain resulted in a slightly, but not significantly, higher incidence of new GID post infection compared to other predominant strains. Only the incidence of new GID of the Delta was significantly lower than those of other strains.

Possible mechanisms of post-infection GID

Post-infection GID could be a result of direct viral invasion, hyperinflammation, microbiome dysbiosis, side effects of COVID-19 treatment, and psychological stress. There is evidence of direct SARS-CoV-2 infection in the GI system via the angiotensin-converting enzyme 2 (ACE2) receptors in the GI system43,44,45,46. Zou et al. used single-cell RNA sequencing to identify organs vulnerable to SARS-CoV-2, finding that esophageal and ileal epithelial cells, which have high ACE2 levels, are at high risk9. Qi et al. found high ACE2 expression in liver cholangiocytes, colon colonocytes, esophagus keratinocytes, ileum and rectum endothelial cells, and stomach epithelial cells47.

COVID-19 can trigger hyperinflammation, including in the GI tract48. This response can damage the gut lining, leading to increased intestinal permeability or "leaky gut." This condition allows harmful substances to enter the bloodstream, causing chronic inflammation and potentially leading to new GI disorders such as irritable bowel syndrome (IBS) and functional dyspepsia49.

The gut microbiome, essential for GI health, can be significantly disrupted by COVID-19 and its treatment. This disruption can lead to an imbalance between beneficial and harmful bacteria, known as dysbiosis. Dysbiosis can contribute to GI disorders like inflammatory bowel disease (IBD) and small intestinal bacterial overgrowth (SIBO) by promoting inflammation and impairing gut function50.

The treatment of COVID-19 could have adverse effects on GI health. Antiviral medications, antibiotics, corticosteroids, immunomodulators, and supportive care drugs can all contribute to GI symptoms and disorders51,52.

The psychological impact of COVID-19, including stress, anxiety, and depression, can also indirectly affect GI health. The gut-brain axis, a communication network between the gut and the brain, can be disrupted by psychological stress, leading to or exacerbating functional GI disorders53,54. Chronic stress can alter gut motility, increase gut sensitivity, and affect the gut microbiome's composition53,54.

Other covariates

We found that smoking status, diabetes, hypertension, CKD, asthma and obesity to be significant covariates contributing to outcomes. Most of these are known risk factors for GID. For examples, smoking is linked to GI cancers, peptic ulcers, and inflammatory bowel disease, while diabetes is associated with gastroparesis, non-alcoholic fatty liver disease, and higher risks of liver and pancreatic cancers55,56,57,58,59. Hypertension can exacerbate GID like gastroesophageal reflux disease (GERD), especially when treated with medications that increase ulcer risk60. At the same time, CKD is linked to gastritis and GI bleeding due to altered medication metabolism61. Obesity is a major risk factor for GERD, gallstones, and colorectal cancers, and asthma is commonly associated with GERD62.

Our analysis of unmatched data revealed that demographic factors significantly influenced new GID outcomes. Older age was associated with a higher risk for GID, and females had an elevated risk, consistent with previous findings. Black and Asian individuals also showed a higher risk of new GID compared to White individuals, as did Hispanic individuals compared to non-Hispanic individuals. COVID-19 has disproportionately impacted marginalized communities63,64, likely due to limited healthcare access, higher prevalence of underlying health conditions, crowded living conditions, and employment in essential but high-risk roles have compounded the burden of COVID-19 within already vulnerable populations. Our findings suggest that COVID-19 imposed additional disease burden on these groups. Investigating the relationship between demographic factors and post-infection GID may illuminate specific challenges faced by these populations and inform strategies to meet the complex needs of those experiencing lasting health effects following SARS-CoV-2 infection65.

Other respiratory viruses

Acute and post-acute GI manifestations have been reported to be associated with other respiratory virus infections. For example, the influenza virus has been previously found to have been detected in stool samples14,15. Minodier et al. studied the GI effects of influenza, coronavirus, rhinovirus, RSV, adenovirus in 331 patients and found that among the 104 patients with influenza, 56.7% presented with GI symptoms12. Notably, 78.9% of patients with human coronavirus infection had GI infections. A systematic review also conducted by Minodier et al. analyzed 44 studies looking at the pooled prevalence of acute digestive symptoms such as diarrhea, nausea, vomiting, abdominal pain in addition to more severe gastrointestinal effects such as acute appendicitis and hemorrhagic gastritis11. This value ranged from 30.9% (95% CI, 9.8 to 57.5; I ² = 97.5%) for A(H1N1) pdm09 to 2.8% (95% CI, 0.6 to 6.5; I ² = 75.4%) for A(H1N1). In an animal study, IFN-γ produced by lung-derived CCR9 + CD4 + T cells associated with influenza viral infection are recruited into the small intestine, which disrupted the intestinal microbiota66.

Limitations

The strengths and novelties of our study included analysis of new GID (as opposed to symptoms), longer-term post infection follow-up (up to 3.5 years), large sample size, and a diverse patient cohort. We also employed analysis of controls with propensity matching. There are several limitations in our analysis. Our findings were limited to patients who returned to our health system. It is possible that patients who returned were more likely to have more severe COVID-19. However, patient records included those who returned for any medical reason, including but not limited to routine office visits. Electron medical records could have errors including missing data and misclassifications of COVID-19 positive versus COVID-19 negative case. We did not analyze various treatments with respective to outcomes. This was because each patient could have undergone many treatments that could include oxygen therapy, corticosteroids, intubation, invasive mechanical ventilation, antiviral medications, and monoclonal antibody medications, among others. Moreover, these treatments were administered in different combinations and might not have been consistently applied in early pandemic in a control manner to allow such analysis. Vaccine status, which could affect outcomes, was not reliably recorded if patients received vaccine outside our healthcare system. Thus, vaccine status was not incorporated into our model. As with any retrospective study, there could be other unintended patient selection biases and latent confounding factors.

Conclusions

COVID-19 patients are more likely to develop new GI disorders compared to contemporary matched controls. These findings underscore the need for ongoing research and follow-up care strategies for post SARS-CoV-2 to improve GI health.

References

1. Article ADS CAS PubMed PubMed Central MATH Google Scholar

2. Iosifescu, A. L., Hoogenboom, W. S., Buczek, A. J., Fleysher, R. & Duong, T. Q. New-onset and persistent neurological and psychiatric sequelae of COVID-19 compared to influenza: A retrospective cohort study in a large New York City healthcare network. Int. J. Methods Psychiatr. Res. 31, e doi: (1914). https://doi.org/10.1002/mpr.1914 (2022).

3. Lu, J. Y. et al. Long-term outcomes of COVID-19 survivors with hospital AKI: Association with time to recovery from AKI. Nephrol. Dial Transpl. https://doi.org/10.1093/ndt/gfad020 (2023).

Article Google Scholar

4. Article ADS CAS PubMed PubMed Central MATH Google Scholar

5. Dell'Aquila, K. et al. Incidence, characteristics, risk factors and outcomes of diabetic ketoacidosis in COVID-19 patients: Comparison with influenza and pre-pandemic data. Diabetes Obes. Metab. https://doi.org/10.1111/dom.15120 (2023).

6. Article ADS CAS PubMed PubMed Central MATH Google Scholar

7. Khreefa, Z., Barbier, M. T., Koksal, A. R., Love, G. & Del Valle, L. Pathogenesis and mechanisms of SARS-CoV-2 infection in the intestine, liver, and pancreas. Cells 12, doi: (2023). https://doi.org/10.3390/cells12020262

8. Ye, Q. et al. The mechanism and treatment of gastrointestinal symptoms in patients with COVID-19. 319, G245-G252 (2020).

9. Upadhaya, S. D. & Kim, I. H. The impact of weaning stress on gut health and the mechanistic aspects of several feed additives contributing to improved gut health function in weanling piglets-A review. J. A. 11, 2418 (2021).

MATH Google Scholar

10. Moloney, R. D., Desbonnet, L., Clarke, G., Dinan, T. G. & Cryan, J. F. J. M. g. The microbiome: Stress, health and disease. 25, 49-74 (2014).

11. Botteri, E. et al. Smoking and colorectal cancer: A meta-analysis. Jama 300, 2765-2778 (2008).

12. KURATA, J. H. & HAILE, B. M. Epidemiology of peptic ulcer disease. Clin. Gastroenterol. 13, 289-307 (1984).

13. Mahid, S. S., Minor, K. S., Soto, R. E., Hornung, C. A. & Galandiuk, S. in Mayo Clinic Proceedings. 1462-1471 (Elsevier).

14. Bharucha, A. E., Kudva, Y. C. & Prichard, D. O. Diabetic gastroparesis. Endocr. Rev. 40, 1318-1352 (2019).

15. Younossi, Z. M. et al. Global epidemiology of nonalcoholic fatty liver disease-meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 64, 73-84 (2016).

16. Miranda, A. G. et al. Inquiring into the collusion between two-system diseases: Potential impact of hypertension over gastroesophageal reflux disease. Med. Hypotheses. 185, 111297 (2024).

17. Huang, K. W. et al. Different peptic ulcer bleeding risk in chronic kidney disease and end-stage renal disease patients receiving different dialysis. Dig. Dis. Sci. 59, 807-813 (2014).

18. Hampel, H., Abraham, N. S. & El-Serag, H. B. Meta-analysis: Obesity and the risk for gastroesophageal reflux disease and its complications. Ann. Intern. Med. 143, 199-211 (2005).

Download references

Acknowledgements

None.

Funding

Authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

1. Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA

Sagar Changela, Samad Ashraf, Justin Y. Lu, Kevin E. Duong, Sonya Henry, Stephen H. Wang & Tim Q. Duong

2. Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

Stephen H. Wang

3. Center for Health & Data Innovation, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA

Tim Q. Duong

Contributions

SC, SA, JYL, SHW, SH, TQD-designed study, collected data, analyzed data, created tables and figures, drafted paperSC-collected data, analyzed dataSC, SA, JYL, KED- consulted on statistical analysis, edited paperTQD-designed study, edited paper, supervised.

Corresponding author

Correspondence to Tim Q. Duong.

Ethics declarations

Competing interests

The authors declare no competing interests.

Consent to participate

Consent was waived as this retrospective study was approved by the Einstein-Montefiore Institutional Review Board (#2021-13658) with an exemption for informed consent and a HIPAA waiver.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Reprints and permissions

About this article

Cite this article

Changela, S., Ashraf, S., Lu, J.Y. et al. New-onset gastrointestinal disorders in COVID-19 patients 3.5 years post-infection in the inner-city population in the Bronx. Sci Rep 14, 31850 (2024). https://doi.org/10.1038/s41598-024-83232-7
Download citation

Received:

Accepted:

Published:

DOI: https://doi.org/10.1038/s41598-024-83232-7

Get shareable link

Keywords

# data[].attributes.attachment_link.title
New-onset gastrointestinal disorders in COVID-19 patients 3.5 years post-infection in the inner-city population in the Bronx - Scientific Reports

# data[].attributes.attachment_link.description
This study examined the incidence, characteristics, and risk factors of new gastrointestinal disorders (GID) associated with SARS-CoV-2 infection up to 3.5 years post-infection. This retrospective study included 35,102 COVID-19 patients and 682,594 contemporary non-COVID-19 patients without past medical history of GID (controls) from the Montefiore Health System in the Bronx (3/1/2020 to 7/31/2023).

# data[].attributes.attachment_caption
Autores Organización mundial de la salud (OMS)

# data[].attributes.body
Enlances:

https://www.who.int/es/health-topics/coronavirus#tab=tab_1

# data[].attributes.headline
Necesidades alimentarias y comportamientos de salud en la situación de COVID-19

# data[].attributes.subject
Necesidades alimentarias y comportamientos de salud en la situación de COVID-19

# data[].attributes.body
Autor :

Areeya Jirathananuwat

Fecha:

13 de enero del 2025

Enlace:

https://jhpn.biomedcentral.com/articles/10.1186/s41043-024-00724-y

# data[].attributes.attachment_link.title
Food needs and health behaviors in the COVID-19 situation: a case study of quarantined communities in densely populated areas of Bangkok, Thailand - Journal of Health, Population and Nutrition

# data[].attributes.attachment_link.description
Background The Thai government’s initial response to the novel coronavirus disease 2019 (COVID-19) led to confusion and food insecurity in quarantined low-income communities. Although free food programs were initiated, no official assessment of their impact exists. The objective of this study was to evaluate the effectiveness of these food programs by surveying the food requirements, food needs, and health behaviors of quarantined, densely populated communities in Bangkok. Methods A cross-sectional descriptive study was conducted with 410 urban dwellers from quarantined communities who received free food assistance. Data were collected via a questionnaire on food requirements, food hygiene, food needs, and health behaviors during the COVID-19 epidemic. The data were analyzed in terms of frequency, percentage, mean, and standard deviation. The associations between demographic characteristics, food needs, and health behaviors were analyzed using logistic regression. Results The participants demanded dried/canned food (54.9%) and three meals per day (64.9%), while the majority of the food provided consisted of rice and side dishes (96.2%) that were clean and qualified. In consideration of food needs, a high level of demand was observed in the first three levels: enough food, acceptable food, and reliability. Overall, dietary health behaviors were good. Logistic regression analysis revealed that being elderly (AOR = 3.67, 95% CI = 1.63–8.27) and having a moderate to high income level (AOR = 3.93, 95% CI = 2.23–6.94) were positively correlated with food needs. Similarly, good health behaviors were positively correlated with being female (AOR = 1.74, 95% CI = 1.12–2.69), being elderly (AOR = 3.73, 95% CI = 1.72–8.08), and having a moderate to high income level (AOR = 3.76, 95% CI = 2.38–5.93). Conclusions Preparing for future crises requires the consideration of demographic factors that influence food needs, personal choices, and dietary health behaviors. Future food assistance programs should focus on the provision of nonperishable and long-lasting food, which will ensure the consistent availability of three meals per day.

# data[].attributes.headline
Nueva variante del COVID-19 se propaga en el extranjero

# data[].attributes.subject
Nueva variante del COVID-19 se propaga en el extranjero

# data[].attributes.body
Autor: The Toronto Star (Toronto, Ontario)

Fecha: May 29, 2025

Enlace: https://go.gale.com/ps/retrieve.do?tabID=T004&resultListType=RESULT_LIST&searchResultsType=SingleTab&retrievalId=eaf50b46-d221-4c75-9516-5154fb452765&hitCount=5104371&searchType=BasicSearchForm&currentPosition=2&docId=GALE%7CA842470080&docType=Brief+article&sort=Relevance&contentSegment=ZNEW-FullText&prodId=STND&pageNum=1&contentSet=GALE%7CA842470080&searchId=R7&userGroupName=univcv&inPS=true

# data[].attributes.wish_content.attachment_props.alternative_text
Nueva variante del COVID-19 se propaga en el extranjero

# data[].attributes.headline
El nivel socioeconómico se correlaciona con la cobertura de vacunación contra COVID-19

# data[].attributes.subject
El nivel socioeconómico se correlaciona con la cobertura de vacunación contra COVID-19

# data[].attributes.body
Autores:

Enzo Guerrero-Araya ,

César Ravello ,

Mario Rosemblatt y

Tomás Pérez-Acle

Fecha:

09 de enero del 2025

Enlace:

https://www.nature.com/articles/s41598-024-84260-z

# data[].attributes.attachment_link.readable_text
Abstract

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.

Similar content being viewed by others

Article Open access

Article Open access

Introduction

As of August 2024, the pandemic caused by the novel coronavirus, SARS-CoV-2 caused more than 7 million deaths worldwide1 along more than three years since the detection of the first case in China2. 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. 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, Chile3. 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 SES6,7. 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 countries8. 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. Similarly, in Sweden, a higher number of COVID-19-related deaths, occurred in areas of lower SES12, 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 disease13.

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. However, unequal access to vaccines creates enormous risks, prolonging the pandemic, increasing the likelihood of new variants17, and exacerbating existing health, economic, and social inequalities. Moreover17, due to the influence of anti-vaxxer movements driven by the abundance of fake news, anti-science, and the promotion of highly individualistic behaviors21,22, assessing and comprehending the willingness to receive COVID-19 vaccination is essential for the development of more effective public policies aimed at promoting vaccination, while keeping a balance with strict restrictions and possible socioeconomic impact26.

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 populations27,28. In contrast, less affluent nations like Haiti struggled to vaccinate even 5% of their population29. Within countries, a lower proportion of the population receiving the COVID-19 vaccine has been associated with lower SES, where the main factors mediating this relation are the perception of vaccine safety, the influence of anti-vaccine movements, and the effectiveness of vaccination campaigns, as families with lower SES may have less access to accurate sources of information, greater exposure to misinformation, and face more logistical challenges in accessing vaccination services4,23,24,25,30.

In the case of Chile, in addition to its successful COVID-19 vaccination campaign aimed at the general population31, the Chilean government conducted an extensive vaccination program for primary and secondary students. By October 2022, over 90% of children and adolescents aged 6 to 17 were fully vaccinated, having received at least two doses of the vaccine32. 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% 33. 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 venues34.

While the level of vaccination coverage achieved in adults was very high across the whole population regardless of their SES according to the published data31, the case for children and adolescents has not been analyzed. 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. This study aims to evaluate the correlation between SES and COVID-19 vaccination levels in primary and secondary students, in Santiago, the capital of Chile through a longitudinal observational study using publicly available data. Understanding this relationship is crucial for developing effective public health strategies that address disparities in vaccine coverage.

Results

In this study, we determined the SES of the 32 municipalities located in the Metropolitan Area of Santiago, the capital of Chile. For each municipality we computed the median school vaccination coverage 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 social priority index

Since the SPI index used in previous studies is only available for some municipalities, 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; 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; Fig. 1A). Notably, our study also shows that there are no municipalities with high vaccination coverage and low or medium CDI. Conversely, no municipalities with low vaccination coverage and medium or high CDI can be identified (Table 1; Fig. 1 A).

Table 1 SES indices and vaccination coverage for each municipality.

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. 2 E-F).

Progression of inequity in school vaccination coverage over time

With the aim to assess the inequity of the vaccination coverage, we calculated the Gini coefficient 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, upper panels). 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 and state-subsidized schools compared to private 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 and state-subsidized schools. In contrast, private schools demonstrate greater equity in vaccination coverage, regardless of the SES of the municipality in which they are situated. To analyze the data from another perspective, we separated the data according to grade and found that inequity in vaccination is greater in lower grades (Fig. 3, lower panels), and this same pattern is found in the whole country, but with even more marked differences in the younger groups. Again, inequity decreases over time, but the age-dependent pattern remains.

Correlation between school vaccination coverage and SES Indices in Santiago, Chile as of May 26, 2022. Panel A), municipalities are colored according to their school vaccination coverage and their Community Development Index (CDI) in a Bivariate Map. Municipalities are numbered according to their rank in CDI from higher to lower values. The color coding is shown adjacent to the map using a 3 by 3 matrix where the CDI increases from left to right and the vaccination coverage increases from bottom to top. The values are grouped into three levels. The number labeled in each municipality is sorted by decreasing ranking according to the CDI and can be seen in Table 1. (B) The correlation value between six SES Indices and the median school vaccination coverage was evaluated in the 34 municipalities of the Metropolitan Area of Santiago, Chile. ρ = Spearman's rank correlation coefficient; p =  p-value.

Correlation between school vaccination coverage and SES Indices by school category in the Metropolitan Area of Santiago as of May 26, 2022. All maps represent Municipalities from the Metropolitan Area of Santiago. They appear colored according to their school vaccination coverage and their Community Development Index (CDI) in a Bivariate Map. Municipalities are numbered according to their rank in CDI from higher to lower values. The color coding is shown adjacent to the map using a 3 by 3 matrix, where the CDI increases from left to right and the vaccination coverage increases from bottom to top. The values are grouped into three levels. The number labeled in each municipality is sorted following a decreasing ranking according to the CDI, as can be seen in Table 1. Next to each map, the correlation of six SES indices and the median school vaccination coverage is show. ρ = Spearman's rank correlation coefficient; p = p-value. (AB) Public schools' data. (CD) Private subsidized schools' data. (EF) Private schools' data.

Inequity assessment of COVID-19 vaccination coverage in Santiago, Chile. The Gini coefficient is used to evaluate the inequity of the vaccination coverage for each school type at three time points along the COVID-19 pandemic: on November 15, 2021; March 1st, 2022; and May 26, 2022, covering the spread of different SARS-CoV-2 variants: Delta, Omicron and Omicron BA.4-BA.5, respectively. While inequity in vaccination coverage within schools' type tend to diminish over time, it is always lower in private schools. The same pattern but with even higher inequity is found when considering the whole country.

Discussion

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 world31. 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 and state-subsidized schools compared to that of private schools, for all municipalities (Fig. 3); this inequity pattern is even more pronounced when considering data from the whole country. 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. 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 findings36.

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. 1), this trend is maintained along the pandemic (Fig. 2). 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 density37, 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. Unfortunately, we were unable to extend this detailed analysis to the entire country, which is particularly concerning given the higher levels of inequity in vaccination observed. This limitation arises because the SES indices are only available at the municipal level. All the rest of Chilean cities are smaller, and generally consist of a single municipality, thus restricting our ability to conduct a detailed analysis of the SES composition and its heterogeneity. Although the disparity in vaccination coverage diminishes over time, research has demonstrated that delays in vaccination schedules are associated with heightened adverse effects38.

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 rate4,23,24,25,30,36. 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 freedoms39,40. 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 vaccine41. 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, and this hesitance apparently increases for younger children. The fact that our results show that inequity in vaccination coverage increases in younger children gives strength to this factor.

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. In the case of the Chilean vaccination campaign, it was based on an heterogenous network of vaccination centers, with some municipalities having single, large, centralized sites, while others had multiple small venues, so ease of access was very different to the population, although there was no assignment of vaccination centers, so people were free to attend the center of their choosing. Unfortunately, we could not access detailed data on this distribution network, so a more detailed analysis of these factors was not feasible. In any case, such factors are particularly relevant for children, who require accompaniment by an adult to attend 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. In more general terms, other limitations are the lack of adjustment for important confounders, such as individual health conditions, access to healthcare facilities, and government trust, among many others. These factors could potentially influence both SES and vaccination rates, thereby introducing bias into our results. Future research should aim to control for these variables to provide a more accurate assessment of the relationship between SES and vaccination coverage. Another limitation is relying on secondary records, which may have other limitations such as inaccuracies in data reporting, variations in data collection methods across different municipalities, and the potential for missing data. While these secondary records are essential for large-scale studies like ours, they may not capture all nuances or errors in data entry, which could affect the outcomes of the analysis.

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 SES, 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.

Methods

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. The criteria for including indices was high reliability, originating from trusted sources and being last updated no more than three years before the vaccination campaign. While these indices may not represent the most current data publicly available at the time of publication, they should reflect the reality of the different municipalities at the time of the parents' decision to vaccinate their children. Still, it is important to acknowledge the potential for changes in the socioeconomic indices subsequent to the time of data analysis, especially for decision making and policy design for future pandemics.

The Social Priority Index (SPI) is published 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 health42. It has already been used to evaluate the correlation between SES and the impact of COVID-19 ³, however, it is only available for some municipalities of the Metropolitan Region. This index is not updated in a regular schedule, the current version is from 2022, and the previous one is from 2019, which is the one used in our analysis and in the work above.

The School Vulnerability Index (SVI), published yearly 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 Board43. For this study we considered the 2021 data.

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 country44, 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. This is the only index that does not come directly from a government agency, it was published by an academic institution. Even though the publication itself was not peer reviewed, the institution and team behind the study are reputable and the data has been used in different studies45,46.

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)47, while the PHLBS, which is the percentage of People in Households Lacking Basic Services, was extracted from the Social Registry of Households 2020 ⁴⁴. Both instruments are used by the Ministry of Social Development to focus social assistance among the population .

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. In the same line of reasoning, the results show all the publicly available indices related to SES for completeness and to minimize biases in the analysis. 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.

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 website32. The Ministry of Health administers the National Immunization Program (Programa Nacional de Inmunización, PNI), a government initiative that has successfully eradicated important contagious diseases through mandatory vaccination and educational campaigns48. Through this program, the Ministry administered the vaccination centers, where every person had to show their official identification card in order to receive the vaccine, thus generating a reliable record by registering the national identity number (RUT), the date and the specific vaccine received. In the case of primary students, the vaccine rollout is detailed in Table 2. 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 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 May17. 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.

Table 2 Vaccine approval schedule.

School metadata

The Chilean educational system has three main types of schools according to its funding source: Public schools are fully state-funded and under the administration of each municipality, these schools are free of tuition costs so they are attended mostly by low income families; private schools are The Chilean Ministry of Education provides easily accessible metadata for each educational institution on their public repository49. 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 package50. To measure inequity, we computed the Gini Coefficient as half of the relative mean absolute difference:

$$\:G=\frac{{\sum\:}_{i=1}^{N}{\sum\:}_{j=1}^{N}|{x}_{i}-{x}_{j}|}{2{n}^{2}\overline{x}}\:$$

Where N is the number of reported vaccination levels and \(\:{x}_{j}\)are individual percentages of fully vaccinated students and \(\:\overline{x}\) is the mean of the values.

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.

References

1. Ritchie, H. et al. Coronavirus (COVID-19) Deaths. vol. Preprint at (2022). https://ourworldindata.org/covid-deaths (2022).

2. World Health Organization. Listings of WHO's response to COVID-19. (2022). https://www.who.int/news/item/29-06-2020-covidtimeline

3. Mena, G. E. et al. Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile. Science 372, (2021). (1979).

4. 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).

5. Lee, S. K., Sun, J., Jang, S. & Connelly, S. Misinformation of COVID-19 vaccines and vaccine hesitancy. Sci. Rep. 12, 1-11 (2022).

MATH Google Scholar

6. Balicer, R. D. & Ohana, R. Israel's COVID-19 endgame. Science vol. 372 663 Preprint at (2021).

7. World Health Organization. Coronavirus disease situation. Preprint at (2022). https://covid19.who.int/region/amro/country/ht

8. Castillo, C., Dintrans, P. V. & Maddaleno, M. The successful COVID-19 vaccine rollout in Chile: Factors and challenges. Vaccine: X vol. (2021). 9 100114 Preprint at.

9. Ministerio de Educación, Chile. Avance de la vacunación contra el COVID-19 por colegio. Vol. 2022. https://vacunacionescolar.mineduc.cl/ (2022).

10. Ministerio de Educación, Chile. Imparte lineamientos generales para la planificacion del año escolar 2022. https://www.mineduc.cl/wp-content/uploads/sites/19/2021/11/LineamientosEscolar2022.pdf (2021).

11. Ministerio de Salud, Chile. Pase de Movilidad. https://www.chileatiende.gob.cl/fichas/107011/2/pdf (2022).

12. Lazarus, J. V. et al. Revisiting COVID-19 vaccine hesitancy around the world using data from 23 countries in 2021. Nature communications vol. 13 1-14 Preprint at (2022).

13. Ministerio de Desarrollo Social y Familia. C. Índice De Prioridad Social De Comunas 2019. (2019). http://www.desarrollosocialyfamilia.gob.cl/storage/docs/INDICE._DE_PRIORIDAD_SOCIAL_2019.pdf

14. JUNAEB. Indice de Vulnerabilidad Escolar. vol. (2022). https://www.junaeb.cl/ive (2022).

15. Hernández Bonivento, J. et al. Índice de Desarrollo Comunal. Chile. Aplica (2020) (2020). https://doi.org/10.32457/ISBN9789568454944962020-ED1

16. Ministerio de Desarrollo Social y Familia, Chile. Encuesta Casen en Pandemia. vol. Preprint at (2022). http://observatorio.ministeriodesarrollosocial.gob.cl/encuesta-casen-en-pandemia-2020 (2020).

17. González, C. Programa Nacional De inmunizacion en Chile, pasado, presente y futuro. Revista Médica Clínica Las Condes. 31, 225-232 (2020).

Article Google Scholar

18. Ministerio de Educación, Chile. Directorio de Establecimientos Educacionales. Vol. 2022. https://datosabiertos.mineduc.cl/directorio-de- (2022).

Download references

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. Economic support from the CDC USA through the ECLIPSE Initiative (NU38FT2023000086) is greatly appreciated. This work was supported by Vicerrectoría de Investigación y Doctorados de la Universidad San Sebastián - Fondo USS-FIN-24-APCS-37. 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.

Author information

Authors and Affiliations

1. Fundación Ciencia & Vida, Avenida del Valle Norte 725, Huechuraba, Santiago, Chile

Enzo Guerrero-Araya, Cesar Ravello, Mario Rosemblatt & Tomas Perez-Acle

2. Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Bellavista 7, Recoleta, Santiago, Chile

Cesar Ravello & Tomas Perez-Acle

3. Facultad de Medicina y Ciencia, Universidad San Sebastián, Lota, Providencia, Santiago, 2465, Chile

Mario Rosemblatt

Contributions

E. G-A. : Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Visualization, Writing. C. R. : Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing. M. R. : Resources, Supervision, Project Administration, Writing. T. P-A. : Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing. E. G-A. and C. R. share the first authorship. All authors reviewed the manuscript.

Corresponding author

Correspondence to Tomas Perez-Acle.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

† This authors contributed equally to the work.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions

About this article

Cite this article

Guerrero-Araya, E., Ravello, C., Rosemblatt, M. et al. Socioeconomic status correlates with COVID-19 vaccination coverage among primary and secondary students in the most populated city of Chile. Sci Rep 15, 1509 (2025). https://doi.org/10.1038/s41598-024-84260-z
Download citation

Received:

Accepted:

Published:

DOI: https://doi.org/10.1038/s41598-024-84260-z

Get shareable link

Keywords

# data[].attributes.attachment_link.title
Socioeconomic status correlates with COVID-19 vaccination coverage among primary and secondary students in the most populated city of Chile - Scientific Reports

# data[].attributes.attachment_link.description
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.

# data[].attributes.body
Autor: Dra. Denisa Ferastraoa y el Dr. Joe Zein

Fecha: 2023

Enlace: https://www.clinicalkey.es/#!/content/journal/1-s2.0-S2213219823010826