Association between living in municipalities with high crowding conditions and poverty and mortality from COVID-19 in Mexico

The impact of violence and COVID-19 on Mexico's life-expectancy losses and recent bounce-back, 2015-22

BACKGROUND

Before the COVID-19 pandemic, life expectancy in Mexico stagnated from the early 2000s, mainly due to increased homicides. During the pandemic, Mexico experienced sizable excess mortality. We aimed to assess the contribution of violence, COVID-19, and causes of death that were amenable to healthcare to life-expectancy changes between 2015 and 2022 in Mexico.

METHODS

We used administrative mortality and adjusted population estimates to construct life tables. We applied demographic methods to untangle contributions of causes of death to life-expectancy changes by year and sex at the subnational level.

RESULTS

Between 2015 and 2019, life expectancy declined from 71.8 to 71.1 years for males and stagnated at 77.6 years for females. Violence among young males explains most of the decline (54.3%). Between 2019 and 2020, life expectancy decreased by 7.1 and 4.4 years for males and females, respectively. COVID-19 accounted for 55.4% of that change for males and 57.7% for females. In 2021, male life expectancy stagnated but continued to decline for females by 0.44 years due to COVID-19 deaths. In 2022, we observed unequal recovery patterns in life expectancy across regions, as northern states experienced larger improvements than central and southern states.

CONCLUSION

We documented large variations in life-expectancy losses across Mexican states before, during, and after the COVID-19 pandemic. Before the pandemic, violence accounted for most of the male life-expectancy losses. During the pandemic, following COVID-19 deaths, mortality due to diabetes and causes that were amenable to healthcare contributed considerably to observed losses, with an uneven impact on the sexes.

Jerusalem's CoVID-19 Experience-The Effect of Ethnicity on Disease Prevalence and Adherence to Testing

BACKGROUND

The management of the SARS-CoV-2 pandemic depends amongst other factors on disease prevalence in the general population. The gap between the true rate of infection and the detected rate of infection may vary, especially between sub-groups of the population. Identifying subpopulations with high rates of undetected infection can guide authorities to direct resource distribution in order to improve health equity.

METHODS

A cross-sectional epidemiological survey was conducted between April and July 2021 in the Pediatric Emergency Department of the Shaare Zedek Medical Center, Jerusalem, Israel. We compared three categories: unconfirmed disease (UD), positive serology test result with no history of positive PCR; confirmed disease (CD), history of a positive PCR test result, regardless of serology test result; and no disease (ND), negative serology and no history of PCR. These categories were applied to local prevailing subpopulations: ultra-orthodox Jews (UO), National Religious Jews (NRJ), secular Jews (SJ), and Muslim Arabs (MA).

RESULTS

Comparing the different subpopulations groups, MAs and UOs had the greatest rate of confirmed or unconfirmed disease. MA had the highest rate of UD and UO had the highest rate of CD. UD significantly correlated with ethnicity, with a low prevalence in NRJ and SJ. UD was also associated with larger family size and housing density defined as family size per number of rooms.

CONCLUSION

This study highlights the effect of ethnicity on disease burden. These findings should serve to heighten awareness to disease burden in weaker populations and direct a suitable prevention program to each subpopulation's needs. Early awareness and possible intervention may lower morbidity and mortality.

Exploring Spatial Inequalities in COVID-19 Mortality and Their Association With Multidimensional Poverty in Colombia: A Spatial Analysis Study

Objectives

The objective is to examine spatial inequalities in COVID-19 mortality rates in Colombia in relation to the spatial distribution of multidimensional poverty.

Methods

A retrospective spatial epidemiological study was conducted in Colombia from 2020 to 2022. Spatial statistics such as Moran's I index, LISA analysis, and simultaneous autoregressive conditional (SAC) regression models were used.

Results

The Moran's I index for different years was as follows: 2020: 0.3 (p = 0.0001), 2021: 0.27 (p = 0.0001), and 2022: 0.26 (p = 0.0001). In 2020, the significant variables were low educational achievement, barriers to early childhood care, child labor, school non-attendance, informal employment, lack of health insurance, inadequate floor material, and critical overcrowding. In 2021, the significant variables were low educational achievement, critical overcrowding, inadequate excreta disposal, and lack of access to water sources. In 2022, the significant variables were school lag and inadequate excreta disposal.

Conclusion

This study revealed that in Colombia, a series of socioeconomic and health factors are interconnected and contribute to COVID-19 mortality. These changes may reflect various socioeconomic, political, and environmental dynamics that shifted during the pandemic years.

Socioeconomic factors and COVID-19 mortality in immune-mediated rheumatic diseases patients: regional analysis from Argentina, Mexico and Brazil

BACKGROUND

SARS-CoV-2 infection has become a major international issue, not only from a medical point of view, but also social, economic and political. Most of the available information comes from the United States, Europe, and China, where the population and the socioeconomic status are very different from Latin American countries. This study evaluates the effect of regional socioeconomic characteristics on mortality due SARS-CoV-2 infection in patients with immune-mediated rheumatic diseases (IMRD) from Argentina, Mexico and Brazil.

METHODS

Data from three national registries, SAR-COVID (Argentina), CMR-COVID (Mexico) and ReumaCoV-Brasil (Brazil), were combined. Adult IMRD patients with SARS-CoV-2 infection were recruited. National data for each province/state, including population density, number of physicians per inhabitant, income, unemployment, GINI index, Municipal Human Development Index (MHDI), stringency index, vaccination rate and most frequent viral strains per period were assessed as risk factors for mortality due to COVID-19.

RESULTS

A total of 4744 patients were included, 2534 (53.4%) from SAR-COVID, 1166 (24.6%) from CMRCOVID and 1044 (22.0%) from ReumaCoV-Brasil. Mortality due to COVID-19 was 5.4%. In the multivariable analysis, higher number of physicians per 1000 inhabitants and being infected during the vaccination period of each country were associated with lower mortality. After adjustment for socioeconomic factors, there was no association with country of residence and mortality.

CONCLUSION

These findings corroborate the complex interplay between socioeconomic factors, rheumatic disease activity, and regional disparities as determinants of death due to COVID-19 in Argentina, Brazil and Mexico. Thus, this research provides valuable insights for guiding public health policies and clinical practice in the ongoing fight against the COVID-19 pandemic.

Death from COVID-19 in contexts of social deprivation in Mexico

Introduction

Poverty is one of the macro factors that has been little studied in terms of its effect on death from COVID-19 since most studies have focused only on investigating whether the pandemic increased poverty or not. With that on mind, the present study aims to analyze how the social deprivations that comprise the measurement of municipal poverty in interaction with health comorbidities and sociodemographic characteristics, increased the probability of death from COVID-19.

Methods

The study is cross-sectional and covers daily reports on the conditions of COVID-19 in the Mexican population for almost 2 years. Using data from the National Epidemiological Surveillance System and the National Council for Evaluation of the Social Development Policy (N = 5,387,981), we employ a Generalized Linear Mixed Model (GLMM), specifically a binomial generalized linear mixed model.

Results

The findings indicate that, besides comorbidities, sociodemographic traits, and clinical aspects, living in a municipality where one or more of the social deprivations exist increases the probability of death. Specifically, in those municipalities where there is deprivation in education, social security, and food, as well as deprivation due to access to health services and deprivation in household services, the probability of death was greater.

Discussion

Living in a municipality with one or more of the social deprivations that compose poverty generated a greater probability of death. Each one of them or together, shows that poverty is a substantial factor for a pandemic like COVID-19 to worsen contagion and death, becoming a circle from which it is difficult to escape.

Association of race/ethnicity and severe housing problems with COVID-19 deaths in the United States: Analysis of the first three waves

The objective of this study is to assess the associations of race/ethnicity and severe housing problems with COVID-19 death rates in the US throughout the first three waves of the COVID-19 pandemic in the US. We conducted a cross-sectional study using a negative binomial regression model to estimate factors associated with COVID-19 deaths in 3063 US counties between March 2020 and July 2021 by wave and pooled across all three waves. In Wave 1, counties with larger percentages of Black, Hispanic, American Indian and Alaska Native (AIAN), and Asian American and Pacific Islander (AAPI) residents experienced a greater risk of deaths per 100,000 residents of +22.82 (95% CI 15.09, 30.56), +7.50 (95% CI 1.74, 13.26), +13.52 (95% CI 8.07, 18.98), and +5.02 (95% CI 0.92, 9.12), respectively, relative to counties with larger White populations. By Wave 3, however, the mortality gap declined considerably in counties with large Black, AIAN and AAPI populations: +10.38 (95% CI 4.44, 16.32), +7.14 (95% CI 1.14, 13.15), and +3.72 (95% CI 0.81, 6.63), respectively. In contrast, the gap increased for counties with a large Hispanic population: +13 (95% CI 8.81, 17.20). Housing problems were an important predictor of COVID-19 deaths. However, while housing problems were associated with increased COVID-19 mortality in Wave 1, by Wave 3, they contributed to magnified mortality in counties with large racial/ethnic minority groups. Our study revealed that focusing on a wave-by-wave analysis is critical to better understand how the associations of race/ethnicity and housing conditions with deaths evolved throughout the first three COVID-19 waves in the US. COVID-19 mortality initially took hold in areas characterized by large racial/ethnic minority populations and poor housing conditions. Over time, as the virus spread to predominantly White counties, these disparities decreased substantially but remained sizable.

Respiratory-related deaths and associated factors in Alicho-Weriro district, southern Ethiopia: verbal autopsy data analysis

BACKGROUND

Respiratory diseases disproportionately affect people living in resource-limited settings. However, obtaining information that explains respiratory-related deaths has been difficult, mainly due to a lack of medical certification of death and the fact that most deaths occur outside of health institutions. This study aimed to determine the proportion of respiratory-related deaths and identify associated factors in Alicho-Weriro district, southern Ethiopia, using the verbal autopsy method.

METHODS

A community-based cross-sectional study was conducted from April to June 2022. All deceased people in the study area from January 2020 to December 2021 were included in the study. Trained physicians ascertained the cause of death from verbal autopsy data that were collected using a pre-tested and modified WHO-designed questionnaire. The binary logistic regression models were used to identify factors associated with respiratory-related deaths.

RESULTS

Respiratory-related deaths accounted for 25% of the deaths from all causes, with 20.8% of male and 29.5% of female deaths. Of which, 9.7% were from tuberculosis, 8.3% were from asthma and 6.2% were from acute lower-respiratory tract infections. Moreover, being female (adjusted OR, AOR: 3.3; 95% CI: (1.75 to 6.22)), age 50-64 years (AOR: 9.3; 95% CI: (1.16 to 73.90)), age above 64 years (AOR: 8.9; 95% CI: (1.130 to 70.79)), family size of five persons or more (AOR: 1.9; 95% CI: (1.15 to 3.29)), smoking (AOR: 3.9; 95% CI: (1.86 to 8.35)), using wood and/or animal dung for household cooking (AOR: 6.6; 95% CI: (1.92 to 22.59)) and poor house ventilation (AOR: 3.1; 95% CI: (1.75 to 5.38)) were significantly associated with increased odds of dying from respiratory-related diseases.

CONCLUSION

This study has determined that about a quarter of deaths from all causes were due to respiratory diseases, mainly tuberculosis, asthma and acute lower respiratory tract infections. Therefore, interventions to reduce this burden should focus on supporting early case detection and treatment, promoting healthy lifestyles, exercising women's equality at the household level and improving housing conditions.

A high density of ultra-processed food, alcohol & tobacco retail stores, and social inequalities are associated with higher mortality rates of non-communicable diseases in Mexican adults: 2005 to 2021

BACKGROUND

Non-communicable diseases (NCDs) are the leading causes of mortality in Mexico. Factors contributing to NCDs-related deaths may vary across small geographic areas such as municipalities. We aimed to predict municipal-level factors associated with NCD mortality in Mexican adults from 2005 to 2021 using the small-area analysis (SSA) approach.

METHODS

We gathered data on population sociodemographic, access to healthcare services, and mortality records at the municipal-level from census and public institutions from 2005 to 2021. We identified municipal predictors of NCDs mortality rates (MR) using negative binomial regression models.

RESULTS

A total of 584,052 observations of Mexican adults were analyzed. The national expected NCDs MR per 100,000 inhabitants was 210.7 (95%CI: 196.1-226.7) in 2005 and increased to 322.4 (95%CI: 300.3-346.4) by 2021. Predictors of NCDs mortality (quintile 5 vs. quintile 1) included; indigeneity (IRR = 1.15, 95%CI: 1.12-1.19), poverty (IRR = 1.14, 95%CI: 1.13-1.15), affiliation with Mexican Social Security Institute (IRR = 1.11, 95%CI: 1.09-1.14), households with television (IRR = 1.14, 95%CI: 1.11-1.17), and high density of ultra-processed food, alcohol & tobacco retail stores (IRR = 1.15, 95%CI: 1.13-1.17). The greatest increases in MR were observed in municipalities from Oaxaca (>200% increments).

CONCLUSION

There was an overall increase in NCDs MR from 2005 to 2021, with a significant geographic variation among Mexican municipalities. The results of this study highlight the importance of identifying priority areas in the country that urgently require public policies focused on local factors associated with deaths from NCDs, such as the regulation of the ultra-processed food, alcohol & tobacco retail stores, and efforts to reduce social inequalities.

Association of Socioeconomic Factors and Severity of Bronchiolitis Hospitalizations

It is unclear if socioeconomic status (SES) factors influence severity of illness of patients hospitalized with bronchiolitis. This study was conducted to identify SES factors including the Center for Disease Control and Prevention's Social Vulnerability Composite Index (SVI), estimated income, proportion of minority, proportion of living below poverty, insurance status, and number of household members associated with length of stay (LOS) and intensive care unit admission. Infants hospitalized at a tertiary care urban center for bronchiolitis were identified using International Classification of Diseases codes. Federal information processing system codes were identified from home address and paired with SVI and 2018 census tract. Other measures of SES were obtained from the Federal Financial Institutions Examination Council. Number of household members, insurance, age, sex, and history of prematurity were recorded from patient chart. Length of stay was modeled with mixed effects negative binomial regression and pediatric intensive care unit (PICU) admission with mixed effects logistic regression with random intercept at the census tract and adjustment for clinical factors. A total of 417 infants had median age of 144 days (interquartile range (IQR): 61, 357) and 136 (33%) were born premature. Median LOS was 62 hours (24, 136) with 97 (23%) patients admitted to the PICU. Median household members were 4 (4, 5). For each increase in household member, there was 7% increase in LOS (incidence rate ratio 1.07, 95% confidence interval: 1-1.14, P = .038). Social Vulnerability Composite Index, subcategories, insurance status, estimated income, percent of minority, and percent of poverty did not show any associations with length of hospitalization or PICU admission. Increasing number of household members may be associated with increased bronchiolitis hospital LOS. We find no associations with other SES measures.

Socioeconomic disparities associated with mortality in patients hospitalized for COVID-19 in Colombia

Socioeconomic disparities play an important role in the development of severe clinical outcomes including deaths from COVID-19. However, the current scientific evidence in regard the association between measures of poverty and COVID-19 mortality in hospitalized patients is scant. The objective of this study was to investigate whether there is an association between the Colombian Multidimensional Poverty Index (CMPI) and mortality from COVID-19 in hospitalized patients in Colombia from May 1, 2020 to August 15, 2021. This was an ecological study using individual data on hospitalized patients from the National Institute of Health of Colombia (INS), and municipal level data from the High-Cost Account and the National Administrative Department of Statistics. The main outcome variable was mortality due to COVID-19. The main exposure variable was the CMPI that ranges from 0 to 100% and was categorized into five levels: (i) level I (0%−20%), (ii) level II (20%−40%), (iii) level III (40%−60%), (iv) level IV (60%−80%); and (v) level V (80%−100%). The higher the level, the higher the level of multidimensional poverty. A Bayesian multilevel logistic regression model was applied to estimate Odds Ratio (OR) and their corresponding 95% credible intervals (CI). In addition, a subgroup analysis was performed according to the epidemiological COVID-19 waves using the same model. The odds for dying from COVID-19 was 1.46 (95% CI 1.4–1.53) for level II, 1.41 (95% CI 1.33–1.49) for level III and 1.70 (95% CI 1.54–1.89) for level IV hospitalized COVID-19 patients compared with the least poor patients (CMPI level I). In addition, age and male sex also increased mortality in COVID-19 hospitalized patients. Patients between 26 and 50 years-of-age had 4.17-fold increased odds (95% CI 4.07–4.3) of death compared with younger than 26-years-old patients. The corresponding for 51–75 years-old patients and those above the age of 75 years were 9.17 (95% CI 8.93–9.41) and 17.1 (95% CI 16.63–17.56), respectively. Finally, the odds of death from COVID-19 in hospitalized patients gradually decreased as the pandemic evolved. In conclusion, socioeconomic disparities were a major risk factor for mortality in patients hospitalized for COVID-19 in Colombia.

Cervical cancer prevention program in Mexico disrupted due to COVID-19 pandemic: Challenges and opportunities

Introduction

The COVID-19 pandemic disrupted the preventive services for cervical cancer (CC) control programs in Mexico, which will result in increased mortality. This study aims to assess the impact of the pandemic on the interruption of three preventive actions in the CC prevention program in Mexico.

Methods

This study is a retrospective time series analysis based on administrative records for the uninsured population served by the Mexican Ministry of Health. Patient data were retrieved from the outpatient service information system and the hospital discharge database for the period 2017-2021. Data were aggregated by month, distinguishing a pre-pandemic and a pandemic period, considering April 2020 as the start date of the pandemic. A Poisson time series analysis was used to model seasonal and secular trends. Five process indicators were selected to assess the disruption of the CC program, these were analyzed as monthly data (N=39 pre-pandemic, N=21 during the pandemic). HPV vaccination indicators (number of doses and coverage) and diagnostic characteristics of CC cases were analyzed descriptively. The time elapsed between diagnosis and treatment initiation in CC cases was modeled using restricted cubic splines from robust regression.

Results

Annual HPV vaccination coverage declined dramatically after 2019 and was almost null in 2021. The number of positive Papanicolaou smears decreased by 67.8% (90%CI: -72.3, -61.7) in April-December 2020, compared to their expected values without the pandemic. The immediate pandemic shock (April 2020) in the number of first-time and recurrent colposcopies was -80.5% (95%CI:-83.5, -77.0) and -77.9% (95%CI: -81.0, -74.4), respectively. An increasing trend was observed in the proportion of advanced stage and metastatic CC cases. The fraction of CC cases that did not receive medical treatment or surgery increased, as well as CC cases that received late treatment after diagnosis.

Conclusions

Our analyses show significant impact of the COVID-19 pandemic with declines at all levels of CC prevention and increasing inequalities. The restarting of the preventive programs against CC in Mexico offers an opportunity to put in place actions to reduce the disparities in the burden of disease between socioeconomic levels.

Risk Factors of Severe COVID-19: A Review of Host, Viral and Environmental Factors

The clinical course and outcome of COVID-19 are highly variable, ranging from asymptomatic infections to severe disease and death. Understanding the risk factors of severe COVID-19 is relevant both in the clinical setting and at the epidemiological level. Here, we provide an overview of host, viral and environmental factors that have been shown or (in some cases) hypothesized to be associated with severe clinical outcomes. The factors considered in detail include the age and frailty, genetic polymorphisms, biological sex (and pregnancy), co- and superinfections, non-communicable comorbidities, immunological history, microbiota, and lifestyle of the patient; viral genetic variation and infecting dose; socioeconomic factors; and air pollution. For each category, we compile (sometimes conflicting) evidence for the association of the factor with COVID-19 outcomes (including the strength of the effect) and outline possible action mechanisms. We also discuss the complex interactions between the various risk factors.

COVID-19-related excess mortality - an overview of the current evidence

Analysis of excess deaths, defined as the difference in the total number of deaths in an emergency compared to the number of deaths expected under normal conditions, allows a more reliable assessment of the impact on health systems caused by the global threat of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus-2). So far, data for the two years of the pandemic (2020-2021) indicates the occurrence of 14.9 million excess deaths according to WHO (World Health Organization) estimates. The purpose of the analysis conducted was to define the concept and identify the causes of excess mortality during the COVID-19 pandemic. Inconsistent and unreliable death registration systems; overburdened health systems in low- and middle-income countries; reduced access to medical services for patients with health problems other than COVID-19; the introduction of social distancing and lockdown rules, which translated into increased deaths from psychiatric illnesses and addictions; political considerations and media messages that interfered with vaccination acceptance and adherence; and the additional impact of other natural disasters (hurricanes, floods, drought) were identified as the most important reasons for excess deaths occurrence. The correct identification of country-specific factors and the correct response and countermeasures taken appear crucial in terms of limiting the negative impact of the current pandemic, but also of future threats of a similar nature, in order to reduce excess deaths.

Impact of healthcare strain on access to mechanical ventilation and mortality of hospitalized COVID-19 patients: a retrospective cohort study

OBJECTIVE

Healthcare saturation has been a prominent worry during the COVID-19 pandemic. Increase of hospital beds with mechanical ventilators has been central in Mexico's approach, but it is not known whether this actually improves access to care and the resulting quality of it. This study aimed to determine the impact of healthcare strain and other pre-specified variables on dying from coronavirus disease 2019 (COVID-19) without receiving invasive mechanical ventilation (IMV).

METHODS

A retrospective cohort study was conducted using open data from Mexico City between 8 May 2020 and 5 January 2021. We performed Cox proportional hazards models to identify the strength of the association between proposed variables and the outcomes.

RESULTS

Of 33 797 hospitalized patients with suspected or confirmed COVID-19, 19 820 (58.6%) did not require IMV and survived, 5414 (16.1%) required IMV and were intubated and 8563 (25.3%) required IMV but died without receiving it. A greater occupation of IMV-capable beds increased the hazard of death without receiving IMV (hazard ratio [HR] 1.56, comparing 90% with 50% occupation). Private healthcare was the most protective factor for death without IMV (HR 0.14).

CONCLUSIONS

Higher hospital bed saturation increased the hazard of dying without being intubated and worsened the outcomes among mechanically ventilated patients. Older age also increased the hazard of the outcomes, while private healthcare dramatically decreased them.

Application of Data Science for Cluster Analysis of COVID-19 Mortality According to Sociodemographic Factors at Municipal Level in Mexico

Mexico is among the five countries with the largest number of reported deaths from COVID-19 disease, and the mortality rates associated to infections are heterogeneous in the country due to structural factors concerning population. This study aims at the analysis of clusters related to mortality rate from COVID-19 at the municipal level in Mexico from the perspective of Data Science. In this sense, a new application is presented that uses a machine learning hybrid algorithm for generating clusters of municipalities with similar values of sociodemographic indicators and mortality rates. To provide a systematic framework, we applied an extension of the International Business Machines Corporation (IBM) methodology called Batch Foundation Methodology for Data Science (FMDS). For the study, 1,086,743 death certificates corresponding to the year 2020 were used, among other official data. As a result of the analysis, two key indicators related to mortality from COVID-19 at the municipal level were identified: one is population density and the other is percentage of population in poverty. Based on these indicators, 16 municipality clusters were determined. Among the main results of this research, it was found that clusters with high values of mortality rate had high values of population density and low poverty levels. In contrast, clusters with low density values and high poverty levels had low mortality rates. Finally, we think that the patterns found, expressed as municipality clusters with similar characteristics, can be useful for decision making by health authorities regarding disease prevention and control for reinforcing public health measures and optimizing resource distribution for reducing hospitalizations and mortality.