Effect of altitude on COVID-19 mortality in Ecuador: an ecological study

Construction and validation of a covariate-based model for district-level estimation of excess deaths due to COVID-19 in India

Background

Different statistical approaches for estimating excess deaths due to coronavirus disease 2019 (COVID-19) pandemic have led to varying estimates. In this study, we developed and validated a covariate-based model (CBM) with imputation for prediction of district-level excess deaths in India.

Methods

We used data extracted from deaths registered under the Civil Registration System for 2015-19 for 684 of 713 districts in India to estimate expected deaths for 2020 through a negative binomial regression model (NBRM) and to calculate excess observed deaths. Specifically, we used 15 covariates across four domains (state, health system, population, COVID-19) in a zero inflated NBRM to identify covariates significantly (P < 0.05) associated with excess deaths estimate in 460 districts. We then validated this CBM in 140 districts by comparing predicted and estimated excess. For 84 districts with missing covariates, we validated the imputation with CBM by comparing estimated with predicted excess deaths. We imputed covariate data to predict excess deaths for 29 districts which did not have data on deaths.

Results

The share of elderly and urban population, the under-five mortality rate, prevalence of diabetes, and bed availability were significantly associated with estimated excess deaths and were used for CBM. The mean of the CBM-predicted excess deaths per district (x̄ = 989, standard deviation (SD) = 1588) was not significantly different from the estimated one (x̄ = 1448, SD = 3062) (P = 0.25). The estimated excess deaths (n = 67 540; 95% confidence interval (CI) = 35 431, 99 648) were similar to the predicted excess death (n = 64 570; 95% CI = 54 140, 75 000) by CBM with imputation. The total national estimate of excess deaths for all 713 districts was 794 989 (95% CI = 664 895, 925 082).

Conclusions

A CBM with imputation can be used to predict excess deaths in an appropriate context.

Assessing eco-geographic influences on COVID-19 transmission: a global analysis

COVID-19 has been massively transmitted for almost 3 years, and its multiple variants have caused serious health problems and an economic crisis. Our goal was to identify the influencing factors that reduce the threshold of disease transmission and to analyze the epidemiological patterns of COVID-19. This study served as an early assessment of the epidemiological characteristics of COVID-19 using the MaxEnt species distribution algorithm using the maximum entropy model. The transmission of COVID-19 was evaluated based on human factors and environmental variables, including climate, terrain and vegetation, along with COVID-19 daily confirmed case location data. The results of the SDM model indicate that population density was the major factor influencing the spread of COVID-19. Altitude, land cover and climatic factor showed low impact. We identified a set of practical, high-resolution, multi-factor-based maximum entropy ecological niche risk prediction systems to assess the transmission risk of the COVID-19 epidemic globally. This study provided a comprehensive analysis of various factors influencing the transmission of COVID-19, incorporating both human and environmental variables. These findings emphasize the role of different types of influencing variables in disease transmission, which could have implications for global health regulations and preparedness strategies for future outbreaks.

Conditioning factors in the spreading of Covid-19 - Does geography matter?

There is evidence in literature that the spread of COVID-19 can be influenced by various geographic factors, including territorial features, climate, population density, socioeconomic conditions, and mobility. The objective of the paper is to provide an updated literature review on geographical studies analysing the factors which influenced COVID-19 spreading. This literature review took into account not only the geographical aspects but also the COVID-19-related outcomes (infections and deaths) allowing to discern the potential influencing role of the geographic factors per type of outcome. A total of 112 scientific articles were selected, reviewed and categorized according to subject area, aim, country/region of study, considered geographic and COVID-19 variables, spatial and temporal units of analysis, methodologies, and main findings. Our literature review showed that territorial features may have played a role in determining the uneven geography of COVID-19; for instance, a certain agreement was found regarding the direct relationship between urbanization degree and COVID-19 infections. For what concerns climatic factors, temperature was the variable that correlated the best with COVID-19 infections. Together with climatic factors, socio-demographic ones were extensively taken into account. Most of the analysed studies agreed that population density and human mobility had a significant and direct relationship with COVID-19 infections and deaths. The analysis of the different approaches used to investigate the role of geographic factors in the spreading of the COVID-19 pandemic revealed that the significance/representativeness of the outputs is influenced by the scale considered due to the great spatial variability of geographic aspects. In fact, a more robust and significant association between geographic factors and COVID-19 was found by studies conducted at subnational or local scale rather than at country scale.

Receiver operating characteristic (ROC) to determine cut-off points of clinical and biomolecular markers to discriminate mortality in severe COVID-19 living at high altitude

BACKGROUND

In 2020, Ecuador had one of the highest death rates because of COVID-19. The role of clinical and biomolecular markers in COVID disease prognosis, is still not well supported by available data. In order for these markers to have practical application in clinical decision-making regarding patient treatment and prognosis, it is necessary to know an optimal cut-off point, taking into consideration ethnic differences and geographic conditions.

AIM

To determine the value of clinical and biomolecular markers, to predict mortality of patients with severe COVID-19 living at high altitude.

METHODS

In this study, receiver operating characteristic (ROC) curves, area under the curve (AUC) of ROC, sensitivity, specificity and likelihood ratios were calculated to determine levels of clinical and biomolecular markers that best differentiate survivors versus non-survivors in severe COVID subjects that live at a high altitude setting.

RESULTS

Selected cut-off values for ferritin (≥ 1225 ng/dl, p = 0.026), IL-6 (≥ 11 pg/ml, p = 0.005) and NLR (≥ 22, p = 0.008) at 24 h, as well as PaFiO2 (≤ 164 mmHg, p = 0.015), NLR (≥ 16, p = p = 0.013) and SOFA (≥ 6, p = 0.031) at 72 h, appear to have good discriminating power to differentiate survivors versus non-survivors. Additionally, odds ratios for ferritin (OR = 3.38); IL-6 (OR = 17.07); PaFiO2 (OR = 4.61); NLR 24 h (OR = 4.95); NLR 72 h (OR = 4.46), and SOFA (OR = 3.77) indicate increased risk of mortality when cut-off points were taken into consideration.

CONCLUSIONS

We proposed a straightforward and understandable method to identify dichotomized levels of clinical and biomolecular markers that can discriminate between survivors and non-survivors patients with severe COVID-19 living at high altitudes.

Disparities in COVID-19 incidence and fatality rates at high-altitude

Background

SARS-CoV-2 has affected every demography disproportionately, including even the native highland populations. Hypobaric-hypoxic settings at high-altitude (HA, >2,500 masl) present an extreme environment that impacts the survival of permanent residents, possibly including SARS-CoV-2. Conflicting hypotheses have been presented for COVID-19 incidence and fatality at HA.

Objectives

To evaluate protection or risk against COVID-19 incidence and fatality in humans under hypobaric-hypoxic environment of high-altitude (>2,501 masl).

Methods

Global COVID-19 data of March 2020-21, employed from official websites of the Indian Government, John Hopkins University, and Worldometer were clustered into 6 altitude categories. Clinical cofactors and comorbidities data were evaluated with COVID-19 incidence and fatality. Extensive comparisons and correlations using several statistical tools estimated the risk and protection.

Results

Of relevance, data analyses revealed four distinct responses, namely, partial risk, total risk, partial protection, and total protection from COVID-19 at high-altitude indicating a mixed baggage and complexity of the infection. Surprisingly, it included the countries within the same geographic region. Moreover, body mass index, hypertension, and diabetes correlated significantly with COVID-19 incidence and fatality rate (P ≤ 0.05).

Conclusions

Varied patterns of protection and risk against COVID-19 incidence and fatality were observed among the high-altitude populations. It is though premature to generalize COVID-19 effects on any particular demography without further extensive studies.

Biological Markers to Predict Outcome in Mechanically Ventilated Patients with Severe COVID-19 Living at High Altitude

BACKGROUND

There is not much evidence on the prognostic utility of different biological markers in patients with severe COVID-19 living at high altitude. The objective of this study was to determine the predictive value of inflammatory and hematological markers for the risk of mortality at 28 days in patients with severe COVID-19 under invasive mechanical ventilation, living at high altitude and in a low-resource setting.

METHODS

We performed a retrospective observational study including patients with severe COVID-19, under mechanical ventilation and admitted to the intensive care unit (ICU) located at 2850 m above sea level, between 1 April 2020 and 1 August 2021. Inflammatory (interleukin-6 (IL-6), ferritin, D-dimer, lactate dehydrogenase (LDH)) and hematologic (mean platelet volume (MPV), neutrophil/lymphocyte ratio (NLR), MPV/platelet ratio) markers were evaluated at 24 h and in subsequent controls, and when available at 48 h and 72 h after admission to the ICU. The primary outcome was the association of inflammatory and hematological markers with the risk of mortality at 28 days.

RESULTS

We analyzed 223 patients (median age (1st quartile [Q1]-3rd quartile [Q3]) 51 (26-75) years and 70.4% male). Patients with severe COVID-19 and with IL-6 values at 24 h ≥ 11, NLR values at 24 h ≥ 22, and NLR values at 72 h ≥ 14 were 8.3, 3.8, and 3.8 times more likely to die at 28 days, respectively. The SOFA and APACHE-II scores were not able to independently predict mortality.

CONCLUSIONS

In mechanically ventilated patients with severe COVID-19 and living at high altitude, low-cost and immediately available blood markers such as IL-6 and NLR may predict the severity of the disease in low-resource settings.

Mimicking Gene-Environment Interaction of Higher Altitude Dwellers by Intermittent Hypoxia Training: COVID-19 Preventive Strategies

Cyclooxygenase 2 (COX2) inhibitors have been demonstrated to protect against hypoxia pathogenesis in several investigations. It has also been utilized as an adjuvant therapy in the treatment of COVID-19. COX inhibitors, which have previously been shown to be effective in treating previous viral and malarial infections are strong candidates for improving the COVID-19 therapeutic doctrine. However, another COX inhibitor, ibuprofen, is linked to an increase in the angiotensin-converting enzyme 2 (ACE2), which could increase virus susceptibility. Hence, inhibiting COX2 via therapeutics might not always be protective and we need to investigate the downstream molecules that may be involved in hypoxia environment adaptation. Research has discovered that people who are accustomed to reduced oxygen levels at altitude may be protected against the harmful effects of COVID-19. It is important to highlight that the study's conclusions only applied to those who regularly lived at high altitudes; they did not apply to those who occasionally moved to higher altitudes but still lived at lower altitudes. COVID-19 appears to be more dangerous to individuals residing at lower altitudes. The downstream molecules in the (COX2) pathway have been shown to adapt in high-altitude dwellers, which may partially explain why these individuals have a lower prevalence of COVID-19 infection. More research is needed, however, to directly address COX2 expression in people living at higher altitudes. It is possible to mimic the gene-environment interaction of higher altitude people by intermittent hypoxia training. COX-2 adaptation resulting from hypoxic exposure at altitude or intermittent hypoxia exercise training (IHT) seems to have an important therapeutic function. Swimming, a type of IHT, was found to lower COX-2 protein production, a pro-inflammatory milieu transcription factor, while increasing the anti-inflammatory microenvironment. Furthermore, Intermittent Hypoxia Preconditioning (IHP) has been demonstrated in numerous clinical investigations to enhance patients' cardiopulmonary function, raise cardiorespiratory fitness, and increase tissues' and organs' tolerance to ischemia. Biochemical activities of IHP have also been reported as a feasible application strategy for IHP for the rehabilitation of COVID-19 patients. In this paper, we aim to highlight some of the most relevant shared genes implicated with COVID-19 pathogenesis and hypoxia. We hypothesize that COVID-19 pathogenesis and hypoxia share a similar mechanism that affects apoptosis, proliferation, the immune system, and metabolism. We also highlight the necessity of studying individuals who live at higher altitudes to emulate their gene-environment interactions and compare the findings with IHT. Finally, we propose COX2 as an upstream target for testing the effectiveness of IHT in preventing or minimizing the effects of COVID-19 and other oxygen-related pathological conditions in the future.

Altitud y su relación con la incidencia, letalidad y mortalidad por COVID-19 en Perú: 2020-2021

Introducción. La incidencia, letalidad y mortalidad COVID-19 no ha sido igual en las regiones del Perú, situación que puede estar relacionada con factores pocos estudiados como la altitud; asimismo, características ambientales propias de la altura (presión atmosférica, humedad relativa, etc.) podrían explicar la dinámica de transmisión de la COVID-19. Objetivo. Determinar la relación entre altitud e incidencia, letalidad y mortalidad por COVID-19 en Perú. Materiales y método. Estudio ecológico de grupos múltiples. Se realizó un análisis secundario de datos oficiales COVID-19 de 1874 distritos del Perú reportados hasta febrero de 2021. La variable altitud se categorizó como baja (0-999 msnm), media (1000-2499 msnm) y elevada (≥2500 msnm). Las tasas de incidencia acumulada, letalidad y mortalidad por COVID-19 se calcularon como el número de casos entre la población total de cada distrito multiplicada por 10000, el número de defunciones entre el número de casos multiplicado por 100, y el número de defunciones entre la población total de cada distrito multiplicado por 100000, respectivamente. Para el análisis de los datos se empleó estadística bivariada (coeficiente de correlación de Spearman y prueba de Kruskal-Wallis) y multivariada (regresión lineal múltiple), con un nivel de confianza del 95%. Resultados. Se observó una correlación inversa entre la tasa de incidencia acumulada (1823 distritos) y altitud (Rho:-0.355; p<0.001), es decir, se redujo a mayor altitud, y una correlación directa entre la tasa de letalidad (1526 distritos) y altitud (Rho: 0.131; p<0.001), es decir, aumentó a mayor altitud. Aunque la tasa de mortalidad mostró una correlación inversa con la altitud (Rho:-0.310; p<0.000), esta varía heterogéneamente según niveles altitudinales. En el análisis multivariado, luego de ajustar el modelo por pobreza y densidad poblacional, la altitud se asoció con las tasas de incidencia (p<0.001) y de letalidad (p=0.009), pero no con la de mortalidad (p=0.179). Conclusión. Se observó una correlación inversa entre altitud y la tasa de incidencia de COVID-19 y una correlación directa entre altitud y la tasa de letalidad en Perú durante el periodo de estudio. Finalmente, no se encontró una correlación entre altitud y tasa de mortalidad.

The Association of Latitude and Altitude with COVID-19 Symptoms: A VIRUS: COVID-19 Registry Analysis

Background:

Better delineation of COVID-19 presentations in different climatological conditions might assist with prompt diagnosis and isolation of patients.

Objectives:

To study the association of latitude and altitude with COVID-19 symptomatology.

Methods:

This observational cohort study included 12267 adult COVID-19 patients hospitalized between 03/2020 and 01/2021 at 181 hospitals in 24 countries within the SCCM Discovery VIRUS: COVID-19 Registry. The outcome was symptoms at admission, categorized as respiratory, gastrointestinal, neurological, mucocutaneous, cardiovascular, and constitutional. Other symptoms were grouped as atypical. Multivariable regression modeling was performed, adjusting for baseline characteristics. Models were fitted using generalized estimating equations to account for the clustering.

Results:

The median age was 62 years, with 57% males. The median age and percentage of patients with comorbidities increased with higher latitude. Conversely, patients with comorbidities decreased with elevated altitudes. The most common symptoms were respiratory (80%), followed by constitutional (75%). Presentation with respiratory symptoms was not associated with the location. After adjustment, at lower latitudes (<30º), patients presented less commonly with gastrointestinal symptoms (p<.001, odds ratios for 15º, 25º, and 30º: 0.32, 0.81, and 0.98, respectively). Atypical symptoms were present in 21% of the patients and showed an association with altitude (p=.026, odds ratios for 75, 125, 400, and 600 meters above sea level: 0.44, 0.60, 0.84, and 0.77, respectively).

Conclusions:

We observed geographic variability in symptoms of COVID-19 patients. Respiratory symptoms were most common but were not associated with the location. Gastrointestinal symptoms were less frequent in lower latitudes. Atypical symptoms were associated with higher altitude.