Macrolevel association of COVID-19 with non-communicable disease risk factors in India

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.

An Ayurvedic Medication (Chyawanprash) as a Prophylaxis for Non-Communicable Disease and Communicable Disease: A Protocol for Systematic Review and Meta-Analysis

Globally, non-communicable diseases (NCDs) and communicable diseases (CDs) are on the rise, posing a significant public health threat. A holistic ayurvedic preparation called chyawanprash (CP) has shown positive outcomes in NCDs and CDs. Hence, we aimed to report the outcomes in a systematic manner. To determine the safety, efficacy, healthcare utilization, and quality of life of CP as an optional therapy for NCD and CD management. This systematic review will adhere to PRISMA-P and Cochrane guidelines for methodological considerations. It will evaluate CP efficacy in diverse populations, considering Ayurvedic and non-Ayurvedic comparators. The study design will encompass randomized controlled trials (RCTs) published from 2010 to 2023 in healthcare settings, controlled environments, and communities. We will also analyze primary outcomes related to immunity biomarkers, vital signs, and secondary outcomes such as quality of life. Data sources and search strategy will involve systematic searches in databases such as Cochrane, PubMed, Google Scholar, Web of Science, and Scopus using MeSH terms and Boolean operators. Screening and data extraction will follow a standardized form with four independent reviewers. Quality assessment will use the Cochrane risk of bias tool. The systematic review will provide an exhaustive summary of the effectiveness and safety of CP to address the growing burden of NCDs and CDs. Registration: CRD42023418994.

Changes in dietary markers during the covid-19 pandemic in Brazil

OBJECTIVE

Evaluate changes in the Brazilian population's diet and its determinants during the covid-19 pandemic.

METHODS

We used diet data collected by the Datafolha Institute in 2019 (n = 1,384), 2020 (n =1,214), and 2021 (n = 1,459) from independent and representative samples of the adult population (aged 18 to 55 years) from all socioeconomic classes and geographic regions of Brazil. Food consumption was measured by checking the consumption of 22 sets of food on the day before the survey. The third cycle also included questions about changes in eating habits during the pandemic. We estimated the prevalence of consumption of the food sets in each cycle of the survey and used statistical tests for comparisons of proportions between the three cycles.

RESULTS

Between 2019 and 2020, we observed a significant increase in the consumption of cereals, milk, packaged snacks or salty cookies, and industrialized sauces, as opposed to a decrease in the consumption of eggs. Between 2019 and 2021 and between 2020 and 2021, on the other hand, there was a significant decrease in the consumption of cereals, vegetables, fruits, and industrialized fruit juices and an increase in the consumption of soda, sweets, cookies, sausages, industrialized sauces, and ready meals. When asked about the main changes in the purchase and preparation of meals, 46.3% of the respondents reported consuming more food prepared at home during the pandemic. Regarding changes in eating habits, 48.6% of the respondents reported a change in their eating habits during the pandemic. The main reasons for such changes were greater concern with health (39.1%) and self-reported decreased family income (30.2%).

CONCLUSIONS

The covid-19 pandemic had a negative impact on the diet of the population, and increased consumption of ultra-processed foods was reported for that period.

Obesity as an independent risk factor for COVID-19 severity and mortality

BACKGROUND

Since December 2019, the world has struggled with the COVID-19 pandemic. Even after the introduction of various vaccines, this disease still takes a considerable toll. In order to improve the optimal allocation of resources and communication of prognosis, healthcare providers and patients need an accurate understanding of factors (such as obesity) that are associated with a higher risk of adverse outcomes from the COVID-19 infection.

OBJECTIVES

To evaluate obesity as an independent prognostic factor for COVID-19 severity and mortality among adult patients in whom infection with the COVID-19 virus is confirmed.

SEARCH METHODS

MEDLINE, Embase, two COVID-19 reference collections, and four Chinese biomedical databases were searched up to April 2021.

SELECTION CRITERIA

We included case-control, case-series, prospective and retrospective cohort studies, and secondary analyses of randomised controlled trials if they evaluated associations between obesity and COVID-19 adverse outcomes including mortality, mechanical ventilation, intensive care unit (ICU) admission, hospitalisation, severe COVID, and COVID pneumonia. Given our interest in ascertaining the independent association between obesity and these outcomes, we selected studies that adjusted for at least one factor other than obesity. Studies were evaluated for inclusion by two independent reviewers working in duplicate.  DATA COLLECTION AND ANALYSIS: Using standardised data extraction forms, we extracted relevant information from the included studies. When appropriate, we pooled the estimates of association across studies with the use of random-effects meta-analyses. The Quality in Prognostic Studies (QUIPS) tool provided the platform for assessing the risk of bias across each included study. In our main comparison, we conducted meta-analyses for each obesity class separately. We also meta-analysed unclassified obesity and obesity as a continuous variable (5 kg/m2 increase in BMI (body mass index)). We used the GRADE framework to rate our certainty in the importance of the association observed between obesity and each outcome. As obesity is closely associated with other comorbidities, we decided to prespecify the minimum adjustment set of variables including age, sex, diabetes, hypertension, and cardiovascular disease for subgroup analysis.  MAIN RESULTS: We identified 171 studies, 149 of which were included in meta-analyses.  As compared to 'normal' BMI (18.5 to 24.9 kg/m2) or patients without obesity, those with obesity classes I (BMI 30 to 35 kg/m2), and II (BMI 35 to 40 kg/m2) were not at increased odds for mortality (Class I: odds ratio [OR] 1.04, 95% confidence interval [CI] 0.94 to 1.16, high certainty (15 studies, 335,209 participants); Class II: OR 1.16, 95% CI 0.99 to 1.36, high certainty (11 studies, 317,925 participants)). However, those with class III obesity (BMI 40 kg/m2 and above) may be at increased odds for mortality (Class III: OR 1.67, 95% CI 1.39 to 2.00, low certainty, (19 studies, 354,967 participants)) compared to normal BMI or patients without obesity. For mechanical ventilation, we observed increasing odds with higher classes of obesity in comparison to normal BMI or patients without obesity (class I: OR 1.38, 95% CI 1.20 to 1.59, 10 studies, 187,895 participants, moderate certainty; class II: OR 1.67, 95% CI 1.42 to 1.96, 6 studies, 171,149 participants, high certainty; class III: OR 2.17, 95% CI 1.59 to 2.97, 12 studies, 174,520 participants, high certainty). However, we did not observe a dose-response relationship across increasing obesity classifications for ICU admission and hospitalisation.

AUTHORS' CONCLUSIONS

Our findings suggest that obesity is an important independent prognostic factor in the setting of COVID-19. Consideration of obesity may inform the optimal management and allocation of limited resources in the care of COVID-19 patients.

Accelerating policy response to curb non-communicable diseases: an imperative to mitigate the dual public health crises of non-communicable diseases and COVID-19 in India

Non-communicable Diseases (NCDs) are a threat to public health and sustainable development. NCDs were equated to being a 'pandemic' before COVID-19 originated. Globally, NCDs caused approximately 74% of deaths (2019). India accounted for nearly 14.5% of these deaths. NCDs and COVID-19 have a lethal bi-directional relationship with both exacerbating each other's impact. Health systems and populations, particularly in Low- and Middle-Income Countries (LMICs) like India have among the highest burden of COVID-19. This narrative review tracks key policy and programmatic developments on NCD prevention and control in India, with a focus on commercially-driven risk factors (tobacco and alcohol use, unhealthy diet, physical inactivity, and air pollution), and the corresponding NCD targets. It identifies lacunae and recommends urgent policy-focussed multi-dimensional action, to ameliorate the dual impact of NCDs and COVID-19. India's comprehensive response to NCDs can steer national, regional and global progress towards time-bound NCD targets and NCD-related Sustainable Development Goals (SDGs).

Funding

This work is supported by the Commonwealth Foundation. None of the authors were paid to write this article by a pharmaceutical company or other agency. The authors were not precluded from accessing data and accept responsibility to submit for publication.

Association of greenness with COVID-19 deaths in India: An ecological study at district level

BACKGROUND

The world has witnessed a colossal death toll due to the novel coronavirus disease-2019 (COVID-19). A few environmental epidemiology studies have identified association of environmental factors (air pollution, greenness, temperature, etc.) with COVID-19 incidence and mortality, particularly in developed countries. India, being one of the most severely affected countries by the pandemic, still has a dearth of research exploring the linkages of environment and COVID-19 pandemic.

OBJECTIVES

We evaluate whether district-level greenness exposure is associated with a reduced risk of COVID-19 deaths in India.

METHODS

We used average normalized difference vegetation index (NDVI) from January to March 2019, derived by Oceansat-2 satellite, to represent district-level greenness exposure. COVID-19 death counts were obtained through May 1, 2021 (around the peak of the second wave) from an open portal: covid19india.org. We used hierarchical generalized negative binomial regressions to check the associations of greenness with COVID-19 death counts. Analyses were adjusted for air pollution (PM_2.5), temperature, rainfall, population density, proportion of older adults (50 years and above), sex ratio over age 50, proportions of rural population, household overcrowding, materially deprived households, health facilities, and secondary school education.

RESULTS

Our analyses found a significant association between greenness and reduced risk of COVID-19 deaths. Compared to the districts with the lowest NDVI (quintile 1), districts within quintiles 3, 4, and 5 have respectively, around 32% [MRR = 0.68 (95% CI: 0.51, 0.88)], 39% [MRR = 0.61 (95% CI: 0.46, 0.80)], and 47% [MRR = 0.53 (95% CI: 0.40, 0.71)] reduced risk of COVID-19 deaths. The association remains consistent for analyses restricted to districts with a rather good overall death registration (>80%).

CONCLUSION

Though cause-of-death statistics are limited, we confirm that exposure to greenness was associated with reduced district-level COVID-19 deaths in India. However, material deprivation and air pollution modify this association.

Characterizing COVID-19 waves in urban and rural districts of India

Understanding spatial determinants, i.e., social, infrastructural, and environmental features of a place, which shape infectious disease is critically important for public health. We present an exploration of the spatial determinants of reported COVID-19 incidence across India's 641 urban and rural districts, comparing two waves (2020-2021). Three key results emerge using three COVID-19 incidence metrics: cumulative incidence proportion (aggregate risk), cumulative temporal incidence rate, and severity ratio. First, in the same district, characteristics of COVID-19 incidences are similar across waves, with the second wave over four times more severe than the first. Second, after controlling for state-level effects, urbanization (urban population share), living standards, and population age emerge as positive determinants of both risk and rates across waves. Third, keeping all else constant, lower shares of workers working from home correlate with greater infection risk during the second wave. While much attention has focused on intra-urban disease spread, our findings suggest that understanding spatial determinants across human settlements is also important for managing current and future pandemics.

Factors associated with differential COVID-19 mortality rates in the SEAR nations: a narrative review

Objectives

Since December 2019, the world has been grappling with the COVID-19 pandemic, which has caused severe loss of lives, the breakdown of health infrastructure, and disruption of the global economy. There is growing evidence on mortality patterns in high-income countries. However, similar evidence from low/middle-income nations is lacking. Our review aimed to describe COVID-19 mortality patterns in the WHO-SEAR nations, and explore the associated factors in order to explain such trends.

Methods

A systematic and comprehensive search was undertaken in PubMed and Google Scholar to obtain maximum hits on COVID-19 mortality and its determinants in the SEAR, using a combination of MeSH terms and Boolean operators. The data were narratively synthesized in detail under appropriate themes.

Results

Our search identified 6411 unique records. Mortality patterns were described in terms of important demographical and epidemiological indicators. Gaps in available evidence and paucity of adequate research in this area were also highlighted.

Conclusions

This review examined significant contributors to COVID-19 mortality across SEAR nations, while emphasizing issues relating to insufficient studies and data quality, and reporting challenges and other concerns in resource-constrained settings. There is a compelling need for more work in this area, to help inform decision making and improve public-health response.

Educational status and COVID-19 related outcomes in India: hospital-based cross-sectional study

OBJECTIVE

Association of educational status, as marker of socioeconomic status, with COVID-19 outcomes has not been well studied. We performed a hospital-based cross-sectional study to determine its association with outcomes.

METHODS

Successive patients of COVID-19 presenting at government hospital were recruited. Demographic and clinical details were obtained at admission, and in-hospital outcomes were assessed. Cohort was classified according to self-reported educational status into group 1: illiterate or ≤primary; group 2: higher secondary; and group 3: some college. To compare intergroup outcomes, we performed logistic regression.

RESULTS

4645 patients (men 3386, women 1259) with confirmed COVID-19 were recruited. Mean age was 46±18 years, most lived in large households and 30.5% had low educational status. Smoking or tobacco use was in 29.5%, comorbidities in 28.6% and low oxygen concentration (SpO₂ <95%) at admission in 30%. Average length of hospital stay was 6.8±3.7 days, supplemental oxygen was provided in 18.4%, high flow oxygen or non-invasive ventilation 7.1% and mechanical ventilation 3.6%, 340 patients (7.3%) died. Group 1 patients had more tobacco use, hypoxia at admission, lymphocytopaenia, and liver and kidney dysfunction. In group 1 versus groups 2 and 3, requirement of oxygen (21.6% vs 16.7% and 17.0%), non-invasive ventilation (8.0% vs 5.9% and 7.1%), invasive ventilation (4.6% vs 3.5% and 3.1%) and deaths (10.0% vs 6.8% and 5.5%) were significantly greater (p<0.05). OR for deaths were higher in group 1 (1.91, 95% CI 1.46 to 2.51) and group 2 (1.24, 95% CI 0.93 to 1.66) compared with group 3. Adjustment for demographic and comorbidities led to some attenuation in groups 1 (1.44, 95% CI 1.07 to 1.93) and 2 (1.38, 95% CI 1.02 to 1.85); this persisted with adjustments for clinical parameters and oxygen support in groups 1 (1.38, 95% CI 0.99 to 1.93) and 2 (1.52, 95% CI 1.01 to 2.11).

CONCLUSION

Low educational status patients with COVID-19 in India have significantly greater adverse in-hospital outcomes and mortality.

TRIAL REGISTRATION NUMBER

REF/2020/06/034036.

Cardiovascular risk factors and outcomes in COVID-19: A hospital-based study in India

BACKGROUND & OBJECTIVES

Presence of cardiovascular (CV) risk factors enhance adverse outcomes in COVID-19. To determine association of risk factors with clinical outcomes in India we performed a study.

METHODS

Successive virologically confirmed adult patients of COVID-19 at a government hospital were recruited at admission and data on clinical presentation and in-hospital outcomes were obtained. The cohort was classified according to age, sex, hypertension, diabetes and tobacco use. In-hospital death was the primary outcome. Logistic regression was performed to compared outcomes in different groups.

RESULTS

From April to September 2020 we recruited 4645 (men 3386, women 1259) out of 5103 virologically confirmed COVID-19 patients (91.0%). Mean age was 46±18y, hypertension was in 17.8%, diabetes in 16.6% and any tobacco-use in 29.5%. Duration of hospital stay was 6.8±3.7 days, supplemental oxygen was in 18.4%, non-invasive ventilation in 7.1%, mechanical ventilation in 3.6% and 7.3% died. Unadjusted and age-sex adjusted odds ratio(OR) and 95% confidence intervals(CI) for in-hospital mortality, respectively, were: age ≥60y vs <40y, OR 8.47(95% CI 5.87-12.21) and 8.49(5.88-12.25), age 40-59y vs <40y 3.69(2.53-5.38) and 3.66(2.50-5.33), men vs women 1.88(1.41-2.51) and 1.26(0.91-1.48); hypertension 2.22(1.74-2.83) and 1.32(1.02-1.70), diabetes 1.88(1.46-2.43) and 1.16(0.89-1.52); and tobacco 1.29(1.02-1.63) and 1.28(1.00-1.63). Need for invasive and non-invasive ventilation was greater among patients in age-groups 40-49 and ≥60y and hypertension. Multivariate adjustment for social factors, clinical features and biochemical tests attenuated significance of all risk factors.

CONCLUSION

Cardiovascular risk factors, age, male sex, hypertension, diabetes and tobacco-use, are associated with greater risk of in-hospital death among COVID-19 patients.