Do wildfires exacerbate COVID-19 infections and deaths in vulnerable communities? Evidence from California
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PROVIDER: S-EPMC9705198 | biostudies-literature | 2022 Nov
REPOSITORIES: biostudies-literature
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Project description:The world is in the grip of the COVID-19 pandemic. Public health measures that can reduce the risk of infection and death in addition to quarantines are desperately needed. This article reviews the roles of vitamin D in reducing the risk of respiratory tract infections, knowledge about the epidemiology of influenza and COVID-19, and how vitamin D supplementation might be a useful measure to reduce risk. Through several mechanisms, vitamin D can reduce risk of infections. Those mechanisms include inducing cathelicidins and defensins that can lower viral replication rates and reducing concentrations of pro-inflammatory cytokines that produce the inflammation that injures the lining of the lungs, leading to pneumonia, as well as increasing concentrations of anti-inflammatory cytokines. Several observational studies and clinical trials reported that vitamin D supplementation reduced the risk of influenza, whereas others did not. Evidence supporting the role of vitamin D in reducing risk of COVID-19 includes that the outbreak occurred in winter, a time when 25-hydroxyvitamin D (25(OH)D) concentrations are lowest; that the number of cases in the Southern Hemisphere near the end of summer are low; that vitamin D deficiency has been found to contribute to acute respiratory distress syndrome; and that case-fatality rates increase with age and with chronic disease comorbidity, both of which are associated with lower 25(OH)D concentration. To reduce the risk of infection, it is recommended that people at risk of influenza and/or COVID-19 consider taking 10,000 IU/d of vitamin D3 for a few weeks to rapidly raise 25(OH)D concentrations, followed by 5000 IU/d. The goal should be to raise 25(OH)D concentrations above 40-60 ng/mL (100-150 nmol/L). For treatment of people who become infected with COVID-19, higher vitamin D3 doses might be useful. Randomized controlled trials and large population studies should be conducted to evaluate these recommendations.
| S-EPMC7231123 | biostudies-literature
Project description:Previous studies have reported associations between air pollution and COVID-19 morbidity and mortality, but most have limited their exposure assessment to a large area, have not used individual-level variables, nor studied infections. We examined 3.1 million SARS-CoV-2 infections and 49,691 COVID-19 deaths that occurred in California from February 2020 to February 2021 to evaluate risks associated with long-term neighborhood concentrations of particulate matter less than 2.5 μm in diameter (PM2.5). We obtained individual address data on SARS-CoV-2 infections and COVID-19 deaths and assigned 2000-2018 1km-1km gridded PM2.5 surfaces to census block groups. We included individual covariate data on age and sex, and census block data on race/ethnicity, air basin, Area Deprivation Index, and relevant comorbidities. Our analyses were based on generalized linear mixed models utilizing a Poisson distribution. Those living in the highest quintile of long-term PM2.5 exposure had risks of SARS-CoV-2 infections 20% higher and risks of COVID-19 mortality 51% higher, compared to those living in the lowest quintile of long-term PM2.5 exposure. Those living in the areas of highest long-term PM2.5 exposure were more likely to be Hispanic and more vulnerable, based on the Area Deprivation Index. The increased risks for SARS-CoV-2 Infections and COVID-19 mortality associated with highest long-term PM2.5 concentrations at the neighborhood-level in California were consistent with a growing body of literature from studies worldwide, and further highlight the importance of reducing levels of air pollution to protect public health.
| S-EPMC9316717 | biostudies-literature
Project description:Wildfires can be detrimental to urban and rural communities, causing impacts in the form of psychological stress, direct physical injury, and smoke-related morbidity and mortality. This study examined the area burned by wildfires over the entire state of California from the years 2000 to 2020 in order to quantify and identify whether burned area and fire frequency differed across Census tracts according to socioeconomic indicators over time. Wildfire data were obtained from the California Fire and Resource Assessment Program (FRAP) and National Interagency Fire Center (NIFC), while demographic data were obtained from the American Community Survey. Results showed a doubling in the number of Census tracts that experienced major wildfires and a near doubling in the number of people residing in wildfire-impacted Census tracts, mostly due to an over 23,000 acre/year increase in the area burned by wildfires over the last two decades. Census tracts with a higher fire frequency and burned area had lower proportions of minority groups on average. However, when considering Native American populations, a greater proportion resided in highly impacted Census tracts. Such Census tracts also had higher proportions of older residents. In general, high-impact Census tracts tended to have higher proportions of low-income residents and lower proportions of high-income residents, as well as lower median household incomes and home values. These findings are important to policymakers and state agencies as it relates to environmental justice and the allocation of resources before, during, and after wildfires in the state of California.
| S-EPMC8068328 | biostudies-literature
Project description:The US government imposed two travel restriction policies to prevent the spread of the COVID-19 but may have funneled asymptomatic air travelers to selected major airports and transportation hubs. Using the most recent JHU COVID-19 database, American Community Survey, Airport and Amtrak data form Bureau of Transportation Statistics from 3132 US counties we ran negative binomial regressions and Cox regression models to explore the associations between COVID-19 cases and death rates and proximity to airports, train stations, and public transportation. Counties within 25 miles of an airport had 1.392 times the rate of COVID-19 cases and 1.545 times the rate of COVID-19 deaths in comparison to counties that are more than 50 miles from an airport. More effective policies to detect and isolate infected travelers are needed. Policymakers and officials in transportation and public health should collaborate to promulgate policies and procedures to protect travelers and transportation workers from COVID-19.
| S-EPMC7714661 | biostudies-literature
Project description:ObjectivesTo evaluate the effectiveness of widespread adoption of masks or face coverings to reduce community transmission of the SARS-CoV-2 virus that causes COVID-19.MethodsWe created an agent-based stochastic network simulation using a variant of the standard SEIR dynamic infectious disease model. We considered a mask order that was initiated 3.5 months after the first confirmed COVID-19 case. We varied the likelihood of individuals wearing masks from 0-100% in steps of 20% (mask adherence) and considered 25% to 90% mask-related reduction in viral transmission (mask efficacy). Sensitivity analyses assessed early (by week 13) versus late (by week 42) adoption of masks and geographic differences in adherence (highest in urban and lowest in rural areas).ResultsIntroduction of mask use with 50% efficacy worn by 50% of individuals reduces the cumulative infection attack rate (IAR) by 27%, the peak prevalence by 49%, and population-wide mortality by 29%. If 90% of individuals wear 50% efficacious masks, this decreases IAR by 54%, peak prevalence by 75%, and population-wide mortality by 55%; similar improvements hold if 70% of individuals wear 75% efficacious masks. Late adoption reduces IAR and deaths by 18% or more compared to no adoption. Lower adoption in rural areas than urban would lead to rural areas having the highest IAR.ConclusionsEven after community transmission of SARS-CoV-2 has been established, adoption of mask-wearing by a majority of community-dwelling individuals can meaningfully reduce the number and outcome of COVID-19 infections over and above physical distancing interventions.HighlightsThis paper shows the impact of widespread adoption of masks in response to the COVID-19 pandemic, with varying levels of population adherence, mask efficacy, and timing of mask adoption.The paper's findings help inform messaging to policymakers at the state or local level considering adding or keeping mask mandates, and to communities to promote widespread adoption of high-quality masks.Adoption of masks by at least half of the population can reduce cumulative infections and population deaths by more than 25%, while decreasing peak prevalence by about 50%. Even greater marginal improvements arise with adoption rates above 70%. The benefits of adopting high-quality masks is above that achieved by mobility changes and distancing alone.Rural and suburban areas are at higher relative risk than urban areas, due to less distancing and lower adoption of masks.
| S-EPMC7852241 | biostudies-literature
Project description:Background/objectivesTo determine the associations of nursing home registered nurse (RN) staffing, overall quality of care, and concentration of Medicaid or racial and ethnic minority residents with 2019 coronavirus disease (COVID-19) confirmed cases and deaths by April 16, 2020, among Connecticut nursing home residents.DesignCross-sectional analysis on Connecticut nursing home (n = 215) COVID-19 report, linked to other nursing home files and county counts of confirmed cases and deaths. Multivariable two-part models determined the associations of key nursing home characteristics with the likelihood of at least one confirmed case (or death) in the facility, and with the count of cases (deaths) among facilities with at least one confirmed case (death).SettingAll Connecticut nursing homes (n = 215).ParticipantsNone.InterventionNone.MeasurementsNumbers of COVID-19 confirmed cases and deaths among residents.ResultsThe average number of confirmed cases was eight per nursing home (zero in 107 facilities), and the average number of confirmed deaths was 1.7 per nursing home (zero in 131 facilities). Among facilities with at least one confirmed case, every 20-minute increase in RN staffing (per resident day) was associated with 22% fewer confirmed cases (incidence rate ratio [IRR] = .78; 95% confidence interval [CI] = .68-.89; P < .001); compared with one- to three-star facilities, four- or five-star facilities had 13% fewer confirmed cases (IRR = .87; 95% CI = .78-.97; P < .015), and facilities with high concentration of Medicaid residents (IRR = 1.16; 95% CI = 1.02-1.32; P = .025) or racial/ethnic minority residents (IRR = 1.15; 95% CI = 1.03-1.29; P = .026) had 16% and 15% more confirmed cases, respectively, than their counterparts. Among facilities with at least one death, every 20-minute increase in RN staffing significantly predicted 26% fewer COVID-19 deaths (IRR = .74; 95% CI = I .55-1.00; P = .047). Other focused characteristics did not show statistically significant associations with deaths.ConclusionNursing homes with higher RN staffing and quality ratings have the potential to better control the spread of the novel coronavirus and reduce deaths. Nursing homes caring predominantly for Medicaid or racial and ethnic minority residents tend to have more confirmed cases.
| S-EPMC7323378 | biostudies-literature
Project description:The complexity of COVID-19 and variations in control measures and containment efforts in different countries have caused difficulties in the prediction and modeling of the COVID-19 pandemic. We attempted to predict the scale of the latter half of the pandemic based on real data using the ratio between the early and latter halves from countries where the pandemic is largely over. We collected daily pandemic data from China, South Korea, and Switzerland and subtracted the ratio of pandemic days before and after the disease apex day of COVID-19. We obtained the ratio of pandemic data and created multiple regression models for the relationship between before and after the apex day. We then tested our models using data from the first wave of the disease from 14 countries in Europe and the US. We then tested the models using data from these countries from the entire pandemic up to March 30, 2021. Results indicate that the actual number of cases from these countries during the first wave mostly fall in the predicted ranges of liniar regression, excepting Spain and Russia. Similarly, the actual deaths in these countries mostly fall into the range of predicted data. Using the accumulated data up to the day of apex and total accumulated data up to March 30, 2021, the data of case numbers in these countries are falling into the range of predicted data, except for data from Brazil. The actual number of deaths in all the countries are at or below the predicted data. In conclusion, a linear regression model built with real data from countries or regions from early pandemics can predict pandemic scales of the countries where the pandemics occur late. Such a prediction with a high degree of accuracy provides valuable information for governments and the public.
| S-EPMC8222531 | biostudies-literature
Project description:With countries across the world facing repeated epidemic waves, it becomes critical to monitor, mitigate and prevent subsequent waves. Common indicators like active case numbers may not be sensitive enough in the presence of systemic inefficiencies like insufficient testing or contact tracing. Test positivity rates are sensitive to testing strategies and cannot estimate the extent of undetected cases. Reproductive numbers estimated from logarithms of new incidences are inaccurate in dynamic scenarios and not sensitive enough to capture changes in efficiencies. Systemic fatigue results in lower testing, inefficient tracing and quarantining thereby precipitating the onset of the epidemic wave. We propose a novel indicator for detecting the slippage of test-trace efficiency based on the number of deaths/hospitalizations resulting from known and hitherto unknown infections. This can also be used to forecast an epidemic wave that is advanced or exacerbated due to a drop in efficiency in situations where the testing has come down drastically and contact tracing is virtually nil as is prevalent currently. Using a modified SEIRD epidemic simulator we show that (i) Ratio of deaths/hospitalizations from an undetected infection to total deaths converges to a measure of systemic test-trace inefficiency. (ii) This index forecasts the slippage in efficiency earlier than other known metrics. (iii) Mitigation triggered by this index helps reduce peak active caseload and eventual deaths. Deaths/hospitalizations accurately track the systemic inefficiencies and detect latent cases. Based on these results we make a strong case that administrations use this metric in the ensemble of indicators. Further, hospitals may need to be mandated to distinctly register deaths/hospitalizations due to previously undetected infections. Thus the proposed metric is an ideal indicator of an epidemic wave that poses the least socio-economic cost while keeping the surveillance robust during periods of pandemic fatigue.
| S-EPMC10022783 | biostudies-literature
Project description:Access to sufficient clean water is important for reducing the risks from COVID-19. It is unclear, however, what influence COVID-19 has had on water insecurities. The objective of this study was to assess the associations between COVID-19 control measures and household water insecurities. A survey of 1559 individuals living in vulnerable communities in five countries (Cambodia, Laos, Myanmar, Thailand, Vietnam) showed that increased needs for clean water to wash hands or facemasks made it more likely a person was water insecure along those dimensions. Water insecurities with respect to handwashing and drinking, in turn, made adoption of the corresponding good practices less likely, whereas in the case of washing facemasks there was no association. Water system infrastructure, environmental conditions such as floods and droughts, as well as gender norms and knowledge, were also important for water insecurities and the adoption of good practices. As domestic water insecurities and COVID-19 control measures are associated with each other, efforts should therefore be directed at identifying and assisting the water insecure at high risk when COVID-19 reaches their communities.Supplementary informationThe online version contains supplementary material available at 10.1007/s10668-022-02182-0.
| S-EPMC8874302 | biostudies-literature
Project description:ImportanceWildfires, intensified by climate change, have known effects on physical health but their effects on mental health are less well characterized. It has been hypothesized that the residential proximity to a large wildfire can exacerbate underlying mental health conditions as evidenced by increased prescriptions of psychotropic medications.ObjectiveTo evaluate the association between the occurrence of large wildfires and the prescription rates of psychotropic medications immediately following the start of the fire.Design, setting, and participantsThis cohortstudy used an interrupted time-series analysis to compare psychotropic medication prescriptions in the 6 weeks before and after each of 25 wildfires. The setting was California counties within metropolitan statistical areas (MSAs) experiencing large wildfires from 2011 through 2018. Participants included individuals residing in California MSAs with prescriptions of psychotropic medications recorded in the Merative MarketScan Research Database (MarketScan) during the study period. Statistical analysis was performed for these 25 large wildfires occurring between September 2011 and November 2018.ExposureResidential proximity to large wildfires that burned more than 25 000 acres occurring in a California county within an MSA.Main outcomes and measuresPrescriptions of psychotropic medications, including antidepressants, antipsychotics, anxiolytics, hypnotics, and mood-stabilizers, with statins as a negative control outcome.ResultsFor the study period, prescription data and patient-level attributes were extracted for 7 115 690 unique individuals (annual mean [range]: 889 461 [455 705-1 426 928] individuals) enrolled in MarketScan and residing in fire-affected MSAs. This study found a statistically significant increase in prescriptions of antidepressants (rate ratio [RR], 1.04 [95% CI, 1.01-1.07]), anxiolytics (RR, 1.05 [95% CI, 1.02-1.09]), and mood-stabilizing medications (RR, 1.06 [95% CI, 1.01-1.13]) in the fire period compared with the prefire baseline. However, the prescriptions of antipsychotics, hypnotics, and the negative control outcome, statins, showed no significant association.Conclusions and relevanceIn this cohort study of large California wildfires, the occurrence of wildfire was associated with increased mental health burden as reflected in increased prescription rates of certain psychotropic medications. The findings underscore the need for further scientific examination into the mental health effects of wildfires and the allocation of mental health resources in disaster responses. California experienced a substantial burden of wildfires from 2011 to 2018, and as wildfires become more intense and frequent in the context of anthropogenic climate change, it is increasingly important to understand and address their mental health effects.
| S-EPMC10897744 | biostudies-literature