COVID-19 pandemic in the United Kingdom

ObjectivesTo describe epidemiological data on cases of COVID-19 and the spread of Severe Acute Respiratory Syndrome Coronavirus 2 in the United Kingdom (UK), and the subsequent policy and technological response to the pandemic, including impact on healthcare, business and the economy.MethodsEpidemiological, business and economic data were extracted from official government sources covering the period 31st January to 13th August 2020; healthcare system data up to end of June 2019.ResultsUK-wide COVID-19 cases and deaths were 313,798 and 46,706 respectively (472 cases and 70 deaths per 100,000 population) by 12th August. There were regional variations in England, with London and North West (756 and 666 cases per 100,000 population respectively) disproportionately affected compared with other regions. As of 11th August, 13,618,470 tests had been conducted in the UK. Increased risk of mortality was associated with age (≥60 years), gender (male) and BAME groups. Since onset of the pandemic, emergency department attendance, primary care utilisation and cancer referrals and inpatient/outpatient referrals have declined; emergency ambulance and NHS111 calls increased. Business sectors most impacted are the arts, entertainment and recreation, followed by accommodation and food services. Government interventions aimed at curtailing the business and economic impact have been implemented, but applications for state benefits have increased.ConclusionsThe impact of COVID-19 on the UK population, health system and economy has been profound. More data are needed to implement the optimal policy and technological responses to preventing further spikes in COVID-19 cases, and to inform strategic planning to manage future pandemics.     

The Influence of Social Distancing on COVID-19 Mortality in US Counties: Cross-sectional Study

BackgroundPrevious studies on the impact of social distancing on COVID-19 mortality in the United States have predominantly examined this relationship at the national level and have not separated COVID-19 deaths in nursing homes from total COVID-19 deaths. This approach may obscure differences in social distancing behaviors by county in addition to the actual effectiveness of social distancing in preventing COVID-19 deaths.ObjectiveThis study aimed to determine the influence of county-level social distancing behavior on COVID-19 mortality (deaths per 100,000 people) across US counties over the period of the implementation of stay-at-home orders in most US states (March-May 2020).MethodsUsing social distancing data from tracked mobile phones in all US counties, we estimated the relationship between social distancing (average proportion of mobile phone usage outside of home between March and May 2020) and COVID-19 mortality (when the state in which the county is located reported its first confirmed case of COVID-19 and up to May 31, 2020) with a mixed-effects negative binomial model while distinguishing COVID-19 deaths in nursing homes from total COVID-19 deaths and accounting for social distancing– and COVID-19–related factors (including the period between the report of the first confirmed case of COVID-19 and May 31, 2020; population density; social vulnerability; and hospital resource availability). Results from the mixed-effects negative binomial model were then used to generate marginal effects at the mean, which helped separate the influence of social distancing on COVID-19 deaths from other covariates while calculating COVID-19 deaths per 100,000 people.ResultsWe observed that a 1% increase in average mobile phone usage outside of home between March and May 2020 led to a significant increase in COVID-19 mortality by a factor of 1.18 (P<.001), while every 1% increase in the average proportion of mobile phone usage outside of home in February 2020 was found to significantly decrease COVID-19 mortality by a factor of 0.90 (P<.001).ConclusionsAs stay-at-home orders have been lifted in many US states, continued adherence to other social distancing measures, such as avoiding large gatherings and maintaining physical distance in public, are key to preventing additional COVID-19 deaths in counties across the country.     

The first months of the COVID-19 pandemic in Spain

BackgroundThe COVID-19 outbreak has led to an unprecedented crisis in Spain. After Italy, the spread of the virus was quick, and Spain became the second epicenter in Europe by number of cases and deceased. To tackle the outbreak and contain the spread, the Spanish authorities undertook exceptional measures based on a generalized lockdown by which the majority of the economic activity ceased for several weeks.ObjectivesThe goal of this paper is to examine the spread of COVID-19 in Spain from February to May 2020, as well as the public policies and technologies used to contain the evolution of the pandemic. In particular it aims to assess the effectivity of the policies applied within the different autonomous communities. Cases are presented until August as well as the main changes in containment and mitigation measures.MethodsData was collected from various official sources, including government reports, press releases and datasets provided by national and international level institutions.ResultsWe show that the main measure to contain the spread of the pandemic was a stringent confinement policy enforced through fines. It resulted in a substantial reduction in the mobility and the economic activity. At a regional level, the negative consequences of the crisis affected differently across regions.     

Responses to COVID-19 in five Latin American countries

BackgroundCOVID-19 reached Latin-American countries slightly later than European countries, around February/March, allowing some emergency preparedness response in countries characterized by low health system capacities and socioeconomic disparities.ObjectiveThis paper focuses on the first months of the pandemic in five Latin American countries: Brazil, Chile, Colombia, Ecuador and Peru. It analyses how the pre-pandemic context, and the government's responses to contain and mitigate the spread together with economic measures have affected the COVID-19 health outcomes.MethodsExtensive qualitative document analysis was conducted focused on publicly-available epidemiological data and federal and state/regional policy documents since the beginning of the pandemic.ResultsThe countries were quick to implement stringent COVID-19 measures and incrementally scaled up their health systems capacity, although tracing and tracking have been poor. All five countries have experienced a large number of cases and deaths due to COVID-19. The analysis on the excess deaths also shows that the impact in deaths is far higher than the official numbers reported to date for some countries.ConclusionDespite the introduction of stringent measures of containment and mitigation, and the scale up of health system capacities, pre-pandemic conditions that characterize these countries (high informal employment, and social inequalities) have undermined the effectiveness of the countries’ responses to the pandemic. The economic support measures put in place were found to be too timid for some countries and introduced too late in most of them. Additionally, the lack of a comprehensive strategy for testing and tracking has also contributed to the failure to contain the spread of the virus.     

Suicide reduction in Canada during the COVID-19 pandemic: lessons informing national prevention strategies for suicide reduction

Objective The objective of this research was to evaluate the impact of federal, public health and social support programs on national suicide rates in Canada.DesignCross-sectional study.SettingCanadian National Database (i.e., Statistics Canada) and Statista.ParticipantsPopulation-level data, and economic and consumer market data.Main Outcome MeasuresSuicide mortality data, population data and unemployment data were obtained from available statistical databases (e.g. Statistics Canada). We quantified suicide rate by dividing the total number of suicide deaths by the national population expressed as a rate per 100,000 population.ResultsOverall suicide mortality rate decreased in Canada from 10.82 deaths per 100,000 in the March 2019 - February 2020 period to 7.34 per 100,000 (i.e. absolute difference of 1300 deaths) in the March 2020 - February 2021 period. The overall Canadian unemployment rate changed from an average monthly rate of 5.7% in 2019 to 9.5% in 2020.Conclusion Our results indicate that for the first post-pandemic interval evaluated (i.e., March 2020 - February 2021), suicide rates in Canada decreased against a background of extraordinary public health measures intended to mitigate community spread of COVID-19. An externality of public health measures was a significant rise in national unemployment rates in population measures of distress. Our results suggest that government interventions that broadly aim to reduce measures of insecurity (i.e., economic, housing, health), and timely psychiatric services, should be prioritised as part of a national suicide reduction strategy, not only during but after termination of the COVID-19 pandemic.     

The COVID-19 pandemic in Italy: Policy and technology impact on health and non-health outcomes

BackgroundItaly was the first Western country to experience a major coronavirus outbreak and consequently faced large-scale health and socio-economic challenges. The Italian government enforced a wide set of homogeneous interventions nationally, despite the differing incidences of the virus throughout the country.ObjectiveThe paper aims to analyse the policies implemented by the government and their impact on health and non-health outcomes considering both scaling-up and scaling-down interventions.MethodsTo categorise the policy interventions, we rely on the comparative and conceptual framework developed by Moy et al. (2020). We investigate the impact of policies on the daily reported number of deaths, case fatality rate, confirmation rate, intensive care unit saturation, and financial and job market indicators across the three major geographical areas of Italy (North, Centre, and South). Qualitative and quantitative data are gathered from mixed sources: Italian national and regional institutions, National Health Research and international organisations. Our analysis contributes to the literature on the COVID-19 pandemic by comparing policy interventions and their outcomes.ResultsOur findings suggest that the strictness and timing of containment and prevention measures played a prominent role in tackling the pandemic, both from a health and economic perspective. Technological interventions played a marginal role due to the inadequacy of protocols and the delay of their implementation.ConclusionsFuture government interventions should be informed by evidence-based decision making to balance, the benefits arising from the timing and stringency of the interventions against the adverse social and economic cost, both in the short and long term.     

Protective Behaviors and Secondary Harms Resulting From Nonpharmaceutical Interventions During the COVID-19 Epidemic in South Africa: Multisite,Prospective Longitudinal Study

BackgroundIn March 2020, South Africa implemented strict nonpharmaceutical interventions (NPIs) to contain the spread of COVID-19. Over the subsequent 5 months, NPI policies were eased in stages according to a national strategy. COVID-19 spread throughout the country heterogeneously; the disease reached rural areas by July and case numbers peaked from July to August. A second COVID-19 wave began in late 2020. Data on the impact of NPI policies on social and economic well-being and access to health care are limited.ObjectiveWe aimed to determine how rural residents in three South African provinces changed their behaviors during the first COVID-19 epidemic wave.MethodsThe South African Population Research Infrastructure Network nodes in the Mpumalanga (Agincourt), KwaZulu-Natal, (Africa Health Research Institute) and Limpopo (Dikgale-Mamabolo-Mothiba) provinces conducted up to 14 rounds of longitudinal telephone surveys among randomly sampled households from rural and periurban surveillance populations every 2-3 weeks. Interviews included questions on the following topics: COVID-19–related knowledge and behaviors, the health and economic impacts of NPIs, and mental health. We analyzed how responses varied based on NPI stringency and household sociodemographics.ResultsIn total, 5120 households completed 23,095 interviews between April and December 2020. Respondents’ self-reported satisfaction with their COVID-19–related knowledge and face mask use rapidly rose to 85% and 95%, respectively, by August. As selected NPIs were eased, the amount of travel increased, economic losses were reduced, and the prevalence of anxiety and depression symptoms fell. When the number of COVID-19 cases spiked at one node in July, the amount of travel dropped rapidly and the rate of missed daily medications doubled. Households where more adults received government-funded old-age pensions reported concerns about economic matters and medication access less often.ConclusionsSouth Africans complied with stringent, COVID-19–related NPIs despite the threat of substantial social, economic, and health repercussions. Government-supported social welfare programs appeared to buffer interruptions in income and health care access during local outbreaks. Epidemic control policies must be balanced against the broader well-being of people in resource-limited settings and designed with parallel support systems when such policies threaten peoples’ income and access to basic services.     

Effects of the COVID-19 pandemic in India: An analysis of policy and technological interventions

ObjectivesFollowing a surge in cases of coronavirus disease 2019 (COVID-19) in June 2020, India became the third-worst affected country worldwide. This study aims to analyse the underlying epidemiological situation in India and explain possible impacts of policy and technological changes.MethodsSecondary data were utilized, including recently published literature from government sources, the COVID-19 India website and local media reports. These data were analysed, with a focus on the impact of policy and technological interventions.ResultsThe spread of COVID-19 in India was initially characterized by fewer cases and lower case fatality rates compared with numbers in many developed countries, primarily due to a stringent lockdown and a demographic dividend. However, economic constraints forced a staggered lockdown exit strategy, resulting in a spike in COVID-19 cases. This factor, coupled with low spending on health as a percentage of gross domestic product (GDP), created mayhem because of inadequate numbers of hospital beds and ventilators and a lack of medical personnel, especially in the public health sector. Nevertheless, technological advances, supported by a strong research base, helped contain the damage resulting from the pandemic.ConclusionsFollowing nationwide lockdown, the Indian economy was hit hard by unemployment and a steep decline in growth. The early implementation of lockdown initially decreased the doubling rate of cases and allowed time to upscale critical medical infrastructure. Measures such as asymptomatic testing, public–private partnerships, and technological advances will be essential until a vaccine can be developed and deployed in India.Public interest summaryThe spread of COVID-19 in India was initially characterized by lower case numbers and fewer deaths compared with numbers in many developed countries. This was mainly due to a stringent lockdown and demographic factors. However, economic constraints forced a staggered lockdown exit strategy, resulting in a spike in COVID-19 cases in June 2020. Subsequently, India became the third-worst affected country worldwide. Low spending on health as a percentage of gross domestic product (GDP) meant there was a shortage of hospital beds and ventilators and a lack of medical personnel, especially in the public health sector. Nevertheless, technological advances, supported by a strong research base, helped contain the health and economic damage resulting from the pandemic. In the future, measures such as asymptomatic testing, public–private partnerships, and technological advances will be essential until a vaccine against COVID-19 can be developed and rolled-out in India.     

Excess Mortality During the COVID-19 Pandemic in Jordan: Secondary Data Analysis

BackgroundAll-cause mortality and estimates of excess deaths are commonly used in different countries to estimate the burden of COVID-19 and assess its direct and indirect effects.ObjectiveThis study aimed to analyze the excess mortality during the COVID-19 pandemic in Jordan in April-December 2020.MethodsOfficial data on deaths in Jordan for 2020 and previous years (2016-2019) were obtained from the Department of Civil Status. We contrasted mortality rates in 2020 with those in each year and the pooled period 2016-2020 using a standardized mortality ratio (SMR) measure. Expected deaths for 2020 were estimated by fitting the overdispersed Poisson generalized linear models to the monthly death counts for the period of 2016-2019.ResultsOverall, a 21% increase in standardized mortality (SMR 1.21, 95% CI 1.19-1.22) occurred in April-December 2020 compared with the April-December months in the pooled period 2016-2019. The SMR was more pronounced for men than for women (SMR 1.26, 95% CI 1.24-1.29 vs SMR 1.12, 95% CI 1.10-1.14), and it was statistically significant for both genders (P<.05). Using overdispersed Poisson generalized linear models, the number of expected deaths in April-December 2020 was 12,845 (7957 for women and 4888 for men). The total number of excess deaths during this period was estimated at 4583 (95% CI 4451-4716), with higher excess deaths in men (3112, 95% CI 3003-3221) than in women (1503, 95% CI 1427-1579). Almost 83.66% of excess deaths were attributed to COVID-19 in the Ministry of Health database. The vast majority of excess deaths occurred in people aged 60 years or older.ConclusionsThe reported COVID-19 death counts underestimated mortality attributable to COVID-19. Excess deaths could reflect the increased deaths secondary to the pandemic and its containment measures. The majority of excess deaths occurred among old age groups. It is, therefore, important to maintain essential services for the elderly during pandemics.     

Testing surge capacity—A Canadian COVID-19 experience,Ontario's surge capacity for the first wave

As of September 1st 2020, over 42 000 COVID-19 cases and 2 800 COVID-19-related deaths have been confirmed in Ontario, Canada. Testing enables quick identification of cases, which results in effective contact tracing and containment of virus spread. Faced with a lack of surge capacity in the public health laboratory system at the start, health officials implemented changes to testing and laboratory infrastructure to significantly expand testing capacity to include 1) the centralization of resources; and 2) the integration of private and independent labs into the COVID-19 testing program. With these changes, testing capacity has grown from approximately 4,000/day in March to 32,000/day by the end of August, 2020. Eligibility criteria for testing has expanded to increase sensitivity and include testing of asymptomatic individuals. Along with previous outbreaks, the COVID-19 pandemic has highlighted the need for integration of testing surge capacity in public health systems before outbreaks occur. This paper details the development and implementation of a COVID-19 testing program in Ontario from January 2020 to September 2020 during the first-wave of the pandemic. The goal of this analysis is to explore the historical precedence, present influences, and future implications of the program.     

COVID-19 mortality burden and comorbidity patterns among decedents with and without intellectual and developmental disability in the US

BackgroundWhile there is ample evidence of increased COVID-19 mortality risk among people with intellectual and developmental disability (IDD), research has not documented whether this higher risk resulted in increased COVID-19 mortality burden in the US or whether comorbidity patterns among COVID-19 deaths are similar or distinct for people with IDD.ObjectiveTo determine the differences in COVID-19 mortality burden between decedents with and without IDD during the first year of the pandemic.MethodsThis study uses 2020 US death certificate data to compare COVID-19 mortality burden and comorbidity patterns among decedents with and without IDD.ResultsCOVID-19 was the leading cause of death among decedents with IDD in 2020, compared with the 3rd leading cause among decedents without IDD. The proportion of deaths from COVID-19 was also higher for decedents with compared to without IDD. Comorbidities resulting from COVID-19 were similar among decedents with and without IDD, but there were some differences among reported pre-existing conditions, notably higher rates of hypothyroidism and seizures among decedents with IDD.ConclusionThe COVID-19 mortality burden was greater for people with than without IDD during the first year of the pandemic. The continued practice of postmortem diagnostic overshadowing prevents analyzing whether this difference continues through today. Action is needed by the Centers for Disease Control and Prevention to mitigate this data inequity. Out of an abundance of caution, medical providers should carefully monitor symptoms among COVID-19 patients with IDD diagnosed with hypothyroidism and/or seizures.     

Quantifying the Effect of Public Activity Intervention Policies on COVID-19 Pandemic Containment Using Epidemiologic Data From 145 Countries

ObjectivesMost countries have adopted public activity intervention policies to control the coronavirus disease 2019 (COVID-19) pandemic. Nevertheless, empirical evidence of the effectiveness of different interventions on the containment of the epidemic was inconsistent.MethodsWe retrieved time-series intervention policy data for 145 countries from the Oxford COVID-19 Government Response Tracker from December 31, 2019, to July 1, 2020, which included 8 containment and closure policies. We investigated the association of timeliness, stringency, and duration of intervention with cumulative infections per million population on July 1, 2020. We introduced a novel counterfactual estimator to estimate the effects of these interventions on COVID-19 time-varying reproduction number (R_t).ResultsThere is some evidence that earlier implementation, longer durations, and more strictness of intervention policies at the early but not middle stage were associated with reduced infections of COVID-19. The counterfactual model proved to have controlled for unobserved time-varying confounders and established a valid causal relationship between policy intervention and R_t reduction. The average intervention effect revealed that all interventions significantly decrease R_t after their implementation. R_t decreased by 30% (22%-41%) in 25 to 32 days after policy intervention. Among the 8 interventions, school closing, workplace closing, and public events cancellation demonstrated the strongest and most consistent evidence of associations.ConclusionsOur study provides more reliable evidence of the quantitative effects of policy interventions on the COVID-19 epidemic and suggested that stricter public activity interventions should be implemented at the early stage of the epidemic for improved containment.     

Reporting and Availability of COVID-19 Demographic Data by US Health Departments (April to October 2020): Observational Study

BackgroundThere is an urgent need for consistent collection of demographic data on COVID-19 morbidity and mortality and sharing it with the public in open and accessible ways. Due to the lack of consistency in data reporting during the initial spread of COVID-19, the Equitable Data Collection and Disclosure on COVID-19 Act was introduced into the Congress that mandates collection and reporting of demographic COVID-19 data on testing, treatments, and deaths by age, sex, race and ethnicity, primary language, socioeconomic status, disability, and county. To our knowledge, no studies have evaluated how COVID-19 demographic data have been collected before and after the introduction of this legislation.ObjectiveThis study aimed to evaluate differences in reporting and public availability of COVID-19 demographic data by US state health departments and Washington, District of Columbia (DC) before (pre-Act), immediately after (post-Act), and 6 months after (6-month follow-up) the introduction of the Equitable Data Collection and Disclosure on COVID-19 Act in the Congress on April 21, 2020.MethodsWe reviewed health department websites of all 50 US states and Washington, DC (N=51). We evaluated how each state reported age, sex, and race and ethnicity data for all confirmed COVID-19 cases and deaths and how they made this data available (ie, charts and tables only or combined with dashboards and machine-actionable downloadable formats) at the three timepoints.ResultsWe found statistically significant increases in the number of health departments reporting age-specific data for COVID-19 cases (P=.045) and resulting deaths (P=.002), sex-specific data for COVID-19 deaths (P=.003), and race- and ethnicity-specific data for confirmed cases (P=.003) and deaths (P=.005) post-Act and at the 6-month follow-up (P<.05 for all). The largest increases were race and ethnicity state data for confirmed cases (pre-Act: 18/51, 35%; post-Act: 31/51, 61%; 6-month follow-up: 46/51, 90%) and deaths due to COVID-19 (pre-Act: 13/51, 25%; post-Act: 25/51, 49%; and 6-month follow-up: 39/51, 76%). Although more health departments reported race and ethnicity data based on federal requirements (P<.001), over half (29/51, 56.9%) still did not report all racial and ethnic groups as per the Office of Management and Budget guidelines (pre-Act: 5/51, 10%; post-Act: 21/51, 41%; and 6-month follow-up: 27/51, 53%). The number of health departments that made COVID-19 data available for download significantly increased from 7 to 23 (P<.001) from our initial data collection (April 2020) to the 6-month follow-up, (October 2020).ConclusionsAlthough the increased demand for disaggregation has improved public reporting of demographics across health departments, an urgent need persists for the introduced legislation to be passed by the Congress for the US states to consistently collect and make characteristics of COVID-19 cases, deaths, and vaccinations available in order to allocate resources to mitigate disease spread.     

Racial and ethnic disparities in COVID-19 vaccine uptake: A mediation framework

BackgroundPrevious research suggests that racial and ethnic minority groups especially Black Americans showed stronger COVID-19 vaccine hesitancy and resistance, which may result from a lack of trust toward the government and vaccine manufacturers, among other sociodemographic and health factors.ObjectivesThe current study explored potential social and economic, clinical, and psychological factors that may have mediated racial and ethnic disparities in COVID-19 vaccine uptake among US adults.MethodsA sample of 6078 US individuals was selected from a national longitudinal survey administered in 2020–2021. Baseline characteristics were collected in December 2020, and respondents were followed up to July 2021. Racial and ethnic disparities in time to vaccine initiation and completion (based on a 2-dose regimen) were first assessed with the Kaplan-Meier Curve and log-rank test, and then explored with the Cox proportional hazards model adjusting for potential time-varying mediators, such as education, income, marital status, chronic health conditions, trust in vaccine development and approval processes, and perceived risk of infection.ResultsPrior to mediator adjustment, Black and Hispanic Americans had slower vaccine initiation and completion than Asian Americans and Pacific Islanders and White Americans (p’s < 0.0001). After accounting for the mediators, there were no significant differences in vaccine initiation or completion between each minoritized group as compared to White Americans. Education, household income, marital status, chronic health conditions, trust, and perceived infection risk were potential mediators.ConclusionRacial and ethnic disparities in COVID-19 vaccine uptake were mediated through social and economic conditions, psychological influences, and chronic health conditions. To address the racial and ethnic inequity in vaccination, it is important to target the social, economic, and psychological forces behind it.     

United but divided: Policy responses and people’s perceptions in the EU during the COVID-19 outbreak

To understand the public sentiment toward the measures used by policymakers for COVID-19 containment, a survey among representative samples of the population in seven European countries was carried out in the first two weeks of April 2020. The study addressed people’s support for containment policies, worries about COVID-19 consequences, and trust in sources of information. Citizens were overall satisfied with their government’s response to the pandemic; however, the extent of approval differed across countries and policy measures. A north-south divide in public opinion was noticeable across the European states. It was particularly pronounced for intrusive policy measures, such as mobile data use for movement tracking, economic concerns, and trust in the information from the national government. Considerable differences in people’s attitudes were noticed within countries, especially across individual regions and age groups. The findings suggest that the epidemic acts as a stressor, causing health and economic anxieties even in households that were not directly affected by the virus. At the same time, the burden of stress was unequally distributed across regions and age groups. Based on the data collected, we draw lessons from the containment stage and identify several insights that can facilitate the design of lockdown exit strategies and future containment policies so that a high level of compliance can be expected.     

COVID-19: The need for an Australian economic pandemic response plan

ObjectivesPandemics pressure national governments to respond swiftly. Mitigation efforts created an imbalance between population health, capacity of the healthcare system and economic prosperity. Each pandemic arising from a new virus is unknown territory for policy makers, and there is considerable uncertainty of the appropriateness of responses and outcomes.MethodsA qualitative approach was used to review mixed sources of data including Australian reports, official government publications, and COVID-19 data to discern robust future responses. Publicly available epidemiological and economic data were utilised to provide insight into the impact of the pandemic on Australia's healthcare system and economy.ResultsPolicies implemented by the Australian Government to mitigate the spread of COVID-19 impacted the healthcare sector and economy. This paper incorporates lessons learned to inform optimal economic preparedness. The rationale for an economic response plan concomitant with the health pandemic plan is explored to guide Australian Government policy makers in ensuring holistic and robust solutions for future pandemics.ConclusionsIn future, an Australian Economic Pandemic Response Plan will aid in health and economic system preparedness, whilst a strong Australian economy and strategic planning will ensure resilience to future pandemics.     

Estimating and Characterizing COVID-19 Deaths,Puerto Rico,March–July 2020

ObjectivesUsing the Council of State and Territorial Epidemiologists (CSTE) classification guidelines, we characterized coronavirus disease 2019 (COVID-19)–associated confirmed and probable deaths in Puerto Rico during March–July 2020. We also estimated the total number of possible deaths due to COVID-19 in Puerto Rico during the same period.MethodsWe described data on COVID-19–associated mortality, in which the lower bound was the sum of confirmed and probable COVID-19 deaths and the upper bound was excess mortality, estimated as the difference between observed deaths and average expected deaths. We obtained data from the Puerto Rico Department of Health COVID-19 Mortality Surveillance System, the Centers for Disease Control and Prevention’s National Electronic Disease Surveillance System Base System, and the National Center for Health Statistics.ResultsDuring March–July 2020, 225 COVID-19–associated deaths were identified in Puerto Rico (119 confirmed deaths and 106 probable deaths). The median age of decedents was 73 (interquartile range, 59-83); 60 (26.7%) deaths occurred in the Metropolitana region, and 140 (62.2%) deaths occurred among men. Of the 225 decedents, 180 (83.6%) had been hospitalized and 93 (41.3%) had required mechanical ventilation. Influenza and pneumonia (48.0%), sepsis (28.9%), and respiratory failure (27.1%) were the most common conditions contributing to COVID-19 deaths based on death certificates. Based on excess mortality calculations, as many as 638 COVID-19–associated deaths could have occurred during the study period, up to 413 more COVID-19–associated deaths than originally reported.ConclusionsIncluding probable deaths per the CSTE guidelines and monitoring all-cause excess mortality can lead to a better estimation of COVID-19–associated deaths and serve as a model to enhance mortality surveillance in other US jurisdictions.     

Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data

BackgroundPrevious studies have shown that electronic cigarette (e-cigarette) users might be more vulnerable to COVID-19 infection and could develop more severe symptoms if they contract the disease owing to their impaired immune responses to viral infections. Social media platforms such as Twitter have been widely used by individuals worldwide to express their responses to the current COVID-19 pandemic.ObjectiveIn this study, we aimed to examine the longitudinal changes in the attitudes of Twitter users who used e-cigarettes toward the COVID-19 pandemic, as well as compare differences in attitudes between e-cigarette users and nonusers based on Twitter data.MethodsThe study dataset containing COVID-19–related Twitter posts (tweets) posted between March 5 and April 3, 2020, was collected using a Twitter streaming application programming interface with COVID-19–related keywords. Twitter users were classified into two groups: Ecig group, including users who did not have commercial accounts but posted e-cigarette–related tweets between May 2019 and August 2019, and non-Ecig group, including users who did not post any e-cigarette–related tweets. Sentiment analysis was performed to compare sentiment scores towards the COVID-19 pandemic between both groups and determine whether the sentiment expressed was positive, negative, or neutral. Topic modeling was performed to compare the main topics discussed between the groups.ResultsThe US COVID-19 dataset consisted of 4,500,248 COVID-19–related tweets collected from 187,399 unique Twitter users in the Ecig group and 11,479,773 COVID-19–related tweets collected from 2,511,659 unique Twitter users in the non-Ecig group. Sentiment analysis showed that Ecig group users had more negative sentiment scores than non-Ecig group users. Results from topic modeling indicated that Ecig group users had more concerns about deaths due to COVID-19, whereas non-Ecig group users cared more about the government’s responses to the COVID-19 pandemic.ConclusionsOur findings show that Twitter users who tweeted about e-cigarettes had more concerns about the COVID-19 pandemic. These findings can inform public health practitioners to use social media platforms such as Twitter for timely monitoring of public responses to the COVID-19 pandemic and educating and encouraging current e-cigarette users to quit vaping to minimize the risks associated with COVID-19.