Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 20 de 81
Filter
Add filters

Document Type
Year range
1.
BMC Infect Dis ; 21(1): 686, 2021 Jul 16.
Article in English | MEDLINE | ID: covidwho-1571742

ABSTRACT

BACKGROUND: Associations between community-level risk factors and COVID-19 incidence have been used to identify vulnerable subpopulations and target interventions, but the variability of these associations over time remains largely unknown. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020. METHODS: Using publicly available sociodemographic, occupational, environmental, and mobility datasets, we developed mixed-effect, adjusted Poisson regression models to depict associations between these variables and town-level COVID-19 case incidence data across five distinct time periods from March to October 2020. We examined town-level demographic variables, including population proportions by race, ethnicity, and age, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PM2.5), and institutional facilities. We calculated incidence rate ratios (IRR) associated with these predictors and compared these values across the multiple time periods to assess variability in the observed associations over time. RESULTS: Associations between key predictor variables and town-level incidence varied across the five time periods. We observed reductions over time in the association with percentage of Black residents (IRR = 1.12 [95%CI: 1.12-1.13]) in early spring, IRR = 1.01 [95%CI: 1.00-1.01] in early fall) and COVID-19 incidence. The association with number of long-term care facility beds per capita also decreased over time (IRR = 1.28 [95%CI: 1.26-1.31] in spring, IRR = 1.07 [95%CI: 1.05-1.09] in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidences of COVID-19 throughout the pandemic (e.g., IRR = 1.30 [95%CI: 1.27-1.33] in spring, IRR = 1.20 [95%CI: 1.17-1.22] in fall). Towns with higher proportions of Latinx residents also had sustained elevated incidence over time (IRR = 1.19 [95%CI: 1.18-1.21] in spring, IRR = 1.14 [95%CI: 1.13-1.15] in fall). CONCLUSIONS: Town-level COVID-19 risk factors varied with time in this study. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence may have decreased across the first 8 months of the pandemic, perhaps indicating greater success in risk mitigation in selected communities. Our approach can be used to evaluate effectiveness of public health interventions and target specific mitigation efforts on the community level.


Subject(s)
COVID-19/epidemiology , Occupations/statistics & numerical data , Social Environment , Transportation/statistics & numerical data , Adult , Aged , Aged, 80 and over , COVID-19/ethnology , Female , Health Status Disparities , Humans , Incidence , Income/statistics & numerical data , Male , Massachusetts/epidemiology , Middle Aged , Movement/physiology , Pandemics , Residence Characteristics/statistics & numerical data , Risk Factors , SARS-CoV-2/physiology , Socioeconomic Factors , Time Factors , Vulnerable Populations/ethnology , Vulnerable Populations/statistics & numerical data , Young Adult
2.
BMJ Glob Health ; 6(4)2021 04.
Article in English | MEDLINE | ID: covidwho-1476465

ABSTRACT

INTRODUCTION: Little evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in São Paulo state, Brazil, and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities. METHODS: We conducted a cross-sectional study using hospitalised severe acute respiratory infections notified from March to August 2020 in the Sistema de Monitoramento Inteligente de São Paulo database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple data sets for individual-level and spatiotemporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour and comorbidities. RESULTS: Throughout the study period, patients living in the 40% poorest areas were more likely to die when compared with patients living in the 5% wealthiest areas (OR: 1.60, 95% CI 1.48 to 1.74) and were more likely to be hospitalised between April and July 2020 (OR: 1.08, 95% CI 1.04 to 1.12). Black and Pardo individuals were more likely to be hospitalised when compared with White individuals (OR: 1.41, 95% CI 1.37 to 1.46; OR: 1.26, 95% CI 1.23 to 1.28, respectively), and were more likely to die (OR: 1.13, 95% CI 1.07 to 1.19; 1.07, 95% CI 1.04 to 1.10, respectively) between April and July 2020. Once hospitalised, patients treated in public hospitals were more likely to die than patients in private hospitals (OR: 1.40%, 95% CI 1.34% to 1.46%). Black individuals and those with low education attainment were more likely to have one or more comorbidities, respectively (OR: 1.29, 95% CI 1.19 to 1.39; 1.36, 95% CI 1.27 to 1.45). CONCLUSIONS: Low-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to quality healthcare, ability to self-isolate and the higher prevalence of comorbidities.


Subject(s)
COVID-19/ethnology , COVID-19/mortality , Hospital Mortality/ethnology , Pneumonia, Viral , Poverty Areas , Residence Characteristics/statistics & numerical data , Adult , Aged , Aged, 80 and over , Brazil/epidemiology , Cross-Sectional Studies , Female , Health Status Disparities , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , SARS-CoV-2 , Seroepidemiologic Studies , Socioeconomic Factors
3.
Lancet Glob Health ; 9(11): e1517-e1527, 2021 11.
Article in English | MEDLINE | ID: covidwho-1472216

ABSTRACT

BACKGROUND: Over 1 year since the first reported case, the true COVID-19 burden in Ethiopia remains unknown due to insufficient surveillance. We aimed to investigate the seroepidemiology of SARS-CoV-2 among front-line hospital workers and communities in Ethiopia. METHODS: We did a population-based, longitudinal cohort study at two tertiary teaching hospitals involving hospital workers, rural residents, and urban communities in Jimma and Addis Ababa. Hospital workers were recruited at both hospitals, and community participants were recruited by convenience sampling including urban metropolitan settings, urban and semi-urban settings, and rural communities. Participants were eligible if they were aged 18 years or older, had provided written informed consent, and were willing to provide blood samples by venepuncture. Only one participant per household was recruited. Serology was done with Elecsys anti-SARS-CoV-2 anti-nucleocapsid assay in three consecutive rounds, with a mean interval of 6 weeks between tests, to obtain seroprevalence and incidence estimates within the cohorts. FINDINGS: Between Aug 5, 2020, and April 10, 2021, we did three survey rounds with a total of 1104 hospital workers and 1229 community residents participating. SARS-CoV-2 seroprevalence among hospital workers increased strongly during the study period: in Addis Ababa, it increased from 10·9% (95% credible interval [CrI] 8·3-13·8) in August, 2020, to 53·7% (44·8-62·5) in February, 2021, with an incidence rate of 2223 per 100 000 person-weeks (95% CI 1785-2696); in Jimma Town, it increased from 30·8% (95% CrI 26·9-34·8) in November, 2020, to 56·1% (51·1-61·1) in February, 2021, with an incidence rate of 3810 per 100 000 person-weeks (95% CI 3149-4540). Among urban communities, an almost 40% increase in seroprevalence was observed in early 2021, with incidence rates of 1622 per 100 000 person-weeks (1004-2429) in Jimma Town and 4646 per 100 000 person-weeks (2797-7255) in Addis Ababa. Seroprevalence in rural communities increased from 18·0% (95% CrI 13·5-23·2) in November, 2020, to 31·0% (22·3-40·3) in March, 2021. INTERPRETATION: SARS-CoV-2 spread in Ethiopia has been highly dynamic among hospital worker and urban communities. We can speculate that the greatest wave of SARS-CoV-2 infections is currently evolving in rural Ethiopia, and thus requires focused attention regarding health-care burden and disease prevention. FUNDING: Bavarian State Ministry of Sciences, Research, and the Arts; Germany Ministry of Education and Research; EU Horizon 2020 programme; Deutsche Forschungsgemeinschaft; and Volkswagenstiftung.


Subject(s)
COVID-19/epidemiology , Personnel, Hospital/statistics & numerical data , Residence Characteristics/statistics & numerical data , Adult , Ethiopia/epidemiology , Female , Humans , Longitudinal Studies , Male , Middle Aged , Models, Statistical , Seroepidemiologic Studies , Young Adult
4.
Lancet Public Health ; 6(11): e805-e816, 2021 11.
Article in English | MEDLINE | ID: covidwho-1467001

ABSTRACT

BACKGROUND: High-resolution data for how mortality and longevity have changed in England, UK are scarce. We aimed to estimate trends from 2002 to 2019 in life expectancy and probabilities of death at different ages for all 6791 middle-layer super output areas (MSOAs) in England. METHODS: We performed a high-resolution spatiotemporal analysis of civil registration data from the UK Small Area Health Statistics Unit research database using de-identified data for all deaths in England from 2002 to 2019, with information on age, sex, and MSOA of residence, and population counts by age, sex, and MSOA. We used a Bayesian hierarchical model to obtain estimates of age-specific death rates by sharing information across age groups, MSOAs, and years. We used life table methods to calculate life expectancy at birth and probabilities of death in different ages by sex and MSOA. FINDINGS: In 2002-06 and 2006-10, all but a few (0-1%) MSOAs had a life expectancy increase for female and male sexes. In 2010-14, female life expectancy decreased in 351 (5·2%) of 6791 MSOAs. By 2014-19, the number of MSOAs with declining life expectancy was 1270 (18·7%) for women and 784 (11·5%) for men. The life expectancy increase from 2002 to 2019 was smaller in MSOAs where life expectancy had been lower in 2002 (mostly northern urban MSOAs), and larger in MSOAs where life expectancy had been higher in 2002 (mostly MSOAs in and around London). As a result of these trends, the gap between the first and 99th percentiles of MSOA life expectancy for women increased from 10·7 years (95% credible interval 10·4-10·9) in 2002 to reach 14·2 years (13·9-14·5) in 2019, and for men increased from 11·5 years (11·3-11·7) in 2002 to 13·6 years (13·4-13·9) in 2019. INTERPRETATION: In the decade before the COVID-19 pandemic, life expectancy declined in increasing numbers of communities in England. To ensure that this trend does not continue or worsen, there is a need for pro-equity economic and social policies, and greater investment in public health and health care throughout the entire country. FUNDING: Wellcome Trust, Imperial College London, Medical Research Council, Health Data Research UK, and National Institutes of Health Research.


Subject(s)
Life Expectancy/trends , Mortality/trends , Adolescent , Adult , Aged , Aged, 80 and over , Bayes Theorem , Child , Child, Preschool , England/epidemiology , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Registries , Residence Characteristics/statistics & numerical data , Risk Assessment , Spatio-Temporal Analysis , Young Adult
5.
Am J Public Health ; 111(8): 1443-1447, 2021 08.
Article in English | MEDLINE | ID: covidwho-1456160

ABSTRACT

To investigate how heat-health behaviors changed in summer 2020 compared with previous summers, our community-academic partnership conducted telephone surveys to collect data on cooling behaviors, safety concerns, and preferences for cooling alternatives for 101 participants living in Alabama. Participants indicating they would visit cooling centers declined from 23% in previous summers to 10% in summer 2020. The use of cooling centers and other public spaces may be less effective in reducing heat-related illness because of safety concerns amid the COVID-19 pandemic and police brutality.


Subject(s)
African Americans/statistics & numerical data , COVID-19/epidemiology , Health Behavior , Heat Stress Disorders/prevention & control , Hot Temperature , Residence Characteristics/statistics & numerical data , Alabama , COVID-19/psychology , Housing , Humans
6.
MMWR Morb Mortal Wkly Rep ; 70(35): 1220-1222, 2021 Sep 03.
Article in English | MEDLINE | ID: covidwho-1414162

ABSTRACT

In-person instruction during the COVID-19 pandemic concerns educators, unions, parents, students, and public health officials as they plan to create a safe and supportive learning environment for children and adolescents (1). Los Angeles County (LAC), the nation's largest county, has an estimated population of 10 million, including 1.7 million children and adolescents aged 5-17 years (2). LAC school districts moved to remote learning for some or all students in transitional kindergarten* through grade 12 (TK-12) schools during the 2020-21 school year (3). Schools that provided in-person instruction were required by LAC Health Officer orders to implement prevention measures such as symptom screening, masking, physical distancing, cohorting, and contact tracing (4). This analysis compares COVID-19 case rates in TK-12 schools among students and staff members who attended school in person with LAC case rates during September 2020-March 2021.


Subject(s)
COVID-19/epidemiology , Residence Characteristics/statistics & numerical data , Schools/statistics & numerical data , Adolescent , Adult , Aged , Child , Child, Preschool , Female , Humans , Los Angeles/epidemiology , Male , Middle Aged , Young Adult
7.
JAMA Netw Open ; 4(9): e2122260, 2021 09 01.
Article in English | MEDLINE | ID: covidwho-1391521

ABSTRACT

Importance: Domestic violence (DV) has become a growing public health concern during the COVID-19 pandemic because individuals may be sheltering in place with abusers and facing mounting economic and health-related stresses. Objective: To analyze associations of the 2020 COVID-19 stay-at-home (SH) order with DV police reporting and resource availability, including differences by community area racial/ethnic composition. Design, Setting, and Participants: This longitudinal cohort study assessed DV police reports (January-June 2020) obtained from the Chicago, Illinois, Police Department and DV resource availability (March and August 2020) obtained from the NowPow community resource database, both for 77 community areas in Chicago. Data were analyzed July through December 2020. Exposures: The COVID-19 SH order effective March 21, 2020. Main Outcomes and Measures: Monthly rates of DV police reports and DV resource availability per 100 000 persons. Results: Of 77 community areas in Chicago, 28 (36.4%) were majority Black, 19 (24.7%) majority Hispanic/Latinx, 18 (23.4%) majority White, and 12 (15.6%) a different or no majority race/ethnicity, representing an estimated population of 2 718 555 individuals. For each community area, the SH order was associated with a decrease in the rate of DV police reports by 21.8 (95% CI, -30.48 to -13.07) crimes per 100 000 persons per month relative to the same months in 2019. Compared with White majority community areas, Black majority areas had a decrease in the rate of DV police reports by 40.8 (95% CI, -62.93 to -18.75) crimes per 100 000 persons per month relative to the same months in 2019. The SH order was also associated with a decrease in DV resource availability at a rate of 5.1 (95% CI, -7.55 to -2.67) resources per 100 000 persons, with the largest decreases for mental health (-4.3 [95% CI, -5.97 to -2.66] resources per 100 000 persons) and personal safety (-2.4 [95% CI, -4.40 to -0.41] resources per 100 000 persons). The Black majority south side of Chicago had a larger decrease in resource availability (-6.7 [95% CI, -12.92 to -0.46] resources per 100 000 persons) than the White majority north side. Conclusions and Relevance: In this longitudinal cohort study, the rate of DV police reports decreased after the SH order was implemented in Chicago. This decrease was largely observed in Black majority communities, whereas there was no significant change in White majority communities. These findings may reflect decreased DV incidence but may also reflect an exacerbation of underreporting. In addition, DV resource availability decreased disproportionately on the predominantly Black south side of Chicago.


Subject(s)
Domestic Violence/statistics & numerical data , Police/statistics & numerical data , Adult , COVID-19/epidemiology , Chicago/epidemiology , Communicable Disease Control/legislation & jurisprudence , Domestic Violence/ethnology , Female , Humans , Longitudinal Studies , Male , Middle Aged , Pandemics , Residence Characteristics/statistics & numerical data , SARS-CoV-2
8.
BMC Med ; 18(1): 160, 2020 05 29.
Article in English | MEDLINE | ID: covidwho-1388759

ABSTRACT

BACKGROUND: Understanding of the role of ethnicity and socioeconomic position in the risk of developing SARS-CoV-2 infection is limited. We investigated this in the UK Biobank study. METHODS: The UK Biobank study recruited 40-70-year-olds in 2006-2010 from the general population, collecting information about self-defined ethnicity and socioeconomic variables (including area-level socioeconomic deprivation and educational attainment). SARS-CoV-2 test results from Public Health England were linked to baseline UK Biobank data. Poisson regression with robust standard errors was used to assess risk ratios (RRs) between the exposures and dichotomous variables for being tested, having a positive test and testing positive in hospital. We also investigated whether ethnicity and socioeconomic position were associated with having a positive test amongst those tested. We adjusted for covariates including age, sex, social variables (including healthcare work and household size), behavioural risk factors and baseline health. RESULTS: Amongst 392,116 participants in England, 2658 had been tested for SARS-CoV-2 and 948 tested positive (726 in hospital) between 16 March and 3 May 2020. Black and south Asian groups were more likely to test positive (RR 3.35 (95% CI 2.48-4.53) and RR 2.42 (95% CI 1.75-3.36) respectively), with Pakistani ethnicity at highest risk within the south Asian group (RR 3.24 (95% CI 1.73-6.07)). These ethnic groups were more likely to be hospital cases compared to the white British. Adjustment for baseline health and behavioural risk factors led to little change, with only modest attenuation when accounting for socioeconomic variables. Socioeconomic deprivation and having no qualifications were consistently associated with a higher risk of confirmed infection (RR 2.19 for most deprived quartile vs least (95% CI 1.80-2.66) and RR 2.00 for no qualifications vs degree (95% CI 1.66-2.42)). CONCLUSIONS: Some minority ethnic groups have a higher risk of confirmed SARS-CoV-2 infection in the UK Biobank study, which was not accounted for by differences in socioeconomic conditions, baseline self-reported health or behavioural risk factors. An urgent response to addressing these elevated risks is required.


Subject(s)
Betacoronavirus , Biological Specimen Banks , Coronavirus Infections/epidemiology , Health Status Disparities , Pneumonia, Viral/epidemiology , SARS Virus , Severe Acute Respiratory Syndrome/epidemiology , Adult , COVID-19 , Female , Humans , Male , Middle Aged , Pandemics , Residence Characteristics/statistics & numerical data , Risk Factors , SARS-CoV-2 , Self Report , United Kingdom/epidemiology
9.
JMIR Public Health Surveill ; 7(8): e26604, 2021 08 26.
Article in English | MEDLINE | ID: covidwho-1374196

ABSTRACT

BACKGROUND: Although it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health. OBJECTIVE: This study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning. METHODS: We developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19-related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health. RESULTS: Using 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19-related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; P<.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (r2=0.87). CONCLUSIONS: The COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19-related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.


Subject(s)
COVID-19/epidemiology , Cost of Illness , Residence Characteristics/statistics & numerical data , COVID-19/mortality , Cities/epidemiology , Health Status Indicators , Humans , New York City/epidemiology , Risk Assessment/methods , Risk Factors , Social Determinants of Health , United States/epidemiology , Unsupervised Machine Learning
10.
Sci Rep ; 11(1): 16533, 2021 08 16.
Article in English | MEDLINE | ID: covidwho-1360207

ABSTRACT

The COVID-19 pandemic significantly modified our urban territories. One of the most strongly affected parameters was outdoor noise, caused by traffic and human activity in general, all of which were forced to stop during the spring of 2020. This caused an indubitable noise reduction both inside and outside the home. This study investigates how people reacted to this new unexpected, unwanted and unpredictable situation. Using field measurements, it was possible to demonstrate how the outdoor sound pressure level clearly decreased. Furthermore, by means of an international survey, it was discovered that people had positive reaction to the lower noise level. This preference was generally not related to home typology or location in the city, but rather to a generalized wish to live in a quieter urban environment.


Subject(s)
COVID-19/prevention & control , Communicable Disease Control/standards , Environmental Monitoring/statistics & numerical data , Noise , Personal Satisfaction , Adult , COVID-19/epidemiology , COVID-19/transmission , Cities/statistics & numerical data , Female , Humans , Italy/epidemiology , Male , Middle Aged , Pandemics/prevention & control , Residence Characteristics/statistics & numerical data , Surveys and Questionnaires/statistics & numerical data , Urban Health/statistics & numerical data
11.
Med Care ; 59(10): 888-892, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1337299

ABSTRACT

BACKGROUND: Despite many studies reporting disparities in coronavirus disease-2019 (COVID-19) incidence and outcomes in Black and Hispanic/Latino populations, mechanisms are not fully understood to inform mitigation strategies. OBJECTIVE: The aim was to test whether neighborhood factors beyond individual patient-level factors are associated with in-hospital mortality from COVID-19. We hypothesized that the Area Deprivation Index (ADI), a neighborhood census-block-level composite measure, was associated with COVID-19 mortality independently of race, ethnicity, and other patient factors. RESEARCH DESIGN: Multicenter retrospective cohort study examining COVID-19 in-hospital mortality. SUBJECTS: Inclusion required hospitalization with positive SARS-CoV-2 test or COVID-19 diagnosis at three large Midwestern academic centers. MEASURES: The primary study outcome was COVID-19 in-hospital mortality. Patient-level predictors included age, sex, race, insurance, body mass index, comorbidities, and ventilation. Neighborhoods were examined through the national ADI neighborhood deprivation rank comparing in-hospital mortality across ADI quintiles. Analyses used multivariable logistic regression with fixed site effects. RESULTS: Among 5999 COVID-19 patients median age was 61 (interquartile range: 44-73), 48% were male, 30% Black, and 10.8% died. Among patients who died, 32% lived in the most disadvantaged quintile while 11% lived in the least disadvantaged quintile; 52% of Black, 24% of Hispanic/Latino, and 8.5% of White patients lived in the most disadvantaged neighborhoods.Living in the most disadvantaged neighborhood quintile predicted higher mortality (adjusted odds ratio: 1.74; 95% confidence interval: 1.13-2.67) independent of race. Age, male sex, Medicare coverage, and ventilation also predicted mortality. CONCLUSIONS: Neighborhood disadvantage independently predicted in-hospital COVID-19 mortality. Findings support calls to consider neighborhood measures for vaccine distribution and policies to mitigate disparities.


Subject(s)
COVID-19/epidemiology , Hospital Mortality/trends , Residence Characteristics/statistics & numerical data , Age Factors , COVID-19 Testing , Comorbidity , Humans , Insurance, Health/statistics & numerical data , Male , Middle Aged , Midwestern United States/epidemiology , Poverty/statistics & numerical data , Retrospective Studies , Sex Factors
12.
Am J Epidemiol ; 190(8): 1510-1518, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1337248

ABSTRACT

Preliminary evidence points to higher morbidity and mortality from coronavirus disease 2019 (COVID-19) in certain racial and ethnic groups, but population-based studies using microlevel data are lacking so far. We used register-based cohort data including all adults living in Stockholm, Sweden, between January 31, 2020 (the date of the first confirmed case of COVID-19) and May 4, 2020 (n = 1,778,670) to conduct Poisson regression analyses with region/country of birth as the exposure and underlying cause of COVID-19 death as the outcome, estimating relative risks and 95% confidence intervals. Migrants from Middle Eastern countries (relative risk (RR) = 3.2, 95% confidence interval (CI): 2.6, 3.8), Africa (RR = 3.0, 95% CI: 2.2, 4.3), and non-Sweden Nordic countries (RR = 1.5, 95% CI: 1.2, 1.8) had higher mortality from COVID-19 than persons born in Sweden. Especially high mortality risks from COVID-19 were found among persons born in Somalia, Lebanon, Syria, Turkey, Iran, and Iraq. Socioeconomic status, number of working-age household members, and neighborhood population density attenuated up to half of the increased COVID-19 mortality risks among the foreign-born. Disadvantaged socioeconomic and living conditions may increase infection rates in migrants and contribute to their higher risk of COVID-19 mortality.


Subject(s)
COVID-19/ethnology , COVID-19/mortality , Health Status Disparities , Transients and Migrants/statistics & numerical data , Adult , Cohort Studies , Employment/statistics & numerical data , Female , Humans , Male , Middle Aged , Middle East/ethnology , Registries , Residence Characteristics/statistics & numerical data , Risk Factors , SARS-CoV-2 , Social Class , Sweden/epidemiology
13.
PLoS One ; 16(7): e0255171, 2021.
Article in English | MEDLINE | ID: covidwho-1332000

ABSTRACT

OBJECTIVES: There is limited evidence on how clinical outcomes differ by socioeconomic conditions among patients with coronavirus disease 2019 (COVID-19). Most studies focused on COVID-19 patients from a single hospital. Results based on patients from multiple health systems have not been reported. The objective of this study is to examine variation in patient characteristics, outcomes, and healthcare utilization by neighborhood social conditions among COVID-19 patients. METHODS: We extracted electronic health record data for 23,300 community dwelling COVID-19 patients in New York City between March 1st and June 11th, 2020 from all care settings, including hospitalized patients, patients who presented to the emergency department without hospitalization, and patients with ambulatory visits only. Zip Code Tabulation Area-level social conditions were measured by the Social Deprivation Index (SDI). Using logistic regressions and Cox proportional-hazards models, we examined the association between SDI quintiles and hospitalization and death, controlling for race, ethnicity, and other patient characteristics. RESULTS: Among 23,300 community dwelling COVID-19 patients, 60.7% were from neighborhoods with disadvantaged social conditions (top SDI quintile), although these neighborhoods only account for 34% of overall population. Compared to socially advantaged patients (bottom SDI quintile), socially disadvantaged patients (top SDI quintile) were older (median age 55 vs. 53, P<0.001), more likely to be black (23.1% vs. 6.4%, P<0.001) or Hispanic (25.4% vs. 8.5%, P<0.001), and more likely to have chronic conditions (e.g., diabetes: 21.9% vs. 10.5%, P<0.001). Logistic and Cox regressions showed that patients with disadvantaged social conditions had higher risk for hospitalization (odds ratio: 1.68; 95% confidence interval [CI]: [1.46, 1.94]; P<0.001) and mortality (hazard ratio: 1.91; 95% CI: [1.35, 2.70]; P<0.001), adjusting for other patient characteristics. CONCLUSION: Substantial socioeconomic disparities in health outcomes exist among COVID-19 patients in NYC. Disadvantaged neighborhood social conditions were associated with higher risk for hospitalization, severity of disease, and death.


Subject(s)
COVID-19/pathology , Patient Acceptance of Health Care/statistics & numerical data , Aged , COVID-19/virology , Female , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , New York City , Residence Characteristics/statistics & numerical data , Risk Factors , Socioeconomic Factors
14.
Epidemiol Infect ; 149: e153, 2021 06 24.
Article in English | MEDLINE | ID: covidwho-1294411

ABSTRACT

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic is still ongoing along with the global vaccination efforts against it. Here, we aimed to understand the longevity and strength of anti-SARS-CoV-2 IgG responses in a small community (n = 283) six months following local SARS-COV-2 outbreak in March 2020. Three serological assays were compared and neutralisation capability was also determined. Overall 16.6% (47/283) of the participants were seropositive and 89.4% (42/47) of the IgG positives had neutralising antibodies. Most of the symptomatic individuals confirmed as polymerase chain reaction (PCR) positive during the outbreak were seropositive (30/32, 93.8%) and 33.3% of the individuals who quarantined with a PCR confirmed patient had antibodies. Serological assays comparison revealed that Architect (Abbott) targeting the N protein LIASON® (DiaSorin) targeting the S protein and enzyme-linked immunosorbent assay (ELISA) targeting receptor binding domain detected 9.5% (27/283), 17.3% (49/283) and 17% (48/283), respectively, as IgG positives. The latter two assays highly agreed (kappa = 0.89) between each other. In addition, 95%, (19/20, by ELISA) and 90.9% (20/22, with LIASON) and only 71.4% (15/21, by Architect) of individuals that were seropositive in May 2020 were found positive also in September. The unexpected low rate of overall immunity indicates the absence of un-noticed, asymptomatic infections. Lack of overall high correlation between the assays is attributed mainly to target-mediated antibody responses and suggests that using a single serological assay may be misleading.


Subject(s)
Antibodies, Viral/immunology , COVID-19/epidemiology , Disease Outbreaks , Immunoglobulin G/immunology , SARS-CoV-2/immunology , Adolescent , Adult , Age Factors , Antibodies, Neutralizing/immunology , COVID-19/immunology , Child , Child, Preschool , Disease Outbreaks/statistics & numerical data , Enzyme-Linked Immunosorbent Assay , Female , Humans , Immunity, Herd/immunology , Infant , Infant, Newborn , Israel/epidemiology , Male , Middle Aged , Polymerase Chain Reaction , Residence Characteristics/statistics & numerical data , Seroepidemiologic Studies , Time Factors , Young Adult
15.
JAMA Netw Open ; 4(6): e2113818, 2021 06 01.
Article in English | MEDLINE | ID: covidwho-1274645

ABSTRACT

Importance: Limited information on the transmission and dynamics of SARS-CoV-2 at the city scale is available. Objective: To describe the local spread of SARS-CoV-2 in Valencia, Spain. Design, Setting, and Participants: This single-center epidemiological cohort study of patients with SARS-CoV-2 was performed at University General Hospital in Valencia (population in the hospital catchment area, 364 000), a tertiary hospital. The study included all consecutive patients with COVID-19 isolated at home from the start of the COVID-19 pandemic on February 19 until August 31, 2020. Exposures: Cases of SARS-CoV-2 infection confirmed by the presence of IgM antibodies or a positive polymerase chain reaction test result on a nasopharyngeal swab were included. Cases in which patients with negative laboratory results met diagnostic and clinical criteria were also included. Main Outcomes and Measures: The primary outcome was the characterization of dissemination patterns and connections among the 20 neighborhoods of Valencia during the outbreak. To recreate the transmission network, the inbound and outbound connections were studied for each region, and the relative risk of infection was estimated. Results: In total, 2646 patients were included in the analysis. The mean (SD) age was 45.3 (22.5) years; 1203 (46%) were male and 1442 (54%) were female (data were missing for 1); and the overall mortality was 3.7%. The incidence of SARS-CoV-2 cases was higher in neighborhoods with higher household income (ß2 [for mean income per household] = 0.197; 95% CI, 0.057-0.351) and greater population density (ß1 [inhabitants per km2] = 0.228; 95% CI, 0.085-0.387). Correlations with meteorological variables were not statistically significant. Neighborhood 3, where the hospital and testing facility were located, had the most outbound connections (14). A large residential complex close to the city (neighborhood 20) had the fewest connections (0 outbound and 2 inbound). Five geographically unconnected neighborhoods were of strategic importance in disrupting the transmission network. Conclusions and Relevance: This study of local dissemination of SARS-COV-2 revealed nonevident transmission patterns between geographically unconnected areas. The results suggest that tailor-made containment measures could reduce transmission and that hospitals, including testing facilities, play a crucial role in disease transmission. Consequently, the local dynamics of SARS-CoV-2 spread might inform the strategic lockdown of specific neighborhoods to stop the contagion and avoid a citywide lockdown.


Subject(s)
COVID-19/epidemiology , Catchment Area, Health/statistics & numerical data , Disease Outbreaks/statistics & numerical data , Disease Transmission, Infectious/statistics & numerical data , Residence Characteristics/statistics & numerical data , Adult , Aged , Aged, 80 and over , COVID-19/transmission , Cohort Studies , Female , Geography , Humans , Incidence , Male , Middle Aged , Risk Factors , SARS-CoV-2 , Spain/epidemiology
16.
PLoS One ; 16(6): e0252383, 2021.
Article in English | MEDLINE | ID: covidwho-1264213

ABSTRACT

Estimation of disease prevalence at sub-city neighborhood scale allows early and targeted interventions that can help save lives and reduce public health burdens. However, the cost-prohibitive nature of highly localized data collection and sparsity of representative signals, has made it challenging to identify neighborhood scale prevalence of disease. To overcome this challenge, we utilize alternative data sources, which are both less sparse and representative of localized disease prevalence: using query data from a large commercial search engine, we identify the prevalence of respiratory illness in the United States, localized to census tract geographic granularity. Focusing on asthma and Chronic Obstructive Pulmonary Disease (COPD), we construct a set of features based on searches for symptoms, medications, and disease-related information, and use these to identify illness rates in more than 23 thousand tracts in 500 cities across the United States. Out of sample model estimates from search data alone correlate with ground truth illness rate estimates from the CDC at 0.69 to 0.76, with simple additions to these models raising those correlations to as high as 0.84. We then show that in practice search query data can be added to other relevant data such as census or land cover data to boost results, with models that incorporate all data sources correlating with ground truth data at 0.91 for asthma and 0.88 for COPD.


Subject(s)
Asthma/epidemiology , Information Seeking Behavior , Pulmonary Disease, Chronic Obstructive/epidemiology , Residence Characteristics/statistics & numerical data , Censuses , Chronic Disease/epidemiology , Humans , Models, Statistical , Prevalence , United States/epidemiology
17.
Econ Hum Biol ; 42: 101018, 2021 08.
Article in English | MEDLINE | ID: covidwho-1240311

ABSTRACT

The first wave of Covid-19 pandemic had a geographically heterogeneous impact even within the most severely hit regions. Exploiting a triple-differences methodology, we find that in Italy Covid-19 hit relatively harder in peripheral areas: the excess mortality in peripheral areas was almost double that of central ones in March 2020 (1.2 additional deaths every 1000 inhabitants). We leverage a rich dataset on Italian municipalities to explore mechanisms behind this gradient. We first show that socio-demographic and economic features at municipal level are highly collinear, making it hard to identify single-variable causal relationships. Using Principal Components Analysis we model excess mortality and show that areas with higher excess mortality have lower income, lower education, larger households, lower trade and higher industrial employments, and older population. Our findings highlight a strong centre-periphery gradient in the harshness of Covid-19, which we believe is also highly relevant from a policy-making standpoint.


Subject(s)
COVID-19/epidemiology , Residence Characteristics/statistics & numerical data , COVID-19/mortality , Cities , Humans , Italy/epidemiology , Male , Pandemics , SARS-CoV-2 , Socioeconomic Factors
19.
Health Secur ; 19(S1): S27-S33, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1217797

ABSTRACT

More than a century of research has shown that sociodemographic conditions affect infectious disease transmission. In the late spring and early summer of 2020, reports of the effects of sociodemographic variables on the spread of COVID-19 were used in the media with minimal scientific proof attached. With new cases of COVID-19 surging in the United States at that time, it became essential to better understand how the spread of COVID-19 was varying across all segments of the population. We used hierarchical exponential growth curve modeling techniques to examine whether community socioeconomic characteristics uniquely influence the incidence of reported COVID-19 cases in the urban built environment. We show that as of July 19, 2020, confirmed coronavirus infections in New York City and surrounding areas-one of the early epicenters of the COVID-19 pandemic in the United States-were concentrated along demographic and socioeconomic lines. Furthermore, our data provides evidence that after the onset of the pandemic, timely enactment of physical distancing measures such as school closures was essential to limiting the extent of the coronavirus spread in the population. We conclude that in a pandemic, public health authorities must impose physical distancing measures early on as well as consider community-level factors that associate with a greater risk of viral transmission.


Subject(s)
COVID-19/epidemiology , Residence Characteristics/statistics & numerical data , Urban Population/statistics & numerical data , COVID-19/diagnosis , Cross-Sectional Studies , Humans , Incidence , New York City/epidemiology , Public Health , Risk Factors , Socioeconomic Factors , Spatial Analysis
20.
Scand J Public Health ; 49(1): 41-47, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1207573

ABSTRACT

Aims: For everyone with a positive test for SARS-CoV-2 in Norway, we studied whether age, sex, comorbidity, continent of birth and nursing home residency were risk factors for hospitalization, invasive mechanical ventilation treatment and death. Methods: Data for everyone who had tested positive for SARS-CoV-2 in Norway by end of June 2020 (N = 8569) were linked at the individual level to hospitalization, receipt of invasive mechanical ventilation treatment and death measured to end of July 2020. Underlying comorbidity was proxied by hospital-based in- or outpatient treatment during the two months before the SARS-CoV-2 test. Multivariable generalized linear models were used to assess risk ratios (RRs). Results: Risk of hospitalization was particularly high for elderly (for those aged 90 and above: RR 9.5; 95% confidence interval (CI) 7.1-12.7; comparison group aged below 50), Norwegian residents born in Asia, Africa or Latin-America (RR 2.1; 95% CI 1.9-2.4; comparison group born in Norway), patients with underlying comorbidity (RR 1.6; 95% CI 1.4-1.8) and men (RR 1.3; 95% CI 1.2-1.5). Men and residents born in Africa, Asia and Latin-America were also at higher risk of receiving ventilation treatment and dying, but the mortality risk was especially high for the elderly (for those aged 90 and above: RR 607.9; 95% CI 145.5-2540.1; comparison group aged below 50) and residents in nursing homes (RR 4.2; 95% CI 3.1-5.7). Conclusions: High age was the most important predictor of severe disease and death if infected with SARS-CoV-2, and nursing home residents were at particularly high risk of death.


Subject(s)
COVID-19 , Hospitalization/statistics & numerical data , Respiration, Artificial/statistics & numerical data , SARS-CoV-2/isolation & purification , Aged , Aged, 80 and over , COVID-19/diagnosis , COVID-19/mortality , COVID-19/therapy , COVID-19 Testing , Comorbidity , Female , Humans , Male , Middle Aged , Norway/epidemiology , Nursing Homes/statistics & numerical data , Prospective Studies , Residence Characteristics/statistics & numerical data , Risk Factors
SELECTION OF CITATIONS
SEARCH DETAIL
...