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1.
J Gerontol B Psychol Sci Soc Sci ; 76(7): e268-e274, 2021 08 13.
Article in English | MEDLINE | ID: covidwho-1526159

ABSTRACT

OBJECTIVES: Mexico is among the countries in Latin America hit hardest by coronavirus disease 2019 (COVID-19). A large proportion of older adults in Mexico have high prevalence of multimorbidity and live in poverty with limited access to health care services. These statistics are even higher among adults living in rural areas, which suggest that older adults in rural communities may be more susceptible to COVID-19. The objectives of the article were to compare clinical and demographic characteristics for people diagnosed with COVID-19 by age group, and to describe cases and mortality in rural and urban communities. METHOD: We linked publicly available data from the Mexican Ministry of Health and the Census. Municipalities were classified based on population as rural (<2,500), semirural (≥2,500 and <15,000), semiurban (≥15,000 and <100,000), and urban (≥100,000). Zero-inflated negative binomial models were performed to calculate the total number of COVID-19 cases, and deaths per 1,000,000 persons using the population of each municipality as a denominator. RESULTS: Older adults were more likely to be hospitalized and reported severe cases, with higher mortality rates. In addition, rural municipalities reported a higher number of COVID-19 cases and mortality related to COVID-19 per million than urban municipalities. The adjusted absolute difference in COVID-19 cases was 912.7 per million (95% confidence interval [CI]: 79.0-1746.4) and mortality related to COVID-19 was 390.6 per million (95% CI: 204.5-576.7). DISCUSSION: Urgent policy efforts are needed to mandate the use of face masks, encourage handwashing, and improve specialty care for Mexicans in rural areas.


Subject(s)
COVID-19/epidemiology , Health Services Accessibility/statistics & numerical data , Health Status Disparities , Poverty/statistics & numerical data , Rural Population/statistics & numerical data , Urban Population/statistics & numerical data , Age Factors , Aged , COVID-19/therapy , Female , Humans , Male , Mexico/epidemiology , Rural Health Services/organization & administration , Urban Health Services/organization & administration
2.
J Infect Dev Ctries ; 15(10): 1388-1395, 2021 10 31.
Article in English | MEDLINE | ID: covidwho-1518654

ABSTRACT

INTRODUCTION: Immunization, as a process of fighting against the COVID-19, has gained important research appeal, but very limited endeavor has been paid for vaccine behavioral studies in underdeveloped and developing countries. This study explores the vaccine demand, hesitancy, and nationalism as well as vaccine acceptance and domestic vaccine preference among young adults in Bangladesh. METHODOLOGY: This quantitative study followed the snowball sampling technique and collected responses from 1,018 individuals from various social media platforms. The analysis covered both descriptive and inferential statistics including chi-square, F-statistic, and logistic regression. RESULTS: The findings of the fully-adjusted regression model suggest that the individuals who had more vaccine demand were 3.29 times (95% confidence interval = 2.39-4.54; p < 0.001) higher to accept vaccine compared to those who had no vaccine demand. Conversely, vaccine hesitancy was negatively associated with vaccine acceptance. Here, the odds ratio was found 0.70 (95% confidence interval = 0.62-0.80; p < 0.001), which means that those who had higher vaccine hesitancy were about 30% less likely to accept vaccines than those who had no hesitancy. In addition, the persons who had vaccine nationalism were 1.75 times (95% confidence interval = 1.62-1.88; p < 0.001) more prone to prefer domestic vaccine. CONCLUSIONS: This study suggests that policymakers may take initiatives for making people aware and knowledgeable about the severity and vulnerability to specific health threats. In this concern, perception and efficacy-increasing programs may take part in increasing protection motivation behaviors like vaccine acceptance and (domestic) vaccine preference.


Subject(s)
Attitude to Health , COVID-19 Vaccines/administration & dosage , Health Knowledge, Attitudes, Practice , Motivation , Patient Acceptance of Health Care , Vaccination/psychology , Adolescent , Bangladesh , Cross-Sectional Studies , Female , Humans , Male , Rural Population/statistics & numerical data , SARS-CoV-2/pathogenicity , Surveys and Questionnaires , Urban Population/statistics & numerical data , Vaccination Refusal/psychology , Young Adult
3.
MMWR Morb Mortal Wkly Rep ; 70(42): 1459-1465, 2021 Oct 22.
Article in English | MEDLINE | ID: covidwho-1485568

ABSTRACT

In the United States, 10% of HIV infections diagnosed in 2018 were attributed to unsafe injection drug use or male-to-male sexual contact among persons who inject drugs (PWID) (1). In 2017, among PWID or men who have sex with men and who inject drugs (MSM-ID), 76% of those who received a diagnosis of HIV infection lived in urban areas* (2). To monitor the prevalence of HIV infection and associated behaviors among persons who reported injecting drugs in the past 12 months, including MSM-ID, CDC's National HIV Behavioral Surveillance (NHBS) conducts interviews and HIV testing among populations of persons at high risk for HIV infection (MSM, PWID, and heterosexually active adults at increased risk for HIV infection) in selected metropolitan statistical areas (MSAs) (3). The estimated HIV infection prevalence among PWID in 23 MSAs surveyed in 2018 was 7%. Among HIV-negative PWID, an estimated 26% receptively shared syringes and 68% had condomless vaginal sex during the preceding 12 months. During the same period, 57% had been tested for HIV infection, and 55% received syringes from a syringe services program (SSP). While overall SSP use did not significantly change since 2015, a substantial decrease in SSP use occurred among Black PWID, and HIV prevalence among Black PWID was higher than that among Hispanic and White PWID. These findings underscore the importance of continuing and expanding HIV prevention programs and community-based strategies for PWID, such as those provided by SSPs, especially following service disruptions created by the COVID-19 pandemic (4). Efforts are needed to ensure that PWID have low-barrier access to comprehensive and integrated needs-based SSPs (where legally permissible) that include provision of sterile syringes and safe syringe disposal, HIV and hepatitis C virus (HCV) testing and referrals to HIV and HCV treatment, HIV preexposure prophylaxis, and treatment for substance use and mental health disorders.


Subject(s)
Drug Users/psychology , HIV Infections/epidemiology , Health Risk Behaviors , Substance Abuse, Intravenous/epidemiology , Urban Population/statistics & numerical data , Adolescent , Adult , Drug Users/statistics & numerical data , Female , Humans , Male , Middle Aged , United States/epidemiology , Young Adult
4.
Crit Care Med ; 49(10): 1739-1748, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1475872

ABSTRACT

OBJECTIVES: The coronavirus disease 2019 pandemic has overwhelmed healthcare resources even in wealthy nations, necessitating rationing of limited resources without previously established crisis standards of care protocols. In Massachusetts, triage guidelines were designed based on acute illness and chronic life-limiting conditions. In this study, we sought to retrospectively validate this protocol to cohorts of critically ill patients from our hospital. DESIGN: We applied our hospital-adopted guidelines, which defined severe and major chronic conditions as those associated with a greater than 50% likelihood of 1- and 5-year mortality, respectively, to a critically ill patient population. We investigated mortality for the same intervals. SETTING: An urban safety-net hospital ICU. PATIENTS: All adults hospitalized during April of 2015 and April 2019 identified through a clinical database search. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 365 admitted patients, 15.89% had one or more defined chronic life-limiting conditions. These patients had higher 1-year (46.55% vs 13.68%; p < 0.01) and 5-year (50.00% vs 17.22%; p < 0.01) mortality rates than those without underlying conditions. Irrespective of classification of disease severity, patients with metastatic cancer, congestive heart failure, end-stage renal disease, and neurodegenerative disease had greater than 50% 1-year mortality, whereas patients with chronic lung disease and cirrhosis had less than 50% 1-year mortality. Observed 1- and 5-year mortality for cirrhosis, heart failure, and metastatic cancer were more variable when subdivided into severe and major categories. CONCLUSIONS: Patients with major and severe chronic medical conditions overall had 46.55% and 50.00% mortality at 1 and 5 years, respectively. However, mortality varied between conditions. Our findings appear to support a crisis standards protocol which focuses on acute illness severity and only considers underlying conditions carrying a greater than 50% predicted likelihood of 1-year mortality. Modifications to the chronic lung disease, congestive heart failure, and cirrhosis criteria should be refined if they are to be included in future models.


Subject(s)
COVID-19/therapy , Crisis Intervention/standards , Resource Allocation/methods , Academic Medical Centers/organization & administration , Academic Medical Centers/statistics & numerical data , Adult , COVID-19/epidemiology , Crisis Intervention/methods , Crisis Intervention/statistics & numerical data , Female , Hospitalization/statistics & numerical data , Humans , Male , Massachusetts , Middle Aged , Resource Allocation/statistics & numerical data , Retrospective Studies , Safety-net Providers/organization & administration , Safety-net Providers/statistics & numerical data , Standard of Care/standards , Standard of Care/statistics & numerical data , Urban Population/statistics & numerical data
5.
Am J Public Health ; 111(9): 1610-1619, 2021 09.
Article in English | MEDLINE | ID: covidwho-1435675

ABSTRACT

Objectives. To describe disparities in depression, anxiety, and problem drinking by sexual orientation, sexual behavior, and gender identity during the COVID-19 pandemic. Methods. Data were collected May 21 to July 15, 2020, from 3245 adults living in 5 major US metropolitan areas (Atlanta, Georgia; Chicago, Illinois; New Orleans, Louisiana; New York, New York; and Los Angeles, California). Participants were characterized as cisgender straight or LGBTQ+ (i.e., lesbian, gay, bisexual, and transgender people, and men who have sex with men, and women who have sex with women not identifying as lesbian, gay, bisexual, or transgender). Results. Cisgender straight participants had the lowest levels of depression, anxiety, and problem drinking compared with all other sexual orientation, sexual behavior, and gender identity groups, and, in general, LGBTQ+ participants were more likely to report that these health problems were "more than usual" during the COVID-19 pandemic. Conclusions. LGBTQ+ communities experienced worse mental health and problem drinking than their cisgender straight counterparts during the COVID-19 pandemic. Future research should assess the impact of the pandemic on health inequities. Policymakers should consider resources to support LGBTQ+ mental health and substance use prevention in COVID-19 recovery efforts.


Subject(s)
COVID-19/epidemiology , Mental Health/statistics & numerical data , Sexual Behavior/statistics & numerical data , Sexual and Gender Minorities/psychology , Adolescent , Adult , Aged , Alcoholism/epidemiology , Anxiety/epidemiology , Depression/epidemiology , Female , Humans , Male , Middle Aged , Pandemics , SARS-CoV-2 , Socioeconomic Factors , United States , Urban Population/statistics & numerical data , Young Adult
6.
Int J Equity Health ; 20(1): 203, 2021 09 08.
Article in English | MEDLINE | ID: covidwho-1430428

ABSTRACT

BACKGROUND: To address the challenge of the aging population, community-based care services (CBCS) have been developed rapidly in China as a new way of satisfying the needs of elderly people. Few studies have described the evolution trend of availability of CBCS in rural and urban areas and evaluated their effectiveness. This study aims to show the availability of China's CBCS and further analyze the effect of the CBCS on the cognitive function of elderly people. METHODS: Longitudinal analysis was performed using data from the 2008 to 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). A total of 23937 observations from 8421 elderly people were included in the study. The Chinese version of the Mini-Mental State Examination (MMSE) was used to assess cognitive function. We aggregated similar CBCS to generate three binary variable categories (daily life support, emotional comfort and entertainment services, medical support and health services) indicating the availability of CBCS (1 = yes, 0 = no). Multilevel growth models were employed to estimate the association between CBCS and cognitive function while adjusting for many demographic and socioeconomic characteristics. RESULTS: The availability of CBCS increased a lot from 2008 to 2018 in China. Although the availability of CBCS in urban areas was higher than that in rural areas in 2008, by 2018 the gap narrowed significantly. Emotional comfort and entertainment services (B = 0.331, 95% CI = 0.090 to 0.572) and medical support and health services (B = 1.041, 95% CI = 0.854 to 1.228) were significantly and positively associated with cognitive function after adjusting for the covariates. CONCLUSION: There was a significant increase in the availability of CBCS from 2008 to 2018 in China. This study sheds light on the positive correlation between CBCS and cognitive function among Chinese elderly individuals. The results suggest that policymakers should pay more attention to the development of CBCS and the equity of the supply of CBCS in urban and rural areas.


Subject(s)
Cognition , Community Health Services , Aged , Aged, 80 and over , China , Cognition/physiology , Community Health Services/supply & distribution , Female , Humans , Longitudinal Studies , Male , Rural Population/statistics & numerical data , Urban Population/statistics & numerical data
7.
Sci Rep ; 11(1): 18474, 2021 09 16.
Article in English | MEDLINE | ID: covidwho-1415959

ABSTRACT

Understanding patient progression from symptomatic COVID-19 infection to a severe outcome represents an important tool for improved diagnoses, surveillance, and triage. A series of models have been developed and validated to elucidate hospitalization, admission to an intensive care unit (ICU) and mortality in patients from the Republic of Ireland. This retrospective cohort study of patients with laboratory-confirmed symptomatic COVID-19 infection included data extracted from national COVID-19 surveillance forms (i.e., age, gender, underlying health conditions, occupation) and geographically-referenced potential predictors (i.e., urban/rural classification, socio-economic profile). Generalised linear models and recursive partitioning and regression trees were used to elucidate COVID-19 progression. The incidence of symptomatic infection over the study-period was 0.96% (n = 47,265), of whom 3781 (8%) required hospitalisation, 615 (1.3%) were admitted to ICU and 1326 (2.8%) died. Models demonstrated an increasingly efficacious fit for predicting hospitalization [AUC 0.816 (95% CI 0.809, 0.822)], admission to ICU [AUC 0.885 (95% CI 0.88 0.89)] and death [AUC of 0.955 (95% CI 0.951 0.959)]. Severe obesity (BMI ≥ 40) was identified as a risk factor across all prognostic models; severely obese patients were substantially more likely to receive ICU treatment [OR 19.630] or die [OR 10.802]. Rural living was associated with an increased risk of hospitalization (OR 1.200 (95% CI 1.143-1.261)]. Urban living was associated with ICU admission [OR 1.533 (95% CI 1.606-1.682)]. Models provide approaches for predicting COVID-19 prognoses, allowing for evidence-based decision-making pertaining to targeted non-pharmaceutical interventions, risk-based vaccination priorities and improved patient triage.


Subject(s)
COVID-19/epidemiology , Obesity, Morbid/epidemiology , Adult , Aged , Aged, 80 and over , COVID-19/mortality , Comorbidity , Evidence-Based Medicine , Female , Hospital Mortality , Hospitalization , Humans , Incidence , Intensive Care Units , Ireland/epidemiology , Linear Models , Male , Middle Aged , Population Surveillance , Prognosis , Retrospective Studies , Rural Population/statistics & numerical data , Socioeconomic Factors , Urban Population/statistics & numerical data
8.
PLoS One ; 16(8): e0256496, 2021.
Article in English | MEDLINE | ID: covidwho-1369567

ABSTRACT

BACKGROUND: While vaccines ensure individual protection against COVID-19 infection, delay in receipt or refusal of vaccines will have both individual and community impacts. The behavioral factors of vaccine hesitancy or refusal are a crucial dimension that need to be understood in order to design appropriate interventions. The aim of this study was to explore the behavioral determinants of COVID-19 vaccine acceptance and to provide recommendations to increase the acceptance and uptake of COVID-19 vaccines in Bangladesh. METHODS: We employed a Barrier Analysis (BA) approach to examine twelve potential behavioral determinants (drawn from the Health Belief Model [HBM] and Theory of Reasoned Action [TRA]) of intended vaccine acceptance. We conducted 45 interviews with those who intended to take the vaccine (Acceptors) and another 45 interviews with those who did not have that intention (Non-acceptors). We performed data analysis to find statistically significant differences and to identify which beliefs were most highly associated with acceptance and non-acceptance with COVID-19 vaccines. RESULTS: The behavioral determinants associated with COVID-19 vaccine acceptance in Dhaka included perceived social norms, perceived safety of COVID-19 vaccines and trust in them, perceived risk/susceptibility, perceived self-efficacy, perceived positive and negative consequences, perceived action efficacy, perceived severity of COVID-19, access, and perceived divine will. In line with the HBM, beliefs about the disease itself were highly predictive of vaccine acceptance, and some of the strongest statistically-significant (p<0.001) predictors of vaccine acceptance in this population are beliefs around both injunctive and descriptive social norms. Specifically, Acceptors were 3.2 times more likely to say they would be very likely to get a COVID-19 vaccine if a doctor or nurse recommended it, twice as likely to say that most people they know will get a vaccine, and 1.3 times more likely to say that most close family and friends will get a vaccine. The perceived safety of vaccines was found to be important since Non-acceptors were 1.8 times more likely to say that COVID-19 vaccines are "not safe at all". Beliefs about one's risk of getting COVID-19 disease and the severity of it were predictive of being a vaccine acceptor: Acceptors were 1.4 times more likely to say that it was very likely that someone in their household would get COVID-19, 1.3 times more likely to say that they were very concerned about getting COVID-19, and 1.3 times more likely to say that it would be very serious if someone in their household contracted COVID-19. Other responses of Acceptors on what makes immunization easier may be helpful in programming to boost acceptance, such as providing vaccination through government health facilities, schools, and kiosks, and having vaccinators maintain proper COVID-19 health and safety protocols. CONCLUSION: An effective behavior change strategy for COVID-19 vaccines uptake will need to address multiple beliefs and behavioral determinants, reducing barriers and leveraging enablers identified in this study. National plans for promoting COVID-19 vaccination should address the barriers, enablers, and behavioral determinants found in this study in order to maximize the impact on COVID-19 vaccination acceptance.


Subject(s)
COVID-19/psychology , Vaccination Refusal/statistics & numerical data , Vaccination/psychology , Adult , Attitude , Bangladesh , COVID-19/prevention & control , COVID-19 Vaccines/therapeutic use , Culture , Female , Humans , Male , Middle Aged , Urban Population/statistics & numerical data , Vaccination Refusal/psychology
9.
JMIR Public Health Surveill ; 7(7): e29865, 2021 07 20.
Article in English | MEDLINE | ID: covidwho-1334882

ABSTRACT

BACKGROUND: COVID-19 has disrupted lives and livelihoods and caused widespread panic worldwide. Emerging reports suggest that people living in rural areas in some countries are more susceptible to COVID-19. However, there is a lack of quantitative evidence that can shed light on whether residents of rural areas are more concerned about COVID-19 than residents of urban areas. OBJECTIVE: This infodemiology study investigated attitudes toward COVID-19 in different Japanese prefectures by aggregating and analyzing Yahoo! JAPAN search queries. METHODS: We measured COVID-19 concerns in each Japanese prefecture by aggregating search counts of COVID-19-related queries of Yahoo! JAPAN users and data related to COVID-19 cases. We then defined two indices-the localized concern index (LCI) and localized concern index by patient percentage (LCIPP)-to quantitatively represent the degree of concern. To investigate the impact of emergency declarations on people's concerns, we divided our study period into three phases according to the timing of the state of emergency in Japan: before, during, and after. In addition, we evaluated the relationship between the LCI and LCIPP in different prefectures by correlating them with prefecture-level indicators of urbanization. RESULTS: Our results demonstrated that the concerns about COVID-19 in the prefectures changed in accordance with the declaration of the state of emergency. The correlation analyses also indicated that the differentiated types of public concern measured by the LCI and LCIPP reflect the prefectures' level of urbanization to a certain extent (ie, the LCI appears to be more suitable for quantifying COVID-19 concern in urban areas, while the LCIPP seems to be more appropriate for rural areas). CONCLUSIONS: We quantitatively defined Japanese Yahoo users' concerns about COVID-19 by using the search counts of COVID-19-related search queries. Our results also showed that the LCI and LCIPP have external validity.


Subject(s)
Anxiety/epidemiology , Attitude to Health , COVID-19/psychology , Internet/statistics & numerical data , Search Engine/statistics & numerical data , Adult , Aged , COVID-19/epidemiology , Female , Humans , Japan/epidemiology , Male , Middle Aged , Rural Population/statistics & numerical data , Urban Population/statistics & numerical data
10.
Urology ; 156: 110-116, 2021 10.
Article in English | MEDLINE | ID: covidwho-1331280

ABSTRACT

OBJECTIVE: To examine differences between telephone and video-televisits and identify whether visit modality is associated with satisfaction in an urban, academic general urology practice. METHODS: A cross sectional analysis of patients who completed a televisit at our urology practice (summer 2020) was performed. A Likert-based satisfaction telephone survey was offered to patients within 7 days of their televisit. Patient demographics, televisit modality (telephone vs video), and outcomes of the visit (eg follow-up visit scheduled, orders placed) were retrospectively abstracted from each chart and compared between the telephone and video cohorts. Multivariate regression analysis was used to evaluate variables associated with satisfaction while controlling for potential confounders. RESULTS: A total of 269 patients were analyzed. 73% (196/269) completed a telephone televisit. Compared to the video cohort, the telephone cohort was slightly older (mean 58.8 years vs. 54.2 years, P = .03). There were no significant differences in the frequency of orders placed for medication changes, labs, imaging, or for in-person follow-up visits within 30 days between cohorts. Survey results showed overall 84.7% patients were satisfied, and there was no significant difference between the telephone and video cohorts. Visit type was not associated with satisfaction on multivariable analyses, while use of an interpreter [OR:8.13 (1.00-65.94); P = .05], labs ordered [OR:2.74 (1.12-6.70); P = .03] and female patient gender [OR:2.28 (1.03-5.03); P = .04] were significantly associated with satisfaction. CONCLUSION: Overall, most patients were satisfied with their televisit. Additionally, telephone- and video-televisits were similar regarding patient opinions, patient characteristics, and visit outcome. Efforts to increase access and coverage of telehealth, particularly telephone-televisits, should continue past the COVID-19 pandemic.


Subject(s)
COVID-19/prevention & control , Patient Satisfaction/statistics & numerical data , Telemedicine/methods , Telephone , Urology/statistics & numerical data , Videoconferencing , Adolescent , Adult , African Americans/statistics & numerical data , Aged , Asian Americans/statistics & numerical data , Clinical Laboratory Techniques , Communication Barriers , Cross-Sectional Studies , Female , Humans , Institutional Practice/statistics & numerical data , Language , Male , Middle Aged , Patient Satisfaction/ethnology , Retrospective Studies , SARS-CoV-2 , Sex Factors , Smoking , Surveys and Questionnaires , Transportation , Urban Population/statistics & numerical data , Young Adult
11.
PLoS One ; 16(7): e0254430, 2021.
Article in English | MEDLINE | ID: covidwho-1317144

ABSTRACT

We have investigated the importance of the rate of vaccination to contain COVID-19 in urban areas. We used an extremely simple epidemiological model that is amenable to implementation in an Excel spreadsheet and includes the demographics of social distancing, efficacy of massive testing and quarantine, and coverage and rate of vaccination as the main parameters to model the progression of COVID-19 pandemics in densely populated urban areas. Our model predicts that effective containment of pandemic progression in densely populated cities would be more effectively achieved by vaccination campaigns that consider the fast distribution and application of vaccines (i.e., 50% coverage in 6 months) while social distancing measures are still in place. Our results suggest that the rate of vaccination is more important than the overall vaccination coverage for containing COVID-19. In addition, our modeling indicates that widespread testing and quarantining of infected subjects would greatly benefit the success of vaccination campaigns. We envision this simple model as a friendly, readily accessible, and cost-effective tool for assisting health officials and local governments in the rational design/planning of vaccination strategies.


Subject(s)
COVID-19 Vaccines/administration & dosage , COVID-19/prevention & control , Software , Vaccination/statistics & numerical data , COVID-19/epidemiology , Humans , Models, Statistical , Quarantine/statistics & numerical data , Urban Population/statistics & numerical data , Vaccination/methods
12.
Nutrients ; 13(7)2021 Jun 26.
Article in English | MEDLINE | ID: covidwho-1288965

ABSTRACT

This study aimed to determine the relationships among hyperglycemia (HG), the presence of type 2 diabetes (T2D), and the outcomes of COVID-19. Demographic data, blood glucose levels (BG) measured on admission, and hospital outcomes of COVID-19 patients hospitalized at Boston University Medical Center from 1 March to 4 August 2020 were extracted from the hospital database. HG was defined as BG > 200 mg/dL. Patients with type 1 diabetes or BG < 70 mg/dL were excluded. A total of 458 patients with T2D and 976 patients without T2D were included in the study. The mean ± SD age was 56 ± 17 years and 642 (45%) were female. HG occurred in 193 (42%) and 42 (4%) of patients with and without T2D, respectively. Overall, the in-hospital mortality rate was 9%. Among patients without T2D, HG was statistically significantly associated with mortality, ICU admission, intubation, acute kidney injury, and severe sepsis/septic shock, after adjusting for potential confounders (p < 0.05). However, only ICU admission and acute kidney injury were associated with HG among patients with T2D (p < 0.05). Among the 235 patients with HG, the presence of T2D was associated with decreased odds of mortality, ICU admission, intubation, and severe sepsis/septic shock, after adjusting for potential confounders, including BG (p < 0.05). In conclusion, HG in the subset of patients without T2D could be a strong indicator of high inflammatory burden, leading to a higher risk of severe COVID-19.


Subject(s)
COVID-19/epidemiology , Diabetes Mellitus, Type 2/epidemiology , Hospitalization/statistics & numerical data , Hyperglycemia/epidemiology , Acute Kidney Injury/epidemiology , Adult , Aged , Blood Glucose , Boston/epidemiology , COVID-19/mortality , Cohort Studies , Female , Hospital Mortality , Humans , Intensive Care Units/statistics & numerical data , Intubation/statistics & numerical data , Male , Middle Aged , Retrospective Studies , SARS-CoV-2 , Sepsis/epidemiology , Severity of Illness Index , Urban Population/statistics & numerical data
13.
JAMA Netw Open ; 4(6): e2116425, 2021 06 01.
Article in English | MEDLINE | ID: covidwho-1281193

ABSTRACT

Importance: The COVID-19 pandemic has severely disrupted US educational institutions. Given potential adverse financial and psychosocial effects of campus closures, many institutions developed strategies to reopen campuses in the fall 2020 semester despite the ongoing threat of COVID-19. However, many institutions opted to have limited campus reopening to minimize potential risk of spread of SARS-CoV-2. Objective: To analyze how Boston University (BU) fully reopened its campus in the fall of 2020 and controlled COVID-19 transmission despite worsening transmission in Boston, Massachusetts. Design, Setting, and Participants: This multifaceted intervention case series was conducted at a large urban university campus in Boston, Massachusetts, during the fall 2020 semester. The BU response included a high-throughput SARS-CoV-2 polymerase chain reaction testing facility with capacity to deliver results in less than 24 hours; routine asymptomatic screening for COVID-19; daily health attestations; adherence monitoring and feedback; robust contact tracing, quarantine, and isolation in on-campus facilities; face mask use; enhanced hand hygiene; social distancing recommendations; dedensification of classrooms and public places; and enhancement of all building air systems. Data were analyzed from December 20, 2020, to January 31, 2021. Main Outcomes and Measures: SARS-CoV-2 diagnosis confirmed by reverse transcription-polymerase chain reaction of anterior nares specimens and sources of transmission, as determined through contact tracing. Results: Between August and December 2020, BU conducted more than 500 000 COVID-19 tests and identified 719 individuals with COVID-19, including 496 students (69.0%), 11 faculty (1.5%), and 212 staff (29.5%). Overall, 718 individuals, or 1.8% of the BU community, had test results positive for SARS-CoV-2. Of 837 close contacts traced, 86 individuals (10.3%) had test results positive for COVID-19. BU contact tracers identified a source of transmission for 370 individuals (51.5%), with 206 individuals (55.7%) identifying a non-BU source. Among 5 faculty and 84 staff with SARS-CoV-2 with a known source of infection, most reported a transmission source outside of BU (all 5 faculty members [100%] and 67 staff members [79.8%]). A BU source was identified by 108 of 183 undergraduate students with SARS-CoV-2 (59.0%) and 39 of 98 graduate students with SARS-CoV-2 (39.8%); notably, no transmission was traced to a classroom setting. Conclusions and Relevance: In this case series of COVID-19 transmission, BU used a coordinated strategy of testing, contact tracing, isolation, and quarantine, with robust management and oversight, to control COVID-19 transmission in an urban university setting.


Subject(s)
COVID-19/prevention & control , Infection Control/standards , Universities/trends , Urban Population/statistics & numerical data , Boston/epidemiology , COVID-19/epidemiology , COVID-19/transmission , Contact Tracing/instrumentation , Contact Tracing/methods , Hand Hygiene/methods , Humans , Infection Control/methods , Infection Control/statistics & numerical data , Quarantine/methods , Universities/organization & administration
14.
J Rural Health ; 36(3): 446-456, 2020 06.
Article in English | MEDLINE | ID: covidwho-1276750

ABSTRACT

PURPOSE: This study creates a COVID-19 susceptibility scale at the county level, describes its components, and then assesses the health and socioeconomic resiliency of susceptible places across the rural-urban continuum. METHODS: Factor analysis grouped 11 indicators into 7 distinct susceptibility factors for 3,079 counties in the conterminous United States. Unconditional mean differences are assessed using a multivariate general linear model. Data from 2018 are primarily taken from the US Census Bureau and CDC. RESULTS: About 33% of rural counties are highly susceptible to COVID-19, driven by older and health-compromised populations, and care facilities for the elderly. Major vulnerabilities in rural counties include fewer physicians, lack of mental health services, higher disability, and more uninsured. Poor Internet access limits telemedicine. Lack of social capital and social services may hinder local pandemic recovery. Meat processing facilities drive risk in micropolitan counties. Although metropolitan counties are less susceptible due to healthier and younger populations, about 6% are at risk due to community spread from dense populations. Metropolitan vulnerabilities include minorities at higher health and diabetes risk, language barriers, being a transportation hub that helps spread infection, and acute housing distress. CONCLUSIONS: There is an immediate need to know specific types of susceptibilities and vulnerabilities ahead of time to allow local and state health officials to plan and allocate resources accordingly. In rural areas it is essential to shelter-in-place vulnerable populations, whereas in large metropolitan areas general closure orders are needed to stop community spread. Pandemic response plans should address vulnerabilities.


Subject(s)
Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Rural Population/statistics & numerical data , Urban Population/statistics & numerical data , Age Factors , Betacoronavirus , COVID-19 , Health Services Accessibility/statistics & numerical data , Health Status , Humans , Mental Health , Pandemics , SARS-CoV-2 , Social Capital , Social Work/statistics & numerical data , Socioeconomic Factors , United States/epidemiology
15.
PLoS One ; 16(6): e0253466, 2021.
Article in English | MEDLINE | ID: covidwho-1278199

ABSTRACT

OBJECTIVE: Reports of disparities in COVID-19 mortality rates are emerging in the public health literature as the pandemic continues to unfold. Alcohol misuse varies across the US and is related to poorer health and comorbidities that likely affect the severity of COVID-19 infection. High levels of pre-pandemic alcohol misuse in some counties may have set the stage for worse COVID-19 outcomes. Furthermore, this relationship may depend on how rural a county is, as access to healthcare in rural communities has lagged behind more urban areas. The objective of this study was to test for associations between county-level COVID-19 mortality, pre-pandemic county-level excessive drinking, and county rurality. METHOD: We used national COVID-19 data from the New York Times to calculate county-level case fatality rates (n = 3,039 counties and county equivalents; October 1 -December 31, 2020) and other external county-level data sources for indicators of rurality and health. We used beta regression to model case fatality rates, adjusted for several county-level population characteristics. We included a multilevel component to our model and defined state as a random intercept. Our focal predictor was a single variable representing nine possible combinations of low/mid/high alcohol misuse and low/mid/high rurality. RESULTS: The median county-level COVID-19 case fatality rate was 1.57%. Compared to counties with low alcohol misuse and low rurality (referent), counties with high levels of alcohol and mid (ß = -0.17, p = 0.008) or high levels of rurality (ß = -0.24, p<0.001) demonstrated significantly lower case fatality rates. CONCLUSIONS: Our findings highlight the intersecting roles of county-level alcohol consumption, rurality, and COVID-19 mortality.


Subject(s)
Alcoholism/epidemiology , COVID-19/epidemiology , Rural Population/statistics & numerical data , SARS-CoV-2/isolation & purification , Urban Population/statistics & numerical data , Alcoholism/physiopathology , COVID-19/mortality , COVID-19/virology , Comorbidity , Geography , Health Status Disparities , Humans , Models, Theoretical , Multivariate Analysis , Pandemics/prevention & control , Risk Factors , SARS-CoV-2/physiology , Severity of Illness Index , Socioeconomic Factors , Survival Rate , United States/epidemiology
16.
PLoS One ; 16(6): e0252373, 2021.
Article in English | MEDLINE | ID: covidwho-1262546

ABSTRACT

OBJECTIVE: To assess whether the basic reproduction number (R0) of COVID-19 is different across countries and what national-level demographic, social, and environmental factors other than interventions characterize initial vulnerability to the virus. METHODS: We fit logistic growth curves to reported daily case numbers, up to the first epidemic peak, for 58 countries for which 16 explanatory covariates are available. This fitting has been shown to robustly estimate R0 from the specified period. We then use a generalized additive model (GAM) to discern both linear and nonlinear effects, and include 5 random effect covariates to account for potential differences in testing and reporting that can bias the estimated R0. FINDINGS: We found that the mean R0 is 1.70 (S.D. 0.57), with a range between 1.10 (Ghana) and 3.52 (South Korea). We identified four factors-population between 20-34 years old (youth), population residing in urban agglomerates over 1 million (city), social media use to organize offline action (social media), and GINI income inequality-as having strong relationships with R0, across countries. An intermediate level of youth and GINI inequality are associated with high R0, (n-shape relationships), while high city population and high social media use are associated with high R0. Pollution, temperature, and humidity did not have strong relationships with R0 but were positive. CONCLUSION: Countries have different characteristics that predispose them to greater intrinsic vulnerability to COVID-19. Studies that aim to measure the effectiveness of interventions across locations should account for these baseline differences in social and demographic characteristics.


Subject(s)
Basic Reproduction Number/statistics & numerical data , COVID-19/epidemiology , Income/statistics & numerical data , Social Media/statistics & numerical data , Age Factors , COVID-19/economics , COVID-19/transmission , COVID-19/virology , Databases, Factual , Global Health , Humans , Models, Statistical , Pandemics , SARS-CoV-2/isolation & purification , Socioeconomic Factors , Urban Population/statistics & numerical data
17.
MMWR Morb Mortal Wkly Rep ; 70(22): 818-824, 2021 Jun 04.
Article in English | MEDLINE | ID: covidwho-1257246

ABSTRACT

Disparities in vaccination coverage by social vulnerability, defined as social and structural factors associated with adverse health outcomes, were noted during the first 2.5 months of the U.S. COVID-19 vaccination campaign, which began during mid-December 2020 (1). As vaccine eligibility and availability continue to expand, assuring equitable coverage for disproportionately affected communities remains a priority. CDC examined COVID-19 vaccine administration and 2018 CDC social vulnerability index (SVI) data to ascertain whether inequities in COVID-19 vaccination coverage with respect to county-level SVI have persisted, overall and by urbanicity. Vaccination coverage was defined as the number of persons aged ≥18 years (adults) who had received ≥1 dose of any Food and Drug Administration (FDA)-authorized COVID-19 vaccine divided by the total adult population in a specified SVI category.† SVI was examined overall and by its four themes (socioeconomic status, household composition and disability, racial/ethnic minority status and language, and housing type and transportation). Counties were categorized into SVI quartiles, in which quartile 1 (Q1) represented the lowest level of vulnerability and quartile 4 (Q4), the highest. Trends in vaccination coverage were assessed by SVI quartile and urbanicity, which was categorized as large central metropolitan, large fringe metropolitan (areas surrounding large cities, e.g., suburban), medium and small metropolitan, and nonmetropolitan counties.§ During December 14, 2020-May 1, 2021, disparities in vaccination coverage by SVI increased, especially in large fringe metropolitan (e.g., suburban) and nonmetropolitan counties. By May 1, 2021, vaccination coverage was lower among adults living in counties with the highest overall SVI; differences were most pronounced in large fringe metropolitan (Q4 coverage = 45.0% versus Q1 coverage = 61.7%) and nonmetropolitan (Q4 = 40.6% versus Q1 = 52.9%) counties. Vaccination coverage disparities were largest for two SVI themes: socioeconomic status (Q4 = 44.3% versus Q1 = 61.0%) and household composition and disability (Q4 = 42.0% versus Q1 = 60.1%). Outreach efforts, including expanding public health messaging tailored to local populations and increasing vaccination access, could help increase vaccination coverage in high-SVI counties.


Subject(s)
COVID-19 Vaccines/administration & dosage , Healthcare Disparities/statistics & numerical data , Urban Population/statistics & numerical data , Vaccination Coverage/statistics & numerical data , Vulnerable Populations/statistics & numerical data , Adult , COVID-19/epidemiology , COVID-19/prevention & control , Cities/epidemiology , Humans , Socioeconomic Factors , United States/epidemiology
18.
PLoS One ; 16(5): e0251737, 2021.
Article in English | MEDLINE | ID: covidwho-1238769

ABSTRACT

IMPORTANCE: During pandemics Agent Based Models (ABMs) can model complex, fine-grained behavioural interactions occurring in social networks, that contribute to disease transmission by novel viruses such as SARS-CoV-2. OBJECTIVE: We present a new agent-based model (ABM) called the Discrete-Event, Simulated Social Agent based Network Transmission model (DESSABNeT) and demonstrate its ability to model the spread of COVID-19 in large cities like Sydney, Melbourne and Gold Coast. Our aim was to validate the model with its disease dynamics and underlying social network. DESIGN: DESSABNeT relies on disease transmission within simulated social networks. It employs an epidemiological SEIRD+M (Susceptible, exposed, infected, recovered, died and managed) structure. One hundred simulations were run for each city, with simulated social restrictions closely modelling real restrictions imposed in each location. MAIN OUTCOME(S) AND MEASURE(S): The mean predicted daily incidence of COVID-19 cases were compared to real case incidence data for each city. Reff and health service utilisation outputs were compared to the literature, or for the Gold Coast with daily incidence of hospitalisation. RESULTS: DESSABNeT modelled multiple physical distancing restrictions and predicted epidemiological outcomes of Sydney, Melbourne and the Gold Coast, validating this model for future simulation work. CONCLUSIONS AND RELEVANCE: DESSABNeT is a valid platform to model the spread of COVID-19 in large cities in Australia and potentially internationally. The platform is suitable to model different combinations of social restrictions, or to model contact tracing, predict, and plan for, the impact on hospital and ICU admissions, and deaths; and also the rollout of COVID-19 vaccines and optimal social restrictions during vaccination.


Subject(s)
COVID-19/transmission , Disease Transmission, Infectious/statistics & numerical data , Social Behavior , Urban Population/statistics & numerical data , Australia , COVID-19/epidemiology , Disease Transmission, Infectious/prevention & control , Humans , Models, Statistical , Quarantine/statistics & numerical data
19.
MMWR Morb Mortal Wkly Rep ; 70(20): 759-764, 2021 May 21.
Article in English | MEDLINE | ID: covidwho-1237006

ABSTRACT

Approximately 60 million persons in the United States live in rural counties, representing almost one fifth (19.3%) of the population.* In September 2020, COVID-19 incidence (cases per 100,000 population) in rural counties surpassed that in urban counties (1). Rural communities often have a higher proportion of residents who lack health insurance, live with comorbidities or disabilities, are aged ≥65 years, and have limited access to health care facilities with intensive care capabilities, which places these residents at increased risk for COVID-19-associated morbidity and mortality (2,3). To better understand COVID-19 vaccination disparities across the urban-rural continuum, CDC analyzed county-level vaccine administration data among adults aged ≥18 years who received their first dose of either the Pfizer-BioNTech or Moderna COVID-19 vaccine, or a single dose of the Janssen COVID-19 vaccine (Johnson & Johnson) during December 14, 2020-April 10, 2021 in 50 U.S. jurisdictions (49 states and the District of Columbia [DC]). Adult COVID-19 vaccination coverage was lower in rural counties (38.9%) than in urban counties (45.7%) overall and among adults aged 18-64 years (29.1% rural, 37.7% urban), those aged ≥65 years (67.6% rural, 76.1% urban), women (41.7% rural, 48.4% urban), and men (35.3% rural, 41.9% urban). Vaccination coverage varied among jurisdictions: 36 jurisdictions had higher coverage in urban counties, five had higher coverage in rural counties, and five had similar coverage (i.e., within 1%) in urban and rural counties; in four jurisdictions with no rural counties, the urban-rural comparison could not be assessed. A larger proportion of persons in the most rural counties (14.6%) traveled for vaccination to nonadjacent counties (i.e., farther from their county of residence) compared with persons in the most urban counties (10.3%). As availability of COVID-19 vaccines expands, public health practitioners should continue collaborating with health care providers, pharmacies, employers, faith leaders, and other community partners to identify and address barriers to COVID-19 vaccination in rural areas (2).


Subject(s)
COVID-19 Vaccines/administration & dosage , Healthcare Disparities/statistics & numerical data , Rural Population/statistics & numerical data , Urban Population/statistics & numerical data , Vaccination Coverage/statistics & numerical data , Adolescent , Adult , Aged , COVID-19/epidemiology , COVID-19/prevention & control , Female , Humans , Male , Middle Aged , United States/epidemiology , Young Adult
20.
J Public Health Policy ; 42(3): 373-389, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1236132

ABSTRACT

Social vulnerability indices (SVI) can predict communities' vulnerability and resilience to public health threats such as drought, food insecurity or infectious diseases. Parity has yet to be investigated as an indicator of social vulnerability in young women. We adapted an SVI score, previously used by the US Centre for Disease Control (CDC), and calculated SVI for young urban South African women (n = 1584; median age 21.6, IQR 3.6 years). Social vulnerability was more frequently observed in women with children and increased as parity increased. Furthermore, young women classified as socially vulnerable were 2.84 times (95% CI 2.10-3.70; p < 0.001) more likely to report household food insecurity. We collected this information in 2018-2019, prior to the current global COVID-19 pandemic. With South Africa having declared a National State of Disaster in March 2020, early indicators suggest that this group of women have indeed been disproportionally affected, supporting the utility of such measures to inform disaster relief efforts.


Subject(s)
Food Insecurity , Parity , Urban Population , Vulnerable Populations , Female , Health Promotion , Humans , Pregnancy , Socioeconomic Factors , South Africa , Urban Health , Urban Population/statistics & numerical data , Vulnerable Populations/statistics & numerical data , Young Adult
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