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1.
Sci Rep ; 12(1): 4690, 2022 03 18.
Article in English | MEDLINE | ID: covidwho-1751753

ABSTRACT

The unprecedented behavioural responses of societies have been evidently shaping the COVID-19 pandemic, yet it is a significant challenge to accurately monitor the continuously changing social mixing patterns in real-time. Contact matrices, usually stratified by age, summarise interaction motifs efficiently, but their collection relies on conventional representative survey techniques, which are expensive and slow to obtain. Here we report a data collection effort involving over [Formula: see text] of the Hungarian population to simultaneously record contact matrices through a longitudinal online and sequence of representative phone surveys. To correct non-representative biases characterising the online data, by using census data and the representative samples we develop a reconstruction method to provide a scalable, cheap, and flexible way to dynamically obtain closer-to-representative contact matrices. Our results demonstrate that although some conventional socio-demographic characters correlate significantly with the change of contact numbers, the strongest predictors can be collected only via surveys techniques and combined with census data for the best reconstruction performance. We demonstrate the potential of combined online-offline data collections to understand the changing behavioural responses determining the future evolution of the outbreak, and to inform epidemic models with crucial data.


Subject(s)
COVID-19 , Pandemics , COVID-19/epidemiology , Censuses , Disease Outbreaks , Humans , Surveys and Questionnaires
2.
Ann Epidemiol ; 67: 29-34, 2022 03.
Article in English | MEDLINE | ID: covidwho-1611591

ABSTRACT

PURPOSE: The establishment of community-academic partnerships to digest data and create actionable policy and advocacy steps is of continuing importance. In this paper, we document COVID-19 racial and geographic disparities uncovered via a collaboration between a local health department and university research center. METHODS: We leverage individual level data for all COVID-19 cases aggregated to the census block group level, where group-based trajectory modeling was employed to identify latent patterns of change and continuity in COVID-19 diagnoses. RESULTS: Linking with socioeconomic data from the census, we identified the types of communities most heavily affected by each of Michigan's two waves (in spring and fall of 2020). This includes a geographic and racial gap in COVID-19 cases during the first wave, which is largely eliminated during the second wave. CONCLUSIONS: Our work has been extremely valuable for community partners, informing community-level response toward testing, treatment, and vaccination. In particular, identifying and conducting advocacy on the sizeable racial disparity in COVID-19 cases during the first wave in spring 2020 helped our community nearly eliminate disparities throughout the second wave in fall 2020.


Subject(s)
COVID-19 , COVID-19/epidemiology , Censuses , Humans , Incidence , Michigan/epidemiology
3.
JAMA Netw Open ; 5(1): e2142046, 2022 01 04.
Article in English | MEDLINE | ID: covidwho-1605268

ABSTRACT

Importance: The COVID-19 pandemic has had a distinct spatiotemporal pattern in the United States. Patients with cancer are at higher risk of severe complications from COVID-19, but it is not well known whether COVID-19 outcomes in this patient population were associated with geography. Objective: To quantify spatiotemporal variation in COVID-19 outcomes among patients with cancer. Design, Setting, and Participants: This registry-based retrospective cohort study included patients with a historical diagnosis of invasive malignant neoplasm and laboratory-confirmed SARS-CoV-2 infection between March and November 2020. Data were collected from cancer care delivery centers in the United States. Exposures: Patient residence was categorized into 9 US census divisions. Cancer center characteristics included academic or community classification, rural-urban continuum code (RUCC), and social vulnerability index. Main Outcomes and Measures: The primary outcome was 30-day all-cause mortality. The secondary composite outcome consisted of receipt of mechanical ventilation, intensive care unit admission, and all-cause death. Multilevel mixed-effects models estimated associations of center-level and census division-level exposures with outcomes after adjustment for patient-level risk factors and quantified variation in adjusted outcomes across centers, census divisions, and calendar time. Results: Data for 4749 patients (median [IQR] age, 66 [56-76] years; 2439 [51.4%] female individuals, 1079 [22.7%] non-Hispanic Black individuals, and 690 [14.5%] Hispanic individuals) were reported from 83 centers in the Northeast (1564 patients [32.9%]), Midwest (1638 [34.5%]), South (894 [18.8%]), and West (653 [13.8%]). After adjustment for patient characteristics, including month of COVID-19 diagnosis, estimated 30-day mortality rates ranged from 5.2% to 26.6% across centers. Patients from centers located in metropolitan areas with population less than 250 000 (RUCC 3) had lower odds of 30-day mortality compared with patients from centers in metropolitan areas with population at least 1 million (RUCC 1) (adjusted odds ratio [aOR], 0.31; 95% CI, 0.11-0.84). The type of center was not significantly associated with primary or secondary outcomes. There were no statistically significant differences in outcome rates across the 9 census divisions, but adjusted mortality rates significantly improved over time (eg, September to November vs March to May: aOR, 0.32; 95% CI, 0.17-0.58). Conclusions and Relevance: In this registry-based cohort study, significant differences in COVID-19 outcomes across US census divisions were not observed. However, substantial heterogeneity in COVID-19 outcomes across cancer care delivery centers was found. Attention to implementing standardized guidelines for the care of patients with cancer and COVID-19 could improve outcomes for these vulnerable patients.


Subject(s)
COVID-19/epidemiology , Neoplasms/epidemiology , Pandemics , Rural Population , Social Vulnerability , Urban Population , Aged , Cause of Death , Censuses , Female , Health Facilities , Humans , Intensive Care Units , Male , Middle Aged , Odds Ratio , Registries , Respiration, Artificial , Retrospective Studies , Risk Factors , SARS-CoV-2 , Severity of Illness Index , Spatial Analysis , United States/epidemiology
4.
PLoS One ; 16(11): e0259665, 2021.
Article in English | MEDLINE | ID: covidwho-1542181

ABSTRACT

Health varies by U.S. region of residence. Despite regional heterogeneity in the outbreak of COVID-19, regional differences in physical distancing behaviors over time are relatively unknown. This study examines regional variation in physical distancing trends during the COVID-19 pandemic and investigates variation by race and socioeconomic status (SES) within regions. Data from the 2015-2019 five-year American Community Survey were matched with anonymized location pings data from over 20 million mobile devices (SafeGraph, Inc.) at the Census block group level. We visually present trends in the stay-at-home proportion by Census region, race, and SES throughout 2020 and conduct regression analyses to examine these patterns. From March to December, the stay-at-home proportion was highest in the Northeast (0.25 in March to 0.35 in December) and lowest in the South (0.24 to 0.30). Across all regions, the stay-at-home proportion was higher in block groups with a higher percentage of Blacks, as Blacks disproportionately live in urban areas where stay-at-home rates were higher (0.009 [CI: 0.008, 0.009]). In the South, West, and Midwest, higher-SES block groups stayed home at the lowest rates pre-pandemic; however, this trend reversed throughout March before converging in the months following. In the Northeast, lower-SES block groups stayed home at comparable rates to higher-SES block groups during the height of the pandemic but diverged in the months following. Differences in physical distancing behaviors exist across U.S. regions, with a pronounced Southern and rural disadvantage. Results can be used to guide reopening and COVID-19 mitigation plans.


Subject(s)
COVID-19/epidemiology , Pandemics , Physical Distancing , Social Class , Censuses , Educational Status , Humans , Income , Quarantine , Rural Population , United States/epidemiology , Urban Population
5.
Syst Rev ; 9(1): 63, 2020 03 24.
Article in English | MEDLINE | ID: covidwho-1455998

ABSTRACT

BACKGROUND: A large proportion of the burden of disease is preventable, yet investment in health promotion and disease prevention programmes remains a small share of the total health budget in many countries. The perception that there is paucity of evidence on the cost-effectiveness of public health programmes is seen as a barrier to policy change. The aim of this scoping review is to conduct a census of economic evaluations in primary prevention in order to identify and map the existing evidence. METHODS: This review is an update of a prior census and will include full economic evaluations of primary prevention programmes conducted in a community-based setting that were published between 2014 and 2019. The search of electronic databases (MEDLINE and Embase, and NHS-EED for 2014) will be supplemented by a search for grey literature in OpenGrey and a search of the reference lists of reviews of economic evaluations identified in our searches. Retrieved citations will be imported into Covidence® and independently screened in a two-stage process by two reviewers (abstracts and full papers). Any disagreements on the eligibility of a citation will be resolved by discussion with a third reviewer. Included studies will then be categorised by one independent reviewer according to a four-part typology covering the type of health promotion intervention, the risk factor being tackled, the setting in which the intervention took place and the population most affected by the intervention. New to this version of the census, we will also document whether or not the intervention sets out specifically to address inequalities in health. DISCUSSION: This review will produce an annotated bibliography of all economic evaluations plus a report summarising the current scope and content of the economic evidence (highlighting where it is plentiful and where it is lacking) and describing any changes in the type of economic evidence available for the various categories of disease prevention programmes since the last census. This will allow us to identify where future evaluative efforts should be focused to enhance the economic evidence base regarding primary prevention interventions. SYSTEMATIC REVIEW REGISTRATION: Registration is being sought concurrently.


Subject(s)
Censuses , Primary Prevention , Cost-Benefit Analysis , Delivery of Health Care , Health Promotion , Review Literature as Topic
8.
Int J Epidemiol ; 51(1): 54-62, 2022 02 18.
Article in English | MEDLINE | ID: covidwho-1356686

ABSTRACT

BACKGROUND: In early 2020, Ecuador reported one of the highest surges of per capita deaths across the globe. METHODS: We collected a comprehensive dataset containing individual death records between 2015 and 2020, from the Ecuadorian National Institute of Statistics and Census and the Ecuadorian Ministry of Government. We computed the number of excess deaths across time, geographical locations and demographic groups using Poisson regression methods. RESULTS: Between 1 January and 23 September 2020, the number of excess deaths in Ecuador was 36 402 [95% confidence interval (CI): 35 762-36 827] or 208 per 100 000 people, which is 171% of the expected deaths in that period in a typical year. Only 20% of the excess deaths are attributable to confirmed COVID-19 deaths. Strikingly, in provinces that were most affected by COVID-19 such as Guayas and Santa Elena, the all-cause deaths are more than double the expected number of deaths that would have occurred in a normal year. The extent of excess deaths in men is higher than in women, and the number of excess deaths increases with age. Indigenous populations had the highest level of excess deaths among all ethnic groups. CONCLUSIONS: Overall, the exceptionally high level of excess deaths in Ecuador highlights the enormous burden and heterogeneous impact of COVID-19 on mortality, especially in older age groups and Indigenous populations in Ecuador, which was not fully revealed by COVID-19 death counts. Together with the limited testing in Ecuador, our results suggest that the majority of the excess deaths were likely to be undocumented COVID-19 deaths.


Subject(s)
COVID-19 , Aged , Censuses , Ecuador/epidemiology , Female , Humans , Male , Mortality , SARS-CoV-2
9.
JMIR Public Health Surveill ; 7(8): e29205, 2021 08 05.
Article in English | MEDLINE | ID: covidwho-1357483

ABSTRACT

BACKGROUND: Previous studies have shown that various social determinants of health (SDOH) may have contributed to the disparities in COVID-19 incidence and mortality among minorities and underserved populations at the county or zip code level. OBJECTIVE: This analysis was carried out at a granular spatial resolution of census tracts to explore the spatial patterns and contextual SDOH associated with COVID-19 incidence from a Hispanic population mostly consisting of a Mexican American population living in Cameron County, Texas on the border of the United States and Mexico. We performed age-stratified analysis to identify different contributing SDOH and quantify their effects by age groups. METHODS: We included all reported COVID-19-positive cases confirmed by reverse transcription-polymerase chain reaction testing between March 18 (first case reported) and December 16, 2020, in Cameron County, Texas. Confirmed COVID-19 cases were aggregated to weekly counts by census tracts. We adopted a Bayesian spatiotemporal negative binomial model to investigate the COVID-19 incidence rate in relation to census tract demographics and SDOH obtained from the American Community Survey. Moreover, we investigated the impact of local mitigation policy on COVID-19 by creating the binary variable "shelter-in-place." The analysis was performed on all COVID-19-confirmed cases and age-stratified subgroups. RESULTS: Our analysis revealed that the relative incidence risk (RR) of COVID-19 was higher among census tracts with a higher percentage of single-parent households (RR=1.016, 95% posterior credible intervals [CIs] 1.005, 1.027) and a higher percentage of the population with limited English proficiency (RR=1.015, 95% CI 1.003, 1.028). Lower RR was associated with lower income (RR=0.972, 95% CI 0.953, 0.993) and the percentage of the population younger than 18 years (RR=0.976, 95% CI 0.959, 0.993). The most significant association was related to the "shelter-in-place" variable, where the incidence risk of COVID-19 was reduced by over 50%, comparing the time periods when the policy was present versus absent (RR=0.506, 95% CI 0.454, 0.563). Moreover, age-stratified analyses identified different significant contributing factors and a varying magnitude of the "shelter-in-place" effect. CONCLUSIONS: In our study, SDOH including social environment and local emergency measures were identified in relation to COVID-19 incidence risk at the census tract level in a highly disadvantaged population with limited health care access and a high prevalence of chronic conditions. Results from our analysis provide key knowledge to design efficient testing strategies and assist local public health departments in COVID-19 control, mitigation, and implementation of vaccine strategies.


Subject(s)
COVID-19/epidemiology , Social Determinants of Health , Adolescent , Adult , Aged , Aged, 80 and over , Censuses , Female , Health Equity , Humans , Incidence , Male , Mexico/ethnology , Middle Aged , Minority Groups , Physical Distancing , SARS-CoV-2 , Socioeconomic Factors , Spatial Analysis , Texas/epidemiology , United States , Vulnerable Populations , Young Adult
10.
JMIR Public Health Surveill ; 7(8): e28195, 2021 08 04.
Article in English | MEDLINE | ID: covidwho-1341584

ABSTRACT

BACKGROUND: COVID-19 has been one of the most serious global health crises in world history. During the pandemic, health care systems require accurate forecasts for key resources to guide preparation for patient surges. Forecasting the COVID-19 hospital census is among the most important planning decisions to ensure adequate staffing, number of beds, intensive care units, and vital equipment. OBJECTIVE: The goal of this study was to explore the potential utility of local COVID-19 infection incidence data in developing a forecasting model for the COVID-19 hospital census. METHODS: The study data comprised aggregated daily COVID-19 hospital census data across 11 Atrium Health hospitals plus a virtual hospital in the greater Charlotte metropolitan area of North Carolina, as well as the total daily infection incidence across the same region during the May 15 to December 5, 2020, period. Cross-correlations between hospital census and local infection incidence lagging up to 21 days were computed. A multivariate time-series framework, called the vector error correction model (VECM), was used to simultaneously incorporate both time series and account for their possible long-run relationship. Hypothesis tests and model diagnostics were performed to test for the long-run relationship and examine model goodness of fit. The 7-days-ahead forecast performance was measured by mean absolute percentage error (MAPE), with time-series cross-validation. The forecast performance was also compared with an autoregressive integrated moving average (ARIMA) model in the same cross-validation time frame. Based on different scenarios of the pandemic, the fitted model was leveraged to produce 60-days-ahead forecasts. RESULTS: The cross-correlations were uniformly high, falling between 0.7 and 0.8. There was sufficient evidence that the two time series have a stable long-run relationship at the .01 significance level. The model had very good fit to the data. The out-of-sample MAPE had a median of 5.9% and a 95th percentile of 13.4%. In comparison, the MAPE of the ARIMA had a median of 6.6% and a 95th percentile of 14.3%. Scenario-based 60-days-ahead forecasts exhibited concave trajectories with peaks lagging 2 to 3 weeks later than the peak infection incidence. In the worst-case scenario, the COVID-19 hospital census can reach a peak over 3 times greater than the peak observed during the second wave. CONCLUSIONS: When used in the VECM framework, the local COVID-19 infection incidence can be an effective leading indicator to predict the COVID-19 hospital census. The VECM model had a very good 7-days-ahead forecast performance and outperformed the traditional ARIMA model. Leveraging the relationship between the two time series, the model can produce realistic 60-days-ahead scenario-based projections, which can inform health care systems about the peak timing and volume of the hospital census for long-term planning purposes.


Subject(s)
COVID-19/therapy , Censuses , Forecasting/methods , Hospitals , Models, Theoretical , COVID-19/epidemiology , Humans , Incidence , Multivariate Analysis , North Carolina/epidemiology
11.
Am J Public Health ; 111(S2): S141-S148, 2021 07.
Article in English | MEDLINE | ID: covidwho-1334834

ABSTRACT

OBJECTIVES: To assess the quality of population-level US mortality data in the US Census Bureau Numerical Identification file (Numident) and describe the details of the mortality information as well as the novel person-level linkages available when using the Census Numident. METHODS: We compared all-cause mortality in the Census Numident to published vital statistics from the Centers for Disease Control and Prevention. We provide detailed information on the linkage of the Census Numident to other Census Bureau survey, administrative, and economic data. RESULTS: Death counts in the Census Numident are similar to those from published mortality vital statistics. Yearly comparisons show that the Census Numident captures more deaths since 1997, and coverage is slightly lower going back in time. Weekly estimates show similar trends from both data sets. CONCLUSIONS: The Census Numident is a high-quality and timely source of data to study all-cause mortality. The Census Bureau makes available a vast and rich set of restricted-use, individual-level data linked to the Census Numident for researchers to use. PUBLIC HEALTH IMPLICATIONS: The Census Numident linked to data available from the Census Bureau provides infrastructure for doing evidence-based public health policy research on mortality.


Subject(s)
Cause of Death/trends , Censuses , Centers for Disease Control and Prevention, U.S./statistics & numerical data , Data Collection/methods , Data Collection/statistics & numerical data , Mortality/trends , Vital Statistics , Forecasting , Humans , United States
12.
J Anxiety Disord ; 83: 102455, 2021 10.
Article in English | MEDLINE | ID: covidwho-1333544

ABSTRACT

BACKGROUND: Events from spring to fall 2020, including the COVID-19 pandemic, hate crimes, and social unrest, may have impacted mental health, particularly mood and anxiety disorders. This study compares rates of positive screens for anxiety and depressive disorders in separate U.S. national samples from 2019 and April to September 2020. The analysis includes trends within demographic groups, which have received scant attention. METHODS: Nationally representative probability samples of U.S. adults administered by the U.S. Census Bureau (n = 1.3 million) completed the PHQ-2 screening for depressive disorder and the GAD-2 screening for anxiety disorder. RESULTS: U.S. adults in 2020 were four times more likely to screen positive for depressive and anxiety disorders than in 2019, with the largest increases among males, 18- to 29-year-olds (for depression), Asian Americans, and parents with children in the home. Anxiety and depression rose and fell in tandem with the number of COVID-19 cases in the U.S., as well as increasing during the early June weeks of racial justice protests. CONCLUSIONS: Screens for mood and anxiety disorders remained at elevated levels in spring, summer, and fall 2020, especially among certain groups.


Subject(s)
COVID-19 , Depression , Adult , Anxiety , Anxiety Disorders/diagnosis , Anxiety Disorders/epidemiology , Censuses , Child , Cross-Sectional Studies , Depression/diagnosis , Depression/epidemiology , Humans , Male , Pandemics , SARS-CoV-2
13.
J Am Med Inform Assoc ; 28(9): 1977-1981, 2021 08 13.
Article in English | MEDLINE | ID: covidwho-1276185

ABSTRACT

Hospital census prediction has well-described implications for efficient hospital resource utilization, and recent issues with hospital crowding due to CoVID-19 have emphasized the importance of this task. Our team has been leading an institutional effort to develop machine-learning models that can predict hospital census 12 hours into the future. We describe our efforts at developing accurate empirical models for this task. Ultimately, with limited resources and time, we were able to develop simple yet useful models for 12-hour census prediction and design a dashboard application to display this output to our hospital's decision-makers. Specifically, we found that linear models with ElasticNet regularization performed well for this task with relative 95% error of +/- 3.4% and that this work could be completed in approximately 7 months.


Subject(s)
Censuses , Hospitals , COVID-19 , Humans , Machine Learning
14.
J Med Internet Res ; 23(6): e26242, 2021 06 14.
Article in English | MEDLINE | ID: covidwho-1268222

ABSTRACT

BACKGROUND: The COVID-19 pandemic has amplified the role of telehealth in health care delivery. Regional variation in internet access and telehealth use are well-documented, but the effect of neighborhood factors, including the pervasiveness of broadband internet, on older adults' telehealth usage in the context of internet access is not known. OBJECTIVE: This study aimed to investigate how individual and neighborhood characteristics, including the pervasiveness of neighborhood broadband internet subscription, are associated with engagement in telehealth among older adults with internet access. METHODS: In this cross-sectional study, we included 5117 community-living older adults aged ≥65 years, who participated in the 2017 National Health and Aging Trends Study with census tract-level data for participants' places of residence from the American Community Survey. RESULTS: Of an estimated 35.3 million community-living older adults, 21.1 million (59.7%) were internet users, and of this group, more than one-third (35.8%) engaged in telehealth. In a multivariable regression model adjusted for individual- and neighborhood-level factors, age, education, income, and the pervasiveness of neighborhood broadband internet subscription were associated with engagement in telehealth, while race, health, county metropolitan status, and neighborhood social deprivation were not. Among internet users, living in a neighborhood at the lowest (versus highest) tertile of broadband internet subscription was associated with being 40% less likely to engage in telehealth (adjusted odds ratio 0.61, 95% CI 0.42-0.87), all else equal. CONCLUSIONS: Neighborhood broadband internet stands out as a mutable characteristic that is consequential to engagement in telehealth.


Subject(s)
COVID-19/epidemiology , Telemedicine/statistics & numerical data , Aged , Censuses , Cross-Sectional Studies , Female , Humans , Male , Pandemics , SARS-CoV-2/isolation & purification , Surveys and Questionnaires , Telemedicine/instrumentation , United States
15.
Environ Pollut ; 287: 117584, 2021 Oct 15.
Article in English | MEDLINE | ID: covidwho-1267672

ABSTRACT

Previous nationwide studies have reported links between long-term concentrations of fine particulate matter (PM2.5) and COVID-19 infection and mortality rates. In order to translate these results to the state level, we use Bayesian hierarchical models to explore potential links between long-term PM2.5 concentrations and census tract-level rates of COVID-19 outcomes (infections, hospitalizations, and deaths) in Colorado. We explicitly consider how the uncertainty in PM2.5 estimates affects our results by comparing four different PM2.5 surfaces from academic and governmental organizations. After controlling for 20 census tract-level covariates, we find that our results depend heavily on the choice of PM2.5 surface. Using PM2.5 estimates from the United States EPA, we find that a 1 µg/m3 increase in long-term PM2.5 concentrations is associated with a statistically significant 26% increase in the relative risk of hospitalizations and a 34% increase in mortality. Results for all other surfaces and outcomes were not statistically significant. At the same time, we find a clear association between communities of color and COVID-19 outcomes at the Colorado census tract level that is minimally affected by the choice of PM2.5 surface. A per-interquartile range (IQR) increase in the percent of non-African American people of color was associated with a 31%, 43%, and 56% increase in the relative risk of infection, hospitalization, and mortality respectively, while a per-IQR increase in the proportion of non-Hispanic African Americans was associated with a 4% and 7% increase in the relative risk of infections and hospitalizations. The current disagreement among the different PM2.5 estimates is a key factor limiting our ability to link environmental exposures and health outcomes at the census tract level. These results have strong implications for the implementation of an equitable public health response during the crisis and suggest targeted areas for additional air monitoring in Colorado.


Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Air Pollutants/analysis , Air Pollution/analysis , Bayes Theorem , Censuses , Colorado , Environmental Exposure/analysis , Humans , Particulate Matter/analysis , Risk Factors , SARS-CoV-2 , United States
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.
Salud Publica Mex ; 63(3 May-Jun): 444-451, 2021 May 03.
Article in Spanish | MEDLINE | ID: covidwho-1259814

ABSTRACT

Objetivo. Describir el diseño y los resultados de campo de la Encuesta Nacional de Salud y Nutrición (Ensanut) 2020 so-bre Covid-19. Material y métodos. La Ensanut Covid-19 es una encuesta probabilística de hogares. En este artículo se describen los siguientes elementos del diseño: alcance, muestreo, medición, inferencia y logística. Resultados. Se obtuvieron 10 216 entrevistas de hogar completas y 9 464 resultados sobre seropositividad a SARS-CoV-2. La tasa de respuesta de hogar fue 80% y la de prueba de seropositividad de 44%. Conclusiones. El diseño probabilístico de la Ensa-nut Covid-19 permite hacer inferencias estadísticas válidas sobre parámetros de interés para la salud pública a nivel nacional y regional; en particular, permitirá hacer inferencias de utilidad práctica sobre la prevalencia de seropositividad a SARS-CoV-2 en México. Además, la Ensanut Covid-19 podrá ser comparada con Ensanut previas para identificar potenciales cambios en los estados de salud y nutrición de la población mexicana.


Subject(s)
COVID-19/epidemiology , Health Status Indicators , Nutrition Surveys/methods , Age Distribution , COVID-19/transmission , Censuses , Humans , Mexico/epidemiology , Nutrition Surveys/statistics & numerical data , Prevalence , Rural Health/statistics & numerical data , Sample Size , Urban Health/statistics & numerical data
18.
Oncologist ; 26(8): e1427-e1433, 2021 08.
Article in English | MEDLINE | ID: covidwho-1210191

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted health care systems. However, to date, the trend of hospitalizations in the oncology patient population has not been studied, and the frequency of nosocomial spread to patients with cancer is not well understood. The objectives of this study were to evaluate the impact of COVID-19 on inpatient oncology census and determine the nosocomial rate of COVID-19 in patients with cancer admitted at a large academic center. MATERIALS AND METHODS: Medical records of patients with cancer diagnosed with COVID-19 and admitted were reviewed to evaluate the temporal trends in inpatient oncology census during pre-COVID-19 (January 2019 to February 2020), COVID-19 (March to May 2020), and post-COVID-19 surge (June to August 2020) in the region. In addition, nosocomial infection rates of SARS-CoV-2 were reviewed. RESULTS: Overall, the daily inpatient census was steady in 2019 (median, 103; range, 92-118) and until February 2020 (median, 112; range, 102-114). However, there was a major decline from March to May 2020 (median, 68; range, 57-104), with 45.4% lower admissions during April 2020. As the COVID-19 surge eased, the daily inpatient census over time returned to the pre-COVID-19 baseline (median, 103; range, 99-111). One patient (1/231, 0.004%) tested positive for SARS-CoV-2 13 days after hospitalization, and it is unclear if it was nosocomial or community spread. CONCLUSION: In this study, inpatient oncology admissions decreased substantially during the COVID-19 surge but over time returned to the pre-COVID-19 baseline. With aggressive infection control measures, the rates of nosocomial transmission were exceedingly low and should provide reassurance to those seeking medical care, including inpatient admissions when medically necessary. IMPLICATIONS FOR PRACTICE: The COVID-19 pandemic has had a major impact on the health care system, and cancer patients are a vulnerable population. This study observes a significant decline in the daily inpatient oncology census from March to May 2020 compared with the same time frame in the previous year and examines the potential reasons for this decline. In addition, nosocomial rates of COVID-19 were investigated, and rates were found to be very low. These findings suggest that aggressive infection control measures can mitigate the nosocomial infection risk among cancer patients and the inpatient setting is a safe environment, providing reassurance.


Subject(s)
COVID-19 , Cross Infection , Neoplasms , Censuses , Cross Infection/epidemiology , Humans , Inpatients , Neoplasms/complications , Neoplasms/epidemiology , Pandemics , SARS-CoV-2
19.
Am J Public Health ; 111(6): 1149-1156, 2021 06.
Article in English | MEDLINE | ID: covidwho-1186636

ABSTRACT

Objectives. To understand how stay-at-home orders changed mobility patterns and influenced the spread of COVID-19.Methods. I merged 2020 data from the Virginia Department of Health, Google Mobility Reports, and the US Census to estimate a series of 2-way fixed-effect event-study regression models.Results. A stay-at-home order caused people to increase the amount of time spent at home by 12 percentage points and decrease the time the spent at work by 30 percentage points, retail and recreation venues by 40 percentage points, and grocery stores and pharmacies by 10 percentage points. People did not sustain changes in mobility and gradually returned to prepandemic levels before the stay-at-home order was lifted. In areas where people spent the most time at indoor locations, there was a large increase in COVID-19.Conclusions. A more robust and stricter policy response coordinated at the national level combined with a strong economic response from policymakers could have increased the effectiveness of the stay-at-home order.


Subject(s)
COVID-19 , Quarantine , Travel , COVID-19/epidemiology , COVID-19/transmission , Censuses , Humans , Virginia/epidemiology
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