[Census of experiences implemented during the Covid period in the Novara, vercelli, Vallemaggia and Locarno areas]. / Censimento delle esperienze attuate in epoca Covid nei territori di Novara, Vercelli, Vallemaggia e Locarno.

. Experiences implemented during the Covid period in the Novara, Vercelli, Vallemaggia and Locarno areas. INTRODUCTION: The Covid-19 pandemic promoted the organization of several initiatives for the elderly. AIM: To map the local district initiatives for citizens >65 years active during the Covid pandemic in 3 districts of Piedmont and Ticino Canton. METHODS: The data were collected through interviews, contacting local Institutions, volunteer organisations and associations and through free research on the web and on institutional websites. RESULTS: The 26 interviews were conducted between August and September 2022: 16 in the Novara area, 4 in Vercelli and 6 in Canton Ticino. Forty-six initiatives were collected, mainly addressing social-healthcare needs, of which seven were already active in the pre-covid period; overall eight are still ongoing. The initiatives consisted of listening windows, home support (meals, shopping, face-masks, delivery of clean clothes to hospitalized patients). 31 were activated by public services with the collaboration of voluntary services. CONCLUSIONS: The mapping of the initiatives showed their heterogeneity and the fundamental role of volunteering in guaranteeing the continuity of supporting services. These experiences should be consolidated over time by institutions and the public health service, enhancing the contribution of volunteers.

Hospital quality reporting in the pandemic era: to what extent did hospitals' COVID-19 census burdens impact 30-day mortality among non-COVID Medicare beneficiaries?

OBJECTIVES: Highly visible hospital quality reporting stakeholders in the USA such as the US News & World Report (USNWR) and the Centers for Medicare & Medicaid Services (CMS) play an important health systems role via their transparent public reporting of hospital outcomes and performance. However, during the pandemic, many such quality measurement stakeholders and pay-for-performance programmes in the USA and Europe have eschewed the traditional risk adjustment paradigm, instead choosing to pre-emptively exclude months or years of pandemic era performance data due largely to hospitals' perceived COVID-19 burdens. These data exclusions may lead patients to draw misleading conclusions about where to seek care, while also masking genuine improvements or deteriorations in hospital quality that may have occurred during the pandemic. Here, we assessed to what extent hospitals' COVID-19 burdens (proportion of hospitalised patients with COVID-19) were associated with their non-COVID 30-day mortality rates from March through November 2020 to inform whether inclusion of pandemic-era data may still be appropriate. DESIGN: This was a retrospective cohort study using the 100% CMS Inpatient Standard Analytic File and Master Beneficiary Summary File to include all US Medicare inpatient encounters with admission dates from 1 April 2020 through 30 November 2020, excluding COVID-19 encounters. Using linear regression, we modelled the association between hospitals' COVID-19 proportions and observed/expected (O/E) ratios, testing whether the relationship was non-linear. We calculated alternative hospital O/E ratios after selective pandemic data exclusions mirroring the USNWR data exclusion methodology. SETTING AND PARTICIPANTS: We analysed 4 182 226 consecutive Medicare inpatient encounters from across 2601 US hospitals. RESULTS: The association between hospital COVID-19 proportion and non-COVID O/E 30-day mortality was statistically significant (p<0.0001), but weakly correlated (r2=0.06). The median (IQR) pairwise relative difference in hospital O/E ratios comparing the alternative analysis with the original analysis was +3.7% (-2.5%, +6.7%), with 1908/2571 (74.2%) of hospitals having relative differences within ±10%. CONCLUSIONS: For non-COVID patient outcomes such as mortality, evidence-based inclusion of pandemic-era data is methodologically plausible and must be explored rather than exclusion of months or years of relevant patient outcomes data.

Child Tax Credit, Spending Patterns, and Mental Health: Mediation Analyses of Data from the U.S. Census Bureau's Household Pulse Survey during COVID-19.

This study examined the relationship between the receipt of COVID-19 child tax credit and adult mental health problems in the United States, and we explored whether and the extent to which a wide range of spending patterns of the credit-15 patterns regarding basic necessities, child education, and household expenditure-mediated the relationship. We used COVID-19-specialized data from the U.S. Census Bureau's Household Pulse Survey, a representative population sample (N = 98,026) of adult respondents (18 and older) who participated between 21 July 2021 and 11 July 2022. By conducting mediation analyses with logistic regression, we found relationships between the credit and lower levels of anxiety (odds ratio [OR] = 0.914; 95% confidence interval [CI] = 0.879, 0.952). The OR was substantially mediated by spending on basic necessities such as food and housing costs (proportion mediated = 46% and 44%, respectively). The mediating role was relatively moderate in the case of spending on child education and household expenditure. We also found that spending the credit on savings or investments reduces the effect of the child tax credit on anxiety (-40%) while donations or giving to family were not a significant mediator. Findings on depression were consistent with anxiety. The child tax credit-depression relationships were substantially mediated by spending on food and housing (proportion mediated = 53% and 70%). These mediation analyses suggested that different patterns of credit spending are important mediators of the relationship between the receipt of the child tax credit and mental illnesses. Public health approaches to improve adult mental health during and after the COVID-19 pandemic need to consider the notable mediating role of spending patterns.

Analysis of Socio-demographic, Economic and Individual Reasons for COVID-19 Vaccination Hesitancy in Ecuador: A Nationwide Longitudinal Study.

The current outbreak of SARS-Cov-2, a virus responsible for COVID-19, has infected millions and caused a soaring death toll worldwide. Vaccination represents a powerful tool in our fight against the transmission of SARS-CoV-2. Ecuador is one of the Latin American countries most impacted by COVID-19. Despite free COVID-19 vaccines, Ecuadorians still hesitate to get vaccinated. A multivariate binary logistic regression was used to analyze data from the Ecuadorian National Institute of Statistics and Censuses. This study investigated socio-demographics, economic, and individual reasons associated with a person having "no intention" to receive COVID-19 vaccine across the study period of October 2021 to March 2022. The survey revealed an increase of unvaccinated people having no intention of COVID-19 vaccination from 57.4% (October-December 2021) to 72.9% (January-March 2022). COVID-19 vaccine hesitancy was dependent on factors like sex, age and ethnicity. Socio-economic characteristics and education level were not found to be statistically significant in lack of vaccine intention, but most vaccination hesitancy was due to distrust in the COVID-19 vaccine. People who believed that the vaccine could be unsafe because of possible side effects represented half of the surveyed participants, a proportion that barely diminished during the progress of the vaccination campaign across October-December 2021 (57.04%) and January-March 2022 (49.59%) periods. People who did not believe that the vaccine was effective enough increased from 11.47 to 18.46%. Misbeliefs about effectiveness and safety of vaccines should be considered in the implementation of public health initiatives of communication, education and intervention to improve vaccination campaigns.

Census Tract Patterns and Contextual Social Determinants of Health Associated With COVID-19 in a Hispanic Population From South Texas: A Spatiotemporal Perspective.

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.

Seroprevalence, correlates and kinetics of SARS-CoV-2 nucleocapsid IgG antibody in healthcare workers and nonclinical staff at a tertiary hospital: A prevaccine census study.

BACKGROUND: Healthcare workers and nonclinical staff in medical facilities are perceived to be a high-risk group for acquiring SAR-CoV-2 infection, and more so in countries where COVID-19 vaccination uptake is low. Serosurveillance may best determine the true extent of SARS-CoV-2 infection since most infected HCWs and other staff may be asymptomatic or present with only mild symptoms. Over time, determining the true extent of SARS-CoV-2 infection could inform hospital management and staff whether the preventive measures instituted are effective and valuable in developing targeted solutions. METHODS: This was a census survey study conducted at the Aga Khan University Hospital, Nairobi, between November 2020 and February 2021 before the implementation of the COVID-19 vaccination. The SARS-CoV-2 nucleocapsid IgG test was performed using a chemiluminescent assay. RESULTS: One thousand six hundred thirty-one (1631) staff enrolled, totalling 60% of the workforce. The overall crude seroprevalence was 18.4% and the adjusted value (for assay sensitivity of 86%) was 21.4% (95% CI; 19.2-23.7). The staff categories with higher prevalence included pharmacy (25.6%), outreach (24%), hospital- based nursing (22.2%) and catering staff (22.6%). Independent predictors of a positive IgG result after adjusting for age, sex and comorbidities included prior COVID-19 like symptoms, odds ratio (OR) 2.0 [95% confidence interval (CI) 1.3-3.0, p = 0.001], a prior positive SARS-CoV-2 PCR result OR 12.0 (CI: 7.7-18.7, p<0.001) and working in a clinical COVID-19 designated area, OR 1.9 (CI 1.1-3.3, p = 0.021). The odds of testing positive for IgG after a positive PCR test were lowest if the antibody test was performed more than 2 months later; OR 0.7 (CI: 0.48-0.95, p = 0.025). CONCLUSIONS: The prevalence of anti- SARS-CoV-2 nucleocapsid IgG among HCWs and nonclinical staff was lower than in the general population. Staff working in clinical areas were not at increased risk when compared to staff working in non-clinical areas.

Effects of Social Vulnerability and Spatial Accessibility on COVID-19 Vaccination Coverage: A Census-Tract Level Study in Milwaukee County, USA.

COVID-19 vaccination coverage was studied by race/ethnicity, up-to-date doses, and by how it was affected by social vulnerability and spatial accessibility at the census-tract level in Milwaukee County, WI, USA. Social vulnerability was quantified at the census-tract level by an aggregate index and its sub-components calculated using the principal components analysis method. The spatial accessibility was assessed by clinic-to-population ratio and travel impedance. Ordinary least squares (OLS) and spatial regression models were employed to examine how social vulnerability and spatial accessibility relate to the vaccination rates of different doses. We found great disparities in vaccination rates by race and between areas of low and high social vulnerability. Comparing to non-Hispanic Blacks, the vaccination rate of non-Hispanic Whites in the county is 23% higher (60% vs. 37%) in overall rate (one or more doses), and 20% higher (29% vs. 9%) in booster rate (three or more doses). We also found that the overall social-vulnerability index does not show a statistically significant relationship with the overall vaccination rate when it is defined as the rate of people who have received one or more doses of vaccines. However, after the vaccination rate is stratified by up-to-date doses, social vulnerability has positive effects on one-dose and two-dose rates, but negative effects on booster rate, and the effects of social vulnerability become increasingly stronger and turn to negative for multi-dose vaccination rates, indicating the increasing challenges of high social vulnerability areas to multi-dose vaccination. The large negative effects of socio-economic status on the booster rate suggests the importance of improving general socio-economic conditions to promote multi-dose vaccination rates.

Spatial and Temporal Characterization of Activity in Public Space, 2019-2020.

The data reported here characterize spatial and temporal variation in the ratio of short-to-long-duration visits in public places (i.e., points of interest) in the United States for each week between January 2019 and December 2020. The underlying data on anonymized and aggregated foot traffic to public places is curated by SafeGraph, a geospatial data provider. In this work, we report the estimated number and duration of "short" (i.e., <4 hours) and "long" (i.e., >4 hours) visits to public places at the US census block group level. Long visits are shown to be a good proxy for workers based on formal economic data. We propose that short visits are more likely to represent nonobligate activities: people visiting a public place for leisure, shopping, entertainment, or civic or cultural engagement. Our work constructs a ratio of short to long visits, which can be used to inform population estimates for nonworker use of public space. These data may be useful for understanding how people's use of public space has changed during the COVID-19 pandemic and, more generally, for understanding activity patterns in public.

Identifying essential critical infrastructure workers during the COVID-19 pandemic using standardized industry codes.

BACKGROUND: The Cybersecurity and Infrastructure Security Agency (CISA) produced an advisory list identifying essential critical infrastructure workers (ECIW) during the coronavirus disease 2019 (COVID-19) response. The CISA advisory list is the most common national definition of ECIW but has not been mapped to United States (U.S.) Census industry codes (CICs) to readily identify these worker populations in public health data sources. METHODS: We identified essential critical infrastructure industry designations corresponding to v4.0 of the CISA advisory list for all six-digit North American Industry Classification System (NAICS) codes and cross-walked NAICS codes to CICs. CICs were grouped as essential, non-essential, or mixed essential/non-essential according to component NAICS industries. We also obtained national estimated population sizes for NAICS and Census industries and cross-tabulated Census industry and occupation codes to identify industry-occupation pairs. RESULTS: We produced and made publicly available spreadsheets containing essential industry designations corresponding to v4.0 of the CISA advisory list for NAICS and Census industry titles and codes and population estimates by six-digit NAICS industry, Census industry, and Census industry-occupation pair. The CISA advisory list is highly inclusive and contains most industries and U.S. workers; 71.0% of Census industries comprising 80.6% of workers and 80.7% of NAICS industries comprising 87.1% of workers were designated as essential. CONCLUSIONS: We identified workers in essential critical infrastructure industries as defined by CISA using standardized industry codes. These classifications may support public health interventions and analyses related to the COVID-19 pandemic and future public health crises.

Reconstructing social mixing patterns via weighted contact matrices from online and representative surveys.

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.

Assessment of Regional Variability in COVID-19 Outcomes Among Patients With Cancer in the United States.

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.

U.S. regional differences in physical distancing: Evaluating racial and socioeconomic divides during the COVID-19 pandemic.

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.

Census of economic evaluations in primary prevention 2014-2019: a scoping review protocol.

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.

Excess deaths reveal the true spatial, temporal and demographic impact of COVID-19 on mortality in Ecuador.

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.

Forecasting COVID-19 Hospital Census: A Multivariate Time-Series Model Based on Local Infection Incidence.

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.

Measuring All-Cause Mortality With the Census Numident File.

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.