How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis

BackgroundPrior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe.ObjectiveOur goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions.MethodsWe developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory’s COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground.ResultsForecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs.ConclusionsReal-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.     

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

BackgroundCOVID-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.ObjectiveThe 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.MethodsThe 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.ResultsThe 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.ConclusionsWhen 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.     

Pandemic Care Through Collaboration: Lessons From a COVID-19 Field Hospital

During the surge of Coronavirus Disease 2019 (COVID-19) infections in March and April 2020, many skilled-nursing facilities in the Boston area closed to COVID-19 post-acute admissions because of infection control concerns and staffing shortages. Local government and health care leaders collaborated to establish a 1000-bed field hospital for patients with COVID-19, with 500 respite beds for the undomiciled and 500 post-acute care (PAC) beds within 9 days. The PAC hospital provided care for 394 patients over 7 weeks, from April 10 to June 2, 2020. In this report, we describe our implementation strategy, including organization structure, admissions criteria, and clinical services. Partnership with government, military, and local health care organizations was essential for logistical and medical support. In addition, dynamic workflows necessitated clear communication pathways, clinical operations expertise, and highly adaptable staff.     

Characteristics of Nursing Homes by COVID-19 Cases Among Staff: March to August 2020

ObjectiveTo measure the association between nursing home (NH) characteristics and Coronavirus Disease 2019 (COVID-19) prevalence among NH staff.DesignRetrospective cross-sectional study.Setting and ParticipantsCenters for Disease Control and Prevention COVID-19 database for US NHs between March and August 2020, linked to NH facility characteristics (LTCFocus database) and local COVID-19 prevalence (USA Facts).MethodsWe estimated the associations between NH characteristics, local infection rates, and other regional characteristics and COVID-19 cases among NH staff (nursing staff, clinical staff, aides, and other facility personnel) measured per 100 beds, controlling for the hospital referral regions in which NHs were located to account for local infection control practices and other unobserved characteristics.ResultsOf the 11,858 NHs in our sample, 78.6% reported at least 1 staff case of COVID-19. After accounting for local COVID-19 prevalence, NHs in the highest quartile of confirmed resident cases (413.5 to 920.0 cases per 1000 residents) reported 18.9 more staff cases per 100 beds compared with NHs that had no resident cases. Large NHs (150 or more beds) reported 2.6 fewer staff cases per 100 beds compared with small NHs (<50 beds) and for-profit NHs reported 0.8 fewer staff cases per 100 beds compared with nonprofit NHs. Higher occupancy and more direct-care hours per day were associated with more staff cases (0.4 more cases per 100 beds for a 10% increase in occupancy, and 0.7 more cases per 100 beds for an increase in direct-care staffing of 1 hour per resident day, respectively). Estimates associated with resident demographics, payer mix, or regional socioeconomic characteristics were not statistically significant.Conclusions and ImplicationsThese findings highlight the urgent need to support facilities with emergency resources such as back-up staff and protocols to reduce resident density within the facility, which may help stem outbreaks.     

Soutien au réseau des services de garde montréalais dans la prévention et la gestion de la COVID-19 : un service à 3 pôles

SettingFrom April 2020, in sight of child care reopening, the Direction régionale de santé publique de Montréal (DRSPM) conducted a situational analysis with its child care (CC) partners in order to learn about the challenges they envisioned in their role in preventing and managing COVID-19. The CC partners requested access to preferred public health support.InterventionThe DRSPM established a service consisting of three components: (1) telephone support available 6 to 7 days/week for CC managers facing a COVID-19 situation; (2) a regional committee combining four Montreal representatives of CC associations and one from the Ministère de la Famille; (3) prevention brigades formed by front-line health workers from the Centres intégrés universitaires de santé et de services sociaux (CIUSSS).OutcomesThis health promotion intervention (1) enabled CC services to handle the pandemic with better capability and confidence through facilitating access to accurate and positive information; (2) supported the commitment and collaboration of CC services by acting as a mediator between them and decision-makers; and (3) responded to the psychosocial needs of community members.ImplicationsThis service helped to adjust public policy and promote community resilience by raising awareness of the importance of balancing COVID-19 prevention and the collateral impacts of the pandemic.     

Impact of COVID-19: Nursing challenges to meeting the care needs of people with developmental disabilities

BackgroundPeople with developmental disabilities (DD) are a population at high-risk for poor outcomes related to COVID-19. COVID-19-specific risks, including greater comorbidities and congregate living situations in persons with DD compound existing health disparities. With their expertise in care of persons with DD and understanding of basic principles of infection control, DD nurses are well-prepared to advocate for the needs of people with DD during the COVID-19 pandemic.ObjectiveTo assess the challenges faced by nurses caring for persons with DD during the COVID-19 pandemic and how the challenges impact people with DD.MethodsWe surveyed 556 DD nurses, from April 6–20, 2020. The 35-item mixed-method survey asked nurses to rate the degree of challenges faced in meeting the care needs of people with DD. We analyzed responses based on presence of COVID-19 in the care setting and geographically. One open-ended question elicited challenges not included in the survey, which we analyzed using manifest content analysis.ResultsStartlingly, nurses reported being excluded from COVID-19 planning, and an absence of public health guidelines specific to persons with DD, despite their high-risk status. Obtaining PPE and sanitizers and meeting social-behavioral care needs were the most highly ranked challenges. COVID-19 impacted nurses’ ability to maintain adequate staffing and perform essential aspects of care. No significant geographic differences were noted.ConclusionsDD nurses must be involved in public health planning and policy development to ensure that basic care needs of persons with DD are met, and the disproportionate burden of COVID-19 in this vulnerable population is reduced.     

Length of Hospital Stay and Survival of Hospitalized COVID-19 Patients During the Second Wave of the Pandemic: A Single Centre Retrospective Study from Slovenia

BackgroundAs of writing, there are no publications pertaining to the prediction of COVID-19-related outcomes and length of stay in patients from Slovene hospitals.ObjectivesTo evaluate the length of regular ward and ICU stays and assess the survival of COVID-19 patients to develop better prediction models to forecast hospital capacity and staffing demands in possible further pandemic peaks.MethodsIn this retrospective, single-site study we analysed the length of stay and survival of all patients, hospitalized due to the novel coronavirus (COVID-19) at the peak of the second wave, between November 18th 2020 and January 27th 2021 at the University Clinic Golnik, Slovenia.ResultsOut of 407 included patients, 59% were male. The median length of stay on regular wards was 7.5 (IQR 5–13) days, and the median ICU length of stay was 6 (IQR 4–11) days. Age, male sex, and ICU stay were significantly associated with a higher risk of death. The probability of dying in 21 days at the regular ward was 14.4% (95% CI [10.9–18%]) and at the ICU it was 43.6% (95% CI [19.3-51.8%]).ConclusionThe survival of COVID-19 is strongly affected by age, sex, and the fact that a patient had to be admitted to ICU, while the length of hospital bed occupancy is very similar across different demographic groups. Knowing the length of stay and admission rate to ICU is important for proper planning of resources during an epidemic.     

COVID-19 Vaccine Uptake Among Residents and Staff Members of Assisted Living and Residential Care Communities—Pharmacy Partnership for Long-Term Care Program,December 2020–April 2021

ObjectivesIn December 2020, CDC launched the Pharmacy Partnership for Long-Term Care Program to facilitate COVID-19 vaccination of residents and staff in long-term care facilities (LTCFs), including assisted living (AL) and other residential care (RC) communities. We aimed to assess vaccine uptake in these communities and identify characteristics that might impact uptake.DesignCross-sectional study.Setting and ParticipantsAL/RC communities in the Pharmacy Partnership for Long-Term Care Program that had ≥1 on-site vaccination clinic during December 18, 2020–April 21, 2021.MethodsWe estimated uptake using the cumulative number of doses of COVID-19 vaccine administered and normalizing by the number of AL/RC community beds. We estimated the percentage of residents vaccinated in 3 states using AL census counts. We linked community vaccine administration data with county-level social vulnerability index (SVI) measures to calculate median vaccine uptake by SVI tertile.ResultsIn AL communities, a median of 67 residents [interquartile range (IQR): 48-90] and 32 staff members (IQR: 15-60) per 100 beds received a first dose of COVID-19 vaccine at the first on-site clinic; in RC, a median of 8 residents (IQR: 5-10) and 5 staff members (IQR: 2-12) per 10 beds received a first dose. Among 3 states with available AL resident census data, median resident first-dose uptake at the first clinic was 93% (IQR: 85-108) in Connecticut, 85% in Georgia (IQR: 70-102), and 78% (IQR: 56-91) in Tennessee. Among both residents and staff, cumulative first-dose vaccine uptake increased with increasing social vulnerability related to housing type and transportation.Conclusions and ImplicationsCOVID-19 vaccination of residents and staff in LTCFs is a public health priority. On-site clinics may help to increase vaccine uptake, particularly when transportation may be a barrier. Ensuring steady access to COVID-19 vaccine in LTCFs following the conclusion of the Pharmacy Partnership is critical to maintaining high vaccination coverage among residents and staff.     

Anticipating the Need for Healthcare Resources Following the Escalation of the COVID-19 Outbreak in the Republic of Kazakhstan

ObjectivesThe lack of advance planning in a public health emergency can lead to wasted resources and inadvertent loss of lives. This study is aimed at forecasting the needs for healthcare resources following the expansion of the coronavirus disease 2019 (COVID-19) outbreak in the Republic of Kazakhstan, focusing on hospital beds, equipment, and the professional workforce in light of the developing epidemiological situation and the data on resources currently available.MethodsWe constructed a forecast model of the epidemiological scenario via the classic susceptible-exposed-infected-removed (SEIR) approach. The World Health Organization’s COVID-19 Essential Supplies Forecasting Tool was used to evaluate the healthcare resources needed for the next 12 weeks.ResultsOver the forecast period, there will be 104 713.7 hospital admissions due to severe disease and 34 904.5 hospital admissions due to critical disease. This will require 47 247.7 beds for severe disease and 1929.9 beds for critical disease at the peak of the COVID-19 outbreak. There will also be high needs for all categories of healthcare workers and for both diagnostic and treatment equipment. Thus, Republic of Kazakhstan faces the need for a rapid increase in available healthcare resources and/or for finding ways to redistribute resources effectively.ConclusionsRepublic of Kazakhstan will be able to reduce the rates of infections and deaths among its population by developing and following a consistent strategy targeting COVID-19 in a number of inter-related directions.     

Participatory COVID-19 Surveillance Tool in Rural Appalachia: Real-Time Disease Monitoring and Regional Response

IntroductionFew US studies have examined the usefulness of participatory surveillance during the coronavirus disease 2019 (COVID-19) pandemic for enhancing local health response efforts, particularly in rural settings. We report on the development and implementation of an internet-based COVID-19 participatory surveillance tool in rural Appalachia.MethodsA regional collaboration among public health partners culminated in the design and implementation of the COVID-19 Self-Checker, a local online symptom tracker. The tool collected data on participant demographic characteristics and health history. County residents were then invited to take part in an automated daily electronic follow-up to monitor symptom progression, assess barriers to care and testing, and collect data on COVID-19 test results and symptom resolution.ResultsNearly 6500 county residents visited and 1755 residents completed the COVID-19 Self-Checker from April 30 through June 9, 2020. Of the 579 residents who reported severe or mild COVID-19 symptoms, COVID-19 symptoms were primarily reported among women (n = 408, 70.5%), adults with preexisting health conditions (n = 246, 70.5%), adults aged 18-44 (n = 301, 52.0%), and users who reported not having a health care provider (n = 131, 22.6%). Initial findings showed underrepresentation of some racial/ethnic and non–English-speaking groups.Practical ImplicationsThis low-cost internet-based platform provided a flexible means to collect participatory surveillance data on local changes in COVID-19 symptoms and adapt to guidance. Data from this tool can be used to monitor the efficacy of public health response measures at the local level in rural Appalachia.     

Continuing inequalities in COVID-19 mortality in England and Wales,and the changing importance of regional,over local,deprivation

BackgroundObservational studies have highlighted that where individuals live is far more important for risk of dying with COVID-19, than for dying of other causes. Deprivation is commonly proposed as explaining such differences. During the period of localised restrictions in late 2020, areas with higher restrictions tended to be more deprived. We explore how this impacted the relationship between deprivation and mortality and see whether local or regional deprivation matters more for inequalities in COVID-19 mortality.MethodsWe use publicly available population data on deaths due to COVID-19 and all-cause mortality between March 2020 and April 2021 to investigate the scale of spatial inequalities. We use a multiscale approach to simultaneously consider three spatial scales through which processes driving inequalities may act. We go on to explore whether deprivation explains such inequalities.ResultsAdjusting for population age structure and number of care homes, we find highest regional inequality in October 2020, with a COVID-19 mortality rate ratio of 5.86 (95% CI 3.31 to 19.00) for the median between-region comparison. We find spatial context is most important, and spatial inequalities higher, during periods of low mortality. Almost all unexplained spatial inequality in October 2020 is removed by adjusting for deprivation. During October 2020, one standard deviation increase in regional deprivation was associated with 20% higher local mortality (95% CI, 1.10 to 1.30).ConclusionsSpatial inequalities are greatest in periods of lowest overall mortality, implying that as mortality declines it does not do so equally. During the prolonged period of low restrictions and low mortality in summer 2020, spatial inequalities strongly increased. Contrary to previous months, we show that the strong spatial patterning during autumn 2020 is almost entirely explained by deprivation. As overall mortality declines, policymakers must be proactive in detecting areas where this is not happening, or risk worsening already strong health inequalities.     

The impact of COVID-19 on routine pediatric vaccination delivery in Brazil

IntroductionChildhood vaccination rates have decreased significantly during the COVID-19 pandemic. The Brazilian immunization program, Programa Nacional de Imunização (PNI), is a model effort, achieving immunization rates comparable to high-income countries. This study aimed to evaluate the impact of the COVID-19 pandemic in pediatric vaccinations administered by the PNI, as a proxy of adherence to vaccinations during 2020.MethodsData on the number of vaccines administered to children under 10 years of age nationally and in each of Brazil’s five regions were extracted from Brazil’s federal health delivery database. Population adjusted monthly vaccination rates from 2015 through 2019 were determined, and autoregressive integrated moving average (ARIMA) models were used to forecast expected vaccinated rates in 2020. We compared the forecasts to reported vaccine administrations to assess adequacy of pediatric vaccine delivery during the COVID-19 pandemic.ResultsFrom January 2015 to February 2020, the average rate of vaccine administration to children was 53.4 per 100,000. After February 2020, this rate decreased to 50.4, a 9.4% drop compared to 2019 and fell outside of forecasted ranges in December 2020. In Brazil's poorest region, the North, vaccine delivery fell outside of the forecasted ranges earlier in 2020 but subsequently rebounded, meeting expected targets by the end of 2020. However, in Brazil's wealthiest South and Southeast regions, initial vaccine delivery fell and remained well below forecasted rates through the end of 2020.ConclusionIn Brazil, despite a model national pediatric vaccination program with an over 95% national coverage, vaccination rates decreased during the COVID-19 pandemic. Coordinated governmental efforts have ameliorated some of the decrease, but more efforts are needed to ensure continued protection from preventable communicable diseases for children globally.     

Influenza cases in nine aged care facilities in Sydney,Australia over a three-year surveillance period, 2018—2020

BackgroundInfluenza outbreaks in aged care facilities are a major public health concern. In response to the severe 2017 influenza season in Australia, enhanced influenza vaccines were introduced from 2018 onwards for those over 65 and more emphasis was placed on improving vaccination rates among aged care staff. During the COVID-19 pandemic, these efforts were then further escalated to reduce the additional burden that influenza could pose to facilities.MethodsAn observational epidemiological study was conducted from 2018 to 2020 in nine Sydney (Australia) aged care facilities of the same provider. De-identified vaccination data and physical layout data were collected from participating facility managers from 2018 to 2020. Active surveillance of influenza-like illness was carried out from 2018 to 2020 influenza seasons. Correlation and Poisson regression analyses were carried out to explore the relationship between physical layout variables to occurrence of influenza cases.ResultsInfluenza cases were low in 2018 and 2019, and there were no confirmed influenza cases identified in 2020. Vaccination rates increased among staff by 50.5% and residents by 16.8% over the three-year period of surveillance from 2018 to 2020. For each unit increase in total number of beds, common areas, single rooms, all types of rooms (including double occupancy rooms), the influenza cases increased by 1.02 (95% confidence interval:1.018–1.025), 1.04 (95% confidence interval: 1.019–1.073), 1.03 (95% confidence interval: 1.016–1 0.038) and 1.02 (95% confidence interval:1.005–1.026) times which were found to be statistically significant. For each unit increase in the proportion of shared rooms, influenza cases increased by 1.004 (95% confidence interval:1.0001–1.207) which was found to be statistically significant.ConclusionsThere is a relationship between influenza case counts and aspects of the physical layout such as facility size, and this should be considered in assessing risk of outbreaks in aged care facilities. Increased vaccination rates in staff and COVID-19 prevention and control measures may have eliminated influenza in the studied facilities in 2020.     

COVID-19 outcomes among people with intellectual and developmental disability in California: The importance of type of residence and skilled nursing care needs

BackgroundPeople with intellectual and developmental disabilities (IDD) appear to be at greater risk for severe outcomes from COVID-19. The roles of congregate living and skilled nursing care needs in this disparity are unclear.ObjectiveTo determine the impact of residential setting and level of skilled nursing care on COVID-19 outcomes for people receiving IDD services, compared to those not receiving IDD services.MethodsUtilizing publicly available California data on COVID-19 outcomes for people receiving IDD services (early May through October 2, 2020), we report outcomes based on seven types of residence, differentiated by number of residents and level of skilled nursing care provided. We compared these results to the larger California published outcomes.ResultsCompared to Californians not receiving IDD services, in general, those receiving IDD services had a 60% lower case rate, but 2.8 times higher case-fatality rate. COVID-19 outcomes varied significantly among Californians receiving IDD services by type of residence and skilled nursing care needs: higher rates of diagnosis in settings with larger number of residents, higher case-fatality and mortality rates in settings that provided 24-h skilled nursing care.ConclusionsDiagnosis with COVID-19 among Californians receiving IDD services appears to be related to the number of individuals within the residence, while adverse COVID-19 outcomes were associated with level of skilled nursing care. When data is available, future research should examine whether these relationships persist even when controlling for age and pre-existing conditions.     

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

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

A country-level analysis comparing hospital capacity and utilisation during the first COVID-19 wave across Europe

BackgroundThe exponential increase in SARS-CoV-2 infections during the first wave of the pandemic created an extraordinary overload and demand on hospitals, especially intensive care units (ICUs), across Europe. European countries have implemented different measures to address the surge ICU capacity, but little is known about the extent. The aim of this paper is to compare the rates of hospitalised COVID-19 patients in acute and ICU care and the levels of national surge capacity for intensive care beds across 16 European countries and Lombardy region during the first wave of the pandemic (28 February to 31 July).MethodsFor this country level analysis, we used data on SARS-CoV-2 cases, current and/or cumulative hospitalised COVID-19 patients and current and/or cumulative COVID-19 patients in ICU care. To analyse whether capacities were exceeded, we also retrieved information on the numbers of hospital beds, and on (surge) capacity of ICU beds during the first wave of the COVID-19 pandemic from the COVID-19 Health System Response Monitor (HSRM). Treatment days and mean length of hospital stay were calculated to assess hospital utilisation.ResultsHospital and ICU capacity varied widely across countries. Our results show that utilisation of acute care bed capacity by patients with COVID-19 did not exceed 38.3% in any studied country. However, the Netherlands, Sweden, and Lombardy would not have been able to treat all patients with COVID-19 requiring intensive care during the first wave without an ICU surge capacity. Indicators of hospital utilisation were not consistently related to the number of SARS-CoV-2 infections. The mean number of hospital days associated with one SARS-CoV-2 case ranged from 1.3 (Norway) to 11.8 (France).ConclusionIn many countries, the increase in ICU capacity was important to accommodate the high demand for intensive care during the first COVID-19 wave.     

Community surveillance of COVID-19 by village health volunteers,Thailand

ProblemTo control the increasing spread of coronavirus disease 2019 (COVID-19), the government of Thailand enforced the closure of public and business areas in Bangkok on 22 March 2020. As a result, large numbers of unemployed workers returned to their hometowns during April 2020, increasing the risk of spreading the virus across the entire country.ApproachIn anticipation of the large-scale movement of unemployed workers, the Thai government trained existing village health volunteers to recognize the symptoms of COVID-19 and educate members of their communities. Provincial health offices assembled COVID-19 surveillance teams of these volunteers to identify returnees from high-risk areas, encourage self-quarantine for 14 days, and monitor and report the development of any relevant symptoms.Local settingDespite a significant and recent expansion of the health-care workforce to meet sustainable development goal targets, there still exists a shortage of professional health personnel in rural areas of Thailand. To compensate for this, the primary health-care system includes trained village health volunteers who provide basic health care to their communities.Relevant changesVillage health volunteers visited more than 14 million households during March and April 2020. Volunteers identified and monitored 809 911 returnees, and referred a total of 3346 symptomatic patients to hospitals by 13 July 2020.Lessons learntThe timely mobilization of Thailand’s trusted village health volunteers, educated and experienced in infectious disease surveillance, enabled the robust response of the country to the COVID-19 pandemic. The virus was initially contained without the use of a costly country-wide lockdown or widespread testing.     

Excess Mortality Among Assisted Living Residents With Dementia During the COVID-19 Pandemic

ObjectiveTo evaluate whether assisted living (AL) residents with Alzheimer’s disease and related dementias (ADRD) experienced a greater rate of excess all-cause mortality during the first several months of the COVID-19 pandemic compared to residents without ADRD, and to compare excess all-cause mortality rates in memory care vs general AL among residents with ADRD.DesignRetrospective cohort study.Setting and ParticipantsTwo cohorts of AL residents enrolled in Medicare Fee-For-Service who resided in 9-digit ZIP codes corresponding to US AL communities of ≥25 beds during calendar year 2019 or 2020.MethodBy linking Medicare claims and Vital Statistics data, we examined the weekly excess all-cause mortality rate, comparing the rate from March 12, 2020, to December 31, 2020, to the rate from January 1, 2019, to March 11, 2020. We adjusted for demographics, chronic conditions, AL community size, and county fixed effects.ResultsOf the 286,350 residents in 2019 and the 273,601 in 2020 identified in these cohorts, approximately 31% had a diagnosis of ADRD. Among all AL residents, the excess weekly mortality rate in 2020 was 49.1 per 100,000 overall during the pandemic. Compared to residents without ADRD, residents with ADRD experienced 33.4 more excess deaths per 100,000 during the pandemic. Among residents with ADRD, those who resided in memory care communities did not experience a statistically significant different mortality rate than residents who lived in general AL.Conclusions and ImplicationsAL residents with ADRD were more vulnerable to mortality during COVID-19 than residents without ADRD, a finding similar to those reported in other settings such as nursing homes. Additionally, the study provides important new information that residents with ADRD in memory care communities may not have been at differential risk of COVID-19 mortality when compared to residents with ADRD in general AL, despite prior research suggesting they have more advanced dementia.     

Implementation of Telemedicine in a Tertiary Hospital–Based Ambulatory Practice in Detroit During the COVID-19 Pandemic: Observational Study

BackgroundThe COVID-19 pandemic, caused by SARS-CoV-2, has forced the health care delivery structure to change rapidly. The pandemic has further widened the disparities in health care and exposed vulnerable populations. Health care services caring for such populations must not only continue to operate but create innovative methods of care delivery without compromising safety. We present our experience of incorporating telemedicine in our university hospital–based outpatient clinic in one of the worst-hit areas in the world.ObjectiveOur goal is to assess the adoption of a telemedicine service in the first month of its implementation in outpatient practice during the COVID-19 pandemic. We also want to assess the need for transitioning to telemedicine, the benefits and challenges in doing so, and ongoing solutions during the initial phase of the implementation of telemedicine services for our patients.MethodsWe conducted a prospective review of clinic operations data from the first month of a telemedicine rollout in the outpatient adult ambulatory clinic from April 1, 2020, to April 30, 2020. A telemedicine visit was defined as synchronous audio-video communication between the provider and patient for clinical care longer than 5 minutes or if the video visit converted to a telephone visit after 5 minutes due to technical problems. We recorded the number of telemedicine visits scheduled, visits completed, and the time for each visit. We also noted the most frequent billing codes used based on the time spent in the patient care and the number of clinical tasks (eg, activity suggested through diagnosis or procedural code) that were addressed remotely by the physicians.ResultsDuring the study period, we had 110 telemedicine visits scheduled, of which 94 (85.4%) visits were completed. The average duration of the video visit was 35 minutes, with the most prolonged visit lasting 120 minutes. Of 94 patients, 24 (25.54%) patients were recently discharged from the hospital, and 70 (74.46%) patients were seen for urgent care needs. There was a 50% increase from the baseline in the number of clinical tasks that were addressed by the physicians during the pandemic.ConclusionsThere was a high acceptance of telemedicine services by the patients, which was evident by a high show rate during the COVID-19 pandemic in Detroit. With limited staffing, restricted outpatient work hours, a shortage of providers, and increased outpatient needs, telemedicine was successfully implemented in our practice.