Risk of severe COVID-19 infection among adults with prior exposure to children.

Susceptibility and severity of COVID-19 infection vary widely. Prior exposure to endemic coronaviruses, common in young children, may protect against SARS-CoV-2. We evaluated risk of severe COVID-19 among adults with and without exposure to young children in a large, integrated healthcare system. Adults with children 0-5 years were matched 1:1 to adults with children 6-11 years, 12-18 years, and those without children based upon a COVID-19 propensity score and risk factors for severe COVID-19. COVID-19 infections, hospitalizations, and need for intensive care unit (ICU) were assessed in 3,126,427 adults, of whom 24% (N = 743,814) had children 18 years or younger, and 8.8% (N = 274,316) had a youngest child 0-5 years. After 1:1 matching, propensity for COVID-19 infection and risk factors for severe COVID-19 were well balanced between groups. Rates of COVID-19 infection were slightly higher for adults with exposure to older children (incident risk ratio, 1.09, 95% confidence interval, [1.05-1.12] and IRR 1.09 [1.05-1.13] for adults with children 6-11 and 12-18, respectively), compared to those with children 0-5 years, although no difference in rates of COVID-19 illness requiring hospitalization or ICU admission was observed. However, adults without exposure to children had lower rates of COVID-19 infection (IRR 0.85, [0.83-0.87]) but significantly higher rates of COVID-19 hospitalization (IRR 1.49, [1.29-1.73]) and hospitalization requiring ICU admission (IRR 1.76, [1.19-2.58]) compared to those with children aged 0-5. In a large, real-world population, exposure to young children was associated with less severe COVID-19 illness. Endemic coronavirus cross-immunity may play a role in protection against severe COVID-19.

Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments.

BACKGROUND: In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. RESULTS: We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. CONCLUSIONS: Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

Default Palliative Care Consultation for Seriously Ill Hospitalized Patients: A Pragmatic Cluster Randomized Trial.

Importance: Increasing inpatient palliative care delivery is prioritized, but large-scale, experimental evidence of its effectiveness is lacking. Objective: To determine whether ordering palliative care consultation by default for seriously ill hospitalized patients without requiring greater palliative care staffing increased consultations and improved outcomes. Design, Setting, and Participants: A pragmatic, stepped-wedge, cluster randomized trial was conducted among patients 65 years or older with advanced chronic obstructive pulmonary disease, dementia, or kidney failure admitted from March 21, 2016, through November 14, 2018, to 11 US hospitals. Outcome data collection ended on January 31, 2019. Intervention: Ordering palliative care consultation by default for eligible patients, while allowing clinicians to opt-out, was compared with usual care, in which clinicians could choose to order palliative care. Main Outcomes and Measures: The primary outcome was hospital length of stay, with deaths coded as the longest length of stay, and secondary end points included palliative care consult rate, discharge to hospice, do-not-resuscitate orders, and in-hospital mortality. Results: Of 34 239 patients enrolled, 24 065 had lengths of stay of at least 72 hours and were included in the primary analytic sample (10 313 in the default order group and 13 752 in the usual care group; 13 338 [55.4%] women; mean age, 77.9 years). A higher percentage of patients in the default order group received palliative care consultation than in the standard care group (43.9% vs 16.6%; adjusted odds ratio [aOR], 5.17 [95% CI, 4.59-5.81]) and received consultation earlier (mean [SD] of 3.4 [2.6] days after admission vs 4.6 [4.8] days; P < .001). Length of stay did not differ between the default order and usual care groups (percent difference in median length of stay, -0.53% [95% CI, -3.51% to 2.53%]). Patients in the default order group had higher rates of do-not-resuscitate orders at discharge (aOR, 1.40 [95% CI, 1.21-1.63]) and discharge to hospice (aOR, 1.30 [95% CI, 1.07-1.57]) than the usual care group, and similar in-hospital mortality (4.7% vs 4.2%; aOR, 0.86 [95% CI, 0.68-1.08]). Conclusions and Relevance: Default palliative care consult orders did not reduce length of stay for older, hospitalized patients with advanced chronic illnesses, but did improve the rate and timing of consultation and some end-of-life care processes. Trial Registration: ClinicalTrials.gov Identifier: NCT02505035.

Essential childhood immunization in 43 low- and middle-income countries: Analysis of spatial trends and socioeconomic inequalities in vaccine coverage.

BACKGROUND: Globally, access to life-saving vaccines has improved considerably in the past 5 decades. However, progress has started to slow down and even reverse in recent years. Understanding subnational heterogeneities in essential child immunization will be critical for closing the global vaccination gap. METHODS AND FINDINGS: We use vaccination information for over 220,000 children across 1,366 administrative regions in 43 low- and middle-income countries (LMICs) from the most recent Demographic and Health Surveys. We estimate essential immunization coverage at the national and subnational levels and quantify socioeconomic inequalities in such coverage using adjusted concentration indices. Within- and between-country variations are summarized via the Theil index. We use local indicator of spatial association (LISA) statistics to identify clusters of administrative regions with high or low values. Finally, we estimate the number of missed vaccinations among children aged 15 to 35 months across all 43 countries and the types of vaccines most often missed. We show that national-level vaccination rates can conceal wide subnational heterogeneities. Large gaps in child immunization are found across West and Central Africa and in South Asia, particularly in regions of Angola, Chad, Nigeria, Guinea, and Afghanistan, where less than 10% of children are fully immunized. Furthermore, children living in these countries consistently lack all 4 basic vaccines included in the WHO's recommended schedule for young children. Across most countries, children from poorer households are less likely to be fully immunized. The main limitations include subnational estimates based on large administrative divisions for some countries and different periods of survey data collection. CONCLUSIONS: The identified heterogeneities in essential childhood immunization, especially given that some regions consistently are underserved for all basic vaccines, can be used to inform the design and implementation of localized intervention programs aimed at eliminating child suffering and deaths from existing and novel vaccine-preventable diseases.

Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration.

BACKGROUND: Hospitalized adults whose condition deteriorates while they are in wards (outside the intensive care unit [ICU]) have considerable morbidity and mortality. Early identification of patients at risk for clinical deterioration has relied on manually calculated scores. Outcomes after an automated detection of impending clinical deterioration have not been widely reported. METHODS: On the basis of a validated model that uses information from electronic medical records to identify hospitalized patients at high risk for clinical deterioration (which permits automated, real-time risk-score calculation), we developed an intervention program involving remote monitoring by nurses who reviewed records of patients who had been identified as being at high risk; results of this monitoring were then communicated to rapid-response teams at hospitals. We compared outcomes (including the primary outcome, mortality within 30 days after an alert) among hospitalized patients (excluding those in the ICU) whose condition reached the alert threshold at hospitals where the system was operational (intervention sites, where alerts led to a clinical response) with outcomes among patients at hospitals where the system had not yet been deployed (comparison sites, where a patient's condition would have triggered a clinical response after an alert had the system been operational). Multivariate analyses adjusted for demographic characteristics, severity of illness, and burden of coexisting conditions. RESULTS: The program was deployed in a staggered fashion at 19 hospitals between August 1, 2016, and February 28, 2019. We identified 548,838 non-ICU hospitalizations involving 326,816 patients. A total of 43,949 hospitalizations (involving 35,669 patients) involved a patient whose condition reached the alert threshold; 15,487 hospitalizations were included in the intervention cohort, and 28,462 hospitalizations in the comparison cohort. Mortality within 30 days after an alert was lower in the intervention cohort than in the comparison cohort (adjusted relative risk, 0.84, 95% confidence interval, 0.78 to 0.90; P<0.001). CONCLUSIONS: The use of an automated predictive model to identify high-risk patients for whom interventions by rapid-response teams could be implemented was associated with decreased mortality. (Funded by the Gordon and Betty Moore Foundation and others.).

Bovine lactoferrin for the prevention of COVID-19 infection in health care personnel: a double-blinded randomized clinical trial (LF-COVID).

Lactoferrin (LF) has in vitro antiviral activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aimed to determine the effect of bovine lactoferrin (bLF) in the prevention of SARS-CoV-2 infection in health care personnel. A randomized, double-blinded, placebo-controlled clinical trial was conducted in two tertiary hospitals that provide care to patients with SARS-CoV-2 infection in Lima, Peru. Daily supplementation with 600 mg of enteral bLF versus placebo for 90 days was compared. Participants were weekly screened for symptoms suggestive of SARS-CoV-2 infection and molecular testing was performed on suspected episodes. A serological test was obtained from all participants at the end of the intervention. The main outcome included symptomatic and asymptomatic cases. A sub-analysis explored the time to symptomatic infection. Secondary outcomes were the severity, frequency, and duration of symptomatic infection. The study was prematurely cancelled due to the availability of vaccines against SARS-CoV-2 in Peru. 209 participants were enrolled and randomized, 104 received bLF and 105 placebo. SARS-CoV-2 infection occurred in 11 (10.6%) participants assigned to bLF and in 9 (8.6%) participants assigned to placebo without significant differences (Incidence Rate Ratio = 1.23, 95%CI 0.51-3.06, p-value = 0.64). There was no significant effect of bLF on time to symptomatic infection (Hazard Ratio = 1.61, 95%CI 0.62-4.19, p-value = 0.3). There were no significant differences in secondary outcomes. A significant effect of bLF in preventing SARS-CoV-2 infection was not proven. Further studies are needed to assess the effect of bLF supplementation on SARS-CoV-2 infection.Clinical trial registration ClinicalTrials.gov Identifier: NCT04526821, https://clinicaltrials.gov/ct2/show/NCT04526821?term=LACTOFERRIN&cond=COVID-19&cntry=PE&city=Lima&draw=2&rank=1 .

Association of ICU Admission and Outcomes in Sepsis and Acute Respiratory Failure.

Rationale: Many decisions to admit patients to the ICU are not grounded in evidence regarding who benefits from such triage, straining ICU capacity and limiting its cost-effectiveness. Objectives: To measure the benefits of ICU admission for patients with sepsis or acute respiratory failure. Methods: At 27 United States hospitals across two health systems from 2013 to 2018, we performed a retrospective cohort study using two-stage instrumental variable quantile regression with a strong instrument (hospital capacity strain) governing ICU versus ward admission among high-acuity patients (i.e., laboratory-based acute physiology score v2 ⩾ 100) with sepsis and/or acute respiratory failure who did not require mechanical ventilation or vasopressors in the emergency department. Measurements and Main Results: Among patients with sepsis (n = 90,150), admission to the ICU was associated with a 1.32-day longer hospital length of stay (95% confidence interval [CI], 1.01-1.63; P < 0.001) (when treating deaths as equivalent to long lengths of stay) and higher in-hospital mortality (odds ratio, 1.48; 95% CI, 1.13-1.88; P = 0.004). Among patients with respiratory failure (n = 45,339), admission to the ICU was associated with a 0.82-day shorter hospital length of stay (95% CI, -1.17 to -0.46; P < 0.001) and reduced in-hospital mortality (odds ratio, 0.75; 95% CI, 0.57-0.96; P = 0.04). In sensitivity analyses of length of stay, excluding, ignoring, or censoring death, results were similar in sepsis but not in respiratory failure. In subgroup analyses, harms of ICU admission for patients with sepsis were concentrated among older patients and those with fewer comorbidities, and the benefits of ICU admission for patients with respiratory failure were concentrated among older patients, highest-acuity patients, and those with more comorbidities. Conclusions: Among high-acuity patients with sepsis who did not require life support in the emergency department, initial admission to the ward, compared with the ICU, was associated with shorter length of stay and improved survival, whereas among patients with acute respiratory failure, triage to the ICU compared with the ward was associated with improved survival.

A review of immunization legislation for children in English- and Dutch-speaking Caribbean countries.

Objective: To assess the legislative frameworks concerning childhood vaccination in the English- and Dutch-speaking Caribbean and propose a model legislative framework for Caribbean countries. Methods: This study included a survey of 22 countries and territories in the Caribbean regarding legal vaccination mandates for school entry, budget allocations, sanctions, or exemptions. A legal consultant conducted a comprehensive search and analysis of legislation regarding vaccination among 13 Caribbean countries/territories. A comparative analysis of the legislation under five themes-legislative structure, mandatory vaccination, national immunization schedule, sanctions, and exemptions-formed the basis for the proposed model legislation. Results: Among the 22 Caribbean countries/territories, 17 (77%) had legislation mandating vaccination, 16 (94%) mandated vaccination for school entry, 8 (47%) had a dedicated budget for immunization programs, and 13 (76%) had no legislated national schedules. The source of legislation includes six (35%) using the Education Act, eight (47%) the Public Health Act, and five (29%) a free-standing Vaccination Act. Three countries/territories-Jamaica, Montserrat, and Saint Lucia-had immunization regulations. In 12 (71%) of the 17 countries with legislation, sanctions were included, and 10 (59%) permitted exemptions for medical or religious/philosophical beliefs. Conclusions: Several countries in the Caribbean have made failure to vaccinate a child an offense. By summarizing the existing legislative frameworks and approaches to immunization in the Caribbean, the analysis guides policymakers in making effective changes to immunization legislation in their own countries.

Adults hospitalized with breakthrough COVID-19 have lower mortality than matched unvaccinated adults.

BACKGROUND: Coronavirus disease 2019 (COVID-19) breakthrough infections are common. OBJECTIVE: Evaluate in-hospital mortality of patients with COVID-19 by vaccination status using retrospective cohort study. METHODS: We generated propensity scores for receipt of full vaccination in adults requiring supplemental oxygen hospitalized at Kaiser Permanente Northern California (1 April 2021 to 30 November 2021) with positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction tests. Optimal matching of fully vaccinated/unvaccinated patients was performed comparing in-hospital mortality. RESULTS: Of 7305 patients, 1463 (20.0%) were full, 138 (1.9%) were partial, and 5704 (78.1%) were unvaccinated. Fully vaccinated were older than partial or unvaccinated (71.0, 63.0, and 54.0 years, respectively, p < 0.001) with more comorbidities (Comorbidity Point Scores 33.0, 22.0, and 10.0, p < 0.001) and immunosuppressant (11.5%, 8.7%, and 3.0%, p < 0.001) or chemotherapy exposure (2.8%, 0.7%, and 0.4%, p < 0.001). Fewer fully vaccinated patients died compared to matched unvaccinated (9.0% vs. 16.3%, p < 0.0001). CONCLUSION: Fully vaccinated patients are less likely to die compared to matched unvaccinated patients.

Development, environmental degradation, and disease spread in the Brazilian Amazon.

The Amazon is Brazil's greatest natural resource and invaluable to the rest of the world as a buffer against climate change. The recent election of Brazil's president brought disputes over development plans for the region back into the spotlight. Historically, the development model for the Amazon has focused on exploitation of natural resources, resulting in environmental degradation, particularly deforestation. Although considerable attention has focused on the long-term global cost of "losing the Amazon," too little attention has focused on the emergence and reemergence of vector-borne diseases that directly impact the local population, with spillover effects to other neighboring areas. We discuss the impact of Amazon development models on human health, with a focus on vector-borne disease risk. We outline policy actions that could mitigate these negative impacts while creating opportunities for environmentally sensitive economic activities.

Heat Waves and Emergency Department Visits Among the Homeless, San Diego, 2012-2019.

Objectives. To determine the effect of heat waves on emergency department (ED) visits for individuals experiencing homelessness and explore vulnerability factors. Methods. We used a unique highly detailed data set on sociodemographics of ED visits in San Diego, California, 2012 to 2019. We applied a time-stratified case-crossover design to study the association between various heat wave definitions and ED visits. We compared associations with a similar population not experiencing homelessness using coarsened exact matching. Results. Of the 24 688 individuals identified as experiencing homelessness who visited an ED, most were younger than 65 years (94%) and of non-Hispanic ethnicity (84%), and 14% indicated the need for a psychiatric consultation. Results indicated a positive association, with the strongest risk of ED visits during daytime (e.g., 99th percentile, 2 days) heat waves (odds ratio = 1.29; 95% confidence interval = 1.02, 1.64). Patients experiencing homelessness who were younger or elderly and who required a psychiatric consultation were particularly vulnerable to heat waves. Odds of ED visits were higher for individuals experiencing homelessness after matching to nonhomeless individuals based on age, gender, and race/ethnicity. Conclusions. It is important to prioritize individuals experiencing homelessness in heat action plans and consider vulnerability factors to reduce their burden. (Am J Public Health. 2022;112(1):98-106. https://doi.org/10.2105/AJPH.2021.306557).

Air pollution and metabolic disorders: Dynamic versus static measures of exposure among Hispanics/Latinos and non-Hispanics.

INTRODUCTION: Exposure to air pollution disproportionately affects racial/ethnic minorities that could contribute to health inequalities including metabolic disorders. However, most existing studies used a static assessment of air pollution exposure (mostly using the residential address) and do not account for activity space when modelling exposure to air pollution. The aim of this study is to understand how exposure to air pollution impacts metabolic disorders biomarkers, how this effect differs according to ethnicity, and for the first time compare these findings with two methods of exposure assessment: dynamic and static measures. METHODS: Among the Community of Mine study, a cross-sectional study conducted in San Diego County, insulin resistance, diabetes, hypertension, obesity, dyslipidemia, and metabolic syndrome (MetS) were assessed. Exposure to air pollution (PM2.5, NO2, traffic) was calculated using static measures around the home, and dynamic measures of mobility derived from Global Positioning Systems (GPS) traces using kernel density estimators to account for exposure variability across space and time. Associations of air pollution with metabolic disorders were quantified using generalized estimating equation models to account for the clustered nature of the data. RESULTS: Among 552 participants (mean age 58.7 years, 42% Hispanic/Latino), Hispanics/Latinos had a higher exposure to PM2.5 compared to non-Hispanics using static measures. In contrast, Hispanics/Latinos had less exposure to PM2.5 using dynamic measures. For all participants, higher dynamic exposure to PM2.5 and NO2 was associated with increased insulin resistance and cholesterol levels, and increased risk of obesity, dyslipidemia and MetS (RR 1.17, 95% CI: 1.07-1.28; RR 1.21, 95% CI: 1.12-1.30, respectively). The association between dynamic PM2.5 exposure and MetS differed by Hispanic/Latino ethnicity. CONCLUSION: These results highlight the importance of considering people's daily mobility in assessing the impact of air pollution on health.

Equitably Allocating Resources during Crises: Racial Differences in Mortality Prediction Models.

Rationale: Crisis standards of care (CSCs) guide critical care resource allocation during crises. Most recommend ranking patients on the basis of their expected in-hospital mortality using the Sequential Organ Failure Assessment (SOFA) score, but it is unknown how SOFA or other acuity scores perform among patients of different races. Objectives: To test the prognostic accuracy of the SOFA score and version 2 of the Laboratory-based Acute Physiology Score (LAPS2) among Black and white patients. Methods: We included Black and white patients admitted for sepsis or acute respiratory failure at 27 hospitals. We calculated the discrimination and calibration for in-hospital mortality of SOFA, LAPS2, and modified versions of each, including categorical SOFA groups recommended in a popular CSC and a SOFA score without creatinine to reduce the influence of race. Measurements and Main Results: Of 113,158 patients, 27,644 (24.4%) identified as Black. The LAPS2 demonstrated higher discrimination (area under the receiver operating characteristic curve [AUC], 0.76; 95% confidence interval [CI], 0.76-0.77) than the SOFA score (AUC, 0.68; 95% CI, 0.68-0.69). The LAPS2 was also better calibrated than the SOFA score, but both underestimated in-hospital mortality for white patients and overestimated in-hospital mortality for Black patients. Thus, in a simulation using observed mortality, 81.6% of Black patients were included in lower-priority CSC categories, and 9.4% of all Black patients were erroneously excluded from receiving the highest prioritization. The SOFA score without creatinine reduced racial miscalibration. Conclusions: Using SOFA in CSCs may lead to racial disparities in resource allocation. More equitable mortality prediction scores are needed.

Prospective evaluation of social risks, physical function, and cognitive function in prediction of non-elective rehospitalization and post-discharge mortality.

BACKGROUND: Increasing evidence suggests that social factors and problems with physical and cognitive function may contribute to patients' rehospitalization risk. Understanding a patient's readmission risk may help healthcare providers develop tailored treatment and post-discharge care plans to reduce readmission and mortality. This study aimed to evaluate whether including patient-reported data on social factors; cognitive status; and physical function improves on a predictive model based on electronic health record (EHR) data alone. METHODS: We conducted a prospective study of 1,547 hospitalized adult patients in 3 Kaiser Permanente Northern California hospitals. The main outcomes were non-elective rehospitalization or death within 30 days post-discharge. Exposures included patient-reported social factors and cognitive and physical function (obtained in a pre-discharge interview) and EHR-derived data for comorbidity burden, acute physiology, care directives, prior utilization, and hospital length of stay. We performed bivariate comparisons using Chi-square, t-tests, and Wilcoxon rank-sum tests and assessed correlations between continuous variables using Spearman's rho statistic. For all models, the results reported were obtained after fivefold cross validation. RESULTS: The 1,547 adult patients interviewed were younger (age, p = 0.03) and sicker (COPS2, p < 0.0001) than the rest of the hospitalized population. Of the 6 patient-reported social factors measured, 3 (not living with a spouse/partner, transportation difficulties, health or disability-related limitations in daily activities) were significantly associated (p < 0.05) with the main outcomes, while 3 (living situation concerns, problems with food availability, financial problems) were not. Patient-reported cognitive (p = 0.027) and physical function (p = 0.01) were significantly lower in patients with the main outcomes. None of the patient-reported variables, singly or in combination, improved predictive performance of a model that included acute physiology and longitudinal comorbidity burden (area under the receiver operator characteristic curve was 0.716 for both the EHR model and maximal performance of a random forest model including all predictors). CONCLUSIONS: In this insured population, incorporating patient-reported social factors and measures of cognitive and physical function did not improve performance of an EHR-based model predicting 30-day non-elective rehospitalization or mortality. While incorporating patient-reported social and functional status data did not improve ability to predict these outcomes, such data may still be important for improving patient outcomes.

Racial Disparities in COVID-19 Testing and Outcomes : Retrospective Cohort Study in an Integrated Health System.

BACKGROUND: Racial disparities exist in outcomes after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. OBJECTIVE: To evaluate the contribution of race/ethnicity in SARS-CoV-2 testing, infection, and outcomes. DESIGN: Retrospective cohort study (1 February 2020 to 31 May 2020). SETTING: Integrated health care delivery system in Northern California. PARTICIPANTS: Adult health plan members. MEASUREMENTS: Age, sex, neighborhood deprivation index, comorbid conditions, acute physiology indices, and race/ethnicity; SARS-CoV-2 testing and incidence of positive test results; and hospitalization, illness severity, and mortality. RESULTS: Among 3 481 716 eligible members, 42.0% were White, 6.4% African American, 19.9% Hispanic, and 18.6% Asian; 13.0% were of other or unknown race. Of eligible members, 91 212 (2.6%) were tested for SARS-CoV-2 infection and 3686 had positive results (overall incidence, 105.9 per 100 000 persons; by racial group, White, 55.1; African American, 123.1; Hispanic, 219.6; Asian, 111.7; other/unknown, 79.3). African American persons had the highest unadjusted testing and mortality rates, White persons had the lowest testing rates, and those with other or unknown race had the lowest mortality rates. Compared with White persons, adjusted testing rates among non-White persons were marginally higher, but infection rates were significantly higher; adjusted odds ratios [aORs] for African American persons, Hispanic persons, Asian persons, and persons of other/unknown race were 2.01 (95% CI, 1.75 to 2.31), 3.93 (CI, 3.59 to 4.30), 2.19 (CI, 1.98 to 2.42), and 1.57 (CI, 1.38 to 1.78), respectively. Geographic analyses showed that infections clustered in areas with higher proportions of non-White persons. Compared with White persons, adjusted hospitalization rates for African American persons, Hispanic persons, Asian persons, and persons of other/unknown race were 1.47 (CI, 1.03 to 2.09), 1.42 (CI, 1.11 to 1.82), 1.47 (CI, 1.13 to 1.92), and 1.03 (CI, 0.72 to 1.46), respectively. Adjusted analyses showed no racial differences in inpatient mortality or total mortality during the study period. For testing, comorbid conditions made the greatest relative contribution to model explanatory power (77.9%); race only accounted for 8.1%. Likelihood of infection was largely due to race (80.3%). For other outcomes, age was most important; race only contributed 4.5% for hospitalization, 12.8% for admission illness severity, 2.3% for in-hospital death, and 0.4% for any death. LIMITATION: The study involved an insured population in a highly integrated health system. CONCLUSION: Race was the most important predictor of SARS-CoV-2 infection. After infection, race was associated with increased hospitalization risk but not mortality. PRIMARY FUNDING SOURCE: The Permanente Medical Group, Inc.

Temporal and Microspatial Heterogeneity in Transmission Dynamics of Coendemic Plasmodium vivax and Plasmodium falciparum in Two Rural Cohort Populations in the Peruvian Amazon.

BACKGROUND: Malaria is highly heterogeneous: its changing malaria microepidemiology needs to be addressed to support malaria elimination efforts at the regional level. METHODS: A 3-year, population-based cohort study in 2 settings in the Peruvian Amazon (Lupuna, Cahuide) followed participants by passive and active case detection from January 2013 to December 2015. Incidence and prevalence rates were estimated using microscopy and polymerase chain reaction (PCR). RESULTS: Lupuna registered 1828 infections (1708 Plasmodium vivax, 120 Plasmodium falciparum; incidence was 80.7 infections/100 person-years (95% confidence interval [CI] , 77.1-84.5). Cahuide detected 1046 infections (1024 P vivax, 20 P falciparum, 2 mixed); incidence was 40.2 infections/100 person-years (95% CI, 37.9-42.7). Recurrent P vivax infections predominated onwards from 2013. According to PCR data, submicroscopic predominated over microscopic infections, especially in periods of low transmission. The integration of parasitological, entomological, and environmental observations evidenced an intense and seasonal transmission resilient to standard control measures in Lupuna and a persistent residual transmission after severe outbreaks were intensively handled in Cahuide. CONCLUSIONS: In 2 exemplars of complex local malaria transmission, standard control strategies failed to eliminate submicroscopic and hypnozoite reservoirs, enabling persistent transmission.

Effect of donor, component, and recipient characteristics on hemoglobin increments following red blood cell transfusion.

Significant research has focused individually on blood donors, product preparation and storage, and optimal transfusion practice. To better understand the interplay between these factors on measures of red blood cell (RBC) transfusion efficacy, we conducted a linked analysis of blood donor and component data with patients who received single-unit RBC transfusions between 2008 and 2016. Hemoglobin levels before and after RBC transfusions and at 24- and 48-hour intervals after transfusion were analyzed. Generalized estimating equation linear regression models were fit to examine hemoglobin increments after RBC transfusion adjusting for donor and recipient demographic characteristics, collection method, additive solution, gamma irradiation, and storage duration. We linked data on 23 194 transfusion recipients who received one or more single-unit RBC transfusions (n = 38 019 units) to donor demographic and component characteristics. Donor and recipient sex, Rh-D status, collection method, gamma irradiation, recipient age and body mass index, and pretransfusion hemoglobin levels were significant predictors of hemoglobin increments in univariate and multivariable analyses (P < .01). For hemoglobin increments 24 hours after transfusion, the coefficient of determination for the generalized estimating equation models was 0.25, with an estimated correlation between actual and predicted values of 0.5. Collectively, blood donor demographic characteristics, collection and processing methods, and recipient characteristics accounted for significant variation in hemoglobin increments related to RBC transfusion. Multivariable modeling allows the prediction of changes in hemoglobin using donor-, component-, and patient-level characteristics. Accounting for these factors will be critical for future analyses of donor and component factors, including genetic polymorphisms, on posttransfusion increments and other patient outcomes.

Hospital-specific Template Matching for Benchmarking Performance in a Diverse Multihospital System.

BACKGROUND: Hospital-specific template matching is a newer method of hospital performance measurement that may be fairer than regression-based benchmarking. However, it has been tested in only limited research settings. OBJECTIVE: The objective of this study was to test the feasibility of hospital-specific template matching assessments in the Veterans Affairs (VA) health care system and determine power to detect greater-than-expected 30-day mortality. RESEARCH DESIGN: Observational cohort study with hospital-specific template matching assessment. For each VA hospital, the 30-day mortality of a representative subset of hospitalizations was compared with the pooled mortality from matched hospitalizations at a set of comparison VA hospitals treating sufficiently similar patients. The simulation was used to determine power to detect greater-than-expected mortality. SUBJECTS: A total of 556,266 hospitalizations at 122 VA hospitals in 2017. MEASURES: A number of comparison hospitals identified per hospital; 30-day mortality. RESULTS: Each hospital had a median of 38 comparison hospitals (interquartile range: 33, 44) identified, and 116 (95.1%) had at least 20 comparison hospitals. In total, 8 hospitals (6.6%) had a significantly lower 30-day mortality than their benchmark, 5 hospitals (4.1%) had a significantly higher 30-day mortality, and the remaining 109 hospitals (89.3%) were similar to their benchmark. Power to detect a standardized mortality ratio of 2.0 ranged from 72.5% to 79.4% for a hospital with the fewest (6) versus most (64) comparison hospitals. CONCLUSIONS: Hospital-specific template matching may be feasible for assessing hospital performance in the diverse VA health care system, but further refinements are needed to optimize the approach before operational use. Our findings are likely applicable to other large and diverse multihospital systems.

Prediction of obstetrical and fetal complications using automated electronic health record data.

An increasing number of delivering women experience major morbidity and mortality. Limited work has been done on automated predictive models that could be used for prevention. Using only routinely collected obstetrical data, this study aimed to develop a predictive model suitable for real-time use with an electronic medical record. We used a retrospective cohort study design with split validation. The denominator consisted of women admitted to a delivery service. The numerator consisted of women who experienced a composite outcome that included both maternal (eg, uterine rupture, postpartum hemorrhage), fetal (eg, stillbirth), and neonatal (eg, hypoxic ischemic encephalopathy) adverse events. We employed machine learning methods, assessing model performance using the area under the receiver operator characteristic curve and number needed to evaluate. A total of 303,678 deliveries took place at 15 study hospitals between January 1, 2010, and March 31, 2018, and 4130 (1.36%) had ≥1 obstetrical complication. We employed data from 209,611 randomly selected deliveries (January 1, 2010, to March 31, 2017) as a derivation dataset and validated our findings on data from 52,398 randomly selected deliveries during the same time period (validation 1 dataset). We then applied our model to data from 41,669 deliveries from the last year of the study (April 1, 2017, to March 31, 2018 [validation 2 dataset]). Our model included 35 variables (eg, demographics, vital signs, laboratory tests, progress of labor indicators). In the validation 2 dataset, a gradient boosted model (area under the receiver operating characteristic curve or c statistic, 0.786) was slightly superior to a logistic regression model (c statistic, 0.778). Using an alert threshold of 4.1%, our final model would flag 16.7% of women and detect 52% of adverse outcomes, with a number needed to evaluate of 20.9 and 0.455 first alerts per day per 1000 annual deliveries. In conclusion, electronic medical record data can be used to predict obstetrical complications. The clinical utility of these automated models has not yet been demonstrated. To conduct interventions to assess whether using these models results in patient benefit, future work will need to focus on the development of clinical protocols suitable for use in interventions.

Validation of Postpartum Hemorrhage Admission Risk Factor Stratification in a Large Obstetrics Population.

OBJECTIVE: This study aimed to evaluate the performance of the California Maternal Quality Care Collaborative (CMQCC) admission risk criteria for stratifying postpartum hemorrhage risk in a large obstetrics population. STUDY DESIGN: Using detailed electronic health record data, we classified 261,964 delivery hospitalizations from Kaiser Permanente Northern California hospitals between 2010 and 2017 into high-, medium-, and low-risk groups based on CMQCC criteria. We used logistic regression to assess associations between CMQCC risk groups and postpartum hemorrhage using two different postpartum hemorrhage definitions, standard postpartum hemorrhage (blood loss ≥1,000 mL) and severe postpartum hemorrhage (based on transfusion, laboratory, and blood loss data). Among the low-risk group, we also evaluated associations between additional present-on-admission factors and severe postpartum hemorrhage. RESULTS: Using the standard definition, postpartum hemorrhage occurred in approximately 5% of hospitalizations (n = 13,479), with a rate of 3.2, 10.5, and 10.2% in the low-, medium-, and high-risk groups. Severe postpartum hemorrhage occurred in 824 hospitalizations (0.3%), with a rate of 0.2, 0.5, and 1.3% in the low-, medium-, and high-risk groups. For either definition, the odds of postpartum hemorrhage were significantly higher in medium- and high-risk groups compared with the low-risk group. Over 40% of postpartum hemorrhages occurred in hospitalizations that were classified as low risk. Among the low-risk group, risk factors including hypertension and diabetes were associated with higher odds of severe postpartum hemorrhage. CONCLUSION: We found that the CMQCC admission risk assessment criteria stratified women by increasing rates of severe postpartum hemorrhage in our sample, which enables early preparation for many postpartum hemorrhages. However, the CMQCC risk factors missed a substantial proportion of postpartum hemorrhages. Efforts to improve postpartum hemorrhage risk assessment using present-on-admission risk factors should consider inclusion of other nonobstetrical factors.