Kenya's experience implementing event-based surveillance during the COVID-19 pandemic.

Event-based surveillance (EBS) can be implemented in most settings for the detection of potential health threats by recognition and immediate reporting of predefined signals. Such a system complements existing case-based and sentinel surveillance systems. With the emergence of the COVID-19 pandemic in early 2020, the Kenya Ministry of Health (MOH) modified and expanded an EBS system in both community and health facility settings for the reporting of COVID-19-related signals. Using an electronic reporting tool, m-Dharura, MOH recorded 8790 signals reported, with 3002 (34.2%) verified as events, across both community and health facility sites from March 2020 to June 2021. A subsequent evaluation found that the EBS system was flexible enough to incorporate the addition of COVID-19-related signals during a pandemic and maintain high rates of reporting from participants. Inadequate resources for follow-up investigations to reported events, lack of supportive supervision for some community health volunteers and lack of data system interoperability were identified as challenges to be addressed as the EBS system in Kenya continues to expand to additional jurisdictions.

A Versatile Prison Psychiatric Pharmacy Program.

BACKGROUND: Before the coronavirus disease 2019 (COVID-19) pandemic, 20% of the adult population in the United States experienced mental illness annually. Since the COVID-19 pandemic, 93% of countries have reported disruptions to mental health services. The demand for services is high whereas infrastructure and qualified professionals are appallingly low. Health care in the correctional setting is unique, where mental illness prevalence is double than that in the community. The intersection of policies and procedures and the beneficence plus nonmaleficence responsibility of health care professionals is exceptionally complex. Studies have shown the potential benefits of pharmacists following patients in chronic care psychiatry visits. OBJECTIVES: An inpatient psychiatric pharmacy clinic was launched to fill gaps to provide safe and up-to-date patient-centric services for more than 240 extremely psychiatrically ill inmate patients at the Federal Correctional Center Butner (FCC Butner), a Federal Bureau of Prisons medical center. METHODS: The inpatient psychiatric pharmacist practiced independently under a collaborative practice agreement and completed mental health clinical visits for a revolving portion of 240 inpatient mental health inmate patients at FCC Butner. The pharmacist provided ancillary services including completing movement disorder testing, monitoring narrow therapeutic index medication laboratory test results, and executing an antipsychotic psychoeducation meeting with other health care departments and inmate patients. RESULTS: Notably, 74% of patients monitored in the specialty program experienced stable or improved symptoms of depression, schizophrenia, and bipolar disorder. Adverse effects, particularly psychiatric-related movement disorders, were also more closely managed. Finally, 43% of the total inmate patient population who previously declined psychiatric medication treatment consented to begin treatment after participation in a pilot antipsychotic psychoeducation meeting. CONCLUSION: The inpatient psychiatric pharmacy program at FCC Butner is a dynamic program that has bolstered the facility's health care mission. The services detailed in this article can be applied to other correctional environments that have a medical outpatient or inpatient presence.

Factors Associated With Receiving Longer Than Recommended Therapy Among Culture-Negative Pulmonary Tuberculosis Patients.

Background: US tuberculosis (TB) guidelines recommend treatment ≥6 months with a regimen composed of multiple effective anti-TB drugs. Since 2003, a 4-month regimen for a specific subset of TB patients has also been recommended. Methods: We used 2011-2018 US National Tuberculosis Surveillance System data to characterize factors associated with 4-month (111-140 days) therapy among adult patients who had completed treatment and were potentially eligible at that time for 4-month therapy (culture-negative pulmonary-only TB, absence of certain risk factors, and initial treatment that included pyrazinamide). We used modified Poisson regression with backward elimination of main effect variables to calculate adjusted relative risks (aRRs). Results: During 2011-2018, 63 393 adults completed TB treatment: 5560 (8.8%) were potentially eligible for 4-month therapy; of these, 5560 patients (79%) received >4-month therapy (median, 193 days or ∼6 months). Patients with cavitary disease were more likely to receive >4-month therapy (aRR, 1.10; 95% CI, 1.07-1.14) vs patients without cavitary disease. Patients more likely to receive 4-month therapy included patients treated by health departments vs private providers only (aRR, 0.94; 95% CI, 0.91-0.98), those in the South and West vs the Midwest, non-US-born persons (aRR, 0.95; 95% CI, 0.91-0.99) vs US-born persons, and aged 25-64 years vs 15-24 years. Conclusions: Most patients potentially eligible for 4-month therapy were treated with standard 6-month courses. Beyond clinical eligibility criteria, other patient- and program-related factors might be more critical determinants of treatment duration.

Estimating COVID-19 Hospitalizations in the United States With Surveillance Data Using a Bayesian Hierarchical Model: Modeling Study.

BACKGROUND: In the United States, COVID-19 is a nationally notifiable disease, meaning cases and hospitalizations are reported by states to the Centers for Disease Control and Prevention (CDC). Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating the burden of COVID-19 from established sentinel surveillance systems is becoming more important. OBJECTIVE: We aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19. METHODS: We estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. Hospitalization rates were calculated from patients hospitalized with a lab-confirmed SARS-CoV-2 test during or within 14 days before admission. We created a model for 6 age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) separately. We identified covariates from multiple data sources that varied by age, state, and month and performed covariate selection for each age group based on 2 methods, Least Absolute Shrinkage and Selection Operator (LASSO) and spike and slab selection methods. We validated our method by checking the sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data. RESULTS: We estimated 3,583,100 (90% credible interval [CrI] 3,250,500-3,945,400) hospitalizations for a cumulative incidence of 1093.9 (992.4-1204.6) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 359 to 1856 per 100,000 between states. The age group with the highest cumulative incidence was those aged ≥85 years (5575.6; 90% CrI 5066.4-6133.7). The monthly hospitalization rate was highest in December (183.7; 90% CrI 154.3-217.4). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks, and timing of peaks between states. CONCLUSIONS: Our novel approach to estimate hospitalizations for COVID-19 has potential to provide sustainable estimates for monitoring COVID-19 burden as well as a flexible framework leveraging surveillance data.

Estimating under-recognized COVID-19 deaths, United States, march 2020-may 2021 using an excess mortality modelling approach.

BACKGROUND: In the United States, Coronavirus Disease 2019 (COVID-19) deaths are captured through the National Notifiable Disease Surveillance System and death certificates reported to the National Vital Statistics System (NVSS). However, not all COVID-19 deaths are recognized and reported because of limitations in testing, exacerbation of chronic health conditions that are listed as the cause of death, or delays in reporting. Estimating deaths may provide a more comprehensive understanding of total COVID-19-attributable deaths. METHODS: We estimated COVID-19 unrecognized attributable deaths, from March 2020-April 2021, using all-cause deaths reported to NVSS by week and six age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) for 50 states, New York City, and the District of Columbia using a linear time series regression model. Reported COVID-19 deaths were subtracted from all-cause deaths before applying the model. Weekly expected deaths, assuming no SARS-CoV-2 circulation and predicted all-cause deaths using SARS-CoV-2 weekly percent positive as a covariate were modelled by age group and including state as a random intercept. COVID-19-attributable unrecognized deaths were calculated for each state and age group by subtracting the expected all-cause deaths from the predicted deaths. FINDINGS: We estimated that 766,611 deaths attributable to COVID-19 occurred in the United States from March 8, 2020-May 29, 2021. Of these, 184,477 (24%) deaths were not documented on death certificates. Eighty-two percent of unrecognized deaths were among persons aged ≥65 years; the proportion of unrecognized deaths were 0•24-0•31 times lower among those 0-17 years relative to all other age groups. More COVID-19-attributable deaths were not captured during the early months of the pandemic (March-May 2020) and during increases in SARS-CoV-2 activity (July 2020, November 2020-February 2021). INTERPRETATION: Estimating COVID-19-attributable unrecognized deaths provides a better understanding of the COVID-19 mortality burden and may better quantify the severity of the COVID-19 pandemic. FUNDING: None.

Global Seasonal Influenza Mortality Estimates: A Comparison of 3 Different Approaches.

Prior to updating global influenza-associated mortality estimates, the World Health Organization convened a consultation in July 2017 to understand differences in methodology and implications for results of 3 influenza mortality projects from the US Centers for Disease Control and Prevention (CDC), the Netherlands Institute for Health Service Research's Global Pandemic Mortality Project II (GLaMOR), and the Institute for Health Metrics and Evaluation (IHME). The expert panel reviewed estimates and discussed differences in data sources, analysis, and modeling assumptions. We performed a comparison analysis of the estimates. Influenza-associated respiratory death counts were comparable between CDC and GLaMOR; the IHME estimate was considerably lower. The greatest country-specific influenza-associated fold differences in mortality rate between CDC and IHME estimates and between GLaMOR and IHME estimates were among countries in Southeast Asia and the Eastern Mediterranean region. The data envelope used for the calculation was one of the major differences (CDC and GLaMOR: all respiratory deaths; IHME: lower-respiratory infection deaths). With the assumption that there is only one cause of death for each death, IHME estimates a fraction of the full influenza-associated respiratory mortality that is measured by the other 2 groups. Wide variability of parameters was observed. Continued coordination between groups could assist with better understanding of methodological differences and new approaches to estimating influenza deaths globally.

Estimated Incidence of Coronavirus Disease 2019 (COVID-19) Illness and Hospitalization-United States, February-September 2020.

BACKGROUND: In the United States, laboratory-confirmed coronavirus disease 2019 (COVID-19) is nationally notifiable. However, reported case counts are recognized to be less than the true number of cases because detection and reporting are incomplete and can vary by disease severity, geography, and over time. METHODS: To estimate the cumulative incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, symptomatic illnesses, and hospitalizations, we adapted a simple probabilistic multiplier model. Laboratory-confirmed case counts that were reported nationally were adjusted for sources of underdetection based on testing practices in inpatient and outpatient settings and assay sensitivity. RESULTS: We estimated that through the end of September, 1 of every 2.5 (95% uncertainty interval [UI]: 2.0-3.1) hospitalized infections and 1 of every 7.1 (95% UI: 5.8-9.0) nonhospitalized illnesses may have been nationally reported. Applying these multipliers to reported SARS-CoV-2 cases along with data on the prevalence of asymptomatic infection from published systematic reviews, we estimate that 2.4 million hospitalizations, 44.8 million symptomatic illnesses, and 52.9 million total infections may have occurred in the US population from 27 February-30 September 2020. CONCLUSIONS: These preliminary estimates help demonstrate the societal and healthcare burdens of the COVID-19 pandemic and can help inform resource allocation and mitigation planning.

Preventing Early Bouncebacks to the Neurointensive Care Unit: A Retrospective Analysis and Quality Improvement Pilot.

BACKGROUND: Early unplanned readmissions of "bouncebacks" to intensive care units are a healthcare quality metric and result in higher mortality and greater cost. Few studies have examined bouncebacks to the neurointensive care unit (neuro-ICU), and we sought to design and implement a quality improvement pilot to reduce that rate. METHODS: First, we performed a retrospective chart review of 504 transfers to identify potential bounceback risk factors. Risk factors were assessed on the day of transfer by the transferring physician identifying patients as "high risk" or "low risk" for bounceback. "High-risk" patients underwent an enhanced transfer process emphasizing interdisciplinary communication and rapid assessment upon transfer during a 9-month pilot. RESULTS: Within the retrospective cohort, 34 of 504 (4.7%) transfers required higher levels of care within 48 h. Respiratory failure and sepsis/hypotension were the most common reasons for bounceback among this group. During the intervention, 8 of 225 (3.6%) transfers bounced back, all of who were labeled "high risk." Being "high risk" was associated with a risk of bounceback (OR not calculable, p = 0.02). Aspiration risk (OR 6.9; 95% CI 1.6-30, p = 0.010) and cardiac arrhythmia (OR 7.1; 95% CI 1.6-32, p = 0.01) were independent predictors of bounceback in multivariate analysis. Bounceback rates trended downward to 2.8% in the final phase (p for trend 0.09). Eighty-five percent of providers responded that the pilot should become standard of care. CONCLUSION: Patients at high risk for bounceback after transfer from the neuro-ICU can be identified using a simple tool. Early augmented multidisciplinary communication and care for high-risk patients may improve their management in the hospital.

Responding to the Opioid Crisis: An Indian Health Service Pharmacist-Led Pain Management Clinic.

An IHS pharmacy pain management clinic has emphasized judicious opioid prescribing, reduced overdose risk in the community, and improved patient functionality and quality of care through close pharmacotherapy monitoring.