Cross-sectional study to assess depression among healthcare workers in Lusaka, Zambia during the COVID-19 pandemic.

OBJECTIVES: We sought to assess depression among healthcare workers (HCWs) in the context of COVID-19 in Lusaka Province, Zambia. DESIGN: This cross-sectional study is nested within a larger study, the Person-Centred Public Health for HIV Treatment in Zambia (PCPH), a cluster-randomised trial to assess HIV care and outcomes. SETTING: The research was conducted in 24 government-run health facilities from 11 August to 15 October 2020 during the first wave of the COVID-19 pandemic in Lusaka, Zambia. PARTICIPANTS: We used convenience sampling to recruit HCW participants who were previously enrolled in the PCPH study, had more than 6 months' experience working at the facility and were voluntarily willing to participate. PRIMARY OUTCOME MEASURES: We implemented the well-validated 9-question Patient Health Questionnaire (PHQ-9) to assess HCW depression. We used mixed-effects, adjusted Poisson regression to estimate the marginal probability of HCWs experiencing depression that may warrant intervention (PHQ-9 score ≥5) by healthcare facility. RESULTS: We collected PHQ-9 survey responses from 713 professional and lay HCWs. Overall, 334 (46.8%, 95% CI 43.1%, 50.6%) HCWs recorded a PHQ-9 score ≥5, indicating the need for further assessment and potential intervention for depression. We identified significant heterogeneity across facilities and observed a greater proportion of HCWs with symptoms of depression in facilities providing COVID-19 testing and treatment services. CONCLUSIONS: Depression may be a concern for a large proportion of HCWs in Zambia. Further work to understand the magnitude and aetiologies of depression among HCWs in the public sector is needed to design effective prevention and treatment interventions to meet the needs for mental health support and to minimise poor health outcomes.

Multistate Modeling of Clinical Trajectories and Outcomes in the ICU: A Proof-of-Concept Evaluation of Acute Kidney Injury Among Critically Ill Patients With COVID-19

IMPORTANCE: Multistate models yield high-fidelity analyses of the dynamic state transition and temporal dimensions of a clinical condition’s natural history, offering superiority over aggregate modeling techniques for addressing these types of problems. OBJECTIVES: To demonstrate the utility of these models in critical care, we examined acute kidney injury (AKI) development, progression, and outcomes in COVID-19 critical illness through multistate analyses. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study at an urban tertiary-care academic hospital in the United States. All patients greater than or equal to 18 years in an ICU with COVID-19 in 2020, excluding patients with preexisting end-stage renal disease. MAIN OUTCOMES AND MEASURES: Using electronic health record data, we determined AKI presence/stage in discrete 12-hour time windows and fit multistate models to determine longitudinal transitions and outcomes. RESULTS: Of 367 encounters, 241 (66%) experienced AKI (maximal stages: 88 stage-1, 49 stage-2, 104 stage-3 AKI [51 received renal replacement therapy (RRT), 53 did not]). Patients receiving RRT overwhelmingly received invasive mechanical ventilation (IMV) (n = 60, 95%) compared with the AKI-without-RRT (n = 98, 53%) and no-AKI groups (n = 39, 32%;p < 0.001), with similar mortality patterns (RRT: n = 36, 57%;AKI: n = 74, 40%;non-AKI: n = 23, 19%;p < 0.001). After 24 hours in the ICU, almost half the cohort had AKI (44.9%;95% CI, 41.6–48.2%). At 7 days after stage-1 AKI, 74.0% (63.6–84.4) were AKI-free or discharged. By contrast, fewer patients experiencing stage-3 AKI were recovered (30.0% [24.1–35.8%]) or discharged (7.9% [5.2–10.7%]) after 7 days. Early AKI occurred with similar frequency in patients receiving and not receiving IMV: after 24 hours in the ICU, 20.9% of patients (18.3–23.6%) had AKI and IMV, while 23.4% (20.6–26.2%) had AKI without IMV. CONCLUSIONS AND RELEVANCE: In a multistate analysis of critically ill patients with COVID-19, AKI occurred early and heterogeneously in the course of critical illness. Multistate methods are useful and underused in ICU care delivery science as tools for understanding trajectories, prognoses, and resource needs.

Hospital trajectories and early predictors of clinical outcomes differ between SARS-CoV-2 and influenza pneumonia.

BACKGROUND: A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients. METHODS: This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers. FINDINGS: Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses. INTERPRETATION: SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated. FUNDING: This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS).

Quantifying inequities in COVID-19 vaccine distribution over time by social vulnerability, race and ethnicity, and location: A population-level analysis in St. Louis and Kansas City, Missouri.

BACKGROUND: Equity in vaccination coverage is a cornerstone for a successful public health response to COVID-19. To deepen understanding of the extent to which vaccination coverage compares with initial strategies for equitable vaccination, we explore primary vaccine series and booster rollout over time and by race/ethnicity, social vulnerability, and geography. METHODS AND FINDINGS: We analyzed data from the Missouri Department of Health and Senior Services on all COVID-19 vaccinations administered across 7 counties in the St. Louis region and 4 counties in the Kansas City region. We compared rates of receiving the primary COVID-19 vaccine series and boosters relative to time, race/ethnicity, zip-code-level Social Vulnerability Index (SVI), vaccine location type, and COVID-19 disease burden. We adapted a well-established tool for measuring inequity-the Lorenz curve-to quantify inequities in COVID-19 vaccination relative to these key metrics. Between 15 December 2020 and 15 February 2022, 1,763,036 individuals completed the primary series and 872,324 received a booster. During early phases of the primary series rollout, Black and Hispanic individuals from high SVI zip codes were vaccinated at less than half the rate of White individuals from low SVI zip codes, but rates increased over time until they were higher than rates in White individuals after June 2021; Asian individuals maintained high levels of vaccination throughout. Increasing vaccination rates in Black and Hispanic communities corresponded with periods when more vaccinations were offered at small community-based sites such as pharmacies rather than larger health systems and mass vaccination sites. Using Lorenz curves, zip codes in the quartile with the lowest rates of primary series completion accounted for 19.3%, 18.1%, 10.8%, and 8.8% of vaccinations while representing 25% of the total population, cases, deaths, or population-level SVI, respectively. When tracking Gini coefficients, these disparities were greatest earlier during rollout, but improvements were slow and modest and vaccine disparities remained across all metrics even after 1 year. Patterns of disparities for boosters were similar but often of much greater magnitude during rollout in fall 2021. Study limitations include inherent limitations in the vaccine registry dataset such as missing and misclassified race/ethnicity and zip code variables and potential changes in zip code population sizes since census enumeration. CONCLUSIONS: Inequities in the initial COVID-19 vaccination and booster rollout in 2 large US metropolitan areas were apparent across racial/ethnic communities, across levels of social vulnerability, over time, and across types of vaccination administration sites. Disparities in receipt of the primary vaccine series attenuated over time during a period in which sites of vaccination administration diversified, but were recapitulated during booster rollout. These findings highlight how public health strategies from the outset must directly target these deeply embedded structural and systemic determinants of disparities and track equity metrics over time to avoid perpetuating inequities in healthcare access.

Evaluation of HIV treatment outcomes with reduced frequency of clinical encounters and antiretroviral treatment refills: A systematic review and meta-analysis.

BACKGROUND: Global HIV treatment programs have sought to lengthen the interval between clinical encounters for people living with HIV (PLWH) who are established on antiretroviral treatment (ART) to reduce the burden of seeking care and to decongest health facilities. The overall effect of reduced visit frequency on HIV treatment outcomes is however unknown. We conducted a systematic review and meta-analysis to evaluate the effect of implementation strategies that reduce the frequency of clinical appointments and ART refills for PLWH established on ART. METHODS AND FINDINGS: We searched databases​ between 1 January 2010 and 9 November 2021 to identify randomized controlled trials (RCTs) and observational studies that compared reduced (6- to 12-monthly) clinical consultation or ART refill appointment frequency to 3- to 6-monthly appointments for patients established on ART. We assessed methodological quality and real-world relevance, and used Mantel-Haenszel methods to generate pooled risk ratios (RRs) with 95% confidence intervals for retention, viral suppression, and mortality. We evaluated heterogeneity quantitatively and qualitatively, and overall evidence certainty using GRADE. Searches yielded 3,955 records, resulting in 10 studies (6 RCTs, 3 observational studies, and 1 study contributing observational and RCT data) representing 15 intervention arms with 33,599 adults (≥16 years) in 8 sub-Saharan African countries. Reduced frequency clinical consultations occurred at health facilities, while reduced frequency ART refills were delivered through facility or community pharmacies and adherence groups. Studies were highly pragmatic, except for some study settings and resources used in RCTs. Among studies comparing reduced clinical consultation frequency (6- or 12-monthly) to 3-monthly consultations, there appeared to be no difference in retention (RR 1.01, 95% CI 0.97-1.04, p = 0.682, 8 studies, low certainty), and this finding was consistent across 6- and 12-monthly consultation intervals and delivery strategies. Viral suppression effect estimates were markedly influenced by under-ascertainment of viral load outcomes in intervention arms, resulting in inconclusive evidence. There was similarly insufficient evidence to draw conclusions on mortality (RR 1.12, 95% CI 0.75-1.66, p = 0.592, 6 studies, very low certainty). For ART refill frequency, there appeared to be little to no difference in retention (RR 1.01, 95% CI 0.98-1.06, p = 0.473, 4 RCTs, moderate certainty) or mortality (RR 1.45, 95% CI 0.63-3.35, p = 0.382, 4 RCTs, low certainty) between 6-monthly and 3-monthly visits. Similar to the analysis for clinical consultations, although viral suppression appeared to be better in 3-monthly arms, effect estimates were markedly influence by under-ascertainment of viral load outcomes in intervention arms, resulting in overall inclusive evidence. This systematic review was limited by the small number of studies available to compare 12- versus 6-monthly clinical consultations, insufficient data to compare implementation strategies, and lack of evidence for children, key populations, and low- and middle-income countries outside of sub-Saharan Africa. CONCLUSIONS: Based on this synthesis, extending clinical consultation intervals to 6 or 12 months and ART dispensing intervals to 6 months appears to result in similar retention to 3-month intervals, with less robust conclusions for viral suppression and mortality. Future research should ensure complete viral load outcome ascertainment, as well as explore mechanisms of effect, outcomes in other populations, and optimum delivery and monitoring strategies to ensure widespread applicability of reduced frequency visits across settings.

Deliberation, Dissent, and Distrust: Understanding Distinct Drivers of Coronavirus Disease 2019 Vaccine Hesitancy in the United States.

BACKGROUND: Despite the availability of safe and efficacious coronavirus disease 2019 vaccines, a significant proportion of the American public remains unvaccinated and does not appear to be immediately interested in receiving the vaccine. METHODS: In this study, we analyzed data from the US Census Bureau's Household Pulse Survey, a biweekly cross-sectional survey of US households. We estimated the prevalence of vaccine hesitancy across states and nationally and assessed the predictors of vaccine hesitancy and vaccine rejection. In addition, we examined the underlying reasons for vaccine hesitancy, grouped into thematic categories. RESULTS: A total of 459 235 participants were surveyed from 6 January to 29 March 2021. While vaccine uptake increased from 7.7% to 47%, vaccine hesitancy rates remained relatively fixed: overall, 10.2% reported that they would probably not get a vaccine and 8.2% that they would definitely not get a vaccine. Income, education, and state political leaning strongly predicted vaccine hesitancy. However, while both female sex and black race were factors predicting hesitancy, among those who were hesitant, these same characteristics predicted vaccine reluctance rather than rejection. Those who expressed reluctance invoked mostly "deliberative" reasons, while those who rejected the vaccine were also likely to invoke reasons of "dissent" or "distrust." CONCLUSIONS: Vaccine hesitancy comprises a sizable proportion of the population and is large enough to threaten achieving herd immunity. Distinct subgroups of hesitancy have distinctive sociodemographic associations as well as cognitive and affective predilections. Segmented public health solutions are needed to target interventions and optimize vaccine uptake.

Understanding Drivers of Coronavirus Disease 2019 (COVID-19) Racial Disparities: A Population-Level Analysis of COVID-19 Testing Among Black and White Populations.

BACKGROUND: Disparities in coronavirus disease 2019 (COVID-19) testing-the pandemic's most critical but limited resource-may be an important but modifiable driver of COVID-19 inequities. METHODS: We analyzed data from the Missouri State Department of Health and Senior Services on all COVID-19 tests conducted in the St Louis and Kansas City regions. We adapted a well-established tool for measuring inequity-the Lorenz curve-to compare COVID-19 testing rates per diagnosed case among Black and White populations. RESULTS: Between 14/3/2020 and 15/9/2020, 606 725 and 328 204 COVID-19 tests were conducted in the St Louis and Kansas City regions, respectively. Over time, Black individuals consistently had approximately half the rate of testing per case than White individuals. In the early period (14/3/2020 to 15/6/2020), zip codes in the lowest quartile of testing rates accounted for only 12.1% and 8.8% of all tests in the St Louis and Kansas City regions, respectively, even though they accounted for 25% of all cases in each region. These zip codes had higher proportions of residents who were Black, without insurance, and with lower median incomes. These disparities were reduced but still persisted during later phases of the pandemic (16/6/2020 to 15/9/2020). Last, even within the same zip code, Black residents had lower rates of tests per case than White residents. CONCLUSIONS: Black populations had consistently lower COVID-19 testing rates per diagnosed case than White populations in 2 Missouri regions. Public health strategies should proactively focus on addressing equity gaps in COVID-19 testing to improve equity of the overall response.

Outcomes Associated With Social Distancing Policies in St Louis, Missouri, During the Early Phase of the COVID-19 Pandemic.

Importance: In the absence of a national strategy in response to the COVID-19 pandemic, many public health decisions fell to local elected officials and agencies. Outcomes of such policies depend on a complex combination of local epidemic conditions and demographic features as well as the intensity and timing of such policies and are therefore unclear. Objective: To use a decision analytical model of the COVID-19 epidemic to investigate potential outcomes if actual policies enacted in March 2020 (during the first wave of the epidemic) in the St Louis region of Missouri had been delayed. Design, Setting, and Participants: A previously developed, publicly available, open-source modeling platform (Local Epidemic Modeling for Management & Action, version 2.1) designed to enable localized COVID-19 epidemic projections was used. The compartmental epidemic model is programmed in R and Stan, uses bayesian inference, and accepts user-supplied demographic, epidemiologic, and policy inputs. Hospital census data for 1.3 million people from St Louis City and County from March 14, 2020, through July 15, 2020, were used to calibrate the model. Exposures: Hypothetical delays in actual social distancing policies (which began on March 13, 2020) by 1, 2, or 4 weeks. Sensitivity analyses were conducted that explored plausible spontaneous behavior change in the absence of social distancing policies. Main Outcomes and Measures: Hospitalizations and deaths. Results: A model of 1.3 million residents of the greater St Louis, Missouri, area found an initial reproductive number (indicating transmissibility of an infectious agent) of 3.9 (95% credible interval [CrI], 3.1-4.5) in the St Louis region before March 15, 2020, which fell to 0.93 (95% CrI, 0.88-0.98) after social distancing policies were implemented between March 15 and March 21, 2020. By June 15, a 1-week delay in policies would have increased cumulative hospitalizations from an observed actual number of 2246 hospitalizations to 8005 hospitalizations (75% CrI: 3973-15 236 hospitalizations) and increased deaths from an observed actual number of 482 deaths to a projected 1304 deaths (75% CrI, 656-2428 deaths). By June 15, a 2-week delay would have yielded 3292 deaths (75% CrI, 2104-4905 deaths)-an additional 2810 deaths or a 583% increase beyond what was actually observed. Sensitivity analyses incorporating a range of spontaneous behavior changes did not avert severe epidemic projections. Conclusions and Relevance: The results of this decision analytical model study suggest that, in the St Louis region, timely social distancing policies were associated with improved population health outcomes, and small delays may likely have led to a COVID-19 epidemic similar to the most heavily affected areas in the US. These findings indicate that an open-source modeling platform designed to accept user-supplied local and regional data may provide projections tailored to, and more relevant for, local settings.

Preferences for COVID-19 vaccine distribution strategies in the US: A discrete choice survey.

BACKGROUND: The COVID-19 vaccination campaign in the US has been immensely successful in vaccinating those who are receptive, further increases in vaccination rates however will require more innovative approaches to reach those who remain hesitant. Developing vaccination strategies that are modelled on what people want could further increase uptake. METHODS AND FINDINGS: To inform COVID-19 vaccine distribution strategies that are aligned with public preferences we conducted a discrete choice experiment among the US public (N = 2,895) between March 15 to March 22, 2021. We applied sampling weights, evaluated mean preferences using mixed logit models, and identified latent class preference subgroups. On average, the public prioritized ease, preferring single to two dose vaccinations (mean preference: -0.29; 95%CI: -0.37 to -0.20), vaccinating once rather than annually (mean preference: -0.79; 95%CI: -0.89 to -0.70) and reducing waiting times at vaccination sites. Vaccine enforcement reduced overall vaccine acceptance (mean preference -0.20; 95%CI: -0.30 to -0.10), with a trend of increasing resistance to enforcement with increasing vaccine hesitancy. Latent class analysis identified four distinct preference phenotypes: the first prioritized inherent "vaccine features" (46.1%), the second were concerned about vaccine "service delivery" (8.8%), a third group desired "social proof" of vaccine safety and were susceptible to enforcement (13.2%), and the fourth group were "indifferent" to vaccine and service delivery features and resisted enforcement (31.9%). CONCLUSIONS: This study identifies several critical insights for the COVID-19 public health response. First, identifying preference segments is essential to ensure that vaccination services meet the needs of diverse population subgroups. Second, making vaccination easy and promoting autonomy by simplifying services and offering the public choices (where feasible) may increase uptake in those who remain deliberative. And, third vaccine mandates have the potential to increase vaccination rates in susceptible groups but may simultaneously promote control aversion and resistance in those who are most hesitant.

Public Preferences for Social Distancing Policy Measures to Mitigate the Spread of COVID-19 in Missouri.

Importance: Policies to promote social distancing can minimize COVID-19 transmission but come with substantial social and economic costs. Quantifying relative preferences among the public for such practices can inform locally relevant policy prioritization and optimize uptake. Objective: To evaluate relative utilities (ie, preferences) for COVID-19 pandemic social distancing strategies against the hypothetical risk of acquiring COVID-19 and anticipated income loss. Design, Setting, and Participants: This survey study recruited individuals living in the Missouri area from May to June 2020 via randomly distributed unincentivized social media advertisements and local recruitment platforms for members of minority racial and ethnic groups. Participants answered 6 questions that asked them to choose between 2 hypothetical counties where business closures, social distancing policy duration, COVID-19 infection risk, and income loss varied. Main Outcomes and Measures: Reweighted population-level relative preferences (utilities) for social distancing policies, subgroups, and latent classes. Results: The survey had a 3% response rate (3045 of 90 320). Of the 2428 respondents who completed the survey, 1669 (75%) were 35 years and older, 1536 (69%) were women, and 1973 (89%) were White. After reweighting to match Missouri population demographic characteristics, the strongest preference was for the prohibition of large gatherings (mean preference, -1.43; 95% CI, -1.67 to -1.18), with relative indifference to the closure of social and lifestyle venues (mean preference, 0.05; 95% CI, -0.08 to 0.17). There were weak preferences to keep outdoor venues (mean preference, 0.50; 95% CI, 0.39 to 0.61) and schools (mean preference, 0.18; 95% CI, 0.05 to 0.30) open. Latent class analysis revealed 4 distinct preference phenotypes in the population: risk averse (48.9%), conflicted (22.5%), prosocial (14.9%), and back to normal (13.7%), with men twice as likely as women to belong to the back to normal group than the risk averse group (relative risk ratio, 2.19; 95% CI, 1.54 to 3.12). Conclusions And relevance: In this survey study using a discrete choice experiment, public health policies that prohibited large gatherings, as well as those that closed social and lifestyle venues, appeared to be acceptable to the public. During policy implementation, these activities should be prioritized for first-phase closures. These findings suggest that policy messages that address preference heterogeneity (eg, focusing on specific preference subgroups or targeting men) could improve adherence to social distancing measures for COVID-19 and future pandemics.

The Clinical Course of Coronavirus Disease 2019 in a US Hospital System: A Multistate Analysis.

There are limited data on longitudinal outcomes for coronavirus disease 2019 (COVID-19) hospitalizations that account for transitions between clinical states over time. Using electronic health record data from a hospital network in the St. Louis, Missouri, region, we performed multistate analyses to examine longitudinal transitions and outcomes among hospitalized adults with laboratory-confirmed COVID-19 with respect to 15 mutually exclusive clinical states. Between March 15 and July 25, 2020, a total of 1,577 patients in the network were hospitalized with COVID-19 (49.9% male; median age, 63 years (interquartile range, 50-75); 58.8% Black). Overall, 34.1% (95% confidence interval (CI): 26.4, 41.8) had an intensive care unit admission and 12.3% (95% CI: 8.5, 16.1) received invasive mechanical ventilation (IMV). The risk of decompensation peaked immediately after admission; discharges peaked around days 3-5, and deaths plateaued between days 7 and 16. At 28 days, 12.6% (95% CI: 9.6, 15.6) of patients had died (4.2% (95% CI: 3.2, 5.2) had received IMV) and 80.8% (95% CI: 75.4, 86.1) had been discharged. Among those receiving IMV, 35.1% (95% CI: 28.2, 42.0) remained intubated after 14 days; after 28 days, 37.6% (95% CI: 30.4, 44.7) had died and only 37.7% (95% CI: 30.6, 44.7) had been discharged. Multistate methods offer granular characterizations of the clinical course of COVID-19 and provide essential information for guiding both clinical decision-making and public health planning.