Comparison of patient exit interviews with unannounced standardised patients for assessing HIV service delivery in Zambia: A study nested within a cluster randomised trial

OBJECTIVES: To compare unannounced standardised patient approach (eg, mystery clients) with typical exit interviews for assessing patient experiences in HIV care (eg, unfriendly providers, long waiting times). We hypothesise standardised patients would report more negative experiences than typical exit interviews affected by social desirability bias. SETTING: Cross-sectional surveys in 16 government-operated HIV primary care clinics in Lusaka, Zambia providing antiretroviral therapy (ART). PARTICIPANTS: 3526 participants aged ≥18 years receiving ART participated in the exit surveys between August 2019 and November 2021. INTERVENTION: Systematic sample (every n OUTCOME MEASURES: We compared patient experience among patients who received brief training prior to their care visit (explaining each patient experience construct in the exit survey, being anonymous, without manipulating behaviour) with those who did not undergo training on the survey prior to their visit. RESULTS: Among 3526 participants who participated in exit surveys, 2415 were untrained (56% female, median age 40 (IQR: 32-47)) and 1111 were trained (50% female, median age 37 (IQR: 31-45)). Compared with untrained, trained patients were more likely to report a negative care experience overall (adjusted prevalence ratio (aPR) for aggregate sum score: 1.64 (95% CI: 1.39 to 1.94)), with a greater proportion reporting feeling unwelcome by providers (aPR: 1.71 (95% CI: 1.20 to 2.44)) and witnessing providers behaving rude (aPR: 2.28 (95% CI: 1.63 to 3.19)). CONCLUSION: Trained patients were more likely to identify suboptimal care. They may have understood the items solicited better or felt empowered to be more critical. We trained existing patients, unlike studies that use \u27standardised patients\u27 drawn from outside the patient population. This low-cost strategy could improve patient-centred service delivery elsewhere. TRIAL REGISTRATION NUMBER: Assessment was nested within a parent study; www.pactr.org registered the parent study (PACTR202101847907585)

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

Understanding contributors to racial and ethnic inequities in COVID-19 incidence and mortality rates

BACKGROUND: Racial inequities in Coronavirus 2019 (COVID-19) have been reported over the course of the pandemic, with Black, Hispanic/Latinx, and Native American individuals suffering higher case rates and more fatalities than their White counterparts. METHODS: We used a unique statewide dataset of confirmed COVID-19 cases across Missouri, linked with historical statewide hospital data. We examined differences by race and ethnicity in raw population-based case and mortality rates. We used patient-level regression analyses to calculate the odds of mortality based on race and ethnicity, controlling for comorbidities and other risk factors. RESULTS: As of September 10, 2020 there were 73,635 confirmed COVID-19 cases in the State of Missouri. Among the 64,526 case records (87.7% of all cases) that merged with prior demographic and health care utilization data, 12,946 (20.1%) were Non-Hispanic (NH) Black, 44,550 (69.0%) were NH White, 3,822 (5.9%) were NH Other/Unknown race, and 3,208 (5.0%) were Hispanic. Raw cumulative case rates for NH Black individuals were 1,713 per 100,000 population, compared with 2,095 for NH Other/Unknown, 903 for NH White, and 1,218 for Hispanic. Cumulative COVID-19-related death rates for NH Black individuals were 58.3 per 100,000 population, compared with 38.9 for NH Other/Unknown, 19.4 for NH White, and 14.8 for Hispanic. In a model that included insurance source, history of a social determinant billing code in the patient\u27s claims, census block travel change, population density, Area Deprivation Index, and clinical comorbidities, NH Black race (OR 1.75, 1.51-2.04, p\u3c0.001) and NH Other/Unknown race (OR 1.83, 1.36-2.46, p\u3c0.001) remained strongly associated with mortality. CONCLUSIONS: In Missouri, COVID-19 case rates and mortality rates were markedly higher among NH Black and NH Other/Unknown race than among NH White residents, even after accounting for social and clinical risk, population density, and travel patterns during COVID-19

Leveraging CD4 cell count at entry into care to monitor success of human immunodeficiency virus prevention, treatment, and public health programming in the greater St Louis area between 2017 and 2020

CD4 cell count at entry into human immunodeficiency virus (HIV) care is a useful indicator of success of multiple steps in HIV public health programming. We demonstrate that CD4 cell count at care initiation was stable in St Louis between 2017 and 2019 but declined in 2020. Missouri efforts in the Ending the HIV Epidemic plan should focus on rapidly identifying individuals with undiagnosed HIV infection

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

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

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\u27s 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

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

Profiles of HIV care disruptions among adult patients lost to follow-up in Zambia: A latent class analysis

BACKGROUND: Patients report varied barriers to HIV care across multiple domains, but specific barrier patterns may be driven by underlying, but unobserved, behavioral profiles. METHODS: We traced a probability sample of patients lost to follow-up (\u3e90 days late) as of July 31, 2015 from 64 clinics in Zambia. Among those found alive, we ascertained patient-reported reasons for care disruptions. We performed latent class analysis to identify patient subgroups with similar patterns of reasons reported and assessed the association between class membership and care status (ie, disengaged versus silently transferred to a new site). RESULTS: Among 547 patients, we identified 5 profiles of care disruptions: (1) Livelihood and Mobility (30.6% of the population) reported work/school obligations and mobility/travel as reasons for care disruptions; (2) Clinic Accessibility (28.9%) reported challenges with attending clinic; (3) Mobility and Family (21.9%) reported family obligations, mobility/travel, and transport-related reasons; (4) Doubting Need for HIV care (10.2%) reported uncertainty around HIV status or need for clinical care, and (5) Multidimensional Barriers to Care (8.3%) reported numerous (mean 5.6) reasons across multiple domains. Patient profiles were significantly associated with care status. The Doubting Need for HIV Care class were mostly disengaged (97.9%), followed by the Multidimensional Barriers to Care (62.8%), Clinic Accessibility (62.4%), Livelihood and Mobility (43.6%), and Mobility and Family (23.5%) classes. CONCLUSION: There are distinct HIV care disruption profiles that are strongly associated with patients\u27 current engagement status. Interventions targeting these unique profiles may enable more effective and tailored strategies for improving HIV treatment outcomes