Reduced renal function is associated with progression to AIDS but not with overall mortality in HIV-infected kenyan adults not initially requiring combination antiretroviral therapy

<p>Abstract</p> <p>Background</p> <p>The World Health Organization (WHO) has recently recommended that antiretrovirals be initiated in all individuals with CD4 counts of less than 350 cells/mm³. For countries with resources too limited to expand care to all such patients, it would be of value to able to identify and target populations at highest risk of HIV progression. Renal disease has been identified as a risk factor for disease progression or death in some populations.</p> <p>Methods</p> <p>Times to meeting combination antiretroviral therapy (cART) initiation criteria (developing either a CD4 count < 200 cells/mm³or WHO stage 3 or 4 disease) and overall mortality were evaluated in cART-naïve, HIV-infected Kenyan adults with CD4 cell counts ≥200/mm³and with WHO stage 1 or 2 disease. Cox proportional hazard regression models were used to evaluate the associations between renal function and these endpoints.</p> <p>Results</p> <p>We analyzed data of 7383 subjects with a median follow-up time of 59 (interquartile range, 27-97) weeks. In Cox regression analyses adjusted for age, sex, WHO disease stage, CD4 cell count and haemoglobin, estimated creatinine clearance (CrCl) < 60 mL/min was significantly associated with shorter times to meeting cART initiation criteria (HR 1.34; 95% CI, 1.23-1.52) and overall mortality (HR 1.73; 95% CI, 1.19-2.51) compared with CrCl ≥60 mL/min. Estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m²was associated with shorter times to meeting cART initiation criteria (HR 1.39; 95% CI, 1.22-1.58), but not with overall mortality. CrCl and eGFR remained associated with shorter times to cART initiation criteria, but neither was associated with mortality, in weight-adjusted analyses.</p> <p>Conclusions</p> <p>In this large natural history study, reduced renal function was strongly associated with faster HIV disease progression in adult Kenyans not initially meeting cART initiation criteria. As such, renal function measurement in resource-limited settings may be an inexpensive method to identify those most in need of cART to prevent progression to AIDS. The initial association between reduced CrCl, but not reduced eGFR, and greater mortality was explained by the low weights in this population.</p

A semiparametric method for the analysis of outcomes during a gap in HIV care under incomplete outcome ascertainment

Objectives: Estimation of the cascade of HIV care is essential for evaluating care and treatment programs, informing policy makers and assessing targets such as 90-90-90. A challenge to estimating the cascade based on electronic health record concerns patients "churning" in and out of care. Correctly estimating this dynamic phenomenon in resource-limited settings, such as those found in sub-Saharan Africa, is challenging because of the significant death under-reporting. An approach to partially recover information on the unobserved deaths is a double-sampling design, where a small subset of individuals with a missed clinic visit is intensively outreached in the community to actively ascertain their vital status. This approach has been adopted in several programs within the East Africa regional IeDEA consortium, the context of our motivating study. The objective of this paper is to propose a semiparametric method for the analysis of competing risks data with incomplete outcome ascertainment. Methods: Based on data from double-sampling designs, we propose a semiparametric inverse probability weighted estimator of key outcomes during a gap in care, which are crucial pieces of the care cascade puzzle. Results: Simulation studies suggest that the proposed estimators provide valid estimates in settings with incomplete outcome ascertainment under a set of realistic assumptions. These studies also illustrate that a naïve complete-case analysis can provide seriously biased estimates. The methodology is applied to electronic health record data from the East Africa IeDEA Consortium to estimate death and return to care during a gap in care. Conclusions: The proposed methodology provides a robust approach for valid inferences about return to care and death during a gap in care, in settings with death under-reporting. Ultimately, the resulting estimates will have significant consequences on program construction, resource allocation, policy and decision making at the highest levels

How Many SARS-CoV-2–Infected People Require Hospitalization? Using Random Sample Testing to Better Inform Preparedness Efforts

Context: Existing hospitalization ratios for COVID-19 typically use case counts in the denominator, which problematically underestimates total infections because asymptomatic and mildly infected persons rarely get tested. As a result, surge models that rely on case counts to forecast hospital demand may be inaccurately influencing policy and decision-maker action. Objective: Based on SARS-CoV-2 prevalence data derived from a statewide random sample (as opposed to relying on reported case counts), we determine the infection-hospitalization ratio (IHR), defined as the percentage of infected individuals who are hospitalized, for various demographic groups in Indiana. Furthermore, for comparison, we show the extent to which case-based hospitalization ratios, compared with the IHR, overestimate the probability of hospitalization by demographic group. Design: Secondary analysis of statewide prevalence data from Indiana, COVID-19 hospitalization data extracted from a statewide health information exchange, and all reported COVID-19 cases to the state health department. Setting: State of Indiana as of April 30, 2020. Main Outcome Measure(s): Demographic-stratified IHRs and case-hospitalization ratios. Results: The overall IHR was 2.1% and varied more by age than by race or sex. Infection-hospitalization ratio estimates ranged from 0.4% for those younger than 40 years to 9.2% for those older than 60 years. Hospitalization rates based on case counts overestimated the IHR by a factor of 10, but this overestimation differed by demographic groups, especially age. Conclusions: In this first study of the IHR based on population prevalence, our results can improve forecasting models of hospital demand—especially in preparation for the upcoming winter period when an increase in SARS CoV-2 infections is expected

CD4 trajectory adjusting for dropout among HIV-positive patients receiving combination antiretroviral therapy in an East African HIV care centre

Objective: Estimates of CD4 response to antiretroviral therapy (ART) obtained by averaging data from patients in care, overestimate population CD4 response and treatment program effectiveness because they do not consider data from patients who are deceased or not in care. We use mathematical methods to assess and adjust for this bias based on patient characteristics. Design: We examined data from 25,261 HIV-positive patients from the East Africa IeDEA Consortium. Methods: We used inverse probability of censoring weighting (IPCW) to represent patients not in care by patients in care with similar characteristics. We address two questions: What would the median CD4 be “had everyone starting ART remained on observation?” and “were everyone starting ART maintained on treatment?” Results: Routine CD4 count estimates were higher than adjusted estimates even under the best-case scenario of maintaining all patients on treatment. Two years after starting ART, differences between estimates diverged from 30 cells/µL, assuming similar mortality and treatment access among dropouts as patients in care, to over 100 cells/µL assuming 20% lower survival and 50% lower treatment access among dropouts. When considering only patients in care, the proportion of patients with CD4 above 350 cells/µL was 50% adjusted to below 30% when accounting for patients not in care. One-year mortality diverged 6–14% from the naïve estimates depending on assumptions about access to care among lost patients. Conclusions: Ignoring mortality and loss to care results in over-estimation of ART response for patients starting treatment and exaggerates the efficacy of treatment programs administering it

Association of Health Status and Nicotine Consumption with SARS-CoV-2 positivity rates

BACKGROUND: Much of what is known about COVID-19 risk factors comes from patients with serious symptoms who test positive. While risk factors for hospitalization or death include chronic conditions and smoking; less is known about how health status or nicotine consumption is associated with risk of SARS-CoV-2 infection among individuals who do not present clinically. METHODS: Two community-based population samples (including individuals randomly and nonrandomly selected for statewide testing, n = 8214) underwent SARS-CoV-2 testing in nonclinical settings. Each participant was tested for current (viral PCR) and past (antibody) infection in either April or June of 2020. Before testing, participants provided demographic information and self-reported health status and nicotine and tobacco behaviors (smoking, chewing, vaping/e-cigarettes). Using descriptive statistics and a bivariate logistic regression model, we examined the association between health status and use of tobacco or nicotine with SARS-CoV-2 positivity on either PCR or antibody tests. RESULTS: Compared to people with self-identified "excellent" or very good health status, those reporting "good" or "fair" health status had a higher risk of past or current infections. Positive smoking status was inversely associated with SARS-CoV-2 infection. Chewing tobacco was associated with infection and the use of vaping/e-cigarettes was not associated with infection. CONCLUSIONS: In a statewide, community-based population drawn for SARS-CoV-2 testing, we find that overall health status was associated with infection rates. Unlike in studies of COVID-19 patients, smoking status was inversely associated with SARS-CoV-2 positivity. More research is needed to further understand the nature of this relationship

Evaluating the Impact of a HIV Low-Risk Express Care Task-Shifting Program: A Case Study of the Targeted Learning Roadmap

In conducting studies on an exposure of interest, a systematic roadmap should be applied for translating causal questions into statistical analyses and interpreting the results. In this paper we describe an application of one such roadmap applied to estimating the joint effect of both time to availability of a nurse-based triage system (low risk express care (LREC)) and individual enrollment in the program among HIV patients in East Africa. Our study population is comprised of 16;513 subjects found eligible for this task-shifting program within 15 clinics in Kenya between 2006 and 2009, with each clinic starting the LREC program between 2007 and 2008. After discretizing followup into 90-day time intervals, we targeted the population mean counterfactual outcome (i.e. counterfactual probability of either dying or being lost to follow up) at up to 450 days after initial LREC eligibility under three fixed treatment interventions. These were (i) under no program availability during the entire follow-up, (ii) under immediate program availability at initial eligibility, but non-enrollment during the entire follow-up, and (iii) under immediate program availability and enrollment at initial eligibility. We further estimated the controlled direct effect of immediate program availability compared to no program availability, under a hypothetical intervention to prevent individualenrollment in the program. Targeted minimum loss-based estimation was used to estimate the mean outcome, while Super Learning was implemented to estimate the required nuisance parameters. Analyses were conducted with the ltmle R package; analysis code is available at an online repository as an R package. Results showed that at 450 days, the probability of in-care survival for subjects with immediate availability and enrollment was 0:93 (95% CI: 0.91, 0.95) and 0:87 (95% CI: 0.86, 0.87) for subjects with immediate availability never enrolling. For subjects without LREC availability, it was 0:91 (95% CI: 0.90, 0.92). Immediate program availability without individualenrollment, compared to no program availability, was estimated to slightly albeit significantly decrease survival by 4% (95% CI 0.03,0.06, p\u3c 0:01). Immediately availability and enrollment resulted in a 7% higher in-care survival compared to immediate availability with non-enrollment after 450 days (95% CI -0.08,-0.05, p\u3c 0:01). The results are consistent with a fairly small impact of both availability and enrollment in the LREC program on in-care survival

Symptoms and symptom clusters associated with SARS-CoV-2 infection in community-based populations: Results from a statewide epidemiological study

Background: Prior studies examining symptoms of COVID-19 are primarily descriptive and measured among hospitalized individuals. Understanding symptoms of SARS-CoV-2 infection in pre-clinical, community-based populations may improve clinical screening, particularly during flu season. We sought to identify key symptoms and symptom combinations in a community-based population using robust methods. Methods: We pooled community-based cohorts of individuals aged 12 and older screened for SARS-CoV-2 infection in April and June 2020 for a statewide seroprevalence study. Main outcome was SARS-CoV-2 positivity. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for individual symptoms as well as symptom combinations. We further employed multivariable logistic regression and exploratory factor analysis (EFA) to examine symptoms and combinations associated with SARS-CoV-2 infection. Results: Among 8214 individuals screened, 368 individuals (4.5%) were RT-PCR positive for SARS-CoV-2. Although two-thirds of symptoms were highly specific (>90.0%), most symptoms individually possessed a PPV <50.0%. The individual symptoms most greatly associated with SARS-CoV-2 positivity were fever (OR=5.34, p<0.001), anosmia (OR=4.08, p<0.001), ageusia (OR=2.38, p=0.006), and cough (OR=2.86, p<0.001). Results from EFA identified two primary symptom clusters most associated with SARS-CoV-2 infection: (1) ageusia, anosmia, and fever; and (2) shortness of breath, cough, and chest pain. Moreover, being non-white (13.6% vs. 2.3%, p<0.001), Hispanic (27.9% vs. 2.5%, p<0.001), or living in an Urban area (5.4% vs. 3.8%, p<0.001) was associated with infection. Conclusions: Symptoms can help distinguish SARS-CoV-2 infection from other respiratory viruses, especially in community or urgent care settings where rapid testing may be limited. Symptoms should further be structured in clinical documentation to support identification of new cases and mitigation of disease spread by public health. These symptoms, derived from asymptomatic as well as mildly infected individuals, can also inform vaccine and therapeutic clinical trials.This work was supported by a grant from the State of Indiana to the IU Fairbanks School of Public Health to conduct seroprevalence testing in the state population. Dr. Dixon receives funding from the U.S. National Library of Medicine (T15LM012502) as well as the U.S. Centers for Disease Control and Prevention (U18DP006500) and the Indiana State Department of Health to support disease surveillance research. The funder had no role in study design, data collection, data analysis, data interpretation, or writing of the paper

A model for extending antiretroviral care beyond the rural health centre

<p>Abstract</p> <p>Background</p> <p>A major obstacle facing many lower-income countries in establishing and maintaining HIV treatment programmes is the scarcity of trained health care providers. To address this shortage, the World Health Organization has recommend task shifting to HIV-infected peers.</p> <p>Methods</p> <p>We designed a model of HIV care that utilizes HIV-infected patients, community care coordinators (CCCs), to care for their clinically stable peers with the assistance of preprogrammed personal digital assistants (PDAs). Rather than presenting for the standard of care, monthly clinic visits, in this model, patients were seen every three months in clinics and monthly by their CCCs in the community during the interim two months. This study was conducted in Kosirai Division, western Kenya, where eight of the 24 sub-locations (defined geographic areas) within the division were randomly assigned to the intervention with the remainder used as controls.</p> <p>Prior to entering the field, CCCs underwent intensive didactic training and mentoring related to the assessment and support of HIV patients, as well as the use of PDAs. PDAs were programmed with specific questions and to issue alerts if responses fell outside of pre-established parameters. CCCs were regularly evaluated in six performance areas. An impressionistic analysis on the transcripts from the monthly group meetings that formed the basis of the continuous feedback and quality improvement programme was used to assess this model.</p> <p>Results</p> <p>All eight of the assigned CCCs successfully passed their training and mentoring, entered the field and remained active for the two years of the study. On evaluation of the CCCs, 89% of their summary scores were documented as superior during Year 1 and 94% as superior during Year 2. Six themes emerged from the impressionistic analysis in Year 1: confidentiality and "community" disclosure; roles and responsibilities; logistics; clinical care partnership; antiretroviral adherence; and PDA issues. At the end of the trial, of those patients not lost to follow up, 64% (56 of 87) in the intervention and 52% (58 of 103) in the control group were willing to continue in the programme (p = 0.26).</p> <p>Conclusion</p> <p>We found that an antiretroviral treatment delivery model that shifted patient monitoring and antiretroviral dispensing tasks into the community by HIV-infected patients was both acceptable and feasible.</p> <p>Trial registration</p> <p>ClinicalTrials.gov ID NCT00371540</p