Long-Term Mortality Following SARS-CoV-2 Infection in Rural Versus Urban Dwellers with Autoimmune or Inflammatory Rheumatic Disease (AIRD): A Retrospective Cohort Analysis from the National COVID Cohort Collaborative (N3C).

OBJECTIVE: Autoimmune or inflammatory rheumatic diseases (AIRD) increase the risk for poor COVID-19 outcomes. While rurality is associated with higher post-COVID-19 mortality in the general population, whether rurality elevates this risk among people with AIRD is unknown. We assessed associations between rurality and post-COVID-19 all-cause mortality, up to 2 years post-infection, among people with AIRD using a large, nationally sampled U.S. METHODS: This retrospective study utilized the National COVID Cohort Collaborative, a medical-records repository containing COVID-19 patient data. We included adults with ≥2 AIRD diagnostic codes and a COVID-19 diagnosis documented between April 2020 and March 2023. Rural residency was categorized using patient residential ZIP Codes. We adjusted for AIRD medications and glucocorticoid usage, age, sex, race and ethnicity, tobacco/substance usage, comorbid burden, and SARS-CoV-2 variant-dominant periods. Multivariable Cox Proportional Hazards with inverse probability treatment weighting assessed associations between rurality and 2-year, all-cause mortality. RESULTS: Among the 86,467 SARS-CoV-2-infected persons with AIRD, we observed a higher risk for 2-year post-COVID-19 mortality in rural versus urban dwellers. Rural-residing persons with AIRD had higher 2-year, all-cause mortality risk (aHR 1.24, 95% CI 1.19-1.29). Use of glucocorticoids, immunosuppressives, and rituximab was associated with a higher risk for 2-year post-COVID-19 mortality, while risk with non-biologic or biologic DMARDs was lower. CONCLUSION: Rural residence in people with AIRD was independently associated with higher post-COVID-19 2-year mortality in a large U.S. cohort after adjusting for background risk factors. Policymakers and healthcare providers should consider these findings when designing interventions to improve outcomes in people with AIRD following SARS-CoV-2 infection, especially among higher-risk rural residents.

Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study.

BACKGROUND: Postacute sequelae of COVID-19 (PASC), also known as long COVID, is a broad grouping of a range of long-term symptoms following acute COVID-19. These symptoms can occur across a range of biological systems, leading to challenges in determining risk factors for PASC and the causal etiology of this disorder. An understanding of characteristics that are predictive of future PASC is valuable, as this can inform the identification of high-risk individuals and future preventative efforts. However, current knowledge regarding PASC risk factors is limited. OBJECTIVE: Using a sample of 55,257 patients (at a ratio of 1 patient with PASC to 4 matched controls) from the National COVID Cohort Collaborative, as part of the National Institutes of Health Long COVID Computational Challenge, we sought to predict individual risk of PASC diagnosis from a curated set of clinically informed covariates. The National COVID Cohort Collaborative includes electronic health records for more than 22 million patients from 84 sites across the United States. METHODS: We predicted individual PASC status, given covariate information, using Super Learner (an ensemble machine learning algorithm also known as stacking) to learn the optimal combination of gradient boosting and random forest algorithms to maximize the area under the receiver operator curve. We evaluated variable importance (Shapley values) based on 3 levels: individual features, temporal windows, and clinical domains. We externally validated these findings using a holdout set of randomly selected study sites. RESULTS: We were able to predict individual PASC diagnoses accurately (area under the curve 0.874). The individual features of the length of observation period, number of health care interactions during acute COVID-19, and viral lower respiratory infection were the most predictive of subsequent PASC diagnosis. Temporally, we found that baseline characteristics were the most predictive of future PASC diagnosis, compared with characteristics immediately before, during, or after acute COVID-19. We found that the clinical domains of health care use, demographics or anthropometry, and respiratory factors were the most predictive of PASC diagnosis. CONCLUSIONS: The methods outlined here provide an open-source, applied example of using Super Learner to predict PASC status using electronic health record data, which can be replicated across a variety of settings. Across individual predictors and clinical domains, we consistently found that factors related to health care use were the strongest predictors of PASC diagnosis. This indicates that any observational studies using PASC diagnosis as a primary outcome must rigorously account for heterogeneous health care use. Our temporal findings support the hypothesis that clinicians may be able to accurately assess the risk of PASC in patients before acute COVID-19 diagnosis, which could improve early interventions and preventive care. Our findings also highlight the importance of respiratory characteristics in PASC risk assessment. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2023.07.27.23293272.

Structural Racism Conceptualization and Operationalization for Research for the U.S. HIV Epidemic: Findings from a Scoping Review and Implications for Advancing Research for Structural Interventions.

In the U.S., inequities by race/ethnicity in health outcomes, such as in the HIV epidemic, are long standing but have come to the forefront during the COVID-19 pandemic. There is growing recognition of the role of structural racism in racialized health inequities, yet the conceptualization and operationalization of structural racism in HIV research lags. We conducted a scoping review of existing published literature, between 1999-April 2024, conceptualizing and measuring structural racism's impact among people living with or at risk for HIV in the U.S. Our initial search yielded 236 unique articles, which after title and abstract screening yielded ten articles meeting full text review criteria. We then extracted key parameters, such as conceptualization, method of measurement of structural racism, study aims, design, and findings. Three of the articles were qualitative studies that conceptualized structural racism using (1) the social network model, (2) individual and structural intersectionality and (3) critical race theory. Operationalization of structural racism within the seven quantitative studies fell into three categories: (1) structural level, (2) a scale of experiences of racism, including structural racism, and (3) using explanatory demographic factors as downstream measures of the effects of structural racism. The variance in the conceptualization and operationalization of structural racism highlights the different interpretations of structural racism in its applications to the field of HIV research. Given the vast racial/ethnic inequities in HIV, we propose three overarching suggestions for next steps in improving the conduct of research on structural racism in HIV: (1) we must prioritize measuring racism past the individual and interpersonal levels to consider systemic factors at a societal level that manifest as structural racism to improve HIV outcomes in the U.S., (2) consider intergenerational effects of structural racism through the use of longitudinal data, and (3) broaden the agenda of structural racism to incorporate other systems of oppression. Additionally, broadening the scope of funding and inclusion of more researchers and individuals with lived experiences to support structural racism research to drive the scientific agenda and design of structural-level interventions will not only bolster achieving the U.S. Ending the HIV Epidemic goals but will do so by addressing inequities.

"No One Needs to be Forced": Qualitative Insights on Competing Priorities between Antiretroviral Therapy and Reproductive Health Planning during the Dolutegravir Rollout.

Potential associations between periconception dolutegravir (DTG) exposure and neural tube defects (NTDs) reported in 2018 caused shifting international and national antiretroviral treatment (ART) guidelines. They sometimes required women to use contraception prior to initiating DTG. To better understand the tensions between ART and family planning (FP) choices, and explore the decision-making processes of women living with HIV (WLHIV) and their healthcare providers (HCPs) employed, we conducted interviews with WLHIV exposed to DTG and their providers in western Kenya from July 2019 to August 2020. For the interviews with WLHIV, we sampled women at varying ages who either continued using DTG, switched to a different ART, or became pregnant while using DTG. We utilized inductive coding and thematic analysis. We conducted 44 interviews with WLHIV and 10 with providers. We found four dominant themes: (1) a range of attitudes about birth defects, (2) nuanced knowledge of DTG and its potential risk of birth defects, (3) significant tensions at the intersection of DTG and FP use with varying priorities amongst WLHIV and their providers for navigating the tensions, and (4) WLHIV desiring autonomy, and provider support for this, in such decision-making. Variations in beliefs were noted between WLHIV and HCPs. WLHIV highlighted the impact of community and social beliefs when discussing their attitudes while HCPs generally reported more medicalized views towards DTG utilization, potential adverse outcomes, and FP selection. Decisions pertaining to ART and FP selection are complex, and HIV treatment guidelines need to better support women's agency and reproductive health justice.

Pharmacovigilance in Pregnancy Studies, Exposures and Outcomes Ascertainment, and Findings from Low- and Middle-Income Countries: A Scoping Review.

INTRODUCTION: Pharmacovigilance (PV), or the ongoing safety monitoring after a medication has been licensed, plays a crucial role in pregnancy, as clinical trials often exclude pregnant people. It is important to understand how pregnancy PV projects operate in low- and middle-income countries (LMICs), where there is a disproportionate lack of PV data yet a high burden of adverse pregnancy outcomes. We conducted a scoping review to assess how exposures and outcomes were measured in recently published pregnancy PV projects in LMICs. METHODS: We utilized a search string, secondary review, and team knowledge to review publications focusing on therapeutic or vaccine exposures among pregnant people in LMICs. We screened abstracts for relevance before conducting a full text review, and documented measurements of exposures and outcomes (categorized as maternal, birth, or neonatal/infant) among other factors, including study topic, setting, and design, comparator groups, and funding sources. RESULTS: We identified 31 PV publications spanning at least 24 LMICs, all focusing on therapeutics or vaccines for infectious diseases, including HIV (n = 17), tuberculosis (TB; n = 9), malaria (n = 7), pertussis, tetanus, and diphtheria (n = 1), and influenza (n = 3). As for outcomes, n = 15, n = 31, and n = 20 of the publications covered maternal, birth, and neonatal/infant outcomes, respectively. Among HIV-specific publications, the primary exposure-outcome relationship of focus was exposure to maternal antiretroviral therapy and adverse outcomes. For TB-specific publications, the main exposures of interest were second-line drug-resistant TB and isoniazid-based prevention therapeutics for pregnant people living with HIV. For malaria-specific publications, the primary exposure-outcome relationship of interest was antimalarial medication exposure during pregnancy and adverse outcomes. Among vaccine-focused publications, the exposure was assessed during a specific time during pregnancy, with an overall interest in vaccine safety and/or efficacy. The study settings were frequently from Africa, designs varied from cohort or cross-sectional studies to clinical trials, and funding sources were largely from high-income countries. CONCLUSION: The published pregnancy PV projects were largely centered in Africa and concerned with infectious diseases. This may reflect the disease burden in LMICs but also funding priorities from high-income countries. As the prevalence of non-communicable diseases increases in LMICs, PV projects will have to broaden their scope. Birth and neonatal/infant outcomes were most reported, with fewer reporting on maternal outcomes and none on longer-term child outcomes; additionally, heterogeneity existed in definitions and ascertainment of specific measures. Notably, almost all projects covered a single therapeutic exposure, missing an opportunity to leverage their projects to cover additional exposures, add scientific rigor, create uniformity across health services, and bolster existing health systems. For many publications, the timing of exposure, specifically by trimester, was crucial to maternal and neonatal safety. While currently published pregnancy PV literature offer insights into the PV landscape in LMICs, further work is needed to standardize definitions and measurements, integrate PV projects across health services, and establish longer-term monitoring.

Real-world performance of point-of-care vs. standard-of-care HIV viral load testing in western Kenya: Secondary analysis of Opt4Kids and Opt4Mamas studies.

Routine HIV viral load testing is important for evaluating HIV treatment outcomes, but conventional viral load testing has many barriers including expensive laboratory equipment and lengthy results return times to patients. A point-of-care viral load testing technology, such as GeneXpert HIV-1 quantification assay, could reduce these barriers by decreasing cost and turnaround time, however real-world performance is limited. We conducted a secondary analysis using 900 samples collected from participants in two studies to examine the performance of GeneXpert as point-of-care viral load compared to standard-of-care testing (which was conducted with two centralized laboratories using traditional HIV-1 RNA PCR quantification assays). The two studies, Opt4Kids (n = 704 participants) and Opt4Mamas (n = 820 participants), were conducted in western Kenya from 2019-2021 to evaluate the effectiveness of a combined intervention strategy, which included point-of-care viral load testing. Paired viral load results were compared using four different thresholds for virological non-suppression, namely ≥50, ≥200, ≥400, ≥1000 copies/ml. At a threshold of ≥1000 copies/mL, paired samples collected on the same day: demonstrated sensitivities of 90.0% (95% confidence interval [CI] 68.3, 98.8) and 66.7% (9.4, 99.2), specificities of 98.4% (95.5, 99.7) and 100% (96.5, 100), and percent agreements of 97.7% (94.6, 99.2) and 99.1% (95.0, 100) in Opt4Kids and Opt4Mamas studies, respectively. When lower viral load thresholds were used and the paired samples were collected an increasing number of days apart, sensitivity, specificity, and percent agreement generally decreased. While specificity and percent agreement were uniformly high, sensitivity was lower than expected. Non-specificity of the standard of care testing may have been responsible for the sensitivity values. Nonetheless, our results demonstrate that GeneXpert may be used reliably to monitor HIV treatment in low- and middle- income countries to attain UNAID's 95-95-95 HIV goals.

Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study.

BACKGROUND: Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition. OBJECTIVE: The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD. METHODS: Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F1-score. RESULTS: Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method's specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection. CONCLUSIONS: This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.

Optimising HIV drug resistance testing laboratory networks in Kenya: insights from systems engineering modelling.

BACKGROUND: HIV drug resistance (DR) is a growing threat to the durability of current and future HIV treatment success. DR testing (DRT) technologies are very expensive and specialised, relying on centralised laboratories in most low and middle-income countries. Modelling for laboratory network with point-of-care (POC) DRT assays to minimise turnaround time (TAT), is urgently needed to meet the growing demand. METHODS: We developed a model with user-friendly interface using integer programming and queueing theory to improve the DRT system in Kisumu County, Kenya. We estimated DRT demand based on both current and idealised scenarios and evaluated a centralised laboratory-only network and an optimised POC DRT network. A one-way sensitivity analysis of key user inputs was conducted. RESULTS: In a centralised laboratory-only network, the mean TAT ranged from 8.52 to 8.55 working days, and the system could not handle a demand proportion exceeding 1.6%. In contrast, the mean TAT for POC DRT network ranged from 1.13 to 2.11 working days, with demand proportion up to 4.8%. Sensitivity analyses showed that expanding DRT hubs reduces mean TAT substantially while increasing the processing rate at national labs had minimal effect. For instance, doubling the current service rate at national labs reduced the mean TAT by only 0.0%-1.9% in various tested scenarios, whereas doubling the current service rate at DRT hubs reduced the mean TAT by 37.5%-49.8%. In addition, faster batching modes and transportation were important factors influencing the mean TAT. CONCLUSIONS: Our model offers decision-makers an informed framework for improving the DRT system using POC in Kenya. POC DRT networks substantially reduce mean TAT and can handle a higher demand proportion than a centralised laboratory-only network, especially for children and pregnant women living with HIV, where there is an immediate push to use DRT results for patient case management.

Pregnancy Outcomes among Pregnant Persons after COVID-19 Vaccination: Assessing Vaccine Safety in Retrospective Cohort Analysis of U.S. National COVID Cohort Collaborative (N3C).

COVID-19 vaccines have been shown to be effective in preventing severe illness, including among pregnant persons. The vaccines appear to be safe in pregnancy, supporting a continuously favorable overall risk/benefit profile, though supportive data for the U.S. over different periods of variant predominance are lacking. We sought to analyze the association of adverse pregnancy outcomes with COVID-19 vaccinations in the pre-Delta, Delta, and Omicron SARS-CoV-2 variants' dominant periods (constituting 50% or more of each pregnancy) for pregnant persons in a large, nationally sampled electronic health record repository in the U.S. Our overall analysis included 311,057 pregnant persons from December 2020 to October 2023 at a time when there were approximately 3.6 million births per year. We compared rates of preterm births and stillbirths among pregnant persons who were vaccinated before or during pregnancy to persons vaccinated after pregnancy or those who were not vaccinated. We performed a multivariable Poisson regression with generalized estimated equations to address data site heterogeneity for preterm births and unadjusted exact models for stillbirths, stratified by the dominant variant period. We found lower rates of preterm birth in the majority of modeled periods (adjusted incidence rate ratio [aIRR] range: 0.42 to 0.85; p-value range: <0.001 to 0.06) and lower rates of stillbirth (IRR range: 0.53 to 1.82; p-value range: <0.001 to 0.976) in most periods among those who were vaccinated before or during pregnancy compared to those who were vaccinated after pregnancy or not vaccinated. We largely found no adverse associations between COVID-19 vaccination and preterm birth or stillbirth; these findings reinforce the safety of COVID-19 vaccination during pregnancy and bolster confidence for pregnant persons, providers, and policymakers in the importance of COVID-19 vaccination for this group despite the end of the public health emergency.

Effectiveness of mRNA Booster Vaccine Against Coronavirus Disease 2019 Infection and Severe Outcomes Among Persons With and Without Immune Dysfunction: A Retrospective Cohort Study of National Electronic Medical Record Data in the United States.

Background: Real-world evidence of coronavirus disease 2019 (COVID-19) messenger RNA (mRNA) booster effectiveness among patients with immune dysfunction are limited. Methods: We included data from patients in the United States National COVID Cohort Collaborative (N3C) who completed ≥2 doses of mRNA vaccination between 10 December 2020 and 27 May 2022. Immune dysfunction conditions included human immunodeficiency virus infection, solid organ or bone marrow transplant, autoimmune diseases, and cancer. We defined incident COVID-19 BTI as positive results from laboratory tests or diagnostic codes 14 days after at least 2 doses of mRNA vaccination; and severe COVID-19 BTI as hospitalization, invasive cardiopulmonary support, and/or death. We used propensity scores to match boosted versus nonboosted patients and evaluated hazards of incident and severe COVID-19 BTI using Cox regression after matching. Results: Among patients without immune dysfunction, the relative effectiveness of booster (3 doses) after 6 months from the primary (2 doses) vaccination against BTI ranged from 69% to 81% during the Delta-predominant period and from 33% to 39% during the Omicron-predominant period. Relative effectiveness against BTI was lower among patients with immune dysfunction but remained statistically significant in both periods. Boosted patients had lower risk of COVID-19-related hospitalization (hazard ratios [HR] ranged from 0.5 [95% confidence interval {CI}, .48-.53] to 0.63 [95% CI, .56-.70]), invasive cardiopulmonary support, or death (HRs ranged from 0.46 [95% CI, .41-.52] to 0.63 [95% CI, .50-.79]) during both periods. Conclusions: Booster vaccines remain effective against severe COVID-19 BTI throughout the Delta- and Omicron-predominant periods, regardless of patients' immune status.

Three-phase generalized raking and multiple imputation estimators to address error-prone data.

Validation studies are often used to obtain more reliable information in settings with error-prone data. Validated data on a subsample of subjects can be used together with error-prone data on all subjects to improve estimation. In practice, more than one round of data validation may be required, and direct application of standard approaches for combining validation data into analyses may lead to inefficient estimators since the information available from intermediate validation steps is only partially considered or even completely ignored. In this paper, we present two novel extensions of multiple imputation and generalized raking estimators that make full use of all available data. We show through simulations that incorporating information from intermediate steps can lead to substantial gains in efficiency. This work is motivated by and illustrated in a study of contraceptive effectiveness among 83 671 women living with HIV, whose data were originally extracted from electronic medical records, of whom 4732 had their charts reviewed, and a subsequent 1210 also had a telephone interview to validate key study variables.

Impact of point-of-care HIV viral load and targeted drug resistance mutation testing on viral suppression among Kenyan pregnant and postpartum women: results from a prospective cohort study (Opt4Mamas).

INTRODUCTION: Lack of viral suppression (VS) among pregnant and breastfeeding women living with HIV poses challenges for maternal and infant health, and viral load (VL) monitoring via centralized laboratory systems faces many barriers. We aimed to determine the impact of point-of-care (POC) VL and targeted drug resistance mutation (DRM) testing in improving VS among pregnant and postpartum women on antiretroviral therapy. METHODS: We conducted a pre/post-intervention prospective cohort study among 820 pregnant women accessing HIV care at five public-sector facilities in western Kenya from 2019 to 2022. The pre-intervention or "control" group consisted of standard-of-care (SOC) centralized VL testing every 6 months and the post-intervention or "intervention" group consisted of a combined strategy of POC VL every 3 months, targeted DRM testing, and clinical management support. The primary outcome was VS (VL ≤1000 copies/ml) at 6 months postpartum; secondary outcomes included uptake and turnaround times for VL testing and sustained VS. RESULTS: At 6 months postpartum, 321/328 (98%) of participants in the intervention group and 339/347 (98%) in the control group achieved VS (aRR 1.00, 95% confidence interval [CI] 0.98, 1.02). When assessing VS using a threshold of <40 copies/ml, VS proportions were lower overall (90-91%) but remained similar between groups. Among women with viraemia (VL>1000 copies/ml) who underwent successful DRM testing in the intervention group, all (46/46, 100%) had some DRMs and 20 (43%) had major DRMs (of which 80% were nucleos(t)ide reverse transcriptase inhibitor mutations). POC VL testing uptake was high (>89%) throughout pregnancy, delivery, and postpartum periods, with a median turnaround time of 1 day (IQR 1, 4) for POC VL in the intervention group and 7 days (IQR 5, 9) for SOC VL in the control group. Sustained VS throughout follow-up was similar between groups with either POC or SOC VL testing (90-91% for <1000 copies/ml, 62-70% for <40 copies/ml). CONCLUSIONS: Our combined strategy markedly decreased turnaround time but did not increase VS rates, which were already very high, or sustained VS among pregnant and postpartum women living with HIV. Further research on how best to utilize POC VL and DRM testing is needed to optimize sustained VS among this population.

HIV Drug Resistance Patterns and Characteristics Associated with Clinically Significant Drug Resistance among Children with Virologic Failure on Antiretroviral Treatment in Kenya: Findings from the Opt4Kids Randomized Controlled Trial.

Increasing HIV drug resistance (DR) among children with HIV (CHIV) on antiretroviral treatment (ART) is concerning. CHIV ages 1-14 years enrolled from March 2019 to December 2020 from five facilities in Kisumu County, Kenya, were included. Children were randomized 1:1 to control (standard-of-care) or intervention (point-of-care viral load (POC VL) testing every three months with targeted genotypic drug resistance testing (DRT) for virologic failure (VF) (≥1000 copies/mL)). A multidisciplinary committee reviewed CHIV with DRT results and offered treatment recommendations. We describe DR mutations and present logistic regression models to identify factors associated with clinically significant DR. We enrolled 704 children in the study; the median age was 9 years (interquartile range (IQR) 7, 12), 344 (49%) were female, and the median time on ART was 5 years (IQR 3, 8). During the study period, 106 (15%) children had DRT results (84 intervention and 22 control). DRT detected mutations associated with DR in all participants tested, with 93 (88%) having major mutations, including 51 (54%) with dual-class resistance. A history of VF in the prior 2 years (adjusted odds ratio (aOR) 11.1; 95% confidence interval (CI) 6.3, 20.0) and less than 2 years on ART at enrollment (aOR 2.2; 95% CI 1.1, 4.4) were associated with increased odds of major DR. DR is highly prevalent among CHIV on ART with VF in Kenya. Factors associated with drug resistance may be used to determine which children should be prioritized for DRT.

Interventions to Vaccinate Zero-Dose Children: A Narrative Review and Synthesis.

Zero-dose children, or children who have not received any routine vaccination, are a priority population for global health policy makers as these children are at high risk of mortality from vaccine-preventable illnesses. We conducted a narrative review to identify potential interventions, both within and outside of the health sector, to reach zero-dose children. We reviewed the peer-reviewed and grey literature and identified 27 relevant resources. Additionally, we interviewed six key informants to enhance the synthesis of our findings. Data were organized into three priority settings: (1) urban slums, (2) remote or rural communities, and (3) conflict settings. We found that zero-dose children in the three priority settings face differing barriers to vaccination and, therefore, require context-specific interventions, such as leveraging slum health committees for urban slums or integrating with existing humanitarian response services for conflict settings. Three predominant themes emerged for grouping the various interventions: (1) community engagement, (2) health systems' strengthening and integration, and (3) technological innovations. The barriers to reaching zero-dose children are multifaceted and nuanced to each setting, therefore, no one intervention is enough. Technological interventions especially must be coupled with community engagement and health systems' strengthening efforts. Evaluations of the suggested interventions are needed to guide scale-up, as the evidence base around these interventions is relatively small.

Harnessing consumer wearable digital biomarkers for individualized recognition of postpartum depression using the All of Us Research Program dataset.

Postpartum depression (PPD), afflicting one in seven women, poses a major challenge in maternal health. Existing approaches to detect PPD heavily depend on in-person postpartum visits, leading to cases of the condition being overlooked and untreated. We explored the potential of consumer wearable-derived digital biomarkers for PPD recognition to address this gap. Our study demonstrated that intra-individual machine learning (ML) models developed using these digital biomarkers can discern between pre-pregnancy, pregnancy, postpartum without depression, and postpartum with depression time periods (i.e., PPD diagnosis). When evaluating variable importance, calories burned from the basal metabolic rate (calories BMR) emerged as the digital biomarker most predictive of PPD. To confirm the specificity of our method, we demonstrated that models developed in women without PPD could not accurately classify the PPD-equivalent phase. Prior depression history did not alter model efficacy for PPD recognition. Furthermore, the individualized models demonstrated superior performance compared to a conventional cohort-based model for the detection of PPD, underscoring the effectiveness of our individualized ML approach. This work establishes consumer wearables as a promising avenue for PPD identification. More importantly, it also emphasizes the utility of individualized ML model methodology, potentially transforming early disease detection strategies.

"I feel drug resistance testing allowed us to make an informed decision": qualitative insights on the role of HIV drug resistance mutation testing among children and pregnant women living with HIV in western Kenya.

BACKGROUND: Pregnant women and children living with HIV in Kenya achieve viral suppression (VS) at lower rates than other adults. While many factors contribute to these low rates, the acquisition and development of HIV drug resistance mutations (DRMs) are a contributing factor. Recognizing the significance of DRMs in treatment decisions, resource-limited settings are scaling up national DRM testing programs. From provider and patient perspectives, however, optimal ways to operationalize and scale-up DRM testing in such settings remain unclear. METHODS: Our mixed methods study evaluates the attitudes towards, facilitators to, and barriers to DRM testing approaches among children and pregnant women on antiretroviral therapy (ART) in five HIV treatment facilities in Kenya. We conducted 68 key informant interviews (KIIs) from December 2019 to December 2020 with adolescents, caregivers, pregnant women newly initiating ART or with a high viral load, and providers, laboratory/facility leadership, and policy makers. Our KII guides covered the following domains: (1) DRM testing experiences in routine care and through our intervention and (2) barriers and facilitators to routine and point-of-care DRM testing scale-up. We used inductive coding and thematic analysis to identify dominant themes with convergent and divergent subthemes. RESULTS: The following themes emerged from our analysis: (1) DRM testing and counseling were valuable to clinical decision-making and reassuring to patients, with timely results allowing providers to change patient ART regimens faster; (2) providers and policymakers desired an amended and potentially decentralized DRM testing process that incorporates quicker sample-to-results turn-around-time, less burdensome procedures, and greater patient and provider "empowerment" to increase comfort with testing protocols; (3) facility-level delays, deriving from overworked facilities and sample tracking difficulties, were highlighted as areas for improvement. CONCLUSIONS: DRM testing has the potential to considerably improve patient health outcomes. Key informants recognized several obstacles to implementation and desired a more simplified, time-efficient, and potentially decentralized DRM testing process that builds provider comfort and confidence with DRM testing protocols. Further investigating the implementation, endurance, and effectiveness of DRM testing training is critical to addressing the barriers and areas of improvement highlighted in our study. TRIAL REGISTRATION: NCT03820323.

Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C).

Objectives: To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods: We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (January 1, 2018-April 7, 2022). HIPPS combines: (1) an extension of a previously published pregnancy episode algorithm, (2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and (3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. Results: We identified 628 165 pregnant persons with 816 471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within 1 week of precision for 475 433 (58.2%) episodes. 62 540 (7.7%) episodes had incident COVID-19 during pregnancy. Discussion: HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. Conclusion: We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data.

Impact of HIV on COVID-19 Outcomes: A Propensity Score Matching Analysis with Varying Age Differences.

To exploratorily test (1) the impact of HIV and aging process among PLWH on COVID-19 outcomes; and (2) whether the effects of HIV on COVID-19 outcomes differed by immunity level. The data used in this study was retrieved from the COVID-19 positive cohort in National COVID Cohort Collaborative (N3C). Multivariable logistic regression models were conducted on populations that were matched using either exact matching or propensity score matching (PSM) with varying age difference between PLWH and non-PLWH to examine the impact of HIV and aging process on all-cause mortality and hospitalization among COVID-19 patients. Subgroup analyses by CD4 counts and viral load (VL) levels were conducted using similar approaches. Among the 2,422,864 adults with a COVID-19 diagnosis, 15,188 were PLWH. PLWH had a significantly higher odds of death compared to non-PLWH until age difference reached 6 years or more, while PLWH were still at an elevated risk of hospitalization across all matched cohorts. The odds of both severe outcomes were persistently higher among PLWH with CD4 < 200 cells/mm3. VL ≥ 200 copies/ml was only associated with higher hospitalization, regardless of the predefined age differences. Age advancement in HIV might significantly contribute to the higher risk of COVID-19 mortality and HIV infection may still impact COVID-19 hospitalization independent of the age advancement in HIV.

Integration of HIV care into maternal and child health services in the global IeDEA consortium.

The WHO recommends the integration of routine HIV services within maternal and child health (MCH) services to reduce the fragmentation of and to promote retention in care for pregnant and postpartum women living with HIV (WWH) and their infants and children exposed to HIV (ICEH). During 2020-2021, we surveyed 202 HIV treatment sites across 40 low- and middle-income countries within the global International epidemiology Databases to Evaluate AIDS (IeDEA) consortium. We determined the proportion of sites providing HIV services integrated within MCH clinics, defined as full [HIV care and antiretroviral treatment (ART) initiation in MCH clinic], partial (HIV care or ART initiation in MCH clinic), or no integration. Among sites serving pregnant WWH, 54% were fully and 21% partially integrated, with the highest proportions of fully integrated sites in Southern Africa (80%) and East Africa (76%) compared to 14%-40% in other regions (i.e., Asia-Pacific; the Caribbean, Central and South America Network for HIV Epidemiology; Central Africa; West Africa). Among sites serving postpartum WWH, 51% were fully and 10% partially integrated, with a similar regional integration pattern to sites serving pregnant WWH. Among sites serving ICEH, 56% were fully and 9% were partially integrated, with the highest proportions of fully integrated sites in East Africa (76%), West Africa (58%) and Southern Africa (54%) compared to ≤33% in the other regions. Integration was heterogenous across IeDEA regions and most prevalent in East and Southern Africa. More research is needed to understand this heterogeneity and the impacts of integration on MCH outcomes globally.

Rituximab is associated with worse COVID-19 outcomes in patients with rheumatoid arthritis: A retrospective, nationally sampled cohort study from the U.S. National COVID Cohort Collaborative (N3C).

OBJECTIVE: To assess whether rituximab (RTX) is associated with worse COVID-19 outcomes among patients with rheumatoid arthritis (RA). METHODS: We used the National COVID Cohort Collaborative (N3C), the largest US cohort of COVID-19 cases and controls, to identify patients with RA (International Classification of Diseases (ICD)-10 code, M05.X or M06.X). Key outcomes were COVID-19-related hospitalization, intensive care unit (ICU) admission, 30-day mortality, and World Health Organization (WHO) classification for COVID-19 severity. We used multivariable logistic regression models to assess the association between RTX use and the odds of COVID-19 outcomes compared with the use of conventional synthetic disease modifying anti-rheumatic drugs (csDMARDs), adjusting for demographics, medical comorbidities, smoking status, body mass index, US region and COVID-19 treatments. RESULTS: A total of 69,549 patients met our eligibility criteria of which 22,956 received a COVID-19 positive diagnosis between 1/1/2020 and 9/16/2021. Median (IQR) age of the cohort was 63 (52-72) years, 76% of the cohort was female, 68% was non-Hispanic/Latinx White, and 73% was non-smokers. Prior to their first COVID-19 diagnosis, 364 patients were exposed to RTX. Compared to the use of csDMARDs, RTX use was associated with an increased odds of COVID-19-related hospitalization (adjusted odds ratio [aOR] 2.1, 95% confidence interval 1.5-3.0), ICU admission (aOR 5.2, 1.8-15.4) and invasive ventilation (aOR 2.7, 1.4-5.5). Results were confirmed in multiple sensitivity analyses. CONCLUSION: Our findings can guide patients, providers, and policymakers regarding the increased risks associated with RTX use during the COVID-19 pandemic. These results can help risk stratification and prognosis-assessment.