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ImportanceDelivery of adjuvant chemotherapy can differ substantially between trial and real-world populations. Adherence metrics like relative dose intensity (RDI) cannot capture the timing of modifications and mask differences in the total amount of chemotherapy received.ObjectiveTo compare oxaliplatin delivery between MOSAIC trial participants and patients treated in the US Oncology Network with stage III colon cancer using a longitudinal cumulative dose (LCD).Design, Setting, and ParticipantsThis cohort study used secondary data from the MOSAIC trial, an international randomized clinical trial (concluded in 2004), and electronic health records from US Oncology (2009-2018), a network of community oncology practices in the US. It included participants in MOSAIC with stage III colon cancer who were randomized to receive treatment with oxaliplatin and fluorouracil/leucovorin (n = 663) and US Oncology patients with stage III colon cancer who were treated with a modified FOLFOX-6 regimen (n = 2523).ExposuresOxaliplatin and fluorouracil/leucovorin.Outcomes and MeasuresWe evaluated RDI and LCD over time and at the end of treatment in the MOSAIC and US Oncology populations. We used bootstrapping to estimate 95% confidence bands for LCD differences between the populations.ResultsThe 663 MOSAIC participants (296 women [44.7%]) and 2523 US Oncology patients (1245 women [49.4%]) were generally similar with respect to demographic characteristics. Median RDI was lower in US Oncology (80% in MOSAIC vs 70% in US Oncology). The LCD also suggested differences in the total amount of oxaliplatin received between populations; the final median LCD in US Oncology was 10.2% lower than in MOSAIC, equivalent to receiving 1.2 fewer treatment cycles less of oxaliplatin. This difference only began 133 days into treatment and persisted after accounting for covariates, likely in terms of more frequent oxaliplatin treatment discontinuation in US Oncology patients than their MOSAIC counterparts.Conclusions and RelevanceThe study results suggest that real-world patients in community practice in the US treated with modified FOLFOX 6 received less oxaliplatin than their historical counterparts in the MOSAIC trial, with differences manifesting late in the treatment course. The LCD allowed us to identify the amount and extent of these differences, the timing of which was unclear when using RDI alone.Trial RegistrationClinicalTrials.gov identifier: NCT00275210

Pooled testing has been successfully used to expand SARS-CoV-2 testing, especially in settings requiring high volumes of screening of lower-risk individuals, but efficiency of pooling declines as prevalence rises. We propose a differentiated pooling strategy that independently optimizes pool sizes for distinct groups with different probabilities of infection to further improve the efficiency of pooled testing. We compared the efficiency (results obtained per test kit used) of the differentiated strategy with a traditional pooling strategy in which all samples are processed using uniform pool sizes under a range of scenarios. For most scenarios, differentiated pooling is more efficient than traditional pooling. In scenarios examined here, an improvement in efficiency of up to 3.94 results per test kit could be obtained through differentiated versus traditional pooling, with more likely scenarios resulting in 0.12 to 0.61 additional results per kit. Under circumstances similar to those observed in a university setting, implementation of our strategy could result in an improvement in efficiency between 0.03 to 3.21 results per test kit. Our results can help identify settings, such as universities and workplaces, where differentiated pooling can conserve critical testing resources.

We aim to assess the reporting of key patient-level demographic and clinical characteristics among COVID-19 related randomized controlled trials (RCTs).We queried English-language articles from PubMed, Web of Science, clinicaltrials.gov, and the CDC library of gray literature databases using keywords of "coronavirus," "covid," "clinical trial" and "randomized controlled trial" from January 2020 to June 2021. From the search, we conducted an initial review to rule-out duplicate entries, identify those that met inclusion criteria (i.e., had results), and exclude those that did not meet the definition of an RCT. Lastly, we abstracted the demographic and clinical characteristics reported on within each RCT.From the initial 43 627 manuscripts, our final eligible manuscripts consisted of 149 RCTs described in 137 articles. Most of the RCTs (113/149) studied potential treatments, while fewer studied vaccines (29), prophylaxis strategies (5), and interventions to prevent transmission among those infected (2). Study populations ranged from 10 to 38 206 participants (median = 100, IQR: 60-300). All 149 RCTs reported on age, 147 on sex, 50 on race, and 110 on the prevalence of at least one comorbidity. No RCTs reported on income, urban versus rural residence, or other indicators of socioeconomic status (SES).Limited reporting on race and other markers of SES make it difficult to draw conclusions about specific external target populations without making strong assumptions that treatment effects are homogenous. These findings highlight the need for more robust reporting on the clinical and demographic profiles of patients enrolled in COVID-19 related RCTs.

Selection bias remains a subject of controversy. Existing definitions of selection bias are ambiguous. To improve communication and the conduct of epidemiologic research focused on estimating causal effects, we propose to unify the various existing definitions of selection bias in the literature by considering any bias away from the true causal effect in the referent population (the population before the selection process), due to selecting the sample from the referent population, as selection bias. Given this unified definition, selection bias can be further categorized into two broad types: type 1 selection bias owing to restricting to one or more level(s) of a collider (or a descendant of a collider) and type 2 selection bias owing to restricting to one or more level(s) of an effect measure modifier. To aid in explaining these two types-which can co-occur-we start by reviewing the concepts of the target population, the study sample, and the analytic sample. Then, we illustrate both types of selection bias using causal diagrams. In addition, we explore the differences between these two types of selection bias, and describe methods to minimize selection bias. Finally, we use an example of "M-bias" to demonstrate the advantage of classifying selection bias into these two types.

Background SARS-CoV-2 infection has caused variable clinical outcomes including hospitalization and death. We analyzed state-level data from the North Carolina COVID-19 Surveillance System (NC COVID) to describe demographics of those infected with SARS-CoV-2 and to describe factors associated with infection-fatality in North Carolina. Methods This was a retrospective cohort study using surveillance data on positive SARS-CoV-2-infected individuals (N = 214,179) identified between March 1, 2020, and September 30, 2020. We present descriptive statistics and associations among demographics, medical comorbidities, and SARS-CoV-2 infection-fatality. Results Median age for residents with reported SARS-CoV-2 was 38 (IQR 23–54). Age was strongly correlated with SARS-CoV-2 infection-fatality. Greater infection-fatality was noted among those who identified as Black across all comorbidities. Coexisting chronic disease was associated with greater infection-fatality, with kidney disease demonstrating the strongest association. Limitations A high percentage of missing data for race/ethnicity and comorbidities limits the interpretation of our findings. Data were not available for socioeconomic measures that could aid in better understanding inequities associated with SARS-CoV-2 infection-fatality. Conclusions Among North Carolinians identified with SARS-CoV-2 via surveillance efforts, age, race, and comorbidities were associated with infection-fatality; these findings are similar to those of studies using different source populations in the United States. In addition to age and other nonmodifiable variables, systematic differences in social conditions and opportunity may increase the risk of SARS-CoV-2 infection-fatality among Black Americans compared to other races/ethnicities.

The union of distinct covariate sets, or the superset, is often used in proofs for the identification or the statistical consistency of an estimator when multiple sources of bias are present. However, the use of a superset can obscure important nuances. Here, we provide two illustrative examples: one in the context of missing data on outcomes, and one in which the average causal effect is transported to another target population. As these examples demonstrate, the use of supersets may indicate a parameter is not identifiable when the parameter is indeed identified. Furthermore, a series of exchangeability conditions may lead to successively weaker conditions. Future work on approaches to address multiple biases can avoid these pitfalls by considering the more general case of nonoverlapping covariate sets.

Background Malaria can have deleterious effects early in pregnancy, during placentation. However, malaria testing and treatment are rarely initiated until the second trimester, leaving pregnancies unprotected in the first trimester. To inform potential early intervention approaches, we sought to identify clinical and demographic predictors of first-trimester malaria. Methods We prospectively recruited women from sites in the Democratic Republic of the Congo (DRC), Kenya, and Zambia who participated in the ASPIRIN (Aspirin Supplementation for Pregnancy Indicated risk Reduction In Nulliparas) trial. Nulliparous women were tested for first-trimester Plasmodium falciparum infection by quantitative polymerase chain reaction. We evaluated predictors using descriptive statistics. Results First-trimester malaria prevalence among 1513 nulliparous pregnant women was 6.3% (95% confidence interval [CI], 3.7%–8.8%] in the Zambian site, 37.8% (95% CI, 34.2%–41.5%) in the Kenyan site, and 62.9% (95% CI, 58.6%–67.2%) in the DRC site. First-trimester malaria was associated with shorter height and younger age in Kenyan women in site-stratified analyses, and with lower educational attainment in analyses combining all 3 sites. No other predictors were identified. Conclusions First-trimester malaria prevalence varied by study site in sub-Saharan Africa. The absence of consistent predictors suggests that routine parasite screening in early pregnancy may be needed to mitigate first-trimester malaria in high-prevalence settings.

OBJECTIVES: The aim of this study was to define a smoking cessation ‘cascade’ among USA women with and without HIV and examine differences by sociodemographic characteristics. DESIGN: An observational cohort study using data from smokers participating in the Women’s Interagency HIV Study between 2014 and 2019. METHODS: We followed 1165 women smokers with and without HIV from their first study visit in 2014 or 2015 until an attempt to quit smoking within approximately 3 years of follow-up, initial cessation (i.e. no restarting smoking within approximately 6 months of a quit attempt), and sustained cessation (i.e. no restarting smoking within approximately 12 months of a quit attempt). Using the Aalen-Johansen estimator, we estimated the cumulative probability of achieving each step, accounting for the competing risk of death. RESULTS: Forty-five percent of smokers attempted to quit, 27% achieved initial cessation, and 14% achieved sustained cessation with no differences by HIV status. Women with some post-high school education were more likely to achieve each step than those with less education. Outcomes did not differ by race. Thirty-six percent [95% confidence interval (95% CI): 31–42] of uninsured women attempted to quit compared with 47% (95% CI: 44–50) with Medicaid and 49% (95% CI: 41–59) with private insurance. CONCLUSION: To decrease smoking among USA women with and without HIV, targeted, multistage interventions, and increased insurance coverage are needed to address shortfalls along this cascade.

Inverse probability weights are increasingly used in epidemiological analysis, and estimation and application of weights to address a single bias are well discussed in the literature. Weights to address multiple biases simultaneously (i.e. a combination of weights) have almost exclusively been discussed related to marginal structural models in longitudinal settings where treatment weights (estimated first) are combined with censoring weights (estimated second). In this work, we examine two examples of combined weights for confounding and missingness in a time-fixed setting in which outcome or confounder data are missing, and the estimand is the marginal expectation of the outcome under a time-fixed treatment. We discuss the identification conditions, construction of combined weights and how assumptions of the missing data mechanisms affect this construction. We use a simulation to illustrate the estimation and application of the weights in the two examples. Notably, when only outcome data are missing, construction of combined weights is straightforward; however, when confounder data are missing, we show that in general we must follow a specific estimation procedure which entails first estimating missingness weights and then estimating treatment probabilities from data with missingness weights applied. However, if treatment and missingness are conditionally independent, then treatment probabilities can be estimated among the complete cases.

Integrase strand transfer inhibitor (InSTI)-based regimens have been recommended as first-line antiretroviral therapy (ART) for adults with HIV. But data on long-term effects of InSTI-based regimens on virologic outcomes remain limited. Here we examined whether InSTI improved long-term virologic outcomes compared with efavirenz (EFV).We included adults from the North American AIDS Cohort Collaboration on Research and Design who initiated their first ART regimen containing either InSTI or EFV between 2009 and 2016. We estimated differences in the proportion virologically suppressed up to 7 years of follow-up in observational intention-to-treat and per-protocol analyses.Of 15 318 participants, 5519 (36%) initiated an InSTI-based regimen and 9799 (64%) initiated the EFV-based regimen. In observational intention-to-treat analysis, 81.3% of patients in the InSTI group and 67.3% in the EFV group experienced virologic suppression at 3 months after ART initiation, corresponding to a difference of 14.0% (95% CI 12.4-15.6). At 1 year after ART initiation, the proportion virologically suppressed was 89.5% in the InSTI group and 90.2% in the EFV group, corresponding to a difference of -0.7% (95% CI -2.1 to 0.8). At 7 years, the proportion virologically suppressed was 94.5% in the InSTI group and 92.5% in the EFV group, corresponding to a difference of 2.0% (95% CI -7.3 to 11.3). The observational per-protocol results were similar to intention-to-treat analyses.Although InSTI-based initial ART regimens had more rapid virologic response than EFV-based regimens, the long-term virologic effect was similar. Our findings may inform guidelines regarding preferred initial regimens for HIV treatment.

OBJECTIVE: To evaluate the associations of HIV infection with preterm birth (PTB), and of HIV antiretroviral therapy (ART) with PTB. METHODS: We analysed singleton live-born pregnancies among women from 1995 to 2019 in the Women’s Interagency HIV Study, a prospective cohort of US women with, or at risk for, HIV. The primary exposures were HIV status and ART use before delivery [none, monotherapy or dual therapy, or highly active antiretroviral therapy (HAART)]. The primary outcome was PTB < 34 weeks, and, secondarily, < 28 and < 37 weeks. We analysed self-reported birth data, and separately modelled the associations between HFV and PTB, and between ART and PTB, among women with HIV. We used modified Poisson regression, and adjusted for age, race, parity, tobacco use and delivery year, and, when modelling the impact of ART, duration from HIV diagnosis to delivery, nadir CD4 count, and pre-pregnancy viral load and CD4 count. RESULTS: We analysed 488 singleton deliveries (56% exposed to HIV) to 383 women. The risk of PTB < 34 weeks was similar among women with and without HIV, but the risk of PTB < 37 weeks was higher [32% vs. 23%; adjusted risk ratio (aRR) = 1.43; 95% confidence interval (CI): 1.07–1.91] among women with HIV. The risk of PTB < 34 weeks was lower among women with HIV receiving HAART than among those receiving no ART (7% vs. 26%; aRR:0.19; 95% CI: 0.08–0.44). The associations between HAART and PTB < 28 and < 37 weeks were similar. CONCLUSIONS: Antiretroviral therapy exposure was associated with a decreased risk of PTB among a US cohort of women with HIV. Given the growing concerns about ART and adverse pregnancy outcomes, this finding that ART may be protective for PTB is reassuring.

BACKGROUND: Globally, neonatal mortality accounts for almost half of all deaths in children younger than 5 years. Aetiological agents of neonatal infection are difficult to identify because the clinical signs are non-specific. Using data from the Aetiology of Neonatal Infections in south Asia (ANISA) cohort, we aimed to describe the spectrum of infectious aetiologies of acute neonatal illness categorised post-hoc using the 2015 WHO case definitions of critical illness, clinical severe infection, and fast breathing only. METHODS: Eligible infants were aged 0-59 days with possible serious bacterial infection and healthy infants enrolled in the ANISA study in Bangladesh, India, and Pakistan. We applied a partial latent class Bayesian model to estimate the prevalence of 27 pathogens detectable on PCR, pathogens detected by blood culture only, and illness not attributed to any infectious aetiology. Infants with at least one clinical specimen available were included in the analysis. We assessed the prevalence of these aetiologies according to WHO's case definitions of critically ill, clinical severe infection, and infants with late onset, isolated fast breathing. For the clinical severe definition, we compared the prevalence of signs by bacterial versus viral aetiology. FINDINGS: There were 934 infants (992 episodes) in the critically ill category, 3769 (4000 episodes) in the clinical severe infection category, and 738 (771 episodes) in the late-onset isolated fast breathing category. We estimated the proportion of illness attributable to bacterial infection was 32·7% in infants in the critically ill group, 15·6% in the clinical severe infection group, and 8·8% among infants with late-onset isolated fast breathing group. An infectious aetiology was not identified in 58-82% of infants in these categories. Among 4000 episodes of clinical severe infection, those with bacterial versus viral attribution had higher proportions of hypothermia, movement only when stimulated, convulsions, and poor feeding. INTERPRETATION: Our modelled results generally support the revised WHO case definitions, although a revision of the most severe case definition could be considered. Clinical criteria do not clearly differentiate between young infants with and without infectious aetiologies. Our results highlight the need for improved point-of-care diagnostics, and further study into neonatal deaths and episodes with no identified aetiology, to ensure antibiotic stewardship and targeted interventions. FUNDING: The Bill and Melinda Gates Foundation.

To illustrate the difference between exposure effects and population attributable effects.We examined the effect of mid-pregnancy short cervical length (25 mm) on preterm birth using data from a prospective cohort of pregnant women in Lusaka, Zambia. Preterm birth was live birth or stillbirth before 37 weeks of pregnancy. For estimation, we used multivariable regression and parametric g-computation.Among 1409 women included in the analysis, short cervix was rare (2.4%); 13.6% of births were preterm. Exposure effect estimates were large (marginal risk ratio 2.86, 95% confidence interval [CI] 1.80-4.54), indicating that the preterm birth risk was substantially higher among women with a short cervix compared with women without a short cervix. However, the population attributable effect estimates were close to the null (risk ratio 1.06, 95% CI 1.02-1.10), indicating that an intervention to counteract the impact of short cervix on preterm birth would have minimal effect on the population risk of preterm birth.Although authors often refer to "the" effect, there are actually different types of effects, as we have illustrated here. In planning research, it is important to consider which effect to estimate to ensure that the estimate aligns with the research objective.

In an analysis of randomized trials, use of efavirenz for treatment of human immunodeficiency virus (HIV) infection was associated with increased suicidal thoughts/behaviors. However, analyses of observational data have found no evidence of increased risk. To assess whether population differences might explain this divergence, we transported the effect of efavirenz use from these trials to a specific target population. Using inverse odds weights and multiple imputation, we transported the effect of efavirenz on suicidal thoughts/behaviors in these randomized trials (participants were enrolled in 2001–2007) to a trials-eligible cohort of US adults initiating antiretroviral therapy while receiving HIV clinical care at medical centers between 1999 and 2015. Overall, 8,291 cohort participants and 3,949 trial participants were eligible. Prescription of antidepressants (19% vs. 13%) and injection drug history (16% vs. 10%) were more frequent in the cohort than in the trial participants. Compared with the effect in trials, the estimated hazard ratio for efavirenz on suicidal thoughts/behaviors was attenuated in our target population (trials: hazard ratio (HR) = 2.3 (95% confidence interval (CI): 1.2, 4.4); transported: HR = 1.8 (95% CI: 0.9, 4.4)), whereas the incidence rate difference was similar (trials: HR = 5.1 (95% CI: 1.6, 8.7); transported: HR = 5.4 (95% CI: −0.4, 11.4)). In our target population, there was greater than 20% attenuation of the hazard ratio estimate as compared with the trials-only estimate. Transporting results from trials to a target population is informative for addressing external validity.

OBJECTIVE People with HIV (PWH) experience increased prevalence of obstructive lung disease (OLD), regardless of greater observed smoking behaviors. We investigated whether the effect of incident OLD on mortality differed by HIV and HIV viral suppression among persons who inject drugs (PWID) and report smoking history. DESIGN ALIVE is a longitudinal, observational cohort study of HIV-positive and seronegative PWID. This analysis included participants who had at least one spirometry measure to assess OLD between 2007 and 2016, excluding those who never smoked (5%, n = 62) or had baseline OLD (17%, n = 269). METHODS Incident OLD occurred when the first prebronchodilator forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) less than 0.70 during follow-up. The effect of incident OLD on all-cause mortality was estimated in PWH and seronegative participants using inverse-probability-of-treatment-weighted marginal structural models controlling for baseline (age, race, sex, calendar year, smoking pack-years) and time-varying (smoking intensity, viral suppression, and calendar time) confounders. RESULTS Among 1204 participants, 269 (22.3%) and 157 (13.0%) experienced incident OLD and death, respectively, over a median of five person-years of follow-up. There was no effect of OLD on mortality among seronegative participants [hazard ratio = 0.84, 95% confidence interval (CI): 0.47-1.48]; however, PWH diagnosed with OLD experienced an increased mortality risk (hazard ratio = 1.71, 95% CI: 1.04-2.80) during follow-up. HIV viral suppression did not confound or modify the effect of OLD on mortality among PWH. CONCLUSION There was an apparent effect of OLD on all-cause mortality irrespective of viral suppression among PWH but not among seronegative persons, after accounting for baseline and time-varying confounders. These results highlight the need for enhanced screening and management of OLD among PWH.

Machine learning is gaining prominence in the health sciences, where much of its use has focused on data-driven prediction. However, machine learning can also be embedded within causal analyses, potentially reducing biases arising from model misspecification. Using a question-and-answer format, we provide an introduction and orientation for epidemiologists interested in using machine learning but concerned about potential bias or loss of rigor due to use of “black box” models. We conclude with sample software code that may lower the barrier to entry to using these techniques.

Background The global COVID-19 pandemic has the potential to indirectly impact transmission dynamics and prevention of HIV and other sexually transmitted infections (STI). It is unknown what combined impact reductions in sexual activity and interruptions in HIV/STI services will have on HIV/STI epidemic trajectories. Methods We adapted a model of HIV, gonorrhea, and chlamydia for a population of approximately 103 000 men who have sex with men (MSM) in the Atlanta area. Model scenarios varied the timing, overlap, and relative extent of COVID-19–related sexual distancing and service interruption within 4 service categories (HIV screening, preexposure prophylaxis, antiretroviral therapy, and STI treatment). Results A 50% relative decrease in sexual partnerships and interruption of all clinical services, both lasting 18 months, would generally offset each other for HIV (total 5-year population impact for Atlanta MSM, −227 cases), but have net protective effect for STIs (−23 800 cases). If distancing lasted only 3 months but service interruption lasted 18 months, the total 5-year population impact would be an additional 890 HIV cases and 57 500 STI cases. Conclusions Immediate action to limit the impact of service interruptions is needed to address the indirect effects of the global COVID-19 pandemic on the HIV/STI epidemic.

Background Neurodevelopmental outcomes of asymptomatic children exposed to Zika virus (ZIKV) in utero are not well characterized. Methods We prospectively followed 129 newborns without evidence of congenital Zika syndrome (CZS) up to 24 months of age. Participants were classified as ZIKV exposed or ZIKV unexposed. The Mullen Scales of Early Learning (MSEL) was administered in the participants’ homes at 6, 12, 15, 18, 21, and 24 months of age by trained psychologists. Sociodemographic data, medical history, and infant anthropometry at birth were collected at each home visit. Our primary outcome was the Mullen Early Learning Composite Score (ECL) at 24 months of age between our 2 exposure groups. Secondary outcomes were differences in MSEL subscales over time and at 24 months. Results Of 129 infants in whom exposure status could be ascertained, 32 (24.8%) met criteria for in utero ZIKV exposure and 97 (75.2%) did not. There were no differences in maternal age, maternal educational attainment, birthweight, or gestational age at birth between the 2 exposure groups. The adjusted means and standard errors (SEs) for the ELC score between the ZIKV-exposed children compared to ZIKV-unexposed children were 91.4 (SE, 3.1) vs 96.8 (SE, 2.4) at 12 months and 93.3 (SE, 2.9) vs 95.9 (SE, 2.3) at 24 months. In a longitudinal mixed model, infants born to mothers with an incident ZIKV infection (P = .01) and low-birthweight infants (, Children with in utero Zika virus exposure and without congenital Zika syndrome in Nicaragua had lower neurocognitive scores at 24 months. Our study includes multiple evaluations over time and a contemporary nonexposed comparator group of children, which is currently lacking in the literature.

Background Combination interventions may be an effective way to prevent human immunodeficiency virus (HIV) in adolescent girls and young women. However, current studies are not designed to understand which specific interventions and combinations will be most effective. We estimate the possible impacts of interventions on a combination of factors associated with HIV. Methods We used the g-formula to model interventions on combinations of HIV risk factors to identify those that would prevent the most incident HIV infections, including low school attendance, intimate partner violence, depression, transactional sex, and age-disparate partnerships. We used data from the HIV Prevention Trials Network (HPTN) 068 study in rural South Africa from 2011 to 2017. We estimated HIV incidence under a potential intervention that reduced each risk factor and compared this to HIV incidence under the current distribution of these risk factors. Results Although many factors had strong associations with HIV, potential intervention estimates did not always suggest large reductions in HIV incidence because the prevalence of risk factors was low. When modeling combination effects, an intervention to increase schooling, decrease depression, and decease transactional sex showed the largest reduction in incident infection (risk difference, –1.4%; 95% confidence interval [CI], –2.7% to –.2%), but an intervention on only transactional sex and depression still reduced HIV incidence by –1.3% (95% CI, –2.6% to –.2%). Conclusions To achieve the largest reductions in HIV, both prevalence of the risk factor and strength of association with HIV must be considered. Additionally, intervening on more risk factors may not necessarily result in larger reductions in HIV incidence.

Background Parametric g-computation is an analytic technique that can be used to estimate the effects of exposures, treatments and interventions; it relies on a different set of assumptions than more commonly used inverse probability weighted estimators. Whereas prior work has demonstrated implementations for binary exposures and continuous outcomes, use of parametric g-computation has been limited due to difficulty in implementation in more typical complex scenarios. Methods We provide an easy-to-implement algorithm for parametric g-computation in the setting of a dynamic baseline intervention of a baseline exposure and a time-to-event outcome. To demonstrate the use of our algorithm, we apply it to estimate the effects of interventions to reduce area deprivation on the cumulative incidence of sexually transmitted infections (STIs: gonorrhea, chlamydia or trichomoniasis) among women living with HIV in the Women’s Interagency HIV Study. Results We found that reducing area deprivation by a maximum of 1 tertile for all women would lead to a 2.7% [95% confidence interval (CI): 0.1%, 4.3%] reduction in 4-year STI incidence, and reducing deprivation by a maximum of 2 tertiles would lead to a 4.3% (95% CI: 1.9%, 6.4%) reduction. Conclusions As analytic methods such as parametric g-computation become more accessible, epidemiologists will be able to estimate policy-relevant effects of interventions to better inform clinical and public health practice and policy.

Objectives Subgroup analyses of clinical trial data can be an important tool for understanding when treatment effects differ across populations. That said, even effect estimates from prespecified subgroups in well-conducted trials may not apply to corresponding subgroups in the source population. While this divergence may simply reflect statistical imprecision, there has been less discussion of systematic or structural sources of misleading subgroup estimates. Study Design and Setting We use directed acyclic graphs to show how selection bias caused by associations between effect measure modifiers and trial selection, whether explicit (e.g., eligibility criteria) or implicit (e.g., self-selection based on race), can result in subgroup estimates that do not correspond to subgroup effects in the source population. To demonstrate this point, we provide a hypothetical example illustrating the sorts of erroneous conclusions that can result, as well as their potential consequences. We also provide a tool for readers to explore additional cases. Conclusion Treating subgroups within a trial essentially as random samples of the corresponding subgroups in the wider population can be misleading, even when analyses are conducted rigorously and all findings are internally valid. Researchers should carefully examine associations between (and consider adjusting for) variables when attempting to identify heterogeneous treatment effects.

As policies built on 'Undetectable = Untransmittable' become more popular, use of durable viral suppression (DVS) as an outcome in analyses is increasing. We identified a case series of recent HIV-related publications that study the DVS outcome. The majority did not distinguish between a definition of DVS and the operationalization of that definition. Clearer discussion of DVS, including a formal definition, is needed to ensure better comparability across studies and ultimately better public health outcomes.

OBJECTIVES: We assessed incidence of extrahepatic cancer among people with HIV/HCV coinfection and the potential impact of direct-acting antivirals (DAAs) on extrahepatic cancer risk among people with HIV/HCV coinfection. DESIGN: Our study cohort included adults who initiated HIV care at a CNICS site in the U.S. during 1995-2017, excluding those with previous cancer and without HCV testing. METHODS: We used Cox regression to estimate hazard ratios for extrahepatic cancer incidence among patients with HIV/HCV coinfection compared with those with HIV monoinfection. Standardized morbidity ratio (SMR) weights were used to create a “pseudopopulation” in which all patients were treated with antiretroviral therapy (ART), and to compare extrahepatic cancer incidence among patients with untreated HIV/HCV coinfection with the incidence that would have been observed if they had been successfully treated for HCV. RESULTS: Of 18,422 adults, 1,775 (10%) had HCV RNA and 10,899 (59%) were on ART at baseline. Incidence rates of any extrahepatic cancer among patients with HIV/HCV coinfection and HIV monoinfection were 1,027 and 771 per 100,000 person-years, respectively. In SMR-weighted analyses, the risk of any extrahepatic cancer among patients with untreated HCV coinfection at baseline was similar to the risk if they had been successfully treated for HCV. Patients with untreated HCV coinfection at baseline had higher incidence of kidney, lung, and inflammation-related cancers than if their HCV had been successfully treated, but these associations were not statistically significant. CONCLUSIONS: We did not find evidence that treating HCV coinfection with DAAs would reduce incidence of extrahepatic cancers among people with HIV receiving ART.

BACKGROUND Prior studies suggest neighborhood poverty and deprivation are associated with adverse health outcomes including death, but evidence is limited among persons with HIV, particularly women. We estimated changes in mortality risk from improvement in three measures of area-level socioeconomic context among participants of the Women's Interagency HIV Study. METHODS Starting in October 2013, we linked geocoded residential census block groups to the 2015 Area Deprivation Index (ADI) and two 2012-2016 American Community Survey poverty variables, categorized into national tertiles. We used parametric g-computation to estimate, through March 2018, impacts on mortality of improving each income or poverty measure by one and two tertiles maximum versus no improvement. RESULTS Of 1596 women with HIV (median age 49), 91 (5.7%) were lost to follow-up and 83 (5.2%) died. Most women (62%) lived in a block group in the tertile with the highest proportions of individuals with income:poverty

In this brief communication, we discuss the confusion of mortality with fatality in the interpretation of evidence in the coronavirus disease 2019 (COVID-19) pandemic, and how this confusion affects the translation of science into policy and practice. We discuss how this confusion has influenced COVID-19 policy in France, Sweden, and the United Kingdom and discuss the implications for decision-making about COVID-19 vaccine distribution. We also discuss how this confusion is an example of a more general statistical fallacy we term the “Missing Link Fallacy.”

BACKGROUND: It is not definitively known if persons with HIV (PWH) are more likely to be SARS-CoV-2 tested or test positive than persons without HIV (PWoH). We describe SARS-CoV-2 testing and positivity in 6 large geographically and demographically diverse cohorts of PWH and PWoH in the United States. SETTING: The Corona-Infectious-Virus Epidemiology Team (CIVET) comprises five clinical cohorts within a health system (Kaiser Permanente Northern California, Oakland, CA; Kaiser Permanente Mid-Atlantic States, Rockville, MD; University of North Carolina Health, Chapel Hill, NC; Vanderbilt University Medical Center, Nashville, TN; Veterans Aging Cohort Study) and one interval cohort (MACS/WIHS Combined Cohort Study). METHODS: We calculated the proportion of patients SARS-CoV-2 tested and the test positivity proportion by HIV status from March 1 to December 31, 2020. RESULTS: The cohorts ranged in size from 1,675 to 31,304 PWH and 1,430 to 3,742,604 PWoH. The proportion of PWH who were tested for SARS-CoV-2 (19.6%–40.5% across sites) was significantly higher than PWoH (14.8%–29.4%) in the clinical cohorts. However, among those tested, the proportion of patients with positive SARS-CoV-2 tests was comparable by HIV status; the difference in proportion of SARS-CoV-2 positivity ranged from 4.7% lower to 1.4% higher. CONCLUSION: Although PWH had higher testing proportions compared with PWoH, we did not find evidence of increased positivity in 6 large, diverse populations across the United States. Ongoing monitoring of testing, positivity, and COVID-19 related outcomes in PWH are needed given availability, response, and durability of COVID-19 vaccines; emergence of SARS-CoV-2 variants; and latest therapeutic options.

Background Improved sanitation has been associated with improved child growth in observational studies, but multiple randomized trials that delivered improved sanitation found no effect on child growth. We assessed to what extent differences in the effect estimated in the two study designs (the effect of treatment in observational studies and the effect of treatment assignment in trials) could explain the contradictory results. Methods We used parametric g-computation in five prospective studies (n = 21 524) and 59 cross-sectional Demographic and Health Surveys (DHS; n = 158 439). We compared the average treatment effect (ATE) for improved sanitation on mean length-for-age z-score (LAZ) among children aged Results The ATE was >0.15 z-scores, a clinically meaningful difference, in most prospective studies but in Conclusions Estimates of PIEs corresponding to anticipated trial results were often smaller than clinically meaningful effects. Incongruence between observational associations and null trial results may in part be explained by expected differences between the effects estimated. Using observational ATEs to set expectations for trials may overestimate the impact that sanitation interventions can achieve. PIEs predict realistic effects and should be more routinely estimated.

The number needed to treat (NNT) is a widely used measure of the potential impact of a treatment or intervention, but it is often calculated and discussed in ways which oversimplify critical issues. Specifically, the NNT itself depends on the population under study and the specific form that "treatment" would take in that population. We discuss how understanding the difference between the effect of removing a harmful exposure and the effect of deploying a specific intervention to remove that harmful exposure can affect the calculation and interpretation of an NNT. Our discussion extends a previously described framework distinguishing exposure effects from population intervention effects.

Background The cost of direct-acting antivirals (DAAs) for hepatitis C virus (HCV) prompted many payers to restrict treatment to patients who met non–evidence-based criteria. These restrictions have implications for survival of people with HCV, especially for people with human immunodeficiency virus (HIV)/HCV coinfection who are at high risk for liver disease progression. The goal of this work was to estimate the effects of DAA access policies on 10-year all-cause mortality among people with HIV. Methods The study population included 3056 adults with HIV in the Women’s Interagency HIV Study and Multicenter AIDS Cohort Study from 1 October 1994 through 30 September 2015. We used the parametric g-formula to estimate 10-year all-cause mortality under DAA access policies that included treating (i) all people with HCV; (ii) only people with suppressed HIV; (iii) only people with severe fibrosis; and (iv) only people with HIV suppression and severe fibrosis. Results The 10-year risk difference of treating all coinfected persons with DAAs compared with no treatment was –3.7% (95% confidence interval [CI], –9.1% to .6%). Treating only those with suppressed HIV and severe fibrosis yielded a risk difference of –1.1% (95% CI, –2.8% to .6%), with 51% (95% CI, 38%–59%) of coinfected persons receiving DAAs. Treating a random selection of 51% of coinfected persons at baseline decreased the risk by 1.9% (95% CI, –4.7% to .3%). Conclusions Restrictive DAA access policies may decrease survival compared to treating similar proportions of people with HIV/HCV coinfection with DAAs at random. These findings suggest that lives could be saved by thoughtfully revising access policies.