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Introduction: Growing evidence exists that greenspace exposure can reduce metabolic syndrome risk, a growing public health concern with well-documented inequities across population subgroups. We capitalize on the use of g-computation to simulate the influence of multiple possible interventions on residential greenspace on nine metabolic biomarkers and metabolic syndrome in adults (N = 555) from the 2014-2017 Community of Mine Study living in San Diego County, California. Methods: Normalized difference vegetation index (NDVI) exposure from 2017 was averaged across a 400-m buffer around the participants' residential addresses. Participants' fasting plasma glucose, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglyceride concentrations, systolic and diastolic blood pressure, hemoglobin A1c (%), waist circumference, and metabolic syndrome were assessed as outcomes of interest. Using parametric g-computation, we calculated risk differences for participants being exposed to each decile of the participant NDVI distribution compared to minimum NDVI. Differential health impacts from NDVI exposure by sex, ethnicity, income, and age were examined. Results: We found that a hypothetical increase in NDVI exposure led to a decrease in hemoglobin A1c (%), glucose, and high-density lipoprotein cholesterol concentrations, an increase in fasting total cholesterol, low-density lipoprotein cholesterol, and triglyceride concentrations, and minimal changes to systolic and diastolic blood pressure, waist circumference, and metabolic syndrome. The impact of NDVI changes was greater in women, Hispanic individuals, and those under 65 years old. Conclusions: G-computation helps to simulate the potential health benefits of differential NDVI exposure and identifies which subpopulations can benefit most from targeted interventions aimed at minimizing health disparities.

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INTRODUCTION: Substantial racial and ethnic disparities in hypertension and dementia exist in the United States. We evaluated the effect of maintaining systolic blood pressure (SBP) below clinical thresholds on dementia incidence. METHODS: We included 6806 Multi-Ethnic Study of Atherosclerosis participants (44 to 84 years old). We implemented the parametric g-formula to simulate the hypothetical interventions to reduce SBP below 120 and 140 mmHg over time, accounting for time-varying confounding. We estimated risk ratios (RRs) and risk differences for dementia incidence at 19 years. RESULTS: The RRs (95% confidence intervals [CIs]) comparing an intervention reducing SBP below 120 mmHg to no intervention were 0.93 (0.87 to 0.99) for total sample, 0.95 (0.88 to 1.02) for White, 0.90 (0.79 to 1.02) for Black, 0.90 (0.78 to 1.05) for Latino, and 1.16 (0.83 to 1.55) for Chinese American participants. Results for lowering SBP below 140 mmHg and with death as competing event were attenuated. DISCUSSION: The reduction of SBP below 120 mmHg over time has modest effects on reducing dementia incidence. More work is needed to understand the heterogeneity across racial and ethnic groups. HIGHLIGHTS: There is a potential beneficial effect in lowering SBP to reduce the risk of dementia, which may vary by race and ethnicity. The percentage of participants who would need intervention on blood pressure to meet clinical thresholds is greater for Black and Latino communities. Results are sensitive to the way that death is specified in the research question and analysis.

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The 2018 World Cancer Research Fund/American Institute for Cancer Research recommends sustained strategies of physical activity and diet for cancer prevention, but evidence for long-term prostate cancer risk is limited. Using observational data from 27,859 men in the Health Professionals Follow-up Study, we emulated a target trial of recommendation-based physical activity and dietary strategies and 26-year risks of prostate cancer, adjusting for risk factors via the parametric g-formula. Compared with no intervention, limiting sugar-sweetened beverages showed a 0.4% (0.0-0.9%) lower risk of lethal (metastatic or fatal) disease and 0.5% (0.1-0.9%) lower risk of fatal disease. Restricting consumption of processed foods showed a 0.4-0.9% higher risk of all prostate cancer outcomes. Estimated risk differences for clinically significant disease were close to null for strategies involving fruits and non-starchy vegetables, whole grains and legumes, red meat, and processed meat, as well as under a joint strategy of physical activity and diet. Compared with a "low adherence" strategy, maintaining recommended physical activity levels showed a 0.4% (0.1-0.8%) lower risk of lethal and 0.5% (0.2-0.8%) lower risk of fatal disease. Adhering to specific components of current physical activity and dietary recommendations may help to prevent lethal and fatal prostate cancer over 26 years.

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BACKGROUND: Observational studies are frequently used to estimate the comparative effectiveness of different colorectal cancer (CRC) screening methods due to the practical limitations and time needed to conduct large clinical trials. However, time-varying confounders, e.g. polyp detection in the last screening, can bias statistical results. Recently, generalized methods, or G-methods, have been used for the analysis of observational studies of CRC screening, given their ability to account for such time-varying confounders. Discretization, or the process of converting continuous functions into discrete counterparts, is required for G-methods when the treatment and outcomes are assessed at a continuous scale. DEVELOPMENT: This paper evaluates the interplay between time-varying confounding and discretization, which can induce bias in assessing screening effectiveness. We investigate this bias in evaluating the effect of different CRC screening methods that differ from each other in typical screening frequency. APPLICATION: First, using theory, we establish the direction of the bias. Then, we use simulations of hypothetical settings to study the bias magnitude for varying levels of discretization, frequency of screening and length of the study period. We develop a method to assess possible bias due to coarsening in simulated situations. CONCLUSIONS: The proposed method can inform future studies of screening effectiveness, especially for CRC, by determining the choice of interval lengths where data are discretized to minimize bias due to coarsening while balancing computational costs.

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Interventions aiming to reduce social inequalities of weight status in adolescents usually focus on lifestyle behaviours, but their effectiveness is limited. This study analysed the effect of achieving levels of dietary intake (DI) and/or physical activity (PA) guidelines on reducing social inequalities in weight status among adolescents. We included adolescents from the PRomotion de l'ALIMentation et de l'Activité Physique - INÉgalité de Santé (PRALIMAP-INÈS) trial with weight status data available at baseline and 1-year follow-up (n 1130). PA and DI were measured using the International Physical Activity Questionnaire and a validated FFQ, respectively. We estimated the likelihood of a 1-year reduction in BMI z-score (BMIz) and population risk difference (PRD) under hypothetical DI and PA levels and socio-economic status using the parametric G-formula. When advantaged and less advantaged adolescents maintained their baseline DI and PA, we found social inequalities in weight status, with a PRD of a 1-year reduction in BMIz of -1·6 % (-3·0 %, -0·5 %). These inequalities were not observed when less advantaged adolescents increased their proportion of achieving DI guidelines by 30 % (PRD = 2·2 % (-0·5 %, 5·0 %)) unlike the same increase in PA (PRD = -3·9 % (-6·8 %, -1·3 %)). Finally, social inequalities of weight status were not observed when levels of achievement of both PA and DI guidelines increased by 30 % (PRD = 2·2 % (-0·5 %, 4·0 %)). Enhancing DI rather than PA could be effective in reducing social inequalities in weight status among adolescents. Future interventions aiming to reduce these inequalities should mostly target DI to be effective.

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Assessing population-level effects of vaccines and other infectious disease prevention measures is important to the field of public health. In infectious disease studies, one person's treatment may affect another individual's outcome, that is, there may be interference between units. For example, the use of bed nets to prevent malaria by one individual may have an indirect effect on other individuals living in close proximity. In some settings, individuals may form groups or clusters where interference only occurs within groups, that is, there is partial interference. Inverse probability weighted estimators have previously been developed for observational studies with partial interference. Unfortunately, these estimators are not well suited for studies with large clusters. Therefore, in this paper, the parametric g-formula is extended to allow for partial interference. G-formula estimators are proposed for overall effects, effects when treated, and effects when untreated. The proposed estimators can accommodate large clusters and do not suffer from the g-null paradox that may occur in the absence of interference. The large sample properties of the proposed estimators are derived assuming no unmeasured confounders and that the partial interference takes a particular form (referred to as 'weak stratified interference'). Simulation studies are presented demonstrating the finite-sample performance of the proposed estimators. The Demographic and Health Survey from the Democratic Republic of the Congo is then analyzed using the proposed g-formula estimators to assess the effects of bed net use on malaria.

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This study estimated and compared mortality risks among people living with HIV (PLWH) under the real-world and hypothetical scenarios of PM2.5 concentrations and HIV severity. An open cohort from all PLWH receiving antiretroviral therapy in Sichuan during 2010-2019 was constructed, resulting in 541,515 person-years. Annual mean concentrations of PM2.5 were estimated and linked to PLWH by their residential address. The parametric g-formula were used to assess 3- and 5-year mortality risks under the real-world and hypothetical scenarios of PM2.5 (10-35, 35-50, 50-75 µg/m3) and CD4 concentrations (0-200, 200-500, 500-800, 800-1100 counts/µl). The estimated 3- and 5-year mortality risks among the PLWH were 14.43 % and 19.38 %, respectively, which would decrease substantially when annual PM2.5 concentration were reduced to between 10 and 35 µg/m3 (risk difference [RD] = -3.23 % and - 4.06 %) and would increase when PM2.5 concentration were elevated to between 50 and 75 µg/m3 (RD = 3.59 % and 5.04 %). The mortality risk would increase when CD4 concentration were reduced to <200 counts/µl (RD = 15.90 % and 20.27 %) and would decrease when CD4 concentration were ≥ 200 counts/µl, especially to between 800 and 1100 counts/µl (RD = -9.01 % and - 11.75 %). The elevated concentration of PM2.5 may disproportionately affect individuals with immune deficiency, especially those with more severity. The findings would serve as justifications for future intervention design and policy making to alleviate air pollution and improve environmental justice and health equity.

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BACKGROUND: Previous studies have found that maternal occupational choice can directly impact fetal health and mothers' energy and time available for childcare. Moreover, reduced caregiving time is associated with poorer child health. However, how maternal occupational factors (work nature/income/stress) affect child health has not been fully explored. METHODS: Data were extracted from China Family Panel Studies (CFPS) from 2014 to 2020. A parametric G-formula Model was constructed to simulate the direct impact of maternal occupational factors (work nature, income, and stress) on child health. Furthermore, we explored their indirect effects mediated by maternal physical health, mental health, or occupational factors for the overall sample and by different age groups of the children. RESULTS: Maternal work nature and income had positive direct impacts on child health, especially for children whose mothers were employers of budgeted positions. Maternal work stress had a negative direct impact on children's health. Maternal work stress mediated the positive impact of maternal work income (9%). Maternal work stress affected children's health directly for children aged under 6, while for children aged 7-15, it had an indirect impact mediated by maternal physical health (43%), mental health (44%), and income (47%). CONCLUSION: Attention should be paid to maternal occupational factors (especially to work stress) and to the varying care needs of children from their mothers at different ages to improve children's health and prevent the transmission of maternal occupation to children's health.

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OBJECTIVE: To determine the association between non-adherence to long term chronic obstructive pulmonary disease (COPD) medications and COPD related emergency department (ED) visits and hospitalizations in patients with incident COPD, utilizing time varying measures of adherence as well as accounting for time-varying confounding impacted by prior adherence. STUDY DESIGN AND SETTING: We conducted a population-based retrospective cohort study between 2007-2017 among individuals aged 66 years and older with incident COPD using multiple linked administrative health databases from the province of Ontario, Canada. Adherence to COPD medications was measured using time varying proportion of days covered based on insurance claims for medications dispensed at community pharmacies. The parametric g-formula was used to assess the association between time-varying adherence (in the last 90-days) to COPD medications and risk of COPD related hospitalizations and ED visits while accounting for time varying confounding by COPD severity. RESULTS: Overall, 60,251 individuals with incident COPD were included; mean age was 76 (SD 7) and 59% were male. Mean adherence over the entire follow-up was 23% (SD 0.3). There were 7248 (12%) COPD related ED visits (2.8 events per 100 person years [PY]) and 9188 (15%) COPD related hospitalizations (3.5 events per 100 PY). Compared to those with 0% 90-day adherence, those with adherence between 1-33% had a 19% decreased risk of COPD related ED visits (adjusted risk ratio[aRR]:0.81, 95% confidence interval [CI]:0.78-0.83), those with adherence between 34%-67% had a 18% decreased risk (aRR: 0.82, 95% CI: 0.77-0.85) while those with 68%-100% 90-day adherence had a 63% increased risk of COPD related ED visits (aRR: 1.63, 95% CI: 1.47-1.78). Nearly identical results were obtained for COPD specific hospitalizations. CONCLUSION: After accounting for time varying confounding by COPD severity, the highest time varying 90-days adherence was associated with an increased risk of both COPD related ED visits and hospitalizations compared to the lowest adherence categories. Differences in COPD severity between adherence categories, perception of need for medication management in the higher adherence categories, and potential residual confounding makes it difficult to disentangle the independent effects of adherence from the severity of the condition itself.

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Developing models for individualized, time-varying treatment optimization from observational data with large variable spaces, e.g., electronic health records (EHR), is problematic because of inherent, complex bias that can change over time. Traditional methods such as the g-formula are robust, but must identify critical subsets of variables due to combinatorial issues. Machine learning approaches such as causal survival forests have fewer constraints and can provide fine-tuned, individualized counterfactual predictions. In this study, we aimed to optimize time-varying antibiotic treatment -identifying treatment heterogeneity and conditional treatment effects- against invasive methicillin-resistant Staphylococcus Aureus (MRSA) infections, using statewide EHR data collected in Florida, USA. While many previous studies focused on measuring the effects of the first empiric treatment (i.e., usually vancomycin), our study focuses on dynamic sequential treatment changes, comparing possible vancomycin switches with other antibiotics at clinically relevant time points, e.g., after obtaining a bacterial culture and susceptibility testing. Our study population included adult individuals admitted to the hospital with invasive MRSA. We collected demographic, clinical, medication, and laboratory information from the EHR for these patients. Then, we followed three sequential antibiotic choices (i.e., their empiric treatment, subsequent directed treatment, and final sustaining treatment), evaluating 30-day mortality as the outcome. We applied both causal survival forests and g-formula using different clinical intervention policies. We found that switching from vancomycin to another antibiotic improved survival probability, yet there was a benefit from initiating vancomycin compared to not using it at any time point. These findings show consistency with the empiric choice of vancomycin before confirmation of MRSA and shed light on how to manage switches on course. In conclusion, this application of causal machine learning on EHR demonstrates utility in modeling dynamic, heterogeneous treatment effects that cannot be evaluated precisely using randomized clinical trials.

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BACKGROUND: In view of the high burden of childhood overweight/obesity (OW/OB), it is important to identify targets for interventions that may have the greatest effects on preventing OW/OB in early life. Using methods of causal inference, we studied the effects of sustained behavioral interventions on the long-term risk of developing OW/OB based on a large European cohort. METHODS: Our sample comprised 10 877 children aged 2 to < 10 years at baseline who participated in the well-phenotyped IDEFICS/I.Family cohort. Children were followed from 2007/08 to 2020/21. Applying the parametric g-formula, the 13-year risk of developing OW/OB was estimated under various sustained hypothetical interventions on physical activity, screen time, dietary intake and sleep duration. Interventions imposing adherence to recommendations (e.g. maximum 2 h/day screen time) as well as interventions 'shifting' the behavior by a specified amount (e.g. decreasing screen time by 30 min/day) were compared to 'no intervention' (i.e. maintaining the usual or so-called natural behavior). Separately, the effectiveness of these interventions in vulnerable groups was assessed. RESULTS: The 13-year risk of developing OW/OB was 30.7% under no intervention and 25.4% when multiple interventions were imposed jointly. Meeting screen time and moderate-to-vigorous physical activity (MVPA) recommendations were found to be most effective, reducing the incidence of OW/OB by -2.2 [-4.4;-0.7] and -2.1 [-3.7;-0.8] percentage points (risk difference [95% confidence interval]), respectively. Meeting sleep recommendations (-0.6 [-1.1;-0.3]) had a similar effect as increasing sleep duration by 30 min/day (-0.6 [-0.9;-0.3]). The most effective intervention in children of parents with low/medium educational level was being member in a sports club; for children of mothers with OW/OB, meeting screen time recommendations and membership in a sports club had the largest effects. CONCLUSIONS: While the effects of single behavioral interventions sustained over 13 years were rather small, a joint intervention on multiple behaviors resulted in a relative reduction of the 13-year OW/OB risk by between 10 to 26%. Individually, meeting MVPA and screen time recommendations were most effective. Nevertheless, even under the joint intervention the absolute OW/OB risk remained at a high level of 25.4% suggesting that further strategies to better prevent OW/OB are required.

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We aim to investigate to what extent gender inequality at the labor market explains higher depression risk for older US women compared to men. We analyze data from 35,699 US adults aged 50-80 years that participated in the Health and Retirement Study. The gender gap is calculated as the difference in prevalence in elevated depressive symptoms (score ≥ 3 on the 8-item Center for Epidemiological Studies Depression Scale) between women and men. We employ a dynamic causal decomposition and simulate the life course of a synthetic cohort from ages 50-80 with the longitudinal g-formula and introduce four nested interventions by assigning women the same probabilities of A) being in an employment category, B) occupation class, C) current income and D) prior income group as men, conditional on women's health and family status until age 70. The gender gap in depression risk is 2.9%-points at ages 50-51 which increases to 7.6%-points at ages 70-71. Intervention A decreases the gender gap over ages 50-71 by 1.2%-points (95%CI for change: 2.81 to 0.4), intervention D by 1.64%-points (95%CI for change: 3.28 to -0.15) or 32% (95%CI: 1.39 to 62.83), and the effects of interventions B and C are in between those of A and D. The impact is particularly large for Hispanics and low educated groups. Gender inequalities at the labor market substantially explain the gender gap in depression risk in older US adults. Reducing these inequalities has the potential to narrow the gender gap in depression.

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The noniterative conditional expectation (NICE) parametric g-formula can be used to estimate the causal effect of sustained treatment strategies. In addition to identifiability conditions, the validity of the NICE parametric g-formula generally requires the correct specification of models for time-varying outcomes, treatments, and confounders at each follow-up time point. An informal approach for evaluating model specification is to compare the observed distributions of the outcome, treatments, and confounders with their parametric g-formula estimates under the "natural course." In the presence of loss to follow-up, however, the observed and natural-course risks can differ even if the identifiability conditions of the parametric g-formula hold and there is no model misspecification. Here, we describe 2 approaches for evaluating model specification when using the parametric g-formula in the presence of censoring: 1) comparing factual risks estimated by the g-formula with nonparametric Kaplan-Meier estimates and 2) comparing natural-course risks estimated by inverse probability weighting with those estimated by the g-formula. We also describe how to correctly compute natural-course estimates of time-varying covariate means when using a computationally efficient g-formula algorithm. We evaluate the proposed methods via simulation and implement them to estimate the effects of dietary interventions in 2 cohort studies.

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Many research questions in public health and medicine concern sustained interventions in populations defined by substantive priorities. Existing methods to answer such questions typically require a measured covariate set sufficient to control confounding, which can be questionable in observational studies. Differences-in-differences rely instead on the parallel trends assumption, allowing for some types of time-invariant unmeasured confounding. However, most existing difference-in-differences implementations are limited to point treatments in restricted subpopulations. We derive identification results for population effects of sustained treatments under parallel trends assumptions. In particular, in settings where all individuals begin follow-up with exposure status consistent with the treatment plan of interest but may deviate at later times, a version of Robins' g-formula identifies the intervention-specific mean under stable unit treatment value assumption, positivity, and parallel trends. We develop consistent asymptotically normal estimators based on inverse-probability weighting, outcome regression, and a double robust estimator based on targeted maximum likelihood. Simulation studies confirm theoretical results and support the use of the proposed estimators at realistic sample sizes. As an example, the methods are used to estimate the effect of a hypothetical federal stay-at-home order on all-cause mortality during the COVID-19 pandemic in spring 2020 in the United States.

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Background: Cognitive impairment is an age-relevant intermediate stage where cognition declines to a state between the normal aging process and dementia. Earlier studies reported that depression, inappropriate nighttime sleep duration (NSD), and limited leisure activity engagement are cognitive impairment risk factors among older adults. Thus, we postulated that interventions on depression, sleep duration, and leisure activity engagement can reduce cognitive impairment risk. However, no earlier research ever explored this. Methods: The data of 4,819 respondents aged 60 years and above, without cognitive impairment at baseline and with no prior history of memory-related diseases, including Alzheimer's disease, Parkinson's disease, and encephalatrophy, were obtained from the China Health and Retirement Longitudinal Study (CHARLS) between 2011 and 2018. The parametric g-formula, an analytic tool for estimating standardized outcome distributions using covariate (exposure and confounders)-specific estimates of the outcome distribution, was used to estimate 7-year cumulative cognitive impairment risks among older Chinese adults, under independent hypothetical interventions on depression, NSD, and leisure activity engagement, which was subdivided into social activity (SA) and intellectual activity (IA) for the different intervention combinations. Results: The observed cognitive impairment risk was 37.52%. Independent intervention on IA was the most effective factor in reducing incident cognitive impairment, with a risk ratio (RR) of 0.75 (95% confidence interval [CI]: 0.67-0.82), followed by depression (RR: 0.89, 95% CI: 0.85-0.93) and NSD (RR: 0.88, 95% CI: 0.80-0.95). The joint intervention combining depression, NSD, and IA interventions could reduce the risk by 17.11%, with an RR of 0.56 (95% CI: 0.48-0.65). In subgroup analyses, independent interventions on depression and IA had analogously significant effects on men and women. However, interventions on depression and IA had stronger effects on literate than illiterate individuals. Conclusions: Hypothetical interventions on depression, NSD, and IA reduced cognitive impairment risks among older Chinese adults, both independently and jointly. The findings of the present study suggest that the intervention measures on depression, inappropriate NSD, limited intellectual activities, and their combination may prove to be effective strategies for preventing cognitive impairment among older adults.

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Extending (i.e., generalizing or transporting) causal inferences from a randomized trial to a target population requires assumptions that randomized and nonrandomized individuals are exchangeable conditional on baseline covariates. These assumptions are made on the basis of background knowledge, which is often uncertain or controversial, and need to be subjected to sensitivity analysis. We present simple methods for sensitivity analyses that directly parameterize violations of the assumptions using bias functions and do not require detailed background knowledge about specific unknown or unmeasured determinants of the outcome or modifiers of the treatment effect. We show how the methods can be applied to non-nested trial designs, where the trial data are combined with a separately obtained sample of nonrandomized individuals, as well as to nested trial designs, where the trial is embedded within a cohort sampled from the target population.

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BACKGROUND: Sleep disturbances, frailty, and body pain are widespread in middle-aged and older adults with depression, and have been identified as depression risk factors. However, there is a scarcity of research on the benefits of sleep improvement, frailty amelioration, and pain management on incident depression. METHODS: A total of 8895 respondents aged above 45 years were derived from the China Health and Retirement Longitudinal Study (CHARLS) between 2011 and 2018. The parametric g-formula was used to estimate the 7-year risks of depression under independent hypothetical interventions on nighttime sleep duration (NSD), daytime napping duration (DND), perceived sleep quality (PSQ), frailty, and pain, as well as their various combinations. RESULTS: The observed depression risk was 41.77 %. The independent intervention on frailty was the most effective in lowering incident depression, with a risk ratio (RR) of 0.61 (95 % CI: 0.57-0.64), followed by PSQ (RR: 0.75, 95 % CI: 0.73-0.78), pain (RR: 0.90, 95 % CI: 0.87-0.91), and NSD (RR: 0.96, 95 % CI: 0.93-0.98). In subgroup analysis, intervention on NSD was more effective in men, PSQ was more effective in middle-aged individuals, and frailty and pain were more effective in older persons. The combined intervention of NSD, PSQ, frailty, and pain lowered the risk the greatest (RR: 0.35, 95 % CI: 0.32-0.37). LIMITATIONS: Generalizing our results to other populations should be possible if they have the same distribution of effect modifiers and interference patterns because of the calculation principle of the parametric g-formula. CONCLUSIONS: Interventions for sleep disturbances, frailty, and body pain can minimize the risk of depression.

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BACKGROUND: The possible mediating role of cardiovascular disease (CVD) in the relationship between alcohol use disorders (AUD) and the risk of early-onset (

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BACKGROUND: The etiology of multiple sclerosis (MS) is still not well-demonstrated, and assessment of some risk factors like alcohol consumption has problems like confounding and measurement bias. To determine the causal effect of alcohol consumption on MS after adjusting for alcohol consumption misclassification bias and confounders. METHODS: In a population-based incident case-control study, 547 patients with MS and 1057 healthy people were recruited. A minimally sufficient adjustment set of confounders was derived using the causal directed acyclic graph. The probabilistic bias analysis method (PBAM) using beta, logit-logistic, and triangular probability distributions for sensitivity/specificity to adjust for misclassification bias in self-reporting alcohol consumption and model-based standardization (MBS) to estimate the causal effect of alcohol consumption were used. Population attributable fraction (PAF) estimates with 95% Monte Carlo sensitivity analysis (MCSA) intervals were calculated using PBAM and MBS analysis. Bootstrap was used to deal with random errors. RESULTS: The adjusted risk ratio (95% MCSA interval) from the probabilistic bias analysis and MBS between alcohol consumption and MS using the three distribution was in the range of 1.93 (1.07 to 4.07) to 2.02 (1.15 to 4.69). The risk difference (RD) in all three scenarios was 0.0001 (0.0000 to 0.0005) and PAF was in the range of 0.15 (0.010 to 0.50) to 0.17 (0.001 to 0.47). CONCLUSION: After adjusting for measurement bias, confounding, and random error alcohol consumption had a positive causal effect on the incidence of MS.

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INTRODUCTION: Randomized trials and observational studies have consistently reported rates of sustained virological response (SVR), equivalent to hepatitis C virus (HCV) cure, as high as 95% following treatment with direct-acting antiviral (DAA) treatment in individuals with HIV and HCV co-infection. However, large studies assessing whether SVR rates differ according to demographic and clinical strata are lacking. Additionally, the SVR rates reported in the literature were typically computed in non-random samples of individuals with available post-DAA HCV-RNA measures. Here, we aimed to estimate the probability of SVR after DAA treatment initiation in persons with HIV and HCV co-infection overall and by demographic and clinical characteristics with and without adjustment for missing HCV-RNA testing. METHODS: We included adults with HIV-HCV co-infection who received DAA treatment between 2014 and 2020 in HepCAUSAL, an international collaboration of cohorts from Europe and North America. We estimated the proportions of DAA recipients who had documented SVR (defined as an undetectable HCV-RNA at least 12 weeks after the end of DAA treatment) overall and by strata defined by age, sex, presence of cirrhosis, calendar period, mode of HIV acquisition, CD4 cell count and HCV genotype at DAA treatment. We then compared these rates with those obtained using the parametric g-formula to impute SVR status for individuals with no SVR assessment. RESULTS AND DISCUSSION: A total of 4527 individuals who initiated DAA treatment (88% males, median [IQR] age 56 [50, 62] years) were included. Of the total of 642 (14%) individuals had no HCV-RNA test on or after 12 weeks after the end of treatment. The overall observed and g-formula imputed SVR rates were 93% (95% CI 93, 94) and 94% (95% CI 92, 95), respectively. SVR estimates were similarly high across all strata. A substantial proportion of individuals who received DAA treatment were never assessed for SVR post-DAA and strategies for more systematic routine HCV-RNA testing should be considered. CONCLUSIONS: Our estimates with and without adjustment for missing HCV-RNA testing indicate SVR rates of approximately 95%, like those reported in clinical trials.

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