Longitudinal trajectories of a claims-based frailty measure during adjuvant chemotherapy in women with stage I-III breast cancer.

BACKGROUND: Frailty is a dynamic syndrome characterized by reduced physiological reserve to maintain homeostasis. Prospective studies have reported frailty worsening in women with breast cancer during chemotherapy, with improvements following treatment. We evaluated whether the Faurot frailty index, a validated claims-based frailty measure, could identify changes in frailty during chemotherapy treatment and identified predictors of trajectory patterns. METHODS: We included women (65+ years) with stage I-III breast cancer undergoing adjuvant chemotherapy in the SEER-Medicare database (2003-2019). We estimated the Faurot frailty index (range: 0-1; higher scores indicate greater frailty) at chemotherapy initiation, 4 months postinitiation, and 10 months postinitiation. Changes in frailty were compared to a matched noncancer comparator cohort. We identified patterns of frailty trajectories during the year following chemotherapy initiation using K-means clustering. RESULTS: Twenty-one thousand five hundred and ninety-nine women initiated adjuvant chemotherapy. Mean claims-based frailty increased from 0.037 at initiation to 0.055 4 months postchemotherapy initiation and fell to 0.049 10 months postinitiation. Noncancer comparators experienced a small increase in claims-based frailty over time (0.055-0.062). We identified 6 trajectory patterns: a robust group (78%), 2 resilient groups (16%), and 3 nonresilient groups (6%). Black women and women with claims for home hospital beds, wheelchairs, and Parkinson's disease were more likely to experience nonresilient trajectories. CONCLUSIONS: We observed changes in a claims-based frailty index during chemotherapy that are consistent with prior studies using clinical measures of frailty and identified predictors of nonresilient frailty trajectories. Our study demonstrates the feasibility of using claims-based frailty indices to assess changes in frailty during cancer treatment.

Hepatitis B Virus Prevalence and Transmission in the Households of Pregnant Women in Kinshasa, Democratic Republic of Congo.

Background: The World Health Organization Africa region has high regional hepatitis B virus (HBV) prevalence, and evidence suggests more frequent horizontal HBV transmission than other regions. Context-specific epidemiological studies are needed to inform additional HBV prevention measures. Methods: In the cross-sectional Horizontal and Vertical Transmission of Hepatitis B (HOVER-HBV) study, we introduced HBV surface antigen (HBsAg) screening alongside existing HIV screening as part of routine antenatal care in high-volume maternity clinics in Kinshasa, Democratic Republic of Congo. We recruited households of pregnant women ("index mothers") who were HBsAg-positive and HBsAg-negative, defining households as index-positive and index-negative, respectively. Household members underwent HBsAg testing and an epidemiological survey. We evaluated HBsAg prevalence and potential transmission correlates. Results: We enrolled 1006 participants from 200 households (100 index-positive, 100 index-negative) across Kinshasa. HBsAg-positivity prevalence was more than twice as high in index-positive households (5.0% [95% confidence interval {CI}, 2.8%-7.1%]) as in index-negative households (1.9% [95% CI, .6%-3.2%]). HBsAg-positivity prevalence was 3.3 (95% CI, .9-11.8) times as high among direct offspring in index-positive versus index-negative households. Factors associated with HBsAg positivity included older age, marriage, and having multiple recent partners or any new sexual partners among index mothers; and older age, lower household wealth, sharing nail clippers, and using street salons among offspring in index-positive households. Conclusions: Vertical and horizontal HBV transmission within households is ongoing in Kinshasa. Factors associated with infection reveal opportunities for HBV prevention efforts, including perinatal prevention, protection during sexual contact, and sanitation of shared personal items.

Emotional and functional well-being in long-term breast cancer survivorship.

PURPOSE: Emotional and functional well-being (EWB and FWB) are important components of mental health and quality of life. This study aims to evaluate long-term EWB and FWB in breast cancer (BC) survivors. METHODS: The Carolina Breast Cancer Study Phase 3 oversampled Black and younger (< 50 years in age) women so that they each represent approximately 50% of the study population and assessed participants' EWB and FWB with the Functional Assessment of Cancer Therapy-Breast (FACT-B) at 5- (baseline), 25-, and 84-months post diagnosis. Multinomial logit models were used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for associations between demographic and clinical characteristics and well-being change relative to baseline. RESULTS: Among 2,781 participants with BC, average EWB and FWB improved with time since diagnosis. Persistent FWB decrements were associated with Black race [OR 1.4 (95% CI 1.2-1.7) and 1.3 (95% CI 1.1-1.6), at 25-months and 84-months respectively], older age [OR 1.4 (95% CI 1.1-1.7) and 1.5 (95% CI 1.2-1.8), respectively], no chemotherapy, and recurrence [OR 2.9 (95% CI 1.8-4.8) and 3.1 (95% CI 2.1-4.6), respectively]. EWB decrements were associated with advanced stage and recurrence. Decrements in combined (FWB+EWB) well-being were associated with recurrence at both follow-up survey timepoints [ORs 4.7 (95% CI 2.7-8.0) and 4.3 (95% CI 2.8-6.6), respectively]. CONCLUSIONS: Long-term well-being varies by demographics and clinical features, with Black women and women with aggressive disease at greatest risk of long-term decrements.

Performance of a Claims-Based Frailty Proxy Using Varying Frailty Ascertainment Lookback Windows.

BACKGROUND: Frailty is an aging-related syndrome of reduced physiological reserve to maintain homeostasis. The Faurot frailty index has been validated as a Medicare claims-based proxy for predicting frailty using billing information from a user-specified ascertainment window. OBJECTIVES: We assessed the validity of the Faurot frailty index as a predictor of the frailty phenotype and 1-year mortality using varying frailty ascertainment windows. RESEARCH DESIGN: We identified older adults (66+ y) in Round 5 (2015) of the National Health and Aging Trends Study with Medicare claims linkage. Gold standard frailty was assessed using the frailty phenotype. We calculated the Faurot frailty index using 3, 6, 8, and 12 months of claims prior to the survey or all-available lookback. Model performance for each window in predicting the frailty phenotype was assessed by quantifying calibration and discrimination. Predictive performance for 1-year mortality was assessed by estimating risk differences across claims-based frailty strata. RESULTS: Among 4253 older adults, the 6 and 8-month windows had the best frailty phenotype calibration (calibration slopes: 0.88 and 0.87). All-available lookback had the best discrimination (C-statistic=0.780), but poor calibration. Mortality associations were strongest using a 3-month window and monotonically decreased with longer windows. Subgroup analyses revealed worse performance in Black and Hispanic individuals than counterparts. CONCLUSIONS: The optimal ascertainment window for the Faurot frailty index may depend on the clinical context, and researchers should consider tradeoffs between discrimination, calibration, and mortality. Sensitivity analyses using different durations can enhance the robustness of inferences. Research is needed to improve prediction across racial and ethnic groups.

Higher-order evidence.

Higher-order evidence is evidence about evidence. Epidemiologic examples of higher-order evidence include the settings where the study data constitute first-order evidence and estimates of misclassification comprise the second-order evidence (e.g., sensitivity, specificity) of a binary exposure or outcome collected in the main study. While sampling variability in higher-order evidence is typically acknowledged, higher-order evidence is often assumed to be free of measurement error (e.g., gold standard measures). Here we provide two examples, each with multiple scenarios where second-order evidence is imperfectly measured, and this measurement error can either amplify or attenuate standard corrections to first-order evidence. We propose a way to account for such imperfections that requires third-order evidence. Further illustrations and exploration of how higher-order evidence impacts results of epidemiologic studies is warranted.

Leveraging External Validation Data: The Challenges of Transporting Measurement Error Parameters.

Approaches to address measurement error frequently rely on validation data to estimate measurement error parameters (e.g., sensitivity and specificity). Acquisition of validation data can be costly, thus secondary use of existing data for validation is attractive. To use these external validation data, however, we may need to address systematic differences between these data and the main study sample. Here, we derive estimators of the risk and the risk difference that leverage external validation data to account for outcome misclassification. If misclassification is differential with respect to covariates that themselves are differentially distributed in the validation and study samples, the misclassification parameters are not immediately transportable. We introduce two ways to account for such covariates: (1) standardize by these covariates or (2) iteratively model the outcome. If conditioning on a covariate for transporting the misclassification parameters induces bias of the causal effect (e.g., M-bias), the former but not the latter approach is biased. We provide proof of identification, describe estimation using parametric models, and assess performance in simulations. We also illustrate implementation to estimate the risk of preterm birth and the effect of maternal HIV infection on preterm birth. Measurement error should not be ignored and it can be addressed using external validation data via transportability methods.

Risk of Malaria Following Untreated Subpatent Plasmodium falciparum Infections: Results Over 4 Years From a Cohort in a High-Transmission Area in Western Kenya.

BACKGROUND: People with suspected malaria may harbor Plasmodium falciparum undetected by rapid diagnostic test (RDT). The impact of these subpatent infections on the risk of developing clinical malaria is not fully understood. METHODS: We analyzed subpatent P. falciparum infections using a longitudinal cohort in a high-transmission site in Kenya. Weighted Kaplan-Meier models estimated the risk difference (RD) for clinical malaria during the 60 days following a symptomatic subpatent infection. Stratum-specific estimates by age and transmission season assessed modification. RESULTS: Over 54 months, we observed 1128 symptomatic RDT-negative suspected malaria episodes, of which 400 (35.5%) harbored subpatent P. falciparum. Overall, the 60-day risk of developing clinical malaria was low following all episodes (8.6% [95% confidence interval, 6.7%-10.4%]). In the low-transmission season, the risk of clinical malaria was slightly higher in those with subpatent infection, whereas the opposite was true in the high-transmission season (low-transmission season RD, 2.3% [95% confidence interval, .4%-4.2%]; high-transmission season RD, -4.8% [-9.5% to -.05%]). CONCLUSIONS: The risk of developing clinical malaria among people with undetected subpatent infections is low. A slightly elevated risk in the low-transmission season may merit alternate management, but RDTs identify clinically relevant infections in the high-transmission season.

HIV Prevention Among Men Who Have Sex With Men: Tenofovir Alafenamide Combination Preexposure Prophylaxis Versus Placebo.

BACKGROUND: While noninferiority of tenofovir alafenamide and emtricitabine (TAF/FTC) as preexposure prophylaxis (PrEP) for the prevention of human immunodeficiency virus (HIV) has been shown, interest remains in its efficacy relative to placebo. We estimate the efficacy of TAF/FTC PrEP versus placebo for the prevention of HIV infection. METHODS: We used data from the DISCOVER and iPrEx trials to compare TAF/FTC to placebo. DISCOVER was a noninferiority trial conducted from 2016 to 2017. iPrEx was a placebo-controlled trial conducted from 2007 to 2009. Inverse probability weights were used to standardize the iPrEx participants to the distribution of demographics and risk factors in the DISCOVER trial. To check the comparison, we evaluated whether risk of HIV infection in the shared tenofovir disoproxil fumarate and emtricitabine (TDF/FTC) arms was similar. RESULTS: Notable differences in demographics and risk factors occurred between trials. After standardization, the difference in risk of HIV infection between the TDF/FTC arms was near zero. The risk of HIV with TAF/FTC was 5.8 percentage points lower (95% confidence interval [CI], -2.0% to -9.6%) or 12.5-fold lower (95% CI, .02 to .31) than placebo standardized to the DISCOVER population. CONCLUSIONS: There was a reduction in HIV infection with TAF/FTC versus placebo across 96 weeks of follow-up. CLINICAL TRIALS REGISTRATION: NCT02842086 and NCT00458393.

Comparative Effect of Loop Diuretic Prescription on Mortality and Heart Failure Readmission.

Loop diuretics are a standard pharmacologic therapy in heart failure (HF) management. Although furosemide is most frequently used, torsemide and bumetanide are increasingly prescribed in clinical practice, possibly because of superior bioavailability. Few real-world comparative effectiveness studies have examined outcomes across all 3 loop diuretics. The study goal was to compare the effects of loop diuretic prescribing at HF hospitalization discharge on mortality and HF readmission. We identified patients in Medicare claims data initiating furosemide, torsemide, or bumetanide after an index HF hospitalization from 2007 to 2017. We estimated 6-month risks of all-cause mortality and a composite outcome (HF readmission or all-cause mortality) using inverse probability of treatment weighting to adjust for relevant confounders. We identified 62,632 furosemide, 1,720 torsemide, and 2,389 bumetanide initiators. The 6-month adjusted all-cause mortality risk was lowest for torsemide (13.2%), followed by furosemide (14.5%) and bumetanide (15.6%). The 6-month composite outcome risk was 21.4% for torsemide, 24.7% for furosemide, and 24.9% for bumetanide. Compared with furosemide, the 6-month all-cause mortality risk was 1.3% (95% confidence interval [CI]: -3.7, 1.0) lower for torsemide and 1.0% (95% CI: -1.2, 3.2) higher for bumetanide, and the 6-month composite outcome risk was 3.3% (95% CI: -6.3, -0.3) lower for torsemide and 0.2% (95% CI: -2.5, 2.9) higher for bumetanide. In conclusion, the findings suggested that the first prescribed loop diuretic following HF hospitalization is associated with clinically important differences in morbidity in older patients receiving torsemide, bumetanide, or furosemide. These differences were consistent for the effect of all-cause mortality alone, but were not statistically significant.

Transportability Without Positivity: A Synthesis of Statistical and Simulation Modeling.

Studies designed to estimate the effect of an action in a randomized or observational setting often do not represent a random sample of the desired target population. Instead, estimates from that study can be transported to the target population. However, transportability methods generally rely on a positivity assumption, such that all relevant covariate patterns in the target population are also observed in the study sample. Strict eligibility criteria, particularly in the context of randomized trials, may lead to violations of this assumption. Two common approaches to address positivity violations are restricting the target population and restricting the relevant covariate set. As neither of these restrictions is ideal, we instead propose a synthesis of statistical and simulation models to address positivity violations. We propose corresponding g-computation and inverse probability weighting estimators. The restriction and synthesis approaches to addressing positivity violations are contrasted with a simulation experiment and an illustrative example in the context of sexually transmitted infection testing uptake. In both cases, the proposed synthesis approach accurately addressed the original research question when paired with a thoughtfully selected simulation model. Neither of the restriction approaches was able to accurately address the motivating question. As public health decisions must often be made with imperfect target population information, model synthesis is a viable approach given a combination of empirical data and external information based on the best available knowledge.

Medications for Opioid Use Disorder and Mortality and Hospitalization Among People With Opioid Use-related Infections.

BACKGROUND: Severe skin and soft tissue infections related to injection drug use have increased in concordance with a shift to heroin and illicitly manufactured fentanyl. Opioid agonist therapy medications (methadone and buprenorphine) may improve long-term outcomes by reducing injection drug use. We aimed to examine the association of medication use with mortality among people with opioid use-related skin or soft tissue infections. METHODS: An observational cohort study of Medicaid enrollees aged 18 years or older following their first documented medical encounters for opioid use-related skin or soft tissue infections during 2007-2018 in North Carolina. The exposure was documented medication use (methadone or buprenorphine claim) in the first 30 days following initial infection compared with no medication claim. Using Kaplan-Meier estimators, we examined the difference in 3-year incidence of mortality by medication use, weighted for year, age, comorbidities, and length of hospital stay. RESULTS: In this sample, there were 13,286 people with opioid use-related skin or soft tissue infections. The median age was 37 years, 68% were women, and 78% were white. In Kaplan-Meier curves for the total study population, 12 of every 100 patients died during the first 3 years. In weighted models, for every 100 people who used medications, there were four fewer deaths over 3 years (95% confidence interval = 2, 6). CONCLUSION: In this study, people with opioid use-related skin and soft tissue infections had a high risk of mortality following their initial healthcare visit for infections. Methadone or buprenorphine use was associated with reductions in mortality.

Fusing trial data for treatment comparisons: Single vs multi-span bridging.

While randomized controlled trials (RCTs) are critical for establishing the efficacy of new therapies, there are limitations regarding what comparisons can be made directly from trial data. RCTs are limited to a small number of comparator arms and often compare a new therapeutic to a standard of care which has already proven efficacious. It is sometimes of interest to estimate the efficacy of the new therapy relative to a treatment that was not evaluated in the same trial, such as a placebo or an alternative therapy that was evaluated in a different trial. Such dual-study comparisons are challenging because of potential differences between trial populations that can affect the outcome. In this article, two bridging estimators are considered that allow for comparisons of treatments evaluated in different trials, accounting for measured differences in trial populations. A "multi-span" estimator leverages a shared arm between two trials, while a "single-span" estimator does not require a shared arm. A diagnostic statistic that compares the outcome in the standardized shared arms is provided. The two estimators are compared in simulations, where both estimators demonstrate minimal empirical bias and nominal confidence interval coverage when the identification assumptions are met. The estimators are applied to data from the AIDS Clinical Trials Group 320 and 388 to compare the efficacy of two-drug vs four-drug antiretroviral therapy on CD4 cell counts among persons with advanced HIV. The single-span approach requires weaker identification assumptions and was more efficient in simulations and the application.

Mass Probation: Effects of Sentencing Severity on Mental Health for Black and White Individuals.

BACKGROUND: Incarceration is associated with negative impacts on mental health. Probation, a form of community supervision, has been lauded as an alternative. However, the effect of probation versus incarceration on mental health is unclear. Our objective was to estimate the impact on mental health of reducing sentencing severity at individuals' first adult criminal-legal encounter. METHODS: We used the US National Longitudinal Survey on Youth 1997, a nationally representative dataset of youth followed into their mid-thirties. Restricting to those with an adult encounter (arrest, charge alone or no sentence, probation, incarceration), we used parametric g-computation to estimate the difference in mental health at age 30 (Mental Health Inventory-5) if (1) everyone who received incarceration for their first encounter had received probation and (2) everyone who received probation had received no sentence. RESULTS: Among 1835 individuals with adult encounters, 19% were non-Hispanic Black and 65% were non-Hispanic White. Median age at first encounter was 20. Under hypothetical interventions to reduce sentencing, we did not see better mental health overall (Intervention 1, incarceration to probation: RD = -0.01; CI = -0.02, 0.01; Intervention 2, probation to no sentence: RD = 0.00; CI = -0.01, 0.01) or when stratified by race. CONCLUSION: Among those with criminal-legal encounters, hypothetical interventions to reduce sentencing, including incremental sentencing reductions, were not associated with improved mental health. Future work should consider the effects of preventing individuals' first criminal-legal encounter.

Hepatitis B virus prevalence and transmission in the households of pregnant women in Kinshasa, Democratic Republic of Congo.

Background: Despite routine infant vaccination and blood donor screening, the Democratic Republic of Congo (DRC) has high hepatitis B virus (HBV) prevalence compared to the United States and Europe. Through the cross-sectional Horizontal and Vertical Transmission of Hepatitis B (HOVER-HBV) study, we characterized household prevalence in DRC's capital, Kinshasa, to inform additional prevention efforts. Methods: We introduced HBV surface antigen (HBsAg) screening alongside existing HIV screening as part of routine antenatal care (ANC) in high-volume maternity clinics in Kinshasa. We recruited households of pregnant women who were HBsAg-positive and HBsAg-negative, defining households as "exposed" and "unexposed," respectively. Household members underwent HBsAg testing and an epidemiological survey. We evaluated HBsAg prevalence and potential transmission correlates. Results: We enrolled 1,006 participants from 200 households (100 exposed, 100 unexposed) across Kinshasa. HBsAg prevalence was more than twice as high in exposed households (5.0%; 95% CI: 2.8%-7.1%) as in unexposed households (1.9%; 0.6%-3.2%). Exposed direct offspring had 3.3 (0.9, 11.8) times the prevalence of unexposed direct offspring. Factors associated with HBsAg-positivity included older age, marriage, and having multiple recent partners or any new sexual partners among index mothers; and older age, lower household wealth, sharing nail clippers, and using street salons among exposed offspring. Conclusions: Vertical and horizontal HBV transmission within households is ongoing in Kinshasa. Factors associated with infection reveal opportunities for HBV prevention efforts, including perinatal prevention, protection during sexual contact, and sanitation of shared personal items.

Understanding the effects of universal test and treat on longitudinal HIV care outcomes among South African youth: a retrospective cohort study.

INTRODUCTION: Little is known about the effects of universal test and treat (UTT) policies on HIV care outcomes among youth living with HIV (YLHIV). Moreover, there is a paucity of information regarding when YLHIV are most susceptible to disengagement from care under the newest treatment guidelines. The longitudinal HIV care continuum is an underutilized tool that can provide a holistic understanding of population-level HIV care trajectories and be used to compare treatment outcomes across groups. We aimed to explore effects of the UTT policy on longitudinal outcomes among South African YLHIV and identify temporally precise opportunities for re-engaging this priority population in the UTT era. METHODS: Using medical record data, we conducted a retrospective cohort study among youth aged 18-24 diagnosed with HIV from August 2015-December 2018 in nine health care facilities in South Africa. We used Fine and Gray sub-distribution proportional hazards models to characterize longitudinal care continuum outcomes in the population overall and stratified by treatment era of diagnosis. We estimated the proportion of individuals in each stage of the continuum over time and the restricted mean time spent in each stage in the first year following diagnosis. Sub-group estimates were compared using differences. RESULTS: A total of 420 YLHIV were included. By day 365 following diagnosis, just 23% of individuals had no 90-or-more-day lapse in care and were virally suppressed. Those diagnosed in the UTT era spent less time as ART-naïve (mean difference=-19.3 days; 95% CI: -27.7, -10.9) and more time virally suppressed (mean difference = 17.7; 95% CI: 1.0, 34.4) compared to those diagnosed pre-UTT. Most individuals who were diagnosed in the UTT era and experienced a 90-or-more-day lapse in care disengaged between diagnosis and linkage to care or ART initiation and viral suppression. CONCLUSIONS: Implementation of UTT yielded modest improvements in time spent on ART and virally suppressed among South African YLHIV- however, meeting UNAIDS' 95-95-95 targets remains a challenge. Retention in care and re-engagement interventions that can be implemented between diagnosis and linkage to care and between ART initiation and viral suppression (e.g., longitudinal counseling) may be particularly important to improving care outcomes among South African YLHIV in the UTT era.

Impact of subgroup-specific heterogeneities and dynamic changes in mortality rates on forecasted population size, deaths, and age distribution of persons receiving antiretroviral treatment in the United States: a computer simulation study.

PURPOSE: Model-based forecasts of population size, deaths, and age distribution of people with HIV (PWH) are helpful for public health and clinical services planning but are influenced by subgroup-specific heterogeneities and changes in mortality rates. METHODS: Using an agent-based simulation of PWH in the United States, we examined the impact of distinct approaches to parametrizing mortality rates on forecasted epidemiology of PWH on antiretroviral treatment (ART). We first estimated mortality rates among (1) all PWH, (2) sex-specific, (3) sex-and-race/ethnicity-specific, and (4) sex-race/ethnicity-and-HIV-acquisition-risk-specific subgroups. We then assessed each scenario by (1) allowing unrestricted reductions in age-specific mortality rates over time and (2) restricting the mortality rates among PWH to subgroup-specific mortality thresholds from the general population. RESULTS: Among the eight scenarios examined, those lacking subgroup-specific heterogeneities and those allowing unrestricted reductions in future mortality rates forecasted the lowest number of deaths among all PWH and 9 of the 15 subgroups through 2030. The forecasted overall number and age distribution of people with a history of injection drug use were sensitive to inclusion of subgroup-specific mortality rates. CONCLUSIONS: Our results underscore the potential risk of underestimating future deaths by models lacking subgroup-specific heterogeneities in mortality rates, and those allowing unrestricted reductions in future mortality rates.

Estimation of SARS-CoV-2 Seroprevalence in Central North Carolina: Accounting for Outcome Misclassification in Complex Sample Designs.

BACKGROUND: Population-based seroprevalence studies are crucial to understand community transmission of COVID-19 and guide responses to the pandemic. Seroprevalence is typically measured from diagnostic tests with imperfect sensitivity and specificity. Failing to account for measurement error can lead to biased estimates of seroprevalence. Methods to adjust seroprevalence estimates for the sensitivity and specificity of the diagnostic test have largely focused on estimation in the context of convenience sampling. Many existing methods are inappropriate when data are collected using a complex sample design. METHODS: We present methods for seroprevalence point estimation and confidence interval construction that account for imperfect test performance for use with complex sample data. We apply these methods to data from the Chatham County COVID-19 Cohort (C4), a longitudinal seroprevalence study conducted in central North Carolina. Using simulations, we evaluate bias and confidence interval coverage for the proposed estimator compared with a standard estimator under a stratified, three-stage cluster sample design. RESULTS: We obtained estimates of seroprevalence and corresponding confidence intervals for the C4 study. SARS-CoV-2 seroprevalence increased rapidly from 10.4% in January to 95.6% in July 2021 in Chatham County, North Carolina. In simulation, the proposed estimator demonstrates desirable confidence interval coverage and minimal bias under a wide range of scenarios. CONCLUSION: We propose a straightforward method for producing valid estimates and confidence intervals when data are based on a complex sample design. The method can be applied to estimate the prevalence of other infections when estimates of test sensitivity and specificity are available.

Accounting for nonmonotone missing data using inverse probability weighting.

Inverse probability weighting can be used to correct for missing data. New estimators for the weights in the nonmonotone setting were introduced in 2018. These estimators are the unconstrained maximum likelihood estimator (UMLE) and the constrained Bayesian estimator (CBE), an alternative if UMLE fails to converge. In this work we describe and illustrate these estimators, and examine performance in simulation and in an applied example estimating the effect of anemia on spontaneous preterm birth in the Zambia Preterm Birth Prevention Study. We compare performance with multiple imputation (MI) and focus on the setting of an observational study where inverse probability of treatment weights are used to address confounding. In simulation, weighting was less statistically efficient at the smallest sample size and lowest exposure prevalence examined (n = 1500, 15% respectively) but in other scenarios statistical performance of weighting and MI was similar. Weighting had improved computational efficiency taking, on average, 0.4 and 0.05 times the time for MI in R and SAS, respectively. UMLE was easy to implement in commonly used software and convergence failure occurred just twice in >200 000 simulated cohorts making implementation of CBE unnecessary. In conclusion, weighting is an alternative to MI for nonmonotone missingness, though MI performed as well as or better in terms of bias and statistical efficiency. Weighting's superior computational efficiency may be preferred with large sample sizes or when using resampling algorithms. As validity of weighting and MI rely on correct specification of different models, both approaches could be implemented to check agreement of results.