BackgroundSubstantial resources and patient commitment are required to successfully scale-up antiretroviral therapy (ART) and provide appropriate HIV management in resource-limited settings. We used pharmacy refill records to evaluate risk factors for loss to follow-up (LTFU) and non-adherence to ART in a large treatment cohort in Nigeria.Methods and FindingsWe reviewed clinic records of adult patients initiating ART between March 2005 and July 2006 at five health facilities. Patients were classified as LTFU if they did not return >60 days from their expected visit. Pharmacy refill rates were calculated and used to assess non-adherence. We identified risk factors associated with LTFU and non-adherence using Cox and Generalized Estimating Equation (GEE) regressions, respectively. Of 5,760 patients initiating ART, 26% were LTFU. Female gender (p<0.001), post-secondary education (p = 0.03), and initiating treatment with zidovudine-containing (p = 0.004) or tenofovir-containing (p = 0.05) regimens were associated with decreased risk of LTFU, while patients with only primary education (p = 0.02) and those with baseline CD4 counts (cell/ml3) >350 and <100 were at a higher risk of LTFU compared to patients with baseline CD4 counts of 100–200. The adjusted GEE analysis showed that patients aged <35 years (p = 0.005), who traveled for >2 hours to the clinic (p = 0.03), had total ART duration of >6 months (p<0.001), and CD4 counts >200 at ART initiation were at a higher risk of non-adherence. Patients who disclosed their HIV status to spouse/family (p = 0.01) and were treated with tenofovir-containing regimens (p≤0.001) were more likely to be adherent.ConclusionsThese findings formed the basis for implementing multiple pre-treatment visit preparation that promote disclosure and active community outreaching to support retention and adherence. Expansion of treatment access points of care to communities to diminish travel time may have a positive impact on adherence.

Background Maternal prepregnancy body mass index (BMI) is associated with several infant outcomes, but it is unclear whether these associations reflect causal relationships. We conducted a study of interpregnancy change in BMI (IPC-BMI) to improve understanding of the associations between BMI and large for gestational age (LGA), small for gestational age (SGA), and preterm birth (PTB). Methods Birth certificate data from 2481 linked sibling pairs (Texas, 2005–2012) were used to estimate IPC-BMI and evaluate its association with LGA, SGA, and PTB in the younger sibling of the pair. Multivariable logistic regression was used to estimate adjusted odds ratios (aOR) and 95% confidence intervals (CI) using data from the full sample and within strata defined by prepregnancy BMI for the older sibling. Results On average, women gained 1.1 BMI units between pregnancies. In the full sample, interpregnancy BMI decreases were associated with reduced odds of LGA and increased odds of SGA and PTB (IPC-BMI < -1 versus 0 to < 1: LGA aOR 0.7, 95% CI 0.4, 1.1; SGA aOR 1.6, 95% CI 1.0, 2.7; PTB aOR 1.9, 95% CI 1.3, 2.8). In stratified analyses, similar associations were observed in some, but not all, strata. Findings for interpregnancy BMI increases were less consistent, with little evidence for associations between these outcomes and the most extreme IPC-BMI increases. Conclusions There is growing evidence that interpregnancy BMI decreases are associated with LGA, SGA, and PTB. However, taken as a whole, the literature provides insufficient evidence to establish causal links between maternal BMI and these outcomes.

Background Few studies have systematically evaluated birth defect co‐occurrence patterns, perhaps, in part, due to the lack of software designed to implement large‐scale, complex analytic methods. Methods We created an R‐based platform, “co‐occurring defect analysis” (CODA), designed to implement analyses of birth defect co‐occurrence patterns in birth defect registries. CODA uses an established algorithm for calculating the observed‐to‐expected ratio of a given birth defect combination, accounting for the known tendency of birth defects to co‐occur nonspecifically. To demonstrate CODA's feasibility, we evaluated the computational time needed to assess 2‐ to 5‐way combinations of major birth defects in the Texas Birth Defects Registry (TBDR) (1999–2014). We report on two examples of pairwise patterns, defects co‐occurring with trisomy 21 or with non‐syndromic spina bifida, to demonstrate proof‐of‐concept. Results We evaluated combinations of 175 major birth defects among 206,784 infants in the TBDR. CODA performed efficiently in the data set, analyzing 1.5 million 5‐way combinations in 18 hr. As anticipated, we identified large observed‐to‐expected ratios for the birth defects that co‐occur with trisomy 21 or spina bifida. Conclusions CODA is available for application to birth defect data sets and can be used to better understand co‐occurrence patterns. Co‐occurrence patterns elucidated by using CODA may be helpful for identifying new birth defect associations and may provide etiological insights regarding potentially shared pathogenic mechanisms. CODA may also have wider applications, such as assessing patterns of additional types of co‐occurrence patterns in other large data sets (e.g., medical records).