Severity of neonatal opioid withdrawal syndrome with prenatal exposure to serotonin reuptake inhibitors.

OBJECTIVE: To evaluate the severity of neonatal opioid withdrawal syndrome (NOWS) in infants prenatally exposed to medications for opioid use disorder (MOUD) and serotonin reuptake inhibitors (SRI). METHODS: A prospective cohort included 148 maternal-infant pairs categorized into MOUD (n = 127) and MOUD + SRI (n = 27) groups. NOWS severity was operationalized as the infant's need for pharmacologic treatment with opioids, duration of hospitalization, and duration of treatment. The association between prenatal SRI exposure and the need for pharmacologic treatment (logistic regression), time-to-discharge, and time-to-treatment discontinuation (Cox proportional hazards modeling) was examined after adjusting for the type of maternal MOUD, use of hydroxyzine, other opioids, benzodiazepines/sedatives, alcohol, tobacco, marijuana, gestational age, and breastfeeding. RESULTS: Infants in the MOUD + SRI group were more likely to receive pharmacologic treatment for NOWS (OR = 3.58; 95% CI: 1.31; 9.76) and had a longer hospitalization (median: 11 vs. 6 days; HR = 0.54; 95% CI: 0.33; 0.89) compared to the MOUD group. With respect to time-to-treatment discontinuation, no association was observed in infants who received treatment (HR = 0.59; 95% CI: 0.26, 1.32); however, significant differences were observed in the entire sample (HR = 0.55; 95% CI: 0.34, 0.89). CONCLUSIONS: Use of SRIs among pregnant women on MOUD might be associated with more severe NOWS. IMPACT: A potential drug-drug interaction between maternal SRIs and opioid medications that inhibit the reuptake of serotonin has been hypothesized but not carefully evaluated in clinical studies. Results of this prospective cohort indicate that the use of SRIs among pregnant women on MOUD is associated with more severe neonatal opioid withdrawal syndrome. This is the first prospective study which carefully examined effect modification between the type of maternal MOUD and SRI use on neonatal outcomes. This report lays the foundation for treatment optimization in pregnant women with co-occurring mental health and substance use disorders.

A Novel Approach to Prenatal Measurement of the Fetal Frontal Lobe Using Three-Dimensional Sonography.

OBJECTIVE: While prenatal 3D ultrasonography results in improved diagnostic accuracy, no data are available on biometric assessment of the fetal frontal lobe. This study was designed to assess feasibility of a standardized approach to biometric measurement of the fetal frontal lobe and to construct frontal lobe growth trajectories throughout gestation. STUDY DESIGN: A sonographic 3D volume set was obtained and measured in 101 patients between 16.1 and 33.7 gestational weeks. Measurements were obtained by two independent raters. To model the relationship between gestational age and each frontal lobe measurement, flexible linear regression models were fit using penalized regression splines. RESULTS: The sample contained an ethnically diverse population (7.9% Native Americans, 45.5% Hispanic/Latina). There was high inter-rater reliability (correlation coefficients: 0.95, 1.0, and 0.87 for frontal lobe length, width, and height; p-values < 0.001). Graphs of the growth trajectories and corresponding percentiles were estimated as a function of gestational age. The estimated rates of frontal lobe growth were 0.096 cm/week, 0.247 cm/week, and 0.111 cm/week for length, width, and height. CONCLUSION: To our knowledge, this is the first study to examine fetal frontal lobe growth trajectories through 3D prenatal ultrasound examination. Such normative data will allow for future prenatal evaluation of a particular disease state by 3D ultrasound imaging.

A Novel Approach to Prenatal Measurement of the Fetal Frontal Lobe Using Three-Dimensional Sonography.

Objective: To assess the feasibility of a standardized approach to biometric measurement of the fetal frontal lobe and to construct frontal lobe growth trajectories throughout gestation. Study Design: A sonographic 3-dimensional (3D) volume set was obtained and measured in 101 patients between 16.1 and 33.7 gestational weeks. Measurements were obtained by 2 independent raters. To model the relationship between gestational age and each frontal lobe measurement, flexible linear regression models were fit using penalized regression splines. Results: The sample contained an ethnically diverse population (7.9% Native Americans, 45.5% Hispanics/Latinas). There was high interrater reliability (correlation coefficients 0.95, 1.0, and 0.87 for frontal lobe length, width, and height; p values <0.001). Graphs of the growth trajectories and corresponding percentiles were estimated as a function of gestational age. The estimated rates of frontal lobe growth were 0.096 cm/week, 0.247 cm/week, and 0.111 cm/week for length, width, and height, respectively. Conclusion: To our knowledge, this is the first study to examine fetal frontal lobe growth trajectories through 3D prenatal ultrasound examination. Such normative data will allow for future prenatal evaluation of a particular disease state by 3D ultrasound imaging.

Causal Inference Methods for Estimating Long-Term Health Effects of Air Quality Regulations.

INTRODUCTION: The regulatory and policy environment surrounding air quality management warrants new types of epidemiological evidence. Whereas air pollution epidemiology has typically informed previous policies with estimates of exposure-response relationships between pollution and health outcomes, new types of evidence can inform current debates about the actual health impacts of air quality regulations. Directly evaluating specific regulatory strategies is distinct from and complements estimating exposure-response relationships; increased emphasis on assessing the effectiveness of well-defined regulatory interventions will enhance the evidence supporting policy decisions. The goal of this report is to provide new analytic perspectives and statistical methods for what we refer to as "direct"-accountability assessment of the effectiveness of specific air quality regulatory interventions. Toward this end, we sharpened many of the distinctions surrounding accountability assessment initially raised by the HEI Accountability Working Group (2003) through discussion, development, and deployment of statistical methods for drawing causal inferences from observational data. The methods and analyses presented here are unified in their focus on anchoring accountability assessment to the estimation of the causal consequences of well-defined actions or interventions. These analytic perspectives are discussed in the context of two direct-accountability case studies pertaining to four different links in the so-called chain of accountability, the related series of events leading from the intervention to the expected outcomes (see Preface; HEI Accountability Working Group 2003). METHODS: The statistical methods described in this report consist of both established methods for drawing causal inferences from observational data and newly developed methods for assessing causal accountability. We have sharpened the analytic distinctions between studies that directly evaluated the effectiveness of specific policies and those that estimated exposure-response relationships between pollution and health. We emphasized how a potential-outcomes paradigm for causal inference can elevate policy debates by means of more direct evidence of the extent to which complex regulatory interventions affect pollution and health outcomes. We also outlined the potential-outcomes perspective and promoted its use as a means to frame observational studies as approximate randomized experiments. Our newly developed methods for assessing causal accountability draw on propensity scores, principal stratification, causal mediation analysis, spatial hierarchical models, and Bayesian estimation. The first case study made use of health outcomes among approximately four million Medicare beneficiaries living in the Western United States to estimate the causal health impacts of areas designated as being in nonattainment for particulate matter ≤10 µm in aerodynamic diameter (PM10*) according to the 1987 National Ambient Air Quality Standards (NAAQS). The second case study focused on developing and testing our new, advanced methodology for multipollutant accountability assessment by examining the extent to which sulfur dioxide (SO2) scrubbers on coal-fired power plants causally affect emissions of SO2, nitrogen oxides (NO(x)), and carbon dioxide (CO2) as well as the extent to which emissions reductions mediate the causal effect of a scrubber on ambient concentrations of PM2.5. Both case studies were anchored in our compilation of national, linked data on ambient air quality monitoring, weather, population demographics, Medicare hospitalization and mortality outcomes, continuous-emissions monitoring for electricity-generating units (EGUs) in power plants, and a variety of regulatory control interventions. The resulting database has unprecedented accuracy and granularity for conducting the types of accountability assessments presented in this report. A key component of our work was the creation of tools to help distribute our linked database and to facilitate reproducible research. RESULTS: In the first case study, we focused on illustrating the most fundamental features of a causal-inference perspective on direct-accountability assessment. The results indicated that all-cause Medicare mortality and respiratory-related hospitalization rates were causally reduced in areas designated as nonattainment for PM10 during 1990 to 1995 compared with the rates that would have occurred without the designation. In the second case study, which examined power-plant emissions and illustrated our newly developed statistical methods, the results indicated that the presence of an SO2 scrubber causally reduced ambient PM2.5 and that this reduction was mediated almost entirely through causal reductions in SO2 emissions. The results were interpreted in light of the well-documented relationships between scrubbers, power-plant emissions, and PM2.5. CONCLUSION: By grounding accountability research in a potential-outcomes framework and applying our new methods to our collection of national data sets, we were able to provide additional sound evidence of the health effects of long-term, large-scale air quality regulations. This additional, rigorous evidence of the causal effects of well-defined actions augments the existing body of research and ensures that the highest-level epidemiological evidence will continue to support regulatory policies. Ultimately, our research contributed to the evidence available to support to the U.S. Environmental Protection Agency (U.S. EPA) and other stakeholders for incorporating health outcomes research into policy development.

Characterization of Pesticide Exposure in a Sample of Pregnant Women in Ecuador.

Few studies have detailed the prenatal pesticide exposure levels of women employed in or residing near large-scale agricultural industries. This study reports pesticide metabolite levels during and shortly after pregnancy in a pilot study of workers in Ecuador. Urine samples were collected for 16 rose workers and 10 nonagricultural workers enrolled into the study in early pregnancy. We measured six nonspecific organophosphatedialkylphosphate (DAP) pesticide metabolites, two alkylenebis-dithiocarbamate pesticide metabolites [ethylene thiourea (ETU) and propylene thiourea (PTU)], 3,5,6-trichloro-2-pyridinol (TCPy), malathion dicarboxylic acid, and two pyrethroid metabolites (2,2-dimethylcyclo propanecarboxylic acid and 3-phenooxybenzoic acid). We collected 141 urine samples (mean: 5.4 per woman). We observed high detection frequencies for five DAP metabolites and ETU, PTU, and TCPy. We report elevated levels of ETU in the entire sample (median 4.24 ng/mL, IQR 2.23, 7.18), suggesting other possible non-occupational pathways of exposure. We found no statistical differences in pesticide levels by current employment status, although the highest pesticide levels were among rose workers. We observed within-woman correlation in TCPy and PTU levels, but not in ETU or DAP levels. The present study is the first to characterize prenatal pesticide exposure levels among working women in Ecuador. Limitations include a small sample size and use of a convenience sample. Strengths include a longitudinal design and multiple urine samples per woman. Results provide an initial characterization of prenatal pesticide exposure levels and how these levels vary over pregnancy in a community impacted by agricultural industry and will inform further studies in the region.

Extending cluster lot quality assurance sampling designs for surveillance programs.

Lot quality assurance sampling (LQAS) has a long history of applications in industrial quality control. LQAS is frequently used for rapid surveillance in global health settings, with areas classified as poor or acceptable performance on the basis of the binary classification of an indicator. Historically, LQAS surveys have relied on simple random samples from the population; however, implementing two-stage cluster designs for surveillance sampling is often more cost-effective than simple random sampling. By applying survey sampling results to the binary classification procedure, we develop a simple and flexible nonparametric procedure to incorporate clustering effects into the LQAS sample design to appropriately inflate the sample size, accommodating finite numbers of clusters in the population when relevant. We use this framework to then discuss principled selection of survey design parameters in longitudinal surveillance programs. We apply this framework to design surveys to detect rises in malnutrition prevalence in nutrition surveillance programs in Kenya and South Sudan, accounting for clustering within villages. By combining historical information with data from previous surveys, we design surveys to detect spikes in the childhood malnutrition rate.

New tools for evaluating LQAS survey designs.

Lot Quality Assurance Sampling (LQAS) surveys have become increasingly popular in global health care applications. Incorporating Bayesian ideas into LQAS survey design, such as using reasonable prior beliefs about the distribution of an indicator, can improve the selection of design parameters and decision rules. In this paper, a joint frequentist and Bayesian framework is proposed for evaluating LQAS classification accuracy and informing survey design parameters. Simple software tools are provided for calculating the positive and negative predictive value of a design with respect to an underlying coverage distribution and the selected design parameters. These tools are illustrated using a data example from two consecutive LQAS surveys measuring Oral Rehydration Solution (ORS) preparation. Using the survey tools, the dependence of classification accuracy on benchmark selection and the width of the 'grey region' are clarified in the context of ORS preparation across seven supervision areas. Following the completion of an LQAS survey, estimation of the distribution of coverage across areas facilitates quantifying classification accuracy and can help guide intervention decisions.

Estimating HIV prevalence from surveys with low individual consent rates: annealing individual and pooled samples.

: Many HIV prevalence surveys are plagued by the problem that a sizeable number of surveyed individuals do not consent to contribute blood samples for testing. One can ignore this problem, as is often done, but the resultant bias can be of sufficient magnitude to invalidate the results of the survey, especially if the number of non-responders is high and the reason for refusing to participate is related to the individual's HIV status. One reason for refusing to participate may be for reasons of privacy. For those individuals, we suggest offering the option of being tested in a pool. This form of testing is less certain than individual testing, but, if it convinces more people to submit to testing, it should reduce the potential for bias and give a cleaner answer to the question of prevalence. This paper explores the logistics of implementing a combined individual and pooled testing approach and evaluates the analytical advantages to such a combined testing strategy. We quantify improvements in a prevalence estimator based on this combined testing strategy, relative to an individual testing only approach and a pooled testing only approach. Minimizing non-response is key for reducing bias, and, if pooled testing assuages privacy concerns, offering a pooled testing strategy has the potential to substantially improve HIV prevalence estimates.

The effect of clustering on lot quality assurance sampling: a probabilistic model to calculate sample sizes for quality assessments.

BACKGROUND: Traditional Lot Quality Assurance Sampling (LQAS) designs assume observations are collected using simple random sampling. Alternatively, randomly sampling clusters of observations and then individuals within clusters reduces costs but decreases the precision of the classifications. In this paper, we develop a general framework for designing the cluster(C)-LQAS system and illustrate the method with the design of data quality assessments for the community health worker program in Rwanda. RESULTS: To determine sample size and decision rules for C-LQAS, we use the beta-binomial distribution to account for inflated risk of errors introduced by sampling clusters at the first stage. We present general theory and code for sample size calculations.The C-LQAS sample sizes provided in this paper constrain misclassification risks below user-specified limits. Multiple C-LQAS systems meet the specified risk requirements, but numerous considerations, including per-cluster versus per-individual sampling costs, help identify optimal systems for distinct applications. CONCLUSIONS: We show the utility of C-LQAS for data quality assessments, but the method generalizes to numerous applications. This paper provides the necessary technical detail and supplemental code to support the design of C-LQAS for specific programs.

Effect of concurrent sexual partnerships on rate of new HIV infections in a high-prevalence, rural South African population: a cohort study.

BACKGROUND: Concurrent sexual partnerships are widely believed to be one of the main drivers of the HIV epidemic in sub-Saharan Africa. This view is supported by theoretical models predicting that increases in prevalence of concurrent partnerships could substantially increase the rate of spread of the disease. However, the effect of concurrent partnerships on HIV incidence has not been appropriately tested in a sub-Saharan African setting. METHODS: For this population-based cohort study, we used data from the Africa Centre demographic surveillance site in KwaZulu-Natal, South Africa, to try to find support for the concurrency hypothesis. We used a moving-window approach to construct estimates of the geographical variation in reported concurrent and lifetime partners in sexually active men aged 15-55 years (n=2153) across the study area. We then followed up 7284 HIV-negative women (≥15 years of age) in the population and quantified the effect of the sexual behaviour profiles of men in the surrounding local community on a woman's hazard of HIV acquisition. FINDINGS: During 5 years' follow-up, 693 new female HIV infections occurred (incidence 3.60 cases per 100 person-years). We identified substantial intercommunity heterogeneity in the estimated point-prevalence of partnership concurrency (range 4.0-76.3%; mean 31.5%) and mean number of lifetime sexual partners (3.4-12.9; mean 6.3) in sexually active men in this population. After adjustment for individual-level sexual behaviour and demographic, socioeconomic, and environmental factors associated with HIV acquisition, mean lifetime number of partners of men in the immediate local community was predictive of hazard of HIV acquisition in women (adjusted hazard ratio [HR] 1.08, 95% CI 1.03-1.14, p=0.004), whereas a high prevalence of partnership concurrency in the same local community was not associated with any increase in risk of HIV acquisition (adjusted HR 1.02, 95% CI 0.95-1.09, p=0.556). INTERPRETATION: We find no evidence to suggest that concurrent partnerships are an important driver of HIV incidence in this typical high-prevalence rural African population. Our findings suggest that in similar hyperendemic sub-Saharan African settings, there is a need for straightforward, unambiguous messages aimed at the reduction of multiple partnerships, irrespective of whether those partnerships overlap in time. FUNDING: US National Institute of Child Health and Human Development; Wellcome Trust.

A geostatistical approach to large-scale disease mapping with temporal misalignment.

Temporal boundary misalignment occurs when area boundaries shift across time (e.g., census tract boundaries change at each census year), complicating the modeling of temporal trends across space. Large area-level datasets with temporal boundary misalignment are becoming increasingly common in practice. The few existing approaches for temporally misaligned data do not account for correlation in spatial random effects over time. To overcome issues associated with temporal misalignment, we construct a geostatistical model for aggregate count data by assuming that an underlying continuous risk surface induces spatial correlation between areas. We implement the model within the framework of a generalized linear mixed model using radial basis splines. Using this approach, boundary misalignment becomes a nonissue. Additionally, this disease-mapping framework facilitates fast, easy model fitting by using a penalized quasilikelihood approximation to maximum likelihood estimation. We anticipate that the method will also be useful for large disease-mapping datasets for which fully Bayesian approaches are infeasible. We apply our method to assess socioeconomic trends in breast cancer incidence in Los Angeles between the periods 1988-1992 and 1998-2002.

Maternal verbal scaffolding: association with higher language skills for 20-month-old children with prenatal polysubstance exposure.

BACKGROUND: The number of children with prenatal polysubstance exposure is increasing. Supportive mother-child interaction is a protective factor, which can ameliorate adverse effects of prenatal polysubstance exposure on developmental outcomes. AIM: To examine the role of maternal verbal scaffolding on cognitive and language development in children with prenatal polysubstance exposure. STUDY DESIGN: Pregnant women were recruited, and we prospectively followed mother-child dyads to 20 months of age. This analysis included 66 dyads (33 healthy controls and 33 with prenatal polysubstance exposure). Multivariable linear regression modelling was used to examine the cross-sectional association between maternal scaffolding and Bayley Scales of Infant and Toddler Development (BSID-III) score, as well as an interaction between the study group and scaffolding score. OUTCOME MEASURES: The BSID-III cognitive and language score was used. Videotaped mother-child play was coded to obtain a maternal verbal scaffolding score. Effect sizes were measured using average differences in scores between groups. RESULTS: There was no evidence of an association between study group and maternal scaffolding scores. Children in the polysubstance exposure group had lower cognitive and language scores compared to controls, but this association was not statistically significant after controlling for maternal education. Maternal scaffolding was predictive of language scores, with scores increasing by 1.24 points on average (95% CI: 0.42, 2.06) for every 1-point increase in scaffolding score after adjustment for covariates. There was no evidence of a study group-by-scaffolding interaction with respect to the language or cognitive scores. CONCLUSIONS: Maternal scaffolding during play was associated with language development in children with and without prenatal polysubstance exposure.

An approach for quantifying small effects in regression models.

We develop a novel approach for quantifying small effects in regression models. Our method is based on variation in the mean function, in contrast to methods that focus on regression coefficients. Our idea applies in diverse settings such as testing for a negligible trend and quantifying differences in regression functions across strata. Straightforward Bayesian methods are proposed for inference. Four examples are used to illustrate the ideas.

Predictors of English Health Literacy among U.S. Hispanic Immigrants: The importance of language, bilingualism and sociolinguistic environment.

In the United States, data confirm that Spanish-speaking immigrants are particularly affected by the negative health outcomes associated with low health literacy. Although the literature points to variables such as age, educational background and language, only a few studies have investigated the factors that may influence health literacy in this group. Similarly, the role that bilingualism and/or multilingualism play in health literacy assessment continues to be an issue in need of further research. The purpose of this study was to examine the predictors of English health literacy among adult Hispanic immigrants whose self-reported primary language is Spanish, but who live and function in a bilingual community. It also explored issues related to the language of the instrument. An analysis of data collected through a randomized controlled study was conducted. Results identified English proficiency as the strongest predictor of health literacy (p < 0.001). The results further point to the importance of primary and secondary language in the assessment of heath literacy level. This study raises many questions in need of further investigation to clarify how language proficiency and sociolinguistic environment affect health literacy in language minority adults; proposes language approaches that may be more appropriate for measuring health literacy in these populations; and recommends further place-based research to determine whether the connection between language proficiency and health is generalizable to border communities.

Choosing a Cluster Sampling Design for Lot Quality Assurance Sampling Surveys.

Lot quality assurance sampling (LQAS) surveys are commonly used for monitoring and evaluation in resource-limited settings. Recently several methods have been proposed to combine LQAS with cluster sampling for more timely and cost-effective data collection. For some of these methods, the standard binomial model can be used for constructing decision rules as the clustering can be ignored. For other designs, considered here, clustering is accommodated in the design phase. In this paper, we compare these latter cluster LQAS methodologies and provide recommendations for choosing a cluster LQAS design. We compare technical differences in the three methods and determine situations in which the choice of method results in a substantively different design. We consider two different aspects of the methods: the distributional assumptions and the clustering parameterization. Further, we provide software tools for implementing each method and clarify misconceptions about these designs in the literature. We illustrate the differences in these methods using vaccination and nutrition cluster LQAS surveys as example designs. The cluster methods are not sensitive to the distributional assumptions but can result in substantially different designs (sample sizes) depending on the clustering parameterization. However, none of the clustering parameterizations used in the existing methods appears to be consistent with the observed data, and, consequently, choice between the cluster LQAS methods is not straightforward. Further research should attempt to characterize clustering patterns in specific applications and provide suggestions for best-practice cluster LQAS designs on a setting-specific basis.

Ethylglucuronide in maternal hair as a biomarker of prenatal alcohol exposure.

While direct ethanol metabolites, including ethylglucuronide (EtG), play an important role for the confirmation of prenatal alcohol exposure (PAE), their utility is often limited by their short half-lives in blood and urine. Maternal hair allows for a retrospective measure of PAE for up to several months. This study examined the validity of hair EtG (hEtG) relative to self-reporting and five other biomarkers in 85 pregnant women. Patients were recruited from a UNM prenatal clinic, which provides care to women with substance abuse and addiction disorders. The composite index, which was based on self-reported measures of alcohol use and allowed us to classify subjects into PAE (n = 42) and control (n = 43) groups, was the criterion measure used to estimate the sensitivity and specificity of hEtG. Proximal segments of hair were collected at enrollment (average 22.0 gestational weeks) and analyzed by LC-MS/MS. At the same visit, maternal blood and urine specimens were collected for analysis of GGT, %dCDT, PEth, uEtG, and uEtS. The study population included mostly opioid-dependent (80%) patients, a large proportion of ethnic minorities (75.3% Hispanic/Latina, 8.2% American Indian, 4.7% African-American), and patients with low education (48.2% < high school). The mean maternal age at enrollment was 26.7 ± 4.8 years. Hair EtG demonstrated 19% sensitivity and 86% specificity. The sensitivities of other biomarkers were comparable (5-20%) to hEtG but specificities were higher (98-100%). Hair EtG sensitivity improved when combined with other biomarkers, especially with GGT (32.5%) and PEth (27.5%). In addition, validity of hEtG improved in patients with less frequent shampooing and those who did not use hair dyes/chemical treatments. These data suggest that hEtG alone is not a sufficiently sensitive or specific biomarker to be used separately for the identification of PAE, but might be useful in a battery along with other maternal biomarkers.

A novel approach to evaluating the iron and folate status of women of reproductive age in Uzbekistan after 3 years of flour fortification with micronutrients.

BACKGROUND: The Uzbekistan 1996 Demographic Health Survey reported 60.4% of women of reproductive age (WRA) had low hemoglobin concentrations (<120 g/L), and anemia was an important public health problem. Fortification of wheat flour was identified as an appropriate intervention to address anemia due to the ubiquitous consumption of wheat flour. A National Flour Fortification Program (NFFP) was implemented in 2005. METHODOLOGY/PRINCIPAL FINDINGS: After 3-years of the NFFP, a national survey using large country-lot quality assurance sampling was carried out to assess iron, folate, hemoglobin and inflammation status of WRA; the coverage and knowledge of the fortified first grade UzDonMakhsulot (UDM) flour/grey loaf program; and consumption habits of women to investigate the dietary factors associated with anemia. Estimated anemia prevalence was 34.4% (95% CI: 32.0, 36.7), iron depletion 47.5% (95% CI: 45.1, 49.9) and folate deficiency 28.8% (95% CI: 26.8, 30.8); the effect of inflammation was minimal (4% with CRP >5 mg/L). Severe anemia was more prevalent among folate deficient than iron depleted WRA. Presence of UDM first grade flour or the grey loaf was reported in 71.3% of households. Among WRA, 32.1% were aware of UDM fortification; only 3.7% mentioned the benefits of fortification and 12.5% understood causes of anemia. Consumption of heme iron-containing food (91%) and iron absorption enhancers (97%) was high, as was the consumption of iron absorption inhibitors (95%). CONCLUSIONS/SIGNIFICANCE: The NFFP coincided with a substantial decline in the prevalence of anemia. Folate deficiency was a stronger predictor of severe anemia than iron depletion. However, the prevalence of iron depletion was high, suggesting that women are not eating enough iron or iron absorption is inhibited. Fortified products were prevalent throughout Uzbekistan, though UDM flour must be adequately fortified and monitored in the future. Knowledge of fortification and anemia was low, suggesting consumer education should be prioritized.