Lauren Hund

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.

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

BackgroundTraditional 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.ResultsTo 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.ConclusionsWe 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.

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.

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.

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.

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.

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.

Collaborative information exchange using elicit-provide-elicit to reduce risky drinking among college students

ABSTRACT Background: Collaborative information exchange using the elicit-provide-elicit (EPE) technique is a recommended health communication strategy, though little empirical work has evaluated its merit. Methods: This study evaluated the impact of EPE on risky drinking knowledge, attitudes, and behavior. Undergraduate drinkers (n = 150) were randomized to receive information regarding risky drinking limits via collaborative exchange (EPE), provision only, or to a no information control group. Changes in drinking behavior from baseline to 2-week follow-up were assessed. In addition, knowledge of and attitudes towards drinking limits were measured at follow-up. Results: Drinks per drinking day, percent days abstinent, and peak consumption did not improve from baseline to follow-up in any group. At follow-up, the proportion of participants who exceeded daily drinking cutoffs significantly decreased in the EPE and provision-only conditions, but increased among control participants. EPE and provision participants were more likely to accurately recall drinking limits at follow-up, though only about half of these participants correctly recalled limits and knowledge was not associated with risky drinking behavior. Conclusions: Providing information regarding safe drinking limits may impact knowledge and behavior among this population; however, collaborative exchange via EPE as measured in the present study does not appear to provide added benefit when compared with non-collaborative information provision.

Role of concurrency in generalised HIV epidemics – Authors' reply

In response to our population-based study in which we followed up more than 7000 HIV-negative women over 5 years and failed to find evidence that concurrent sexual partnerships are an important driver of HIV incidence, James Shelton, Martina Morris, and Helen Epstein cite studies on HIV incidence in stable concordant HIV-negative partnerships as “direct evidence” for the concurrency hypothesis. However, these studies do not test the concurrency hypothesis (that concurrent sexual partnerships increase the rate of spread of HIV in a population) because they lack a meaningful counterfactual—ie, HIV incidence in people with the same total number of partners over the observation period but in serially monogamous partnerships. Furthermore, Morris and Epsteins claim that the concurrency hypothesis has already been tested and shown to hold runs counter to ongoing public debates on the topic,1, 2 other empirical data,3, 4, 5 and the results of a recent systematic review.6 The claim is also inconsistent with Morris and Epsteins call for a randomised controlled trial to test the hypothesis, which would not be ethically permissible if it had indeed already been shown that the hypothesis held true.