Prevalence, Incidence, and Reversal Pattern of Childhood Stunting From Birth to Age 2 Years in Ethiopia.

Importance: Although there has been a reduction in stunting (low-height-for-age and low-length-for-age), a proxy of malnutrition, the prevalence of malnutrition in Ethiopia is still high. Child growth patterns and estimates of stunting are needed to increase awareness and resources to improve the potential for recovery. Objective: To estimate the prevalence, incidence, and reversal of stunting among children aged 0 to 24 months. Design, Setting, and Participants: This population-based cohort study of the Birhan Maternal and Child Health cohort in North Shewa Zone, Amhara, Ethiopia, was conducted between December 2018 and November 2020. Eligible participants included children aged 0 to 24 months who were enrolled during the study period and had their length measured at least once. Data analysis occurred from Month Year to Month Year. Main Outcomes and Measures: The primary outcome of this study was stunting, defined as length-for-age z score (LAZ) at least 2 SDs below the mean. Z scores were also used to determine the prevalence, incidence, and reversal of stunting at each key time point. Growth velocity was determined in centimeters per month between key time points and compared with global World Health Organization (WHO) standards for the same time periods. Heterogeneity was addressed by excluding outliers in sensitivity analyses using modeled growth trajectories for each child. Results: A total of 4354 children were enrolled, out of which 3674 (84.4%; 1786 [48.7%] female) had their length measured at least once and were included in this study. The median population-level length was consistently below WHO growth standards from birth to 2 years of age. The observed prevalence of stunting was highest by 2 years of age at 57.4% (95% CI, 54.8%-9 60.0%). Incidence of stunting increased over time and reached 51.0% (95% CI, 45.3%-56.6%) between ages 12 and 24 months. Reversal was 63.5% (95% CI, 54.8%-71.4%) by age 6 months and 45.2% (95% CI, 36.0%-54.8%) by age 2 years. Growth velocity point estimate differences were slowest compared with WHO standards during the neonatal period (-1.4 cm/month for girls and -1.6 cm/month for boys). There was substantial heterogeneity in anthropometric measurements. Conclusions and Relevance: The evidence from this cohort study highlights a chronically malnourished population with much of the burden associated with growth faltering during the neonatal periods as well as after 6 months of age. To end all forms of malnutrition, growth faltering in populations such as that in young children in Amhara, Ethiopia, needs to be addressed.

Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population.

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.

Study Designs for Extending Causal Inferences From a Randomized Trial to a Target Population.

In this article, we examine study designs for extending (generalizing or transporting) causal inferences from a randomized trial to a target population. Specifically, we consider nested trial designs, where randomized individuals are nested within a sample from the target population, and nonnested trial designs, including composite data-set designs, where observations from a randomized trial are combined with those from a separately obtained sample of nonrandomized individuals from the target population. We show that the counterfactual quantities that can be identified in each study design depend on what is known about the probability of sampling nonrandomized individuals. For each study design, we examine identification of counterfactual outcome means via the g-formula and inverse probability weighting. Last, we explore the implications of the sampling properties underlying the designs for the identification and estimation of the probability of trial participation.

Timeliness of follow-up after abnormal screening mammogram: variability of facilities.

PURPOSE: To describe the timeliness of follow-up care in community-based settings among women who receive a recommendation for immediate follow-up during the screening mammography process and how follow-up timeliness varies according to facility and facility-level characteristics. MATERIALS AND METHODS: This was an institutional review board-approved and HIPAA-compliant study. Screening mammograms obtained from 1996 to 2007 in women 40-80 years old in the Breast Cancer Surveillance Consortium were examined. Inclusion criteria were a recommendation for immediate follow-up at screening, or subsequent imaging, and observed follow-up within 180 days of the recommendation. Recommendations for additional imaging (AI) and biopsy or surgical consultation (BSC) were analyzed separately. The distribution of time to follow-up care was estimated by using the Kaplan-Meier estimator. RESULTS: Data were available on 214,897 AI recommendations from 118 facilities and 35,622 BSC recommendations from 101 facilities. The median time to subsequent follow-up care after recommendation was 14 days for AI and 16 days for BSC. Approximately 90% of AI follow-up and 81% of BSC follow-up occurred within 30 days. Facilities with higher recall rates tended to have longer AI follow-up times (P < .001). Over the study period, BSC follow-up rates at 15 and 30 days improved (P < .001). Follow-up times varied substantially across facilities. Timely follow-up was associated with larger volumes of the recommended procedures but not notably associated with facility type nor observed facility-level characteristics. CONCLUSION: Most patients with follow-up returned within 3 weeks of the recommendation.

Influence of annual interpretive volume on screening mammography performance in the United States.

PURPOSE: To examine whether U.S. radiologists' interpretive volume affects their screening mammography performance. MATERIALS AND METHODS: Annual interpretive volume measures (total, screening, diagnostic, and screening focus [ratio of screening to diagnostic mammograms]) were collected for 120 radiologists in the Breast Cancer Surveillance Consortium (BCSC) who interpreted 783 965 screening mammograms from 2002 to 2006. Volume measures in 1 year were examined by using multivariate logistic regression relative to screening sensitivity, false-positive rates, and cancer detection rate the next year. BCSC registries and the Statistical Coordinating Center received institutional review board approval for active or passive consenting processes and a Federal Certificate of Confidentiality and other protections for participating women, physicians, and facilities. All procedures were compliant with the terms of the Health Insurance Portability and Accountability Act. RESULTS: Mean sensitivity was 85.2% (95% confidence interval [CI]: 83.7%, 86.6%) and was significantly lower for radiologists with a greater screening focus (P = .023) but did not significantly differ by total (P = .47), screening (P = .33), or diagnostic (P = .23) volume. The mean false-positive rate was 9.1% (95% CI: 8.1%, 10.1%), with rates significantly higher for radiologists who had the lowest total (P = .008) and screening (P = .015) volumes. Radiologists with low diagnostic volume (P = .004 and P = .008) and a greater screening focus (P = .003 and P = .002) had significantly lower false-positive and cancer detection rates, respectively. Median invasive tumor size and proportion of cancers detected at early stages did not vary by volume. CONCLUSION: Increasing minimum interpretive volume requirements in the United States while adding a minimal requirement for diagnostic interpretation could reduce the number of false-positive work-ups without hindering cancer detection. These results provide detailed associations between mammography volumes and performance for policymakers to consider along with workforce, practice organization, and access issues and radiologist experience when reevaluating requirements.

Association between acute care and critical illness hospitalization and cognitive function in older adults.

CONTEXT: Studies suggest that many survivors of critical illness experience long-term cognitive impairment but have not included premorbid measures of cognitive functioning and have not evaluated risk for dementia associated with critical illness. OBJECTIVES: To determine whether decline in cognitive function was greater among older individuals who experienced acute care or critical illness hospitalizations relative to those not hospitalized and to determine whether the risk for incident dementia differed by these exposures. DESIGN, SETTING, AND PARTICIPANTS: Analysis of data from a prospective cohort study from 1994 through 2007 comprising 2929 individuals 65 years old and older without dementia at baseline residing in the community in the Seattle area and belonging to the Group Health Cooperative. Participants with 2 or more study visits were included, and those who had a hospitalization for a diagnosis of primary brain injury were censored at the time of hospitalization. Individuals were screened with the Cognitive Abilities Screening Instrument (CASI) (score range, 0-100) every 2 years at follow-up visits, and those with a score less than 86 underwent a clinical examination for dementia. MAIN OUTCOME MEASURES: Score on the CASI at follow-up study visits and incident dementia diagnosed in study participants, adjusted for baseline cognitive scores, age, and other risk factors. RESULTS: During a mean (SD) follow-up of 6.1 (3.2) years, 1601 participants had no hospitalization, 1287 had 1 or more noncritical illness hospitalizations, and 41 had 1 or more critical illness hospitalizations. The CASI score was assessed more than 45 days after discharge for 94.3% of participants. Adjusted CASI scores averaged 1.01 points lower for visits following acute care illness hospitalization compared with follow-up visits not following any hospitalization (95% confidence interval [CI], -1.33 to -0.70; P < .001) and 2.14 points lower on average for visits following critical illness hospitalization (95% CI, -4.24 to -0.03; P = .047). There were 146 cases of dementia among those not hospitalized, 228 cases of dementia among those with 1 or more noncritical illness hospitalizations, and 5 cases of dementia among those with 1 or more critical illness hospitalizations. The adjusted hazard ratio for incident dementia was 1.4 following a noncritical illness hospitalization (95% CI, 1.1 to 1.7; P = .001) and 2.3 following a critical illness hospitalization (95% CI, 0.9 to 5.7; P = .09). CONCLUSIONS: Among a cohort of older adults without dementia at baseline, those who experienced acute care hospitalization and critical illness hospitalization had a greater likelihood of cognitive decline compared with those who had no hospitalization. Noncritical illness hospitalization was significantly associated with the development of dementia.

Statistical approaches for modeling radiologists' interpretive performance.

Although much research has been conducted to understand the influence of interpretive volume on radiologists' performance of mammography interpretation, the published literature has been unable to achieve consensus on the volume standards required for optimal mammography accuracy. One potential contributing factor is that studies have used different statistical approaches to address the same underlying scientific question. Such studies have relied on multiple mammography interpretations from a sample of radiologists; thus, an important statistical issue is appropriately accounting for dependence, or correlation, among interpretations made by (or clustered within) the same radiologist. The aim of this review is to increase awareness about differences between statistical approaches used to analyze clustered data. Statistical frameworks commonly used to model binary measures of interpretive performance are reviewed, focusing on two broad classes of regression frameworks: marginal and conditional models. Although both frameworks account for dependence in clustered data, the interpretations of their parameters differ; hence, the choice of statistical framework may (implicitly) dictate the scientific question being addressed. Additional statistical issues that influence estimation and inference are also discussed, together with their potential impact on the scientific interpretation of the analysis. This work was motivated by ongoing research being conducted by the National Cancer Institute's Breast Cancer Surveillance Consortium; however, the ideas are relevant to a broad range of settings in which researchers seek to identify and understand sources of variability in clustered binary outcomes.

On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses.

Statistical experiments, more commonly referred to as Monte Carlo or simulation studies, are used to study the behavior of statistical methods and measures under controlled situations. Whereas recent computing and methodological advances have permitted increased efficiency in the simulation process, known as variance reduction, such experiments remain limited by their finite nature and hence are subject to uncertainty; when a simulation is run more than once, different results are obtained. However, virtually no emphasis has been placed on reporting the uncertainty, referred to here as Monte Carlo error, associated with simulation results in the published literature, or on justifying the number of replications used. These deserve broader consideration. Here we present a series of simple and practical methods for estimating Monte Carlo error as well as determining the number of replications required to achieve a desired level of accuracy. The issues and methods are demonstrated with two simple examples, one evaluating operating characteristics of the maximum likelihood estimator for the parameters in logistic regression and the other in the context of using the bootstrap to obtain 95% confidence intervals. The results suggest that in many settings, Monte Carlo error may be more substantial than traditionally thought.

An assessment of the quality of mammography care at facilities treating medically vulnerable populations.

BACKGROUND: Women in medically vulnerable populations, including racial and ethnic minorities, socioeconomically disadvantaged, and residents of rural areas, experience higher breast cancer mortality than do others. Whether mammography facilities that treat vulnerable women demonstrate lower quality of care than other facilities is unknown. OBJECTIVES: To assess the quality of mammography women receive at facilities characterized as serving a high proportion of medically vulnerable populations. RESEARCH DESIGN: We prospectively collected self-reported breast cancer risk factor information, mammography interpretations, and cancer outcomes on 1,579,929 screening mammography examinations from 750,857 women, aged 40-80 years, attending any of 151 facilities in the Breast Cancer Surveillance Consortium between 1998 and 2004. To classify facilities as serving medically vulnerable populations, we used 4 criteria: educational attainment, racial/ethnic minority, household income, and rural/urban residence. RESULTS: After adjustment for patient-level factors known to affect mammography accuracy, facilities serving vulnerable populations had significantly higher mammography specificity than did other facilities: ie, those serving a higher proportion of women who were minorities [odds ratio (OR): 1.32; 95% confidence interval (CI): 1.01-1.73], living in rural areas (1.45; 1.15-1.73), and with lower household income (1.33; 1.05-1.68). We observed no statistically significant differences between facilities in mammography sensitivity. CONCLUSIONS: Facilities serving high proportions of vulnerable populations provide screening mammography with equal or better quality (as reflected in higher specificity with no corresponding decrease in sensitivity) than other facilities. Further research is needed to understand the mechanisms underlying these findings.

Overcoming ecologic bias using the two-phase study design.

Ecologic (aggregate) data are widely available and widely utilized in epidemiologic studies. However, ecologic bias, which arises because aggregate data cannot characterize within-group variability in exposure and confounder variables, can only be removed by supplementing ecologic data with individual-level data. Here the authors describe the two-phase study design as a framework for achieving this objective. In phase 1, outcomes are stratified by any combination of area, confounders, and error-prone (or discretized) versions of exposures of interest. Phase 2 data, sampled within each phase 1 stratum, provide accurate measures of exposure and possibly of additional confounders. The phase 1 aggregate-level data provide a high level of statistical power and a cross-classification by which individuals may be efficiently sampled in phase 2. The phase 2 individual-level data then provide a control for ecologic bias by characterizing the within-area variability in exposures and confounders. In this paper, the authors illustrate the two-phase study design by estimating the association between infant mortality and birth weight in several regions of North Carolina for 2000-2004, controlling for gender and race. This example shows that the two-phase design removes ecologic bias and produces gains in efficiency over the use of case-control data alone. The authors discuss the advantages and disadvantages of the approach.

The separation of timescales in Bayesian survival modeling of the time-varying effect of a time-dependent exposure.

In this paper, we apply flexible Bayesian survival analysis methods to investigate the risk of lymphoma associated with kidney transplantation among patients with end-stage renal disease. Of key interest is the potentially time-varying effect of a time-dependent exposure: transplant status. Bayesian modeling of the baseline hazard and the effect of transplant requires consideration of 2 timescales: time since study start and time since transplantation, respectively. Previous related work has not dealt with the separation of multiple timescales. Using a hierarchical model for the hazard function, both timescales are incorporated via conditionally independent stochastic processes; smoothing of each process is specified via intrinsic conditional Gaussian autoregressions. Features of the corresponding posterior distribution are evaluated from draws obtained via a Metropolis-Hastings-Green algorithm.

The Combination of Ecological and Case-Control Data.

Ecological studies, in which data are available at the level of the group, rather than at the level of the individual, are susceptible to a range of biases due to their inability to characterize within-group variability in exposures and confounders. In order to overcome these biases, we propose a hybrid design in which ecological data are supplemented with a sample of individual-level case-control data. We develop the likelihood for this design and illustrate its benefits via simulation, both in bias reduction when compared to an ecological study, and in efficiency gains relative to a conventional case-control study. An interesting special case of the proposed design is the situation where ecological data are supplemented with case-only data. The design is illustrated using a dataset of county-specific lung cancer mortality rates in the state of Ohio from 1988.

Hierarchical models for combining ecological and case-control data.

The ecological study design suffers from a broad range of biases that result from the loss of information regarding the joint distribution of individual-level outcomes, exposures, and confounders. The consequent nonidentifiability of individual-level models cannot be overcome without additional information; we combine ecological data with a sample of individual-level case-control data. The focus of this article is hierarchical models to account for between-group heterogeneity. Estimation and inference pose serious computational challenges. We present a Bayesian implementation based on a data augmentation scheme where the unobserved data are treated as auxiliary variables. The methods are illustrated with a dataset of county-specific infant mortality data from the state of North Carolina.

Sensitivity analyses for unmeasured confounding assuming a marginal structural model for repeated measures.

Robins introduced marginal structural models (MSMs) and inverse probability of treatment weighted (IPTW) estimators for the causal effect of a time-varying treatment on the mean of repeated measures. We investigate the sensitivity of IPTW estimators to unmeasured confounding. We examine a new framework for sensitivity analyses based on a nonidentifiable model that quantifies unmeasured confounding in terms of a sensitivity parameter and a user-specified function. We present augmented IPTW estimators of MSM parameters and prove their consistency for the causal effect of an MSM, assuming a correct confounding bias function for unmeasured confounding. We apply the methods to assess sensitivity of the analysis of Hernán et al., who used an MSM to estimate the causal effect of zidovudine therapy on repeated CD4 counts among HIV-infected men in the Multicenter AIDS Cohort Study. Under the assumption of no unmeasured confounders, a 95 per cent confidence interval for the treatment effect includes zero. We show that under the assumption of a moderate amount of unmeasured confounding, a 95 per cent confidence interval for the treatment effect no longer includes zero. Thus, the analysis of Hernán et al. is somewhat sensitive to unmeasured confounding. We hope that our research will encourage and facilitate analyses of sensitivity to unmeasured confounding in other applications.