Improved generalized estimating equations for incomplete longitudinal binary data, covariance estimation in small samples, and ordinal data

The focus of this research is to improve existing methods for the marginal modeling of associated categorical outcomes. Generalized estimating equations, based on quasi-likelihood, is in wide use to make inference on marginal mean parameters, especially for categorical data. In the case that response data are not all observed, generalized estimating equations give inconsistent parameter estimates when missingness depends on observed or unobserved outcomes. Inverse-probability weighted generalized estimating equations give valid results if missingness depends only on observed outcomes, and a missingness model is correctly specified. For our first topic we propose specific forms of semi-parametric efficient estimators in marginal models when dropouts for longitudinal binary data are missing at random. The efficiency of inverse-probability weighted generalized estimating equations is also explored in this setting. The other specific topics of concern in this research are related to extensions of generalized estimating equations that allow for modeling associations between categorical outcomes. Although associations are often considered nuisances, it is not uncommon that they are scientifically relevant. It may be of interest in this case to model associations on covariates defined by characteristics of clusters or outcome pairs. Alternating logistic regressions model marginal means of correlated binary outcomes while simultaneously allowing for an association model that parameterizes the odds ratio for outcome pairs. Our second topic concerns point and variance estimation of association parameters for finite samples. Bias adjustments in estimating outcome variance have recently been introduced for small samples in generalized estimating equations. We propose an extension of these adjustments to odds ratio parameters in alternating logistic regressions. The remaining topic of our research concerns generalized estimating equations for ordinal data, for which alternating logistic regressions has recently been adapted. An alternate formulation of alternating logistic regressions based on orthogonalized residuals has been introduced for binary data resolving some problems in the existing procedure, including lack of invariance of the variance estimator to observation order. In our final topic we define this alternate formulation of alternating logistic regressions for correlated ordinal data, and examine its efficiency with regards to estimating within-cluster association parameters

Diarrhea as a risk factor for acute lower respiratory tract infections among young children in low income settings

Diarrhea and acute lower respiratory tract infections (ALRI) are leading causes of morbidity and mortality among children under 5 years of age. We sought to quantify the correlation of diarrhea and respiratory infections within an individual child and to determine if infection with one illness increases the risk of infection with the other during the same time period

Does comorbidity increase the risk of mortality among children under 3 years of age?

Objectives Diarrhoea and pneumonia remain leading causes of morbidity and mortality in children under 5 years of age. Little data is available to quantify the burden of comorbidity and the relationship between comorbid diarrhoea and pneumonia infections and mortality. We sought to quantify the relationship between comorbidity and risk of mortality among young children in two community-based studies conducted among South Asian children. Design Secondary data analysis of two cohort studies. Participants We identified two cohort studies of children under 3 years of age with prospective morbidity at least once every 2 weeks and ongoing mortality surveillance. Outcome measures We calculated the mortality risk for diarrhoea and acute lower respiratory infection (ALRI) episodes and further quantified the risk of mortality when both diseases occur at the same time using a semiparametric additive model. Results Among Nepali children, the estimated additional risk of mortality for comorbid diarrhoea and ALRI was 0.0014 (−0.0033, 0.0060). Among South Indian children, the estimated additional risk of mortality for comorbid diarrhoea and ALRI was 0.0032 (−0.0098, 0.0162). This risk is in addition to the single infection risk of mortality observed among these children. Conclusions We observed an additional risk of mortality in children who experienced simultaneous diarrhoea and ALRI episodes though the CI was wide indicating low statistical support. Additional studies with adequate power to detect the increased risk of comorbidity on mortality are needed to improve confidence around the effect size estimate

Deletion Diagnostics for Alternating Logistic Regressions

Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the intracluster association given by the log odds ratio. The diagnostics are developed within an estimating equations framework that recasts the estimating functions for association parameters based upon conditional residuals into equivalent functions based upon marginal residuals. Extensions of earlier work on GEE diagnostics follow directly, including computational formulae for one-step deletion diagnostics that measure the influence of a cluster of observations on the estimated regression parameters and on the overall marginal mean or association model fit. The diagnostic formulae are evaluated with simulations studies and with an application concerning an assessment of factors associated with health maintenance visits in primary care medical practices. The application and the simulations demonstrate that the proposed cluster-deletion diagnostics for alternating logistic regressions are good approximations of their exact fully iterated counterparts

Global, regional, and national causes of under-5 mortality in 2000-15: an updated systematic analysis with implications for the Sustainable Development Goals.

BACKGROUND: Despite remarkable progress in the improvement of child survival between 1990 and 2015, the Millennium Development Goal (MDG) 4 target of a two-thirds reduction of under-5 mortality rate (U5MR) was not achieved globally. In this paper, we updated our annual estimates of child mortality by cause to 2000-15 to reflect on progress toward the MDG 4 and consider implications for the Sustainable Development Goals (SDG) target for child survival. METHODS: We increased the estimation input data for causes of deaths by 43% among neonates and 23% among 1-59-month-olds, respectively. We used adequate vital registration (VR) data where available, and modelled cause-specific mortality fractions applying multinomial logistic regressions using adequate VR for low U5MR countries and verbal autopsy data for high U5MR countries. We updated the estimation to use Plasmodium falciparum parasite rate in place of malaria index in the modelling of malaria deaths; to use adjusted empirical estimates instead of modelled estimates for China; and to consider the effects of pneumococcal conjugate vaccine and rotavirus vaccine in the estimation. FINDINGS: In 2015, among the 5·9 million under-5 deaths, 2·7 million occurred in the neonatal period. The leading under-5 causes were preterm birth complications (1·055 million [95% uncertainty range (UR) 0·935-1·179]), pneumonia (0·921 million [0·812 -1·117]), and intrapartum-related events (0·691 million [0·598 -0·778]). In the two MDG regions with the most under-5 deaths, the leading cause was pneumonia in sub-Saharan Africa and preterm birth complications in southern Asia. Reductions in mortality rates for pneumonia, diarrhoea, neonatal intrapartum-related events, malaria, and measles were responsible for 61% of the total reduction of 35 per 1000 livebirths in U5MR in 2000-15. Stratified by U5MR, pneumonia was the leading cause in countries with very high U5MR. Preterm birth complications and pneumonia were both important in high, medium high, and medium child mortality countries; whereas congenital abnormalities was the most important cause in countries with low and very low U5MR. INTERPRETATION: In the SDG era, countries are advised to prioritise child survival policy and programmes based on their child cause-of-death composition. Continued and enhanced efforts to scale up proven life-saving interventions are needed to achieve the SDG child survival target. FUNDING: Bill & Melinda Gates Foundation, WHO

An effectiveness-implementation trial protocol to evaluate PrEP initiation among U.S. cisgender women using eHealth tools vs. standard care

BackgroundThe United States' (U.S.) initiative to End the HIV Epidemic aims to reduce new HIV infections in areas of high HIV prevalence. Despite national efforts to reduce HIV incidence, cisgender women continue to represent approximately one out of every five new HIV diagnoses in the U.S. Taking pre-exposure prophylaxis (PrEP) is an effective HIV prevention strategy; however, PrEP initiation among cisgender women is suboptimal, with only 10% of eligible women receiving PrEP prescriptions in 2019.MethodsWe designed a trial to test the effectiveness of interventions to increase PrEP initiation, while evaluating the implementation strategy (hybrid type II trial) in seven obstetrics and gynecology (OB/GYN) clinics (two federally qualified health centers, three community-based, and two academic) in Baltimore, Maryland. A total of 42 OB/GYN providers will be enrolled and randomized (1:1:1) into one of three clinical trial arms (standard of care, patient-level intervention, or multi-level intervention). Eligible patients of enrolled providers will receive a sexual health questionnaire before their appointment through the electronic health record’s (EHR) patient portal. The questionnaire will be scored in three tiers (low, moderate, and high) to assess HIV risk. Patients at low risk will be offered an HIV test only, while those who score medium or high risk will be included in the clinical trial and assigned to the clinical trial arm associated with their provider. Differences in PrEP initiation, our primary outcome, across the three arms will be analyzed using generalized linear mixed-effect models with logistic regression. We will adjust results for demographic differences observed between arms and examine PrEP initiation stratified by patient’s and provider’s race and ethnicity.Additionally, a comprehensive economic analysis for each intervention will be conducted.DiscussionWe hypothesize that gathering information on sensitive sexual behaviors electronically, communicating HIV risk in an understandable and relatable format to patients and OB/GYN providers, and deploying EHR alerts will increase PrEP initiation and HIV testing.Trial RegistrationThe trial is registered with ClinicalTrials.gov (NCT05412433) on 09 June 2022. https://clinicaltrials.gov/ct2/show/NCT05412433?term=NCT05412433&draw=2&rank=1

Anxiety-focused cognitive behavioral therapy delivered by non-specialists to prevent postnatal depression: a randomized, phase 3 trial.

Anxiety experienced by women during pregnancy is highly prevalent, especially in resource-poor settings and strongly predicts postnatal common mental disorders (CMDs), anxiety and depression. We evaluated the effectiveness of an anxiety-focused early prenatal intervention on preventing postnatal CMDs. This study was a phase 3, two-arm, single-blind, randomized controlled trial conducted in Pakistan with women who were ≤22 weeks pregnant and had at least mild anxiety without clinical depression. Participants were randomized to the Happy Mother-Healthy Baby program, based on cognitive behavioral therapy, consisting of six one-on-one intervention sessions in pregnancy delivered by non-specialist providers, or to enhanced care alone. The primary outcome was major depression, generalized anxiety disorder or both at 6 weeks after delivery. Overall, 755 women completed postnatal assessments (380 (50.3%), intervention arm; 375 (49.7%) enhanced-care arm). The primary outcomes were met. Examined jointly, we found 81% reduced odds of having either a major depressive episode (MDE) or moderate-to-severe anxiety for women randomized to the intervention (adjusted odds ratio (aOR) = 0.19, 95% CI 0.14-0.28). Overall, 12% of women in the intervention group developed MDE at 6 weeks postpartum, versus 41% in the control group. We found reductions of 81% and 74% in the odds of postnatal MDE (aOR = 0.19, 95% CI 0.13-0.28) and of moderate-to-severe anxiety (aOR = 0.26, 95% CI 0.17-0.40), respectively. The Happy Mother-Healthy Baby program early prenatal intervention focusing on anxiety symptoms reduced postpartum CMDs. ClinicalTrials.gov identifier NCT03880032

Modelling stunting in LiST: the effect of applying smoothing to linear growth data

Background: The Lives Saved Tool (LiST) is a widely used resource for evidence-based decision-making regarding health program scale-up in low- and middle-income countries. LiST estimates the impact of specified changes in intervention coverage on mortality and stunting among children under 5 years of age. We aimed to improve the estimates of the parameters in LiST that determine the rate at which the effects of interventions to prevent stunting attenuate as children get older.Methods: We identified datasets with serial measurements of children\u27s lengths or heights and used random effects models and restricted cubic splines to model the growth trajectories of children with at least six serial length/height measurements. We applied WHO growth standards to both measured and modelled (smoothed) lengths/heights to determine children\u27s stunting status at multiple ages (1, 6, 12, 24 months). We then calculated the odds ratios for the association of stunting at one age point with stunting at the next ( stunting-to-stunting ORs ) using both measured and smoothed data points. We ran analyses in LiST to compare the impact on intervention effect attenuation of using smoothed rather than measured stunting-to-stunting ORs.Results: A total of 21,786 children with 178,786 length/height measurements between them contributed to our analysis. The odds of stunting at a given age were strongly related to whether a child is stunted at an earlier age, using both measured and smoothed lengths/heights, although the relationship was stronger for smoothed than measured lengths/heights. Using smoothed lengths/heights, we estimated that children stunted at 1 month have 45 times the odds of being stunted at 6 months, with corresponding odds ratios of 362 for the period 6 to 12 months and 175 for the period 12 to 24 months. Using the odds ratios derived from the smoothed data in LiST resulted in a somewhat slower attenuation of intervention effects over time, but substantial attenuation was still observed in the LiST outputs. For example, in Mali the effect of effectively eliminating SGA births reduced prevalence of stunting at age 59 months from 44.4% to 43.7% when using odds ratios derived from measured lengths/heights and from 44.4% to 41.9% when using odds ratios derived from smoothed lengths/heights.Conclusions: Smoothing of children\u27s measured lengths/heights increased the strength of the association between stunting at a given age and stunting at an earlier age. Using odds ratios based on smoothed lengths/heights in LiST resulted in a small reduction in the attenuation of intervention effects with age and thus some increase in the estimated benefits, and may better reflect the true benefits of early nutritional interventions