Inverse Propensity Score Weighting with a Latent Class Exposure: Estimating the Causal Effect of Reported Reasons for Alcohol Use on Problem Alcohol Use 16 Years Later

Latent class analysis (LCA) has proven to be a useful tool for identifying qualitatively different population subgroups who may be at varying levels of risk for negative outcomes. Recent methodological work has improved techniques for linking latent class membership to distal outcomes; however, these techniques do not adjust for potential confounding variables that may provide alternative explanations for observed relations. Inverse propensity score weighting provides a way to account for many confounders simultaneously, thereby strengthening causal inference of the effects of predictors on outcomes. Although propensity score weighting has been adapted to LCA with covariates, there has been limited work adapting it to LCA with distal outcomes. The current study proposes a step-by-step approach for using inverse propensity score weighting together with the "Bolck, Croon, and Hagenaars" approach to LCA with distal outcomes (i.e., the BCH approach), in order to estimate the causal effects of reasons for alcohol use latent class membership during the year after high school (at age 19) on later problem alcohol use (at age 35) with data from the longitudinal sample in the Monitoring the Future study. A supplementary appendix provides evidence for the accuracy of the proposed approach via a small-scale simulation study, as well as sample programming code to conduct the step-by-step approach.

The burden of high workload on the health-related quality of life among home care workers in Northern Sweden

Objective

Previous studies have shown that high workload affects health negatively. However, studies are lacking among home care workers. The aim of this study is to examine the burden of perceived workload on health-related quality of life (HRQoL) among home care workers and to determine whether psychosocial factors modify such a relationship.

Methods

A cross-sectional study was conducted in which 1162 (58% response rate) home care workers participated. The psychosocial factors were measured by QPSnordic. HRQoL was measured by EuroQol 5 dimensions, from which responses were translated into quality-adjusted life year scores (QALY). Propensity scores were used with absolute risk differences (RD). Stratified analysis was used to test the buffer hypothesis of the demand–control–support model.

Results

Personnel with a high workload had a statistically significant 0.035 lower QALY than personnel with a normal workload. This difference was also statistically significant for the Visual Analogue Scale (RD 5.0) and the mobility (RD 0.033) and anxiety/depression scales (RD 0.20) dimensions of EQ-5D. For QALY, the effect of a high workload compared to a normal workload was higher, with low (RD 0.045, significant) compared with high (RD 0.015, non-significant) social support; while it was similar, and non-significant results, for low and high control.

Conclusions

Our study shows that lowered work burden would be beneficial for home care personnel. Furthermore, our results suggest that interventions aimed at increasing social support could reduce work-related illness.

School suspension predicts trichomoniasis five years later in a matched sample

BACKGROUND

Young adults who were suspended from school during adolescence are more likely than matched non-suspended youth to be arrested, on probation, or not graduate from high school, which are STI risk factors. This study evaluates whether suspension is a marker for STI risk among young adults who avoid subsequent negative effects.

METHODS

This study evaluated whether suspension predicts a positive test for chlamydia, gonorrhea, or trichomoniasis in a urine sample using matched sampling in the National Longitudinal Study of Adolescent and Adult Health (Add Health), and evaluated potential mediators between suspension and STI status using causal mediation analysis. We used Mahalanobis and exact matched sampling within propensity score calipers to compare 381 youth suspended for the first time in a 1-year period with 980 non-suspended youth. The suspended and non-suspended youth were similar on 67 pre-suspension variables. We evaluated STI outcomes 5 years after suspension.

RESULTS

Before matching, suspended youth were more likely to test positive for trichomoniasis and gonorrhea, but not chlamydia, than non-suspended youth. Suspended youth were more likely to test positive for trichomoniasis 5 years after suspension than matched non-suspended youth (OR = 2.87 (1.40, 5.99)). Below-median household income before suspension explained 9% of the suspension-trichomoniasis association (p = 0.02), but criminal justice involvement and educational attainment were not statistically significantly mediators.

CONCLUSIONS

School suspension is a marker for STI risk. Punishing adolescents for initial deviance may cause them to associate with riskier sexual networks even if they graduate high school and avoid criminal justice system involvement. Suspension may compound disadvantages for youth from below-median-income families, who have fewer resources for recovering from setbacks.

Recruiting Control Participants into Stroke Biomarker Studies

The number of scientists using -omics technologies to investigate biomarkers with the potential to gauge risk and aid in the diagnosis, treatment, and prognosis of stroke continues to rise, yet there are few resources to aid investigators in recruiting control participants. In this review, we describe two major strategies to match control participants to a stroke cohort-propensity score matching and one-to-one matching-including statistical approaches to gauge the balance between groups. We then explore the advantages and disadvantages of traditional recruitment methods including approaching spouses of enrolled stroke participants, direct recruitment from clinics, community outreach events, approaching retirement communities, and buying samples from a 3rd party vendor. Newer methods to identify controls by screening the electronic health record and using an online screening questionnaire are also described. Finally, we cover compensation for control participants and special considerations. The hope is that this review will serve as a roadmap whereby an investigator can successfully tailor their control recruitment strategy to the research question at hand and the local research environment. While this review is focused on blood-based biomarker studies, the principles will apply to investigators studying a broad range of biological materials.

Assessing causal treatment effect estimation when using large observational datasets

Background

Recently, there has been a heightened interest in developing and evaluating different methods for analysing observational data. This has been driven by the increased availability of large data resources such as Electronic Health Record (EHR) data alongside known limitations and changing characteristics of randomised controlled trials (RCTs). A wide range of methods are available for analysing observational data. However, various, sometimes strict, and often unverifiable assumptions must be made in order for the resulting effect estimates to have a causal interpretation. In this paper we will compare some common approaches to estimating treatment effects from observational data in order to highlight the importance of considering, and justifying, the relevant assumptions prior to conducting an observational analysis.

Methods

A simulation study was conducted based upon a small cohort of patients with chronic obstructive pulmonary disease. Two-stage least squares instrumental variables, propensity score, and linear regression models were compared under a range of different scenarios including different strengths of instrumental variable and unmeasured confounding. The effects of violating the assumptions of the instrumental variables analysis were also assessed. Sample sizes of up to 200,000 patients were considered.

Results

Two-stage least squares instrumental variable methods can yield unbiased treatment effect estimates in the presence of unmeasured confounding provided the sample size is sufficiently large. Adjusting for measured covariates in the analysis reduces the variability in the two-stage least squares estimates. In the simulation study, propensity score methods produced very similar results to linear regression for all scenarios. A weak instrument or strong unmeasured confounding led to an increase in uncertainty in the two-stage least squares instrumental variable effect estimates. A violation of the instrumental variable assumptions led to bias in the two-stage least squares effect estimates. Indeed, these were sometimes even more biased than those from a naïve linear regression model.

Conclusions

Instrumental variable methods can perform better than naïve regression and propensity scores. However, the assumptions need to be carefully considered and justified prior to conducting an analysis or performance may be worse than if the problem of unmeasured confounding had been ignored altogether.

Does height catch-up in schoolchildren have an effect on bone mass in adolescents? Analysis in a Brazilian birth cohort

OBJECTIVE

To analyze the effect of height catch-up at school age on the bone mass of adolescents of a Brazilian birth cohort.

METHODS

A cohort study using data obtained from the three moments (birth, 7/9 years and 18/19 years of age) of the Cohorts-RPS study. Height catch-up was defined based on the difference between the schoolchild's z-score and birth z-score. The adolescents' bone mineral mass was analyzed using the z-score index for the lumbar spine measured by dual emission X-ray absorptiometry. A theoretical model was developed for the proposed analysis using directed acyclic graphs paired through the nearest-neighbor matching propensity score using the STATA software, version 14.0. The level of significance was set at 5%.

RESULTS

Of the 297 studied children, 24.5% achieved height catch-up. The bone mass below the expected for age was observed in 5.39% of the subjects. The mean lumbar spine z-score was -0.34 (±1.01). After the adjustment, no effect was observed between height catch-up at school age and bone mass in adolescents (Coeff=0.598; 95% CI -0.117 to 1.313; p=0.101).

CONCLUSION

Even using the directed acyclic graphs and the causal inference method by adjusting the propensity score, the height catch-up did not seem to affect bone mass in adolescents, a result perhaps related to the sample size.

Does unemployment contribute to poorer health-related quality of life among Swedish adults?

Background

Previous studies have shown that unemployment has negative impacts on various aspects of health. However, little is known about the effect of unemployment on health-related quality of life. Our aim was to examine how unemployment impacts upon health-related quality of life among Swedish adults, and to investigate these effects on population subgroups defined by education level, marital status, previous health, and gender.

Methods

As part of a cross-sectional study, a questionnaire was sent to 2500 randomly selected individuals aged 20 to 64 years living in Sweden in 2016. The questionnaire included the EuroQol 5 dimensions (EQ-5D) instrument and was answered by 967 individuals (39%). Quality-adjusted life year (QALY) scores were derived from the EQ-5D responses. Of the respondents, 113 were unemployed and 724 were employed. We used inverse probability-weighted propensity scores in our analyses to estimate a risk difference. Gender, age, education level, marital status, and previous health were used as covariates in our analyses.

Results

There was a statistically significant lower QALY score by 0.096 points for the unemployed compared to the employed. There were also statistically significant more problems due to unemployment for usual activities (6.6% more), anxiety/depression (23.6% more), and EQ-5D’s Visual Analogue Scale (7.5 point lower score). Grouped analyses indicated a larger negative health effect from becoming unemployed for men, those who are married, and young individuals.

Conclusions

In our study, we show that the health deterioration from unemployment is likely to be large, as our estimated effect implies an almost 10% worse health (in absolute terms) from being unemployed compared to being employed. This further highlights that unemployment is a public health problem that needs more focus. Our study also raises further demands for determining for whom unemployment has the most negative effects and thus suggesting groups of individuals who are in greatest need for labor market measures.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-6825-y) contains supplementary material, which is available to authorized users.

Different statistical techniques dealing with confounding in observational research: measuring the effect of breast-conserving therapy and mastectomy on survival

PURPOSE

Propensity trimming, hierarchical modelling and instrumental variable (IV) analysis are statistical techniques dealing with confounding, cluster-related variation or confounding by severity. This study aimed to explain (dis)advantages of these techniques in estimating the effect of breast-conserving therapy (BCT) and mastectomy on 10-year distant metastasis-free survival (DMFS).

METHODS

All women diagnosed in 2005 with primary T1-2N0-1 breast cancer treated with BCT or mastectomy were selected from the Netherlands Cancer Registry. We used multivariable Cox regression to correct for confounding. Propensity trimming was used to create a more homogeneous population for which the treatment choice was not self-evident. Hospital of surgery was used as hierarchical level to handle hospital-related variation, and as IV to deal with unmeasured confounding.

RESULTS

Multivariable Cox regression showed higher 10-year DMFS for BCT than mastectomy [HR 0.70 (95% CI 0.60-82)]. Propensity trimming on the 10-90th and the 20-80th percentile of the propensity score distribution and hierarchical modelling showed similar HRs. IV analysis showed no significant difference between BCT and mastectomy.

CONCLUSION

Unmeasured confounding is very difficult to eliminate in observational research. We cannot conclude that BCT or mastectomy has a causal relationship with 10-year DMFS. It is crucial to critically evaluate all model's assumptions, and to be careful in drawing firm conclusions.

An overview of commonly used statistical methods in clinical research

Statistics plays an essential role in clinical research by providing a framework for making inferences about a population of interest. In order to interpret research datasets, clinicians involved in clinical research should have an understanding of statistical methodology. This article provides a brief overview of statistical methods that are frequently used in clinical research studies. Descriptive and inferential methods, including regression modeling and propensity scores, are discussed, with focus on the rationale, assumptions, strengths, and limitations to their application.

Improved Survival of Hepatocellular Carcinoma Patients Diagnosed with a Dedicated Screening Programme-a Propensity Score Adjusted Analysis

AIM

To assess the overall survival (OS) in those with hepatocellular carcinoma (HCC) diagnosed within a programmatic, centrally co-ordinated, regional screening programme.

METHODS

A retrospective cohort analysis of consecutive HCC patients diagnosed between 2004 and 2013. Patients were followed up till death or end of study period (30 April 2015). A dedicated screening programme was commenced in 2009 to screen high-risk patients for HCC. Primary objective is to compare the OS between HCC patients diagnosed within the screening group versus those diagnosed outside this group. Other objectives were to compare tumour stage at diagnosis and the proportion having curative treatments in the two groups. Propensity score adjustments were performed to assess the survival benefit.

RESULTS

HCC was diagnosed in 130 subjects during the study period (82.3% males, median [IQR] age 62 [± 19] years and median [IQR] follow-up of 11.3 (± 23.5) months). Ninety-six patients (73.8%) died during the follow-up, and the median (95%CI) OS was 15.7 (9.7-21.8) months. HCC diagnosed within the screening programme had a better OS compared to those diagnosed outside this programme (26.8 vs 11.5 months, p = 0.01). Further, those diagnosed within the programme had an earlier stage HCC ([58.3% vs 23.6%], Ӽ² = 11.3, p = 0.001), and a significant proportion were treated with curative intent ([62.5% vs 31.1%], Ӽ² = 8.3, p = 0.004). Propensity score adjustment showed a 58% reduction in mortality for HCC diagnosed within the screening programme (HR [95%CI] 0.42 [0.20-0.89], p = 0.02).

CONCLUSION

A programmatic, regional HCC screening programme improved the OS and detected tumours at an earlier stage enabling more patients to have curative therapies.

Propensity scores with misclassified treatment assignment: a likelihood-based adjustment

Propensity score methods are widely used in comparative effectiveness research using claims data. In this context, the inaccuracy of procedural or billing codes in claims data frequently misclassifies patients into treatment groups, that is, the treatment assignment ($T$) is often measured with error. In the context of a validation data where treatment assignment is accurate, we show that misclassification of treatment assignment can impact three distinct stages of a propensity score analysis: (i) propensity score estimation; (ii) propensity score implementation; and (iii) outcome analysis conducted conditional on the estimated propensity score and its implementation. We examine how the error in $T$ impacts each stage in the context of three common propensity score implementations: subclassification, matching, and inverse probability of treatment weighting (IPTW). Using validation data, we propose a two-step likelihood-based approach which fully adjusts for treatment misclassification bias under subclassification. This approach relies on two common measurement error-assumptions; non-differential measurement error and transportability of the measurement error model. We use simulation studies to assess the performance of the adjustment under subclassification, and also investigate the method's performance under matching or IPTW. We apply the methods to Medicare Part A hospital claims data to estimate the effect of resection versus biopsy on 1-year mortality among $10\,284$ Medicare beneficiaries diagnosed with brain tumors. The ICD9 billing codes from Medicare Part A inaccurately reflect surgical treatment, but SEER-Medicare validation data are available with more accurate information.

Choice of time period to identify confounders for propensity score matching, affected the estimate: a retrospective cohort study of drug effectiveness in asthmatic children

OBJECTIVES

To control for confounding by indication in comparative (drug) effectiveness studies, propensity score (PS) methods may be used. Since childhood diseases or outcomes often present as acute events, we compared the effect of using different look-back periods in electronic health-care data, to construct PSs. This was applied in our research on the effect of a combination of inhaled corticosteroids/long-acting beta-2 agonists (ICS + LABA), either as fixed combination or used as loose combination (2 separate inhaler devices) in the prevention of severe asthma exacerbations.

METHODS

We created a cohort of children (5-17 years) diagnosed with asthma from the Dutch Integrated Primary Care information database. Within this cohort, we identified new users of ICS + LABA, either as fixed combination or loose combination (2 separate inhaler devices). The outcome of interest was severe asthma exacerbations. PSs for type of treatment were created using comorbidity and drug use history in different time windows: 1 week, 1 month, 3 months, 1 year, and full history prior to the start of treatment. PSs were used for matching subjects in both exposure groups. Time to first asthma exacerbation was analyzed with Cox proportional hazard regression. The results were compared with published clinical trials.

RESULTS

Of 39,682 asthmatic children, 3,500 (8.8%) were new users of either ICS + LABA fixed (3,324 [95.0%]) or loose (176 [5.0%]). The crude hazard ratio (HR) for a severe asthma exacerbation, comparing ICS + LABA fixed to loose was 0.37 (95% confidence interval [CI]: 0.20-0.66). PS-matched HRs (1 week, 1 month, 3 month, 1 year, and full history) were 0.48 (95% CI: 0.22-1.04); 0.60 (95% CI: 0.26-1.38), 0.69 (95% CI: 0.31-1.57), 0.56 (CI: 0.25-1.24), and 0.58 (CI: 0.24-1.36), respectively.

CONCLUSIONS

PS matching can be used to control for confounding in pediatric comparative (drug) effectiveness studies, the impact of different look-back periods to implement the PS is important. Controlling for confounders occurring in the 3 months preceding drug exposure may yield results comparable to clinical trial results.

A Bayesian nonparametric approach to causal inference on quantiles

We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records.

Propensity scores - A brief introduction for resuscitation researchers

Propensity scores are commonly used in observational research. This article provides a brief introduction to propensity scores aimed for resuscitation researchers. We discuss the concept and calculation of the propensity score and how it can be used to adjust for confounding via regression adjustment, stratification, weighting or matching. The interpretation of these method is briefly discussed and the advantages and limitations of propensity scores are presented. Lastly, we provide some practical recommendations for the presentation of studies using propensity scores.

Comparison of surgical outcomes between open and robot-assisted minimally invasive pancreaticoduodenectomy

BACKGROUND

Robot surgery is a new method that maintains advantages and overcomes disadvantages of conventional methods, even in pancreatic surgery. This study aimed to evaluate safety and benefits of robot-assisted minimally invasive pancreaticoduodenectomy (robot PD).

METHODS

This study included 237 patients who underwent PD between 2015 and 2017. Demographics and surgical outcomes were evaluated.

RESULTS

Fifty-one patients underwent robot PD and 186 underwent open PD. Robot PD group had younger age (60.7 vs. 65.4 years, P = 0.006) and lower body mass index (22.7 vs. 24.0, P = 0.007). Robot PD group had lower proportion of patients with firm or hard pancreatic texture (15.7% vs. 38.2%, P = 0.004) and smaller pancreatic duct size (2.3 vs. 3.3 mm, P = 0.002). Two groups had similar operation time (robot vs. open: 335.6 vs. 330.1 min) and complications (15.7% vs. 21.0%), including postoperative pancreatic fistula rate (6.0% vs. 12.0%). Robot PD group had lower postoperative pain score (3.7 vs. 4.1 points, P = 0.008), and shorter postoperative stay (10.6 vs. 15.3 days, P = 0.001).

CONCLUSION

Robot PD is comparable to open PD in early outcomes. Robot PD is safe and feasible and enables early recovery; indication for robot PD is expected to expand in the near future.

Health related quality of life among myocardial infarction survivors in the United States: a propensity score matched analysis

Background

Little is known regarding the health-related quality of life among myocardial infarction (MI) survivors in the United States. The purpose of this population-based study was to identify differences in health-related quality of life domains between MI survivors and propensity score matched controls.

Methods

This retrospective, cross-sectional matched case-control study examined differences in health-related quality of life (HRQoL) among MI survivors of myocardial infarction compared to propensity score matched controls using data from the 2015 Behavioral Risk Factor Surveillance System (BRFSS) survey. Propensity scores were generated via logistic regression for MI survivors and controls based on gender, race/ethnicity, age, body mass index (BMI), smoking status, and comorbidities. Chi-square tests were used to compare differences between MI survivors to controls for demographic variables. A multivariate analysis of HRQoL domains estimated odds ratios. Life satisfaction, sleep quality, and activity limitations were estimated using binary logistic regression. Social support, perceived general health, perceived physical health, and perceived mental health were estimated using multinomial logistic regression. Significance was set at p < 0.05.

Results

The final sample consisted of 16,729 MI survivors matched to 50,187 controls (n = 66,916). Survivors were approximately 2.7 times more likely to report fair/poor general health compared to control (AOR = 2.72, 95% CI: 2.43–3.05) and 1.5 times more likely to report limitations to daily activities (AOR = 1.46, 95% CI: 1.34–1.59). Survivors were more likely to report poor physical health >15 days in the month (AOR = 1.63, 95% CI: 1.46–1.83) and poor mental health >15 days in the month (AOR = 1.25, 95% CI: 1.07–1.46) compared to matched controls. There was no difference in survivors compared to controls in level of emotional support (rarely/never: AOR = 0.75, 95% CI: 0.48–1.18; sometimes: AOR = 0.73, 95% CI: 0.41–1.28), hours of recommended sleep (AOR = 1.14, 95% CI: 0.94–1.38), or life satisfaction (AOR = 1.62, 95% CI: 0.99–2.63).

Conclusion

MI survivors experienced lower HRQoL on domains of general health, physical health, daily activity, and mental health compared to the general population.

Prenatal exposure to PM2.5 and birth weight: A pooled analysis from three North American longitudinal pregnancy cohort studies

A common practice when analyzing multi-site epidemiological data is to include a term for 'site' to account for unmeasured effects at each location. This practice should be carefully considered when site can have complex relationships with important demographic and exposure variables. We leverage data from three longitudinal North American pregnancy cohorts to demonstrate a novel method to assess study heterogeneity and potential combinability of studies for pooled analyses in order to better understand how to consider site in analyses. Results from linear regression and fixed effects meta-regression models run both prior to and following the proposed combinability analyses were compared. In order to exemplify this approach, we examined associations between prenatal exposure to particulate matter and birth weight. Analyses included mother-child dyads (N=1966) from the Asthma Coalition on Community Environment and Social Stress (ACCESS) Project and the PRogramming of Intergenerational Stress Mechanisms (PRISM) study in the northeastern United States, and the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) study in Mexico City. Mothers' daily third trimester exposure to particulate matter≤2.5μm in diameter (PM_2.5) was estimated using a validated satellite-based spatio-temporally resolved model in all studies. Fenton birth weight for gestational age z-scores were calculated. Linear regression analyses within each cohort separately did not find significant associations between PM_2.5 averaged over the third trimester and Fenton z-scores. The initial meta-regression model also did not find significant associations between prenatal PM_2.5 and birthweight. Next, propensity scores and log linear models were used to assess higher order interactions and determine if sites were comparable with regard to sociodemographics and other covariates; these analyses demonstrated that PROGRESS and ACCESS were combinable. Adjusted linear regression models including a 2-level site variable according to the pooling indicated by the log linear models (ACCESS and PROGRESS as one level and PRISM as another) revealed that a 5μg/m³ increase in PM_2.5 was associated with a 0.075 decrease in Fenton z-score (p<0.0001); linear models including a 3-level site variable did not reveal significant associations. By assessing the combinability of heterogeneous populations prior to combining data using a method that more optimally accounts for underlying cohort differences, we were able to identify significant associations between prenatal PM_2.5 exposure and birthweight that were not detected using standard methods.

The perception of fairness in infant care and mothers' postpartum depression

This study investigates a potential causal effect of mothers' perceptions of the fairness of infant care on their postpartum depression. Based on the tenets of equity theory, it is hypothesized that, net of controls, mothers who see infant care as fairly apportioned between themselves and their husbands will be less depressed than others. We utilize data from a longitudinal study of a nonrandom sample of 178 heterosexual couples experiencing the birth of their first child together. The primary focus variable is the mothers' perception in the first couple of months postpartum that infant care is fair to them. Statistical analysis involved the careful chronological sequencing of response variable and controls, along with regression modeling using propensity scores. We find that a perception of fairness is associated with about a quarter of a standard deviation lower depressive symptomatology, controlling for key covariates. Depressive symptomatology is additionally elevated for mothers experiencing more pre-partum depression, and for those who more generally felt, before the birth, that they were overbenefiting in the marriage. This paper contributes to both equity theory and research on postpartum depression. In a scenario in which it is not practical or ethical to randomly assign people to fairness-in-infant-care conditions, we are able to utilize longitudinal data and a natural "experiment," along with propensity-score modeling to attempt to assess the causal impact of fairness in infant care on postpartum depression. The finding that fairness in this arena appears to reduce postpartum depression emphasizes the importance of encouraging father participation in this critical stage of parenting. Limitations of the study with respect to causal inference are also discussed.

Living in violence: Neighborhood domestic violence and small for gestational age births

OBJECTIVES

To determine the association between neighborhood domestic violence and small-for-gestational-age (SGA) birth and to examine if there is a differential impact of neighborhood domestic violence on SGA births by race in a high crime community.

METHODS

This analysis includes all birth records issued in New Orleans, Louisiana from 2011 to 2012 geocoded by census tract (N=177 census tracts, N=8322 women). Hierarchical modeling and ecologic spatial analysis were used to examine the area-effect of neighborhood domestic violence on SGA births, independent of individual-level predictors and accounting for the propensity to live in high domestic violence neighborhoods.

RESULTS

Tests for spatial autocorrelation reveled area-level clustering and overlap of SGA and domestic violent rates. Pregnant women living in high domestic violence areas were more likely to give birth to an SGA infant compared to women in low-domestic violence areas (OR=1.04, 95%CI: 1.01, 1.08), net of the effects of individual-level factors and propensity scores.

CONCLUSION

Neighborhood-level attributes including rates of domestic violence may increase women's risk for SGA birth, highlighting a policy-relevant and potentially amenable exposure.

Adherence to combination DMARD therapy and treatment outcomes in rheumatoid arthritis: a longitudinal study of new and existing DMARD users

Medication adherence is believed to be a major contributor to treatment outcomes yet studies quantifying this relationship as rare in rheumatoid arthritis (RA). To determine the association of adherence to DMARD therapy with treatment outcomes among new and existing DMARD users over 2 years. Relevant clinical parameters were obtained from a longitudinal cohort of RA patients, most of who were treated with combination therapy. Patients were classified as adherent if the proportion of days covered for each DMARD was ≥80%. Outcome measures were the change in the disease activity score in 28 joints (DAS28), simplified disease activity index (SDAI), modified health assessment questionnaires (mHAQ) and proportion of patients who achieved response criteria. An inverse propensity-score weighting method was used to estimate the association of adherence with each outcome. Of 194 patients invited, a total of 111 patients (new = 45 and existing = 66 DMARD users) met study eligibility. DMARD-naive patients demonstrated relatively higher rates of adherence compared to existing users. After controlling for confounding variables, adherence was significantly associated with reduction in DAS28 (β = -1.5, 95% CI of β = - 2.17 to -0.83, p < 0.0001), SDAI (β = -9.44, 95% CI of β = -15.53 to -3.35, p = 0.002) and mHAQ (β = -0.269, 95% CI of β, -0.462 to -0.077, p = 0.017) over 2 years among new patients and adherent patients were more likely to achieve most response criteria compared to non-adherent patients. Such associations were not replicated among existing DMARD users. Adherence to combination DMARD therapy was associated with improvements in disease activity and functional outcomes in the first 2 years of therapy.

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides

Background

Bacterial tyrosine-kinases (BY-kinases), which play an important role in numerous cellular processes, are characterized as a separate class of enzymes and share no structural similarity with their eukaryotic counterparts. However, in silico methods for predicting BY-kinases have not been developed yet. Since these enzymes are involved in key regulatory processes, and are promising targets for anti-bacterial drug design, it is desirable to develop a simple and easily interpretable predictor to gain new insights into bacterial tyrosine phosphorylation. This study proposes a novel SCMBYK method for predicting and characterizing BY-kinases.

Results

A dataset consisting of 797 BY-kinases and 783 non-BY-kinases was established to design the SCMBYK predictor, which achieved training and test accuracies of 97.55 and 96.73%, respectively. Furthermore, the leave-one-phylum-out method was used to predict specific bacterial phyla hosts of target sequences, gaining 97.39% average test accuracy. After analyzing SCMBYK-derived propensity scores, four characteristics of BY-kinases were determined: 1) BY-kinases tend to be composed of α-helices; 2) the amino-acid content of extracellular regions of BY-kinases is expected to be dominated by residues such as Val, Ile, Phe and Tyr; 3) BY-kinases structurally resemble nuclear proteins; 4) different domains play different roles in triggering BY-kinase activity.

Conclusions

The SCMBYK predictor is an effective method for identification of possible BY-kinases. Furthermore, it can be used as a part of a novel drug repurposing method, which recognizes putative BY-kinases and matches them to approved drugs. Among other results, our analysis revealed that azathioprine could suppress the virulence of M. tuberculosis, and thus be considered as a potential antibiotic for tuberculosis treatment.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1371-4) contains supplementary material, which is available to authorized users.

Parental health shocks and schooling: The impact of mutual health insurance in Rwanda

The goal of this study was to look at the educational spill-over effects of health insurance on schooling with a focus on the Rwandan Community Based Health Insurance Programme, the Mutual Health Insurance scheme. Using a two-person general equilibrium overlapping generations model, this paper theoretically analyses the possible effect of health insurance on the relationship between parental health shocks and child schooling. Individuals choose whether or not they want to incur a medical cost by seeking care in order to reduce the effect of health shocks on their labour market availability and productivity. The theoretical results show that, health shocks negatively affect schooling irrespective of insurance status. However, if the health shock is severe (incapacitating) or sudden in nature, there is a discernible mitigating effect of health insurance on the negative impact of parental ill health on child schooling. The results are tested empirically using secondary data from the third Integrated Household Living Conditions Survey (EICV) for Rwanda, collected in 2011. A total of 2401 children between the ages of 13 and 18 are used for the analysis. This age group is selected due to the age of compulsory education in Rwanda. Based on average treatment effect on treated we find a statistically significant difference in attendance between children with MHI affiliated parents and those with uninsured parents of about 0.044. The negative effect of a father being severely ill is significant only for uninsured household. For the case of the mother, this effect is felt by female children with uninsured parents only when the illness is sudden. The observed effects are more pronounced for older children. While the father's ill health (sever or sudden) significantly and negatively affects their working hours, health insurance plays appears to increase their working hours. The effects of health insurance extend beyond health outcomes.

Observational Research Using Propensity Scores

In most observational studies, treatments or other "exposures" (in an epidemiologic sense) do not occur at random. Instead, treatments or other such interventions depend on several patient-related and patient-independent characteristics. Such factors, associated with the receipt vs nonreceipt of treatment, may also be-independently-associated with outcomes. Thus, confounding exists making it difficult to ascertain the true association between treatments and outcomes. Propensity scores (PS) represent an intuitive set of approaches to reduce the influence of such "confounding" factors. PS is a computed probability of treatment, a value that is estimated for each patient in an observational study and then applied (in a variety of ways such as matching, stratification, weighting, etc.) to reduce distortion in the true nature of the association between treatment (or any similar exposure) and outcomes. Despite several advantages, PS-based methods cannot account for unmeasured confounding, ie, for factors that are not being included in the computation of PS.

The Impact of Degree of Exposure to Violent Video Games, Family Background, and Other Factors on Youth Violence

Despite decades of study, no scholarly consensus has emerged regarding whether violent video games contribute to youth violence. Some skeptics contend that small correlations between violent game play and violence-related outcomes may be due to other factors, which include a wide range of possible effects from gender, mental health, and social influences. The current study examines this issue with a large and diverse (49 % white, 21 % black, 18 % Hispanic, and 12 % other or mixed race/ethnicity; 51 % female) sample of youth in eighth (n = 5133) and eleventh grade (n = 3886). Models examining video game play and violence-related outcomes without any controls tended to return small, but statistically significant relationships between violent games and violence-related outcomes. However, once other predictors were included in the models and once propensity scores were used to control for an underlying propensity for choosing or being allowed to play violent video games, these relationships vanished, became inverse, or were reduced to trivial effect sizes. These results offer further support to the conclusion that video game violence is not a meaningful predictor of youth violence and, instead, support the conclusion that family and social variables are more influential factors.