The objective of this study was to determine the association between maternal 25-hydroxyvitamin D (25(OH)D) and the risk of spontaneous preterm birth (sPTB) before 35 weeks’ gestation. A random subcohort from the US Collaborative Perinatal Project (1959–1965) was sampled (n = 2,629) and augmented with all remaining cases of sPTB before 35 weeks’ gestation for a total of 767 cases. Banked serum samples collected at 26 weeks’ gestation or earlier were assayed for 25(OH)D. Constructs for vascular histology and inflammatory histology were developed from placental pathology examinations. There was no relationship between 25(OH)D and sPTB among white women. Among nonwhite mothers, serum 25(OH)D levels of 30–<50, 50–<75, and ≥75 nmol/L were associated with reductions of 1.0–1.6 cases of sPTB per 100 live births and 20%–30% reductions in risk of sPTB compared with 25(OH)D levels less than 30 nmol/L after adjustment for prepregnancy body mass index (weight (kg)/height (m)2), season, and other confounders. This association was driven by inflammation-mediated cases of sPTB and sPTB cases without placental lesions. A sensitivity analysis for unmeasured confounding by exercise, fish intake, and skin color suggested some bias away from the null in the conventional results, but conclusions were generally supported. The vitamin D–sPTB relationship should be examined in modern cohorts with detailed data on skin pigmentation and other covariates.
gestational age; 25-hydroxyvitamin D; inflammation; placenta; pregnancy; preterm birth
The PROmotion of Breastfeeding Intervention Trial (PROBIT) cluster-randomized a program encouraging breastfeeding to new mothers in hospital centers. The original studies indicated that this intervention successfully increased duration of breastfeeding and lowered rates of gastrointestinal tract infections in newborns. Additional scientific and popular interest lies in determining the causal effect of longer breastfeeding on gastrointestinal infection. In this study, we estimate the expected infection count under various lengths of breastfeeding in order to estimate the effect of breastfeeding duration on infection. Due to the presence of baseline and time-dependent confounding, specialized “causal” estimation methods are required. We demonstrate the double-robust method of Targeted Maximum Likelihood Estimation (TMLE) in the context of this application and review some related methods and the adjustments required to account for clustering. We compare TMLE (implemented both parametrically and using a data-adaptive algorithm) to other causal methods for this example. In addition, we conduct a simulation study to determine (1) the effectiveness of controlling for clustering indicators when cluster-specific confounders are unmeasured and (2) the importance of using data-adaptive TMLE.
Causal inference; G-computation; inverse probability weighting; marginal effects; missing data; pediatrics
Marginal structural models were developed as a semiparametric alternative to the G-computation formula to estimate causal effects of exposures. In practice, these models are often specified using parametric regression models. As such, the usual conventions regarding regression model specification apply. This paper outlines strategies for marginal structural model specification, and considerations for the functional form of the exposure metric in the final structural model. We propose a quasi-likelihood information criterion adapted from use in generalized estimating equations. We evaluate the properties of our proposed information criterion using a limited simulation study. We illustrate our approach using two empirical examples. In the first example, we use data from a randomized breastfeeding promotion trial to estimate the effect of breastfeeding duration on infant weight at one year. In the second example, we use data from two prospective cohorts studies to estimate the effect of highly active antiretroviral therapy on CD4 count in an observational cohort of HIV-infected men and women. The marginal structural model specified should reflect the scientific question being addressed, but can also assist in exploration of other plausible and closely related questions. In marginal structural models, as in any regression setting, correct inference depends on correct model specification. Our proposed information criterion provides a formal method for comparing model fit for different specifications.
Bias; Causal inference; Marginal structural model; Regression analysis; Model specification
Background: Health plans must prioritize disease management efforts to reduce hospitalization and mortality rates in heart failure patients.
Methods and Results: We developed a risk model to predict the 5-year risk of mortality or hospitalization for heart failure among patients at a large health maintenance organization. We identified 4696 patients who had an echocardiogram and a heart failure diagnosis from 1999 to 2004.
We observed a 56% five-year risk of hospitalization for heart failure or death (95% confidence interval, 54% to 58%). The hazard ratios for echocardiogram data contributed statistically significantly to the model, but echocardiogram findings did not improve our ability to predict risk accurately once we had accounted for demographic characteristics and clinical findings. A more complex model demonstrated a modest capacity to accurately predict risk. Our risk model discriminated the highest- and lowest-risk patients with limited success–the observed risk was 3 times higher in the highest risk quintile, compared with the lowest-risk quintile.
Conclusions: Using data available from electronic health records, we developed a series of risk-prediction models for poor outcomes in patients with heart failure. We found that a relatively simple model is as effective as a more complex model, but that all the models predict with only modest accuracy. Until better prediction variables are available for heart failure patients, our prediction model may be valuable for prioritizing centralized disease management program efforts by stratifying patients according to their absolute risk of poor outcomes.
Conventional measures of gestational weight gain (GWG), such as average rate of weight gain, are likely correlated with gestational duration. Such correlation could introduce bias to epidemiologic studies of GWG and adverse perinatal outcomes because many perinatal outcomes are also correlated with gestational duration. This study aimed to quantify the extent to which currently-used GWG measures may bias the apparent relation between maternal weight gain and risk of preterm birth. For each woman in a provincial perinatal database registry (British Columbia, Canada, 2000–2009), a total GWG was simulated such that it was uncorrelated with risk of preterm birth. The simulation was based on serial antenatal GWG measurements from a sample of term pregnancies. Simulated GWGs were classified using 3 approaches: total weight gain (kg), average rate of weight gain (kg/week) or adequacy of gestational weight gain in relation to Institute of Medicine recommendations, and their association with preterm birth ≤ 32 weeks was explored using logistic regression. All measures of GWG induced an apparent association between GWG and preterm birth ≤32 weeks even when, by design, none existed. Odds ratios in the lowest fifths of each GWG measure compared with the middle fifths ranged from 4.4 [95% CI 3.6, 5.4] (total weight gain) to 1.6 [95% CI 1.3, 2.0] (Institute of Medicine adequacy ratio). Conventional measures of GWG introduce serious bias to the study of maternal weight gain and preterm birth. A new measure of GWG that is uncorrelated with gestational duration is needed.
Bias; Epidemiologic; Pregnancy; Pregnancy nutrition; Premature birth; Weight gain
To synthesise estimates of the prevalence of cessation attempts among adolescent smokers generally, and according to age and level of cigarette consumption.
PubMed, ERIC, and PsychInfo databases and Internet searches of central data collection agencies.
National population‐based studies published in English between 1990 and 2005 reporting the prevalence, frequency and/or duration of cessation attempts among smokers aged ⩾10 to <20 years.
Five reviewers determined inclusion criteria for full‐text reports. One reviewer extracted data on the design, population characteristics and results from the reports.
In total, 52 studies conformed to the inclusion criteria. The marked heterogeneity that characterised the study populations and survey questions precluded a meta‐analysis. Among adolescent current smokers, the median 6‐month, 12‐month and lifetime cessation attempt prevalence was 58% (range: 22–73%), 68% (range 43–92%) and 71% (range 28–84%), respectively. More than half had made multiple attempts. Among smokers who had attempted cessation, the median prevalence of relapse was 34, 56, 89 and 92% within 1 week, 1 month, 6 months, and 1 year, respectively, following the longest attempt. Younger (age<16 years) and non‐daily smokers experienced a similar or higher prevalence of cessation attempts compared with older (age ⩾16 years) or daily smokers. Moreover, the prevalence of relapse by 6 months following the longest cessation attempt was similar across age and smoking frequency.
The high prevalence of cessation attempts and relapse among adolescent smokers extends to young adolescents and non‐daily smokers. Cessation surveillance, research and program development should be more inclusive of these subgroups.
According to the authors, time-modified confounding occurs when the causal relation between a time-fixed or time-varying confounder and the treatment or outcome changes over time. A key difference between previously described time-varying confounding and the proposed time-modified confounding is that, in the former, the values of the confounding variable change over time while, in the latter, the effects of the confounder change over time. Using marginal structural models, the authors propose an approach to account for time-modified confounding when the relation between the confounder and treatment is modified over time. An illustrative example and simulation show that, when time-modified confounding is present, a marginal structural model with inverse probability-of-treatment weights specified to account for time-modified confounding remains approximately unbiased with appropriate confidence limit coverage, while models that do not account for time-modified confounding are biased. Correct specification of the treatment model, including accounting for potential variation over time in confounding, is an important assumption of marginal structural models. When the effect of confounders on either the treatment or outcome changes over time, time-modified confounding should be considered.
bias (epidemiology); confounding factors (epidemiology); structural model
Control of blood pressure (BP) remains a major challenge in primary care. Innovative interventions to improve BP control are therefore needed. By updating and combining data from 2 previous systematic reviews, we assess the effect of pharmacist interventions on BP and identify potential determinants of heterogeneity.
Methods and Results
Randomized controlled trials (RCTs) assessing the effect of pharmacist interventions on BP among outpatients with or without diabetes were identified from MEDLINE, EMBASE, CINAHL, and CENTRAL databases. Weighted mean differences in BP were estimated using random effect models. Prediction intervals (PI) were computed to better express uncertainties in the effect estimates. Thirty‐nine RCTs were included with 14 224 patients. Pharmacist interventions mainly included patient education, feedback to physician, and medication management. Compared with usual care, pharmacist interventions showed greater reduction in systolic BP (−7.6 mm Hg, 95% CI: −9.0 to −6.3; I2=67%) and diastolic BP (−3.9 mm Hg, 95% CI: −5.1 to −2.8; I2=83%). The 95% PI ranged from −13.9 to −1.4 mm Hg for systolic BP and from −9.9 to +2.0 mm Hg for diastolic BP. The effect tended to be larger if the intervention was led by the pharmacist and was done at least monthly.
Pharmacist interventions – alone or in collaboration with other healthcare professionals – improved BP management. Nevertheless, pharmacist interventions had differential effects on BP, from very large to modest or no effect; and determinants of heterogeneity could not be identified. Determining the most efficient, cost‐effective, and least time‐consuming intervention should be addressed with further research.
hypertension; pharmacist; prediction interval; systematic review; team‐based care
Despite modern effective HIV treatment, hepatitis C virus (HCV) co-infection is associated with a high risk of progression to end-stage liver disease (ESLD) which has emerged as the primary cause of death in this population. Clinical interest lies in determining the impact of clearance of HCV on risk for ESLD. In this case study, we examine whether HCV clearance affects risk of ESLD using data from the multicenter Canadian Co-infection Cohort Study. Complications in this survival analysis arise from the time-dependent nature of the data, the presence of baseline confounders, loss to follow-up, and confounders that change over time, all of which can obscure the causal effect of interest. Additional challenges included non-censoring variable missingness and event sparsity.
In order to efficiently estimate the ESLD-free survival probabilities under a specific history of HCV clearance, we demonstrate the doubly-robust and semiparametric efficient method of Targeted Maximum Likelihood Estimation (TMLE). Marginal structural models (MSM) can be used to model the effect of viral clearance (expressed as a hazard ratio) on ESLD-free survival and we demonstrate a way to estimate the parameters of a logistic model for the hazard function with TMLE. We show the theoretical derivation of the efficient influence curves for the parameters of two different MSMs and how they can be used to produce variance approximations for parameter estimates. Finally, the data analysis evaluating the impact of HCV on ESLD was undertaken using multiple imputations to account for the non-monotone missing data.
Double-robust; Inverse probability of treatment weighting; Kaplan-Meier; Longitudinal data; Marginal structural model; Survival analysis; Targeted maximum likelihood estimation
Angiotensin-converting enzyme (ACE) inhibitors are recommended for patients with chronic kidney disease (CKD) because they slow disease progression. But physicians’ concerns about the risk of hyperkalemia (elevated serum potassium level), a potentially fatal adverse effect, may limit optimal management with ACE-inhibitors. We synthesized known predictors of hyperkalemia into a prognostic risk score to predict the risk of hyperkalemia.
We assembled a retrospective cohort of adult patients with possible CKD (at least one estimated glomerular filtration rate (eGFR) value less than 60 mL/min/1.73m2) who started an ACE-inhibitor (i.e., incident users) between 1998 and 2006 at a health maintenance organization. We followed patients for hyperkalemia: (1) potassium value > 5.5 mmol/L; or, (2) diagnosis code for hyperkalemia. Cox regression synthesized a priori predictors recorded in the electronic medical record into a risk score.
We followed 5,171 patients and 145 experienced hyperkalemia, a 90-day risk of 2.8%. Predictors included: age, eGFR, diabetes, heart failure, potassium supplements, potassium-sparing diuretics, and a high dose for the ACE-inhibitor (lisinopril). The risk score separated high-risk patients (top quintile, observed risk of 6.9%) from low-risk patients (bottom quintile, observed risk of 0.7%). Predicted and observed risks agreed within 1% for each quintile. The risk increased gradually in relation to declining eGFR with no apparent threshold for contraindicating ACE-inhibitors.
The risk score separated high-risk patients (who may need more intensive laboratory monitoring) from low-risk patients. The risk score should be validated in other populations before it is ready for use in clinical practice.
hyperkalemia; chronic kidney disease; ACE-inhibitors; risk score; cohort study; adverse effects
Observational (nonexperimental) studies of the association of infant feeding and subsequent child or adult behavior are prone to residual confounding by subtle differences in psychological attributes and interactional styles of mothers who breastfeed vs those who formula-feed. We followed up 13,889 6.5-year-old Belarusian children who participated in a large cluster-randomized trial of a breastfeeding promotion intervention. Behavior was evaluated using the Strengths and Difficulties Questionnaire (SDQ), completed independently by the children’s parents and teachers. We compared the results of experimental (intention-to-treat, ITT) and observational analyses (based on feeding actually received), both adjusted for clustering. Observational analyses were additionally adjusted for geographic region and urban vs rural residence; child sex, age at follow-up, and birth weight; and maternal and paternal education. No differences between the randomized experimental vs control groups were observed in ITT analyses. In contrast, small but statistically significant associations with weaning prior to 3 months were observed for parent and teacher SDQ scores on total difficulties, conduct problems, and hyperactivity, even after multivariate adjustment. The absence of associations based on ITT analyses, in contrast with the significant associations based on observed BF duration, strongly suggests that the latter are biased by residual confounding.
Motivated by a previously published study of HIV treatment, we simulated data subject to time-varying confounding affected by prior treatment to examine some finite-sample properties of marginal structural Cox proportional hazards models. We compared (a) unadjusted, (b) regression-adjusted, (c) unstabilized and (d) stabilized marginal structural (inverse probability-of-treatment [IPT] weighted) model estimators of effect in terms of bias, standard error, root mean squared error (MSE) and 95% confidence limit coverage over a range of research scenarios, including relatively small sample sizes and ten study assessments. In the base-case scenario resembling the motivating example, where the true hazard ratio was 0.5, both IPT-weighted analyses were unbiased while crude and adjusted analyses showed substantial bias towards and across the null. Stabilized IPT-weighted analyses remained unbiased across a range of scenarios, including relatively small sample size; however, the standard error was generally smaller in crude and adjusted models. In many cases, unstabilized weighted analysis showed a substantial increase in standard error compared to other approaches. Root MSE was smallest in the IPT-weighted analyses for the base-case scenario. In situations where time-varying confounding affected by prior treatment was absent, IPT-weighted analyses were less precise and therefore had greater root MSE compared with adjusted analyses. The 95% confidence limit coverage was close to nominal for all stabilized IPT-weighted but poor in crude, adjusted, and unstabilized IPT-weighted analysis. Under realistic scenarios, marginal structural Cox proportional hazards models performed according to expectations based on large-sample theory and provided accurate estimates of the hazard ratio.
Bias; Causal inference; Marginal structural models; Monte Carlo study
Although administrative health care databases have long been used to evaluate adverse drug effects, responses to drug safety signals have been slow and uncoordinated. We describe the establishment of the Canadian Network for Observational Drug Effect Studies (CNODES), a collaborating centre of the Drug Safety and Effectiveness Network (DSEN). CNODES is a distributed network of investigators and linked databases in British Columbia, Alberta, Saskatchewan, Manitoba, Ontario, Quebec and Nova Scotia. Principles of operation are as follows: (1) research questions are prioritized by the coordinating office of DSEN; (2) the linked data stay within the provinces; (3) for each question, a study team formulates a detailed protocol enabling consistent analyses in each province; (4) analyses are “blind” to results obtained elsewhere; (5) protocol deviations are permitted for technical reasons only; (6) analyses using multivariable methods are lodged centrally with a methods team, which is responsible for combining the results to provide a summary estimate of effect. These procedures are designed to achieve high internal validity of risk estimates and to eliminate the possibility of selective reporting of analyses or outcomes. The value of a coordinated multi-provincial approach is illustrated by projects studying acute renal injury with high-potency statins, community-acquired pneumonia with proton pump inhibitors, and hyperglycemic emergencies with antipsychotic drugs. CNODES is an academically based distributed network of Canadian researchers and data centres with a commitment to rapid and sophisticated analysis of emerging drug safety signals in study populations totalling over 40 million.
We present a model for longitudinal measures of fetal weight as a function of gestational age. We use a linear mixed model, with a Box-Cox transformation of fetal weight values, and restricted cubic splines, in order to flexibly but parsimoniously model median fetal weight. We systematically compare our model to other proposed approaches. All proposed methods are shown to yield similar median estimates, as evidenced by overlapping pointwise confidence bands, except after 40 completed weeks, where our method seems to produce estimates more consistent with observed data. Sex-based stratification affects the estimates of the random effects variance-covariance structure, without significantly changing sex-specific fitted median values. We illustrate the benefits of including sex-gestational age interaction terms in the model over stratification. The comparison leads to the conclusion that the selection of a model for fetal weight for gestational age can be based on the specific goals and configuration of a given study without affecting the precision or value of median estimates for most gestational ages of interest.
multi-level models; fetal growth; small for gestational age
The authors tested whether the relation between gestational weight gain (GWG) and 5 adverse pregnancy outcomes (small-for-gestational-age (SGA) birth, large-for-gestational-age (LGA) birth, spontaneous preterm birth, indicated preterm birth, and unplanned cesarean delivery) differed according to maternal race/ethnicity, smoking, parity, age, and/or height. They also evaluated whether GWG guidelines should be modified for special populations by studying GWG and risk of at least 1 adverse outcome within different subgroups. Data came from a cohort of 23,362 normal-weight mothers who delivered singletons at Magee-Womens Hospital in Pittsburgh, Pennsylvania (2003–2008). Adequacy of GWG was defined as observed GWG divided by recommended GWG. The synergy analysis found that the combination of smoking, black race/ethnicity, primiparity, or short height with poor GWG was associated with an excess risk of SGA birth, while high GWG combined with each of these characteristics diminished risk of LGA birth in comparison with the same GWG among the women's counterparts. Nevertheless, there were no significant or meaningful differences in the risk of at least 1 adverse outcome between the GWG recommended by the Institute of Medicine in 2009 and the GWG that minimized risk of the composite outcome. These findings do not support the tailoring of GWG guidelines on the basis of a mother's smoking status, race/ethnicity, parity, age, or height among normal-weight women.
ethnic groups; gestational age; parity; practice guidelines as topic; pregnancy; smoking; weight gain
Several national and regional central line-associated bloodstream infections (CLABSI) surveillance programs do not require continuous hospital participation. We evaluated the effect of different hospital participation requirements on the validity of annual CLABSI incidence rate benchmarks for intensive care units (ICUs).
We estimated the annual pooled CLABSI incidence rates for both a real regional (<100 ICUs) and a simulated national (600 ICUs) surveillance program, which were used as a reference for the simulations. We simulated scenarios where the annual surveillance participation was randomly or non-randomly reduced. Each scenario's annual pooled CLABSI incidence rate was estimated and compared to the reference rates in terms of validity, bias, and proportion of simulation iterations that presented valid estimates (ideal if≥90%).
All random scenarios generated valid CLABSI incidence rates estimates (bias −0.37 to 0.07 CLABSI/1000 CVC-days), while non-random scenarios presented a wide range of valid estimates (0 to 100%) and higher bias (−2.18 to 1.27 CLABSI/1000 CVC-days). In random scenarios, the higher the number of participating ICUs, the shorter the participation required to generate ≥90% valid replicates. While participation requirements in a countrywide program ranged from 3 to 13 surveillance blocks (1 block = 28 days), requirements for a regional program ranged from 9 to 13 blocks.
Based on the results of our model of national CLABSI reporting, the shortening of participation requirements may be suitable for nationwide ICU CLABSI surveillance programs if participation months are randomly chosen. However, our regional models showed that regional programs should opt for continuous participation to avoid biased benchmarks.
Infants who receive prolonged and exclusive breastfeeding grow more slowly during the first year of life than those who do not. However, infant feeding and growth are dynamic processes in which feeding may affect growth, and prior growth and size may also influence subsequent feeding decisions. The authors carried out an observational analysis of 17,046 Belarusian infants who were recruited between June 1996 and December 1997 and who participated in a cluster-randomized trial of a breastfeeding promotion intervention. To assess the effects of infant size on subsequent feeding, the authors restricted the analysis to infants breastfed (or exclusively breastfed) at the beginning of each follow-up interval and examined associations between weight or length at the beginning of the interval and weaning or discontinuation of exclusive breastfeeding by the end of the interval. Smaller size (especially weight for age) was strongly and statistically significantly associated with increased risks of subsequent weaning and of discontinuing exclusive breastfeeding (adjusted odds ratios = 1.2–1.6), especially between 2 and 6 months, even after adjusment for potential confounding factors and clustered measurement. The authors speculate that similar dynamic processes involving infant crying, other signs of hunger, and supplementation/weaning undermine causal inferences about the “effect” of prolonged and exclusive breastfeeding on slower infant growth.
body size; breast feeding; causal inference; evidence; infant
Smoking paradoxically increases the risk of small-for-gestational-age (SGA) birth but protects against preeclampsia. Some studies have reported a "U-shaped" distribution of fetal growth in preeclamptic pregnancies, but reasons for this are unknown. We investigated whether cigarette smoking interacts with preeclampsia to affect fetal growth, and compared levels of soluble fms-like tyrosine kinase-1 (sFlt-1), a circulating anti-angiogenic protein, in preeclamptic smokers and non-smokers.
From a multicenter cohort of 5337 pregnant women, we prospectively identified 113 women who developed preeclampsia (cases) and 443 controls. Smoking exposure was assessed by self-report and maternal hair nicotine levels. Fetal growth was assessed as z-score of birthweight for gestational age (BWGA). sFlt-1 was measured in plasma samples collected at the 24-26-week visit.
In linear regression, smoking and preeclampsia were each associated with lower BWGA z-scores (β = -0.29; p = 0.008, and β = -0.67; p < 0.0001), but positive interaction was observed between smoking and preeclampsia (β = +0.86; p = 0.0008) such that smoking decreased z-score by -0.29 in controls but increased it by +0.57 in preeclampsia cases. Results were robust to substituting log hair nicotine for self-reported smoking and after adjustment for confounding variables. Mean sFlt-1 levels were lower in cases with hair nicotine levels above vs. below the median (660.4 pg/ml vs. 903.5 pg/ml; p = 0.0054).
Maternal smoking seems to protect against preeclampsia-associated fetal growth restriction and may account, at least partly, for the U-shaped pattern of fetal growth described in preeclamptic pregnancies. Smoking may exert this effect by reducing levels of the anti-angiogenic protein sFlt-1.
That conditioning on a common effect of exposure and outcome may cause selection, or collider-stratification, bias is not intuitive. We provide two hypothetical examples to convey concepts underlying bias due to conditioning on a collider. In the first example, fever is a common effect of influenza and consumption of a tainted egg-salad sandwich. In the second example, case-status is a common effect of a genotype and an environmental factor. In both examples, conditioning on the common effect imparts an association between two otherwise independent variables; we call this selection bias.
Bias; selection; methods; epidemiologic
The authors investigated variations in cognitive ability by gestational age among 13,824 children at age 6.5 years who were born at term with normal weight, using data from a prospective cohort recruited in 1996–1997 in Belarus. The mean differences in the Wechsler Abbreviated Scales of Intelligence were examined by gestational age in completed weeks and by fetal growth after controlling for maternal and family characteristics. Compared with the score for those born at 39–41 weeks, the full-scale intelligence quotient (IQ) score was 1.7 points (95% confidence interval (CI): −2.7, −0.7) lower in children born at 37 weeks and 0.4 points (95% CI: −1.1, 0.02) lower at 38 weeks after controlling for confounders. There was also a graded relation in postterm children: a 0.5-points (95% CI: −2.6, 1.6) lower score at 42 weeks and 6.0 points (95% CI: −15.1, 3.1) lower at 43 weeks. Compared with children born large for gestational age (>90th percentile), children born small for gestational age (<10th percentile) had the lowest IQ, followed by those at the 10th–50th percentile and those at the >50th–90th percentile. These findings suggest that, even among healthy children born at term, cognitive ability at age 6.5 years is lower in those born at 37 or 38 weeks and those with suboptimal fetal growth.
birth weight; cognition; gestational age; term birth
Despite strong laboratory evidence that non-steroidal anti-inflammatory drugs (NSAIDs) could prevent prostate cancer, epidemiological studies have so far reported conflicting results. Most studies were limited by lack of information on dosage and duration of use of the different classes of NSAIDs.
We conducted a nested case-control study using data from Saskatchewan Prescription Drug Plan (SPDP) and Cancer Registry to examine the effects of dose and duration of use of five classes of NSAIDs on prostate cancer risk. Cases (N = 9,007) were men aged ≥40 years diagnosed with prostatic carcinoma between 1985 and 2000, and were matched to four controls on age and duration of SPDP membership. Detailed histories of exposure to prescription NSAIDs and other drugs were obtained from the SPDP.
Any use of propionates (e.g., ibuprofen, naproxen) was associated with a modest reduction in prostate cancer risk (Odds ratio = 0.90; 95%CI 0.84-0.95), whereas use of other NSAIDs was not. In particular, we did not observe the hypothesized inverse association with aspirin use (1.01; 0.95–1.07). There was no clear evidence of dose-response or duration-response relationships for any of the examined NSAID classes.
Our findings suggest modest benefits of at least some NSAIDs in reducing prostate cancer risk.
Our objective was to examine the association between HIV and HCV discordant infection status and the sharing of drug equipment by injection drug users (IDUs). IDUs were recruited from syringe exchange and methadone treatment programmes in Montreal, Canada. Characteristics of participants and their injecting partners were elicited using a structured questionnaire. Among 159 participants and 245 injecting partners, sharing of syringes and drug preparation equipment did not differ between concordant or discordant partners, although HIV-positive subjects did not share with HIV-negative injectors. Sharing of syringes was positively associated with discordant HIV status (OR = 1.85) and negatively with discordant HCV status (OR = 0.65), but both results were not statistically significant. Sharing of drug preparation equipment was positively associated with both discordant HIV (OR = 1.61) and HCV (OR = 1.18) status, but both results were non-significant. Factors such as large injecting networks, frequent mutual injections, younger age, and male gender were stronger predictors of equipment sharing. In conclusion, IDUs do not appear to discriminate drug equipment sharing partners based at least on their HCV infection status. The results warrant greater screening to raise awareness of infection status, post-test counselling to promote status disclosure among partners, and skill-building to avoid equipment sharing between discordant partners.
PMID: 19172434 CAMSID: cams1471
Overadjustment is defined inconsistently. This term is meant to describe control (eg, by regression adjustment, stratification, or restriction) for a variable that either increases net bias or decreases precision without affecting bias. We define overadjustment bias as control for an intermediate variable (or a descending proxy for an intermediate variable) on a causal path from exposure to outcome. We define unnecessary adjustment as control for a variable that does not affect bias of the causal relation between exposure and outcome but may affect its precision. We use causal diagrams and an empirical example (the effect of maternal smoking on neonatal mortality) to illustrate and clarify the definition of overadjustment bias, and to distinguish overadjustment bias from unnecessary adjustment. Using simulations, we quantify the amount of bias associated with overadjustment. Moreover, we show that this bias is based on a different causal structure from confounding or selection biases. Overadjustment bias is not a finite sample bias, while inefficiencies due to control for unnecessary variables are a function of sample size.
The ‘birthweight paradox’ describes the phenomenon whereby birthweight-specific mortality curves cross when stratified on other exposures, most notably cigarette smoking. The paradox has been noted widely in the literature and numerous explanations and corrections have been suggested. Recently, causal diagrams have been used to illustrate the possibility for collider-stratification bias in models adjusting for birthweight. When two variables share a common effect, stratification on the variable representing that effect induces a statistical relation between otherwise independent factors. This bias has been proposed to explain the birthweight paradox.
Causal diagrams may illustrate sources of bias, but are limited to describing qualitative effects. In this paper, we provide causal diagrams that illustrate the birthweight paradox and use a simulation study to quantify the collider-stratification bias under a range of circumstances. Considered circumstances include exposures with and without direct effects on neonatal mortality, as well as with and without indirect effects acting through birthweight on neonatal mortality. The results of these simulations illustrate that when the birthweight-mortality relation is subject to substantial uncontrolled confounding, the bias on estimates of effect adjusted for birthweight may be sufficient to yield opposite causal conclusions, i.e. a factor that poses increased risk appears protective. Effects on stratum-specific birthweight-mortality curves were considered to illustrate the connection between collider-stratification bias and the crossing of the curves. The simulations demonstrate the conditions necessary to give rise to empirical evidence of the paradox.
collider-stratification bias; birthweight; directed acyclic graphs; neonatal nortality
Investigators have long puzzled over the observation that low-birthweight babies of smokers tend to fare better than low-birthweight babies of non-smokers. Similar observations have been made with regard to factors other than smoking status, including socio-economic status, race and parity. Use of standardised birthweights, or birthweight z-scores, has been proposed as an approach to resolve the crossing of the curves that is the hallmark of the so-called birthweight paradox. In this paper, we utilise directed acyclic graphs, analytical proofs and an extensive simulation study to consider the use of z-scores of birthweight and their effect on statistical analysis. We illustrate the causal questions implied by inclusion of birthweight in statistical models, and illustrate the utility of models that include birthweight or z-scores to address those questions.
Both analytically and through a simulation study we show that neither birthweight nor z-score adjustment may be used for effect decomposition. The z-score approach yields an unbiased estimate of the total effect, even when collider-stratification would adversely impact estimates from birthweight-adjusted models; however, the total effect could have been estimated more directly with an unadjusted model. The use of z-scores does not add additional information beyond the use of unadjusted models. Thus, the ability of z-scores to successfully resolve the paradoxical crossing of mortality curves is due to an alteration in the causal parameter being estimated (total effect), rather than adjustment for confounding or effect decomposition or other factors.
birthweight; birthweight paradox; simulation; directed acyclic graphs; z-scores