Estimating the Marginal Causal Effect and Potential Impact of Waterpipe Smoking on Risk of Multiple Sclerosis Using the Targeted Maximum Likelihood Estimation Method: A Large, Population-Based Incident Case-Control Study

Lifetime exposure to smoking and substance abuse may be associated with late-onset multiple sclerosis: a population-based case-control study

BACKGROUND

Late-onset multiple sclerosis (LOMS), defined as the development of MS after the age of 50, has shown a substantial surge in incidence rates and is associated with more rapid progression of disability. Besides, studies have linked tobacco smoking to a higher chance of MS progression. However, the role of smoking on the risk of developing LOMS remains unclear. This study aims to evaluate the possible association between lifetime exposure to cigarette and waterpipe smoking, drug abuse, and alcohol consumption and the risk of LOMS.

METHODS

This population-based case-control study involved LOMS cases and healthy sex and age-matched controls from the general population in Tehran, Iran. The primary data for confirmed LOMS cases were obtained from the nationwide MS registry of Iran (NMSRI), while supplementary data were collected through telephone and on-site interviews. Predesigned questionnaire for multinational case-control studies of MS environmental risk factors was used to evaluate the LOMS risk factors. The study employed Likelihood ratio chi-square test to compare qualitative variables between the two groups and utilized two independent sample t-test to compare quantitative data. Adjusted odds ratio (AOR) for age along with 95% confidence intervals (CI) were calculated using matched logistic regression analysis in SPSS 23.

RESULTS

Totally, 83 LOMS cases and 207 controls were included in the analysis. The female to male ratio in the cases was 1.5: 1. The mean ± SD age of 83 cases and 207 controls was 61.14 ± 5.38) and 61.51 ± 7.67 years, respectively. The mean ± SD expanded disability status scale (EDSS) score was 3.68 ± 2.1. Although the results of waterpipe exposure had no significant effect on LOMS development (P-value: 0.066), ever cigarette-smoked participants had a significantly higher risk of developing LOMS than those who never smoked (AOR: 2.57, 95% CI: 1.44-4.60). Furthermore, people with a history of smoking for more than 20 years had 3.45 times the odds of developing MS than non-smokers. Drug and alcohol abuse were both associated with LOMS in our study; of which opioids (AOR: 5.67, 95% CI: 2.05-15.7), wine (AOR: 3.30, 95% CI: 1.41-7.71), and beer (AOR: 3.12, 95% CI: 1.45-6.69) were found to pose the greatest risk of LOMS, respectively.

CONCLUSION

For the first time, we identified smoking, drug, and alcohol use as potential risk factors for LOMS development. According to the global increase in cigarette smoking and alcohol use, these findings highlight the importance of conducting interventional approaches for prevention.

Waterpipe Tobacco Smoking and Multiple Sclerosis: A Systematic Review and Meta-Analysis

BACKGROUND

Multiple sclerosis (MS), as an autoimmune disease of the central nervous system, has a significant burden among people worldwide. Tobacco smoking is one of the most prevalent habits of patients with different diseases including those with MS, and among the methods of use, waterpipe tobacco smoking is gaining popularity. Herein, we aimed to systematically evaluate the association between waterpipe smoking and MS.

METHODS

Relevant studies were identified to be included in this systematic review and meta-analysis through a systematic search in PubMed, Scopus, Web of Science, and Embase. Studies were screened by the title/abstract and then by their full text. Extraction of data was performed for relevant studies. Odds ratios (ORs) and their 95% confidence intervals (CIs) of the association between waterpipe smoking and MS were used to pool the results observed in each study.

RESULTS

After the screening, a total of five studies were included in our systematic review, comprised of 3,087 individuals, among which there were 1,135 cases with MS. Random-effect meta-analysis revealed that ever-smoking waterpipe had a significant association with MS (OR: 1.73, 95% CI: 1.38-2.17, p value <0.0001). Similarly, past waterpipe smoking was significantly higher in those with MS, compared with controls (OR: 2.17, 95% CI: 1.61-2.92, p value <0.0001). Based on the reported results, smoking both tobacco and waterpipe had an additive association with MS. Finally, no association was found between the Expanded Disability Status Scale (EDSS) and waterpipe smoking.

CONCLUSION

Waterpipe as one of the common ways of tobacco smoking becoming popular has an association with MS, and even the use of waterpipe for a certain period in the lifetime has a significant correlation with MS. Further large-scale studies are needed to confirm these findings. These results could help clinicians in risk-stratifying the patients and to provide better care for the MS population.

Comparison of outcomes between off-pump and on-pump coronary artery bypass graft surgery using collaborative targeted maximum likelihood estimation

There are some discrepancies about the superiority of the off-pump coronary artery bypass grafting (CABG) surgery over the conventional cardiopulmonary bypass (on-pump). The aim of this study was estimating risk ratio of mortality in the off-pump coronary bypass compared with the on-pump using a causal model known as collaborative targeted maximum likelihood estimation (C-TMLE). The data of the Tehran Heart Cohort study from 2007 to 2020 was used. A collaborative targeted maximum likelihood estimation and targeted maximum likelihood estimation, and propensity score (PS) adjustment methods were used to estimate causal risk ratio adjusting for the minimum sufficient set of confounders, and the results were compared. Among 24,883 participants (73.6% male), 5566 patients died during an average of 8.2 years of follow-up. The risk ratio estimates (95% confidence intervals) by unadjusted log-binomial regression model, PS adjustment, TMLE, and C-TMLE methods were 0.86 (0.78-0.95), 0.88 (0.80-0.97), 0.88 (0.80-0.97), and 0.87(0.85-0.89), respectively. This study provides evidence for a protective effect of off-pump surgery on mortality risk for up to 8 years in diabetic and non-diabetic patients.

Additive interaction between dietary inflammatory index and some key risk factors of multiple sclerosis: a population-based incident case-control study

BACKGROUND

Recently, dietary inflammatory index (DII) has been introduced as a significant risk factor for MS. We examined the interaction between dietary inflammatory index and some formerly demonstrated key risk factors of multiple sclerosis (MS).

MATERIAL AND METHODS

We conducted a population-based incident case-control study of 547 MS cases and 1057 controls. Multiplicative and additive interaction were assessed using interaction term in the logistic regression model and synergy index (SI), respectively.

RESULTS

Additive interaction was detected between DII and drug abuse (SI = 2.58; 95% CI: 1.14-5.82), gender (SI = 2.00; 95% CI: 1.39-2.87) and history of depression (SI = 1.68; 95% CI: 1.04-2.72) on the risk scale. The risk of MS in drug abusers with DII ≥ 0 was 10.4-times higher than that in non-drug abusers with DII < 0 (OR = 10.4, 95% CI: 5.12-21.02, P < 0.001). We also found that women with DII ≥ 0 had a 9.2 times larger risk compared with the men with DII < 0(OR = 9.2, 95% CI: 6.3-13.5, P < 0.001). Similarly, the risk of MS was remarkably higher in those with a history of depression and DII >0 (OR = 7.6, 95% CI: 5.1-11.5, P < 0.001). There was no evidence of multiplicative interaction between DII and the other risk factors of MS on the risk scale.

CONCLUSIONS

We identified additive interaction between DII and drug abuse, gender and history of depression on MS. Further studies are needed to understand the underlying mechanisms of these detected interactions.

The effect of smoking on latent hazard classes of metabolic syndrome using latent class causal analysis method in the Iranian population

BACKGROUND

The prevalence of metabolic syndrome is increasing worldwide. Clinical guidelines consider metabolic syndrome as an all or none medical condition. One proposed method for classifying metabolic syndrome is latent class analysis (LCA). One approach to causal inference in LCA is using propensity score (PS) methods. The aim of this study was to investigate the causal effect of smoking on latent hazard classes of metabolic syndrome using the method of latent class causal analysis.

METHODS

In this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS). 4857 participants aged over 20 years with complete information on exposure (smoking) and confounders in the third phase (2005-2008) were included. Metabolic syndrome was evaluated as outcome and latent variable in LCA in the data of the fifth phase (2014-2015). The step-by-step procedure for conducting causal inference in LCA included: (1) PS estimation and evaluation of overlap, (2) calculation of inverse probability-of-treatment weighting (IPTW), (3) PS matching, (4) evaluating balance of confounding variables between exposure groups, and (5) conducting LCA using the weighted or matched data set.

RESULTS

Based on the results of IPTW which compared the low, medium and high risk classes of metabolic syndrome (compared to a class without metabolic syndrome), no association was found between smoking and the metabolic syndrome latent classes. PS matching which compared low and moderate risk classes compared to class without metabolic syndrome, showed that smoking increases the probability of being in the low-risk class of metabolic syndrome (OR: 2.19; 95% CI: 1.32, 3.63). In the unadjusted analysis, smoking increased the chances of being in the low-risk (OR: 1.45; 95% CI: 1.01, 2.08) and moderate-risk (OR: 1.68; 95% CI: 1.18, 2.40) classes of metabolic syndrome compared to the class without metabolic syndrome.

CONCLUSIONS

Based on the results, the causal effect of smoking on latent hazard classes of metabolic syndrome can be different based on the type of PS method. In adjusted analysis, no relationship was observed between smoking and moderate-risk and high-risk classes of metabolic syndrome.

Impact of Alcohol Consumption on Multiple Sclerosis Using Model-based Standardization and Misclassification Adjustment Via Probabilistic Bias Analysis

BACKGROUND

The etiology of multiple sclerosis (MS) is still not well-demonstrated, and assessment of some risk factors like alcohol consumption has problems like confounding and measurement bias. To determine the causal effect of alcohol consumption on MS after adjusting for alcohol consumption misclassification bias and confounders.

METHODS

In a population-based incident case-control study, 547 patients with MS and 1057 healthy people were recruited. A minimally sufficient adjustment set of confounders was derived using the causal directed acyclic graph. The probabilistic bias analysis method (PBAM) using beta, logit-logistic, and triangular probability distributions for sensitivity/specificity to adjust for misclassification bias in self-reporting alcohol consumption and model-based standardization (MBS) to estimate the causal effect of alcohol consumption were used. Population attributable fraction (PAF) estimates with 95% Monte Carlo sensitivity analysis (MCSA) intervals were calculated using PBAM and MBS analysis. Bootstrap was used to deal with random errors.

RESULTS

The adjusted risk ratio (95% MCSA interval) from the probabilistic bias analysis and MBS between alcohol consumption and MS using the three distribution was in the range of 1.93 (1.07 to 4.07) to 2.02 (1.15 to 4.69). The risk difference (RD) in all three scenarios was 0.0001 (0.0000 to 0.0005) and PAF was in the range of 0.15 (0.010 to 0.50) to 0.17 (0.001 to 0.47).

CONCLUSION

After adjusting for measurement bias, confounding, and random error alcohol consumption had a positive causal effect on the incidence of MS.

Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review

PURPOSE

The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments.

METHODS

We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarized the epidemiological discipline, geographical location, expertize of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results.

RESULTS

Of the 89 publications included, 33% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By 2022, 59% of the publications originated from outside the United States and explored up to seven different epidemiological disciplines in 2021-2022. Double-robustness, bias reduction, and model misspecification were the main motivations that drew researchers toward the TMLE framework. Through time, a wide variety of methodological, tutorial, and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE.

CONCLUSIONS

There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits and adoption of TMLE.

The effects of different types of smoking on recovery from attack in hospitalized multiple sclerosis patients

BACKGROUND

Several studies demonstrated the association between tobacco smoking and higher risk and increased progression of multiple sclerosis (MS). Data about the effect of smoking during the recovery from MS attacks is limited. Furthermore, different types of tobacco exposures such as water pipe and passive smoking are not well assessed separately. So this study evaluated the effect of different types of smokes, cigarette and water pipe as well as passive smoking on the function recovery of relapsing-remitting MS (RRMS) attacks METHODS: This cohort study evaluated the adult patients with RRMS and Expanded Disability Status Scale (EDSS) < 5 in the attack phase. Patients were divided into two groups: smokers and non-smokers. The smokers included those who use cigarette, water pipe as well as passive smokers as subgroups for more analyses later. EDSS was monitored after relapse and two months after relapse. Change of EDSS considered as the criteria for functional recovery. The correlation between the amount of consumption and disability level was assessed among smokers by Pearson's correlation test. While, the difference of EDSS between smoker and non-smoker were assessed by Independent samples T-test.

RESULTS

142 patients were evaluated. 79 (55.6%) were smokers (43% male) while 63 (44.4%) were non-smokers (36.5% male). There was a statistically significant difference in change of EDSS between smoker and non-smoker groups, which change of EDSS was higher in non-smoker (-2.62 ± 0.90 non-smoker vs. -1.75 ± 0.76 smoker, P < 0.001). Also, only there was a significantly lesser decline in EDSS after two months in the cigarette smokers in subgroups analyses (P < 0.001). A correlation analysis revealed a significant positive correlation between the number per day of cigarette smoking and EDSS after relapse (r = 0.3, P = 0.03) and a significant positive correlation between minutes per month of smoking of water pipe and EDSS two months after relapse (r = ‎0.6‎‎, P > 0.001).

CONCLUSION

Tobacco smoking especially cigarette smoking is associated with a negative effect on recovery from the attack in patients with RRMS.

Protective effect of intensive glucose lowering therapy on all-cause mortality, adjusted for treatment switching using G-estimation method, the ACCORD trial

Previous analysis of the action to control cardiovascular risk in diabetes showed an increased risk of mortality among patients receiving intensive glucose lowering therapy using conventional regression method with intention to treat approach. This method is biased when time-varying confounder is affected by the previous treatment. We used 15 follow-up visits of ACCORD trial to compare the effect of time-varying intensive vs. standard treatment of glucose lowering drugs on cardiovascular and mortality outcomes in diabetic patients. The treatment effect was estimated using G-estimation and compared with accelerated failure time model using two modeling strategies. The first model adjusted for baseline confounders and the second adjusted for both baseline and time-varying confounders. While the hazard ratio of all-cause mortality for intensive compared to standard therapy in AFT model adjusted for baseline confounders was 1.17 (95% CI 1.01-1.36), the result of time-dependent AFT model  was compatible with both protective and risk effects. However, the hazard ratio estimated by G-estimation was 0.64 (95% CI 0.39-0.92). The results of this study revealed a protective effect of intensive therapy on all-cause mortality compared with standard therapy in ACCORD trial.

Longitudinal effects of lipid indices on incident cardiovascular diseases adjusting for time-varying confounding using marginal structural models: 25 years follow-up of two US cohort studies

Background

This study assesses the effect of blood lipid indices and lipid ratios on cardiovascular diseases (CVDs) using inverse probability-of-exposure weighted estimation of marginal structural models (MSMs).

Methods

A pooled dataset of two US representative cohort studies, including 16736 participants aged 42-84 years with complete information at baseline, was used. The effect of each lipid index, including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), ratios of TC/HDL-C, LDL-C/HDL-C, and TG/HDL-C on coronary heart disease (CHD) and stroke were estimated using weighted Cox regression.

Results

There were 1638 cases of CHD and 1017 cases of stroke during a median follow-up of 17.1 years (interquartile range: 8.5 to 25.7). Compared to optimal levels, the risk of CVD outcomes increased substantially in high levels of TC, LDL-C, TC/HDL-C, and LDL-C/HDL-C. If everyone had always had high levels of TC (≥240 mg/dL), risk of CHD would have been 2.15 times higher, and risk of stroke 1.35 times higher than if they had always had optimal levels (<200 mg/dL). Moreover, if all participants had been kept at very high (≥190 mg/dL) levels of LDL-C, risk of CHD would have been 2.62 times higher and risk of stroke would have been 1.92 times higher than if all participants had been kept at optimal levels, respectively. Our results suggest that high levels of HDL-C may be protective for CHD, but not for stroke. There was also no evidence of an adverse effect of high triglyceride levels on stroke.

Conclusions

Using MSM, this study highlights the effect of TC and LDL-C on CVD, with a stronger effect on CHD than on stroke. There was no evidence for a protective effect of high levels of HDL-C on stroke. Besides, triglyceride was not found to affect stroke.

Longitudinal causal effect of modified creatinine index on all-cause mortality in patients with end-stage renal disease: Accounting for time-varying confounders using G-estimation

BACKGROUND

Standard regression modeling may cause biased effect estimates in the presence of time-varying confounders affected by prior exposure. This study aimed to quantify the relationship between declining in modified creatinine index (MCI), as a surrogate marker of lean body mass, and mortality among end stage renal disease (ESRD) patients using G-estimation accounting appropriately for time-varying confounders.

METHODS

A retrospective cohort of all registered ESRD patients (n = 553) was constructed over 8 years from 2011 to 2019, from 3 hemodialysis centers at Kerman, southeast of Iran. According to changes in MCI, patients were dichotomized to either the decline group or no-decline group. Subsequently the effect of interest was estimated using G-estimation and compared with accelerated failure time (AFT) Weibull models using two modelling strategies.

RESULTS

Standard models demonstrated survival time ratios of 0.91 (95% confidence interval [95% CI]: 0.64 to 1.28) and 0.84 (95% CI: 0.58 to 1.23) in patients in the decline MCI group compared to those in no-decline MCI group. This effect was demonstrated to be 0.57 (-95% CI: 0.21 to 0.81) using G-estimation.

CONCLUSION

Declining in MCI increases mortality in patients with ESRD using G-estimation, while the AFT standard models yield biased effect estimate toward the null.

Population attributable fraction in textbooks: Time to revise

Introduction

The population attributable fraction is an important measure for assessing the impact of intervention on the disease risk in populations, but it is frequently misused in the research literature.

Methods

In this study, we review the definition, calculation, interpretation and assumptions of PAF in 43 textbooks and highlight important shortcomings.

Results

While the Levin formula was proposed as a method of calculation in 29 (67%) textbooks, only in 4 (9%) was the Miettinen formula or its generalization for multilevel exposure recommended to calculate a confounding-adjusted population attributable fraction. Other concepts such as generalized impact fraction and prevented and preventable fractions were briefly discussed in few textbooks.

Discussion

We recommend the authors revise the textbooks in light of our proposed framework for teaching the population attributable fraction.

To Adjust or Not to Adjust: The Role of Different Covariates in Cardiovascular Observational Studies

Covariate adjustment is integral to the validity of observational studies assessing causal effects. It is common practice to adjust for as many variables as possible in observational studies in the hopes of reducing confounding by other variables. However, indiscriminate adjustment for variables using standard regression models may actually lead to biased estimates. In this paper, we differentiate between confounders, mediators, colliders, and effect modifiers. We will discuss that while confounders should be adjusted for in the analysis, one should be wary of adjusting for colliders. Mediators should not be adjusted for when examining the total effect of an exposure on an outcome. Automated statistical programs should not be used to decide which variables to include in causal models. Using a case scenario in cardiology, we will demonstrate how to identify confounders, colliders, mediators and effect modifiers and the implications of adjustment or non-adjustment for each of them.

The causal effect and impact of reproductive factors on breast cancer using super learner and targeted maximum likelihood estimation: a case-control study in Fars Province, Iran

OBJECTIVES

The relationship between reproductive factors and breast cancer (BC) risk has been investigated in previous studies. Considering the discrepancies in the results, the aim of this study was to estimate the causal effect of reproductive factors on BC risk in a case-control study using the double robust approach of targeted maximum likelihood estimation.

METHODS

This is a causal reanalysis of a case-control study done between 2005 and 2008 in Shiraz, Iran, in which 787 confirmed BC cases and 928 controls were enrolled. Targeted maximum likelihood estimation along with super Learner were used to analyze the data, and risk ratio (RR), risk difference (RD), andpopulation attributable fraction (PAF) were reported.

RESULTS

Our findings did not support parity and age at the first pregnancy as risk factors for BC. The risk of BC was higher among postmenopausal women (RR = 3.3, 95% confidence interval (CI) = (2.3, 4.6)), women with the age at first marriage ≥20 years (RR = 1.6, 95% CI = (1.3, 2.1)), and the history of oral contraceptive (OC) use (RR = 1.6, 95% CI = (1.3, 2.1)) or breastfeeding duration ≤60 months (RR = 1.8, 95% CI = (1.3, 2.5)). The PAF for menopause status, breastfeeding duration, and OC use were 40.3% (95% CI = 39.5, 40.6), 27.3% (95% CI = 23.1, 30.8) and 24.4% (95% CI = 10.5, 35.5), respectively.

CONCLUSIONS

Postmenopausal women, and women with a higher age at first marriage, shorter duration of breastfeeding, and history of OC use are at the higher risk of BC.

Longitudinal Causal Effects of Normalized Protein Catabolic Rate on All-Cause Mortality in Patients With End-Stage Renal Disease: Adjusting for Time-Varying Confounders Using the G-Estimation Method

In this study, we aimed to estimate the causal effect of normalized protein catabolic rate (nPCR) on mortality among end-stage renal disease (ESRD) patients in the presence of time-varying confounding affected by prior exposure using g-estimation. Information about 553 ESRD patients was retrospectively collected over an 8-year period (2011-2019) from hemodialysis facilities in Kerman, Iran. nPCR was dichotomized as <1.2 g/kg/day versus ≥1.2 g/kg/day. Then a standard time-varying accelerated failure time (AFT) Weibull model was built, and results were compared with those generated by g-estimation. After appropriate adjustment for time-varying confounders, weighted g-estimation yielded 78% shorter survival time (95% confidence interval (95% CI): -81, -73) among patients with a continuous nPCR <1.2 g/kg/day than among those who had nPCR ≥1.2 g/kg/day during follow-up, though it was 18% (95% CI: -57, 54) in the Weibull model. Moreover, hazard ratio estimates of 4.56 (95% CI: 3.69, 5.37) and 1.20 (95% CI: 0.66, 2.17) were obtained via weighted g-estimation and the Weibull model, respectively. G-estimation indicated that inadequate dietary protein intake characterized by nPCR increases all-cause mortality among ESRD patients, but the Weibull model provided an effect estimate that was substantially biased toward the null.