A new look at risk patterns related to coronary heart disease incidence using survival tree analysis: 12 Years Longitudinal Study

Epidemiological analysis of coronary heart disease and its main risk factors: are their associations multiplicative, additive, or interactive?

OBJECTIVE

The purpose of this study was to discover how considering multiplicative, additive, and interactive effects modifies results of a prospective cohort study on coronary heart disease (CHD) incidence and its main risk factors.

MATERIAL AND METHODS

The Kuopio Ischaemic Heart Disease Risk Factor (KIHD) Study provided the study material, 2682 Eastern Finnish middle-aged men, followed since the 1980s. We applied multiplicative and additive survival models together with different statistical metrics and confidence intervals for risk ratios and risk differences to estimate the nature of associations.

RESULTS

The mean (SD) follow-up time among men who were free of CHD at baseline (n = 1958) was 21.4 (10.4) years, and 717 (37%) of them had the disease and 301 (15%) died for CHD before the end of follow-up. All tested non-modifiable and modifiable risk factors statistically significantly predicted CHD incidence. We detected three interactions: circulating low-density lipoprotein cholesterol (LDL-C) × age, obesity × age, and obesity × smoking of which LDL-C × age was the most evident one. High LDL-C increased the risk of CHD more among men younger than 50 [risk ratio (RR) 2.10] than those older than 50 (RR 1.22). LDL-C status was the only additive covariate. The additive effect of high LDL-C increased almost linearly up to 18 years and then reached a plateau. The simple multiplicative survival model stressed glycemic status as the strongest modifiable risk factor for developing CHD [hazard ratio (HR) for diabetes vs. normoglycemia was 2.69], whereas the model considering interactions and time dependence emphasised the role of LDL-C status (HR for high LDL-C vs. lower than borderline was 4.43). Age was the strongest non-modifiable predictor.

CONCLUSIONS

Including covariate interactions and time dependence in survival models potentially refine results of epidemiological analyses and ease to define the order of importance across CHD risk factors. KEY MESSAGESIncluding covariate interactions and time dependence in survival models potentially refine results of epidemiological analyses on coronary heart disease.Including covariate interactions and time dependence in survival models potentially ease to define the order of importance across coronary heart disease risk factors.

Analysing First Birth Interval by A CART Survival Tree

Background

Birth spacing, especially the first birth interval (FBI), is a suitable index to investigate the delayed fertil- ity that results in a low fertility pattern. Non-parametric familiar alternatives to the Cox proportional hazard regression (CPH) model include survival trees that can automatically discover certain types of covariate interactions according to the survival length. The aim of this research is to study FBI influential factors by applying survival trees.

Materials and Methods

In this cross-sectional study, 610 married women (aged 15-49 years), were selected from different regions of Tehran, Iran in the Winter and Spring of 2017. Classification and regression trees (CART) for the FBI survival tree were fitted by taking into consideration the predictors of each woman’s age, age at first marriage, educational level, partner’s educational level, activity, region, house ownership, kinship, partner’s race, marriage time attitude, and expenditure using R packages.

Results

Since the PH assumption of the CPH model was not confirmed for the covariates of age at first marriage (P=0.001), kinship (P=0.000), partner’s race (P=0.001), and marriage time attitude (P=0.042), the results of this model were not valid. Thus, a CART survival tree was fitted. The validity of the fitted model in assessing FBI was confirmed by the significant result of the log rank test (P<0.01) for the terminal nodes and the value of the separation measure, which was greater than 1. The fitted tree had 13 terminal nodes and the most vital FBI predictor was women’s age. The longest FBI belonged to educated and employed women, ages 30-37 years.

Conclusion

Analysing patterns of birth spacing by selecting the appropriate statistical method provides important informa- tion for health policymakers. In order to formulate appropriate demographic policies, it is essential to take into consideration age, educational level and job status of the women, all of which have essential roles on their decision to have children.

Tobacco Smoking: Findings from 20 Years of the Tehran Lipid and Glucose Study

CONTEXT

Smoking is a global public health priority and accurate data of the local population is essential to improve the health policies against its use. Hence, this study aimed to summarize the important findings available on the prevalence of smoking and its association with non-communicable diseases, documented by one of the largest prospective community-based studies of Iran.

EVIDENCE ACQUISITION

All articles derived from the Tehran Lipid and Glucose Study (TLGS) in the last two decades, from the earliest publications until 30 January 2018 were reviewed for their findings on tobacco smoking.

RESULTS

The prevalence of smoking in non-diabetic adults ≥ 20 years increased between baseline (phase I, 1999 - 2001) and follow-up (phase V, 2008 - 2011) from 25.5% to 35.4% among men and from 3.4% to 6.8% among women. In TLGS adolescents (10 - 18 years) water pipe use increased between 2003 and 2005 from 35.5% to 40.9% among boys and from 19.7% to 26.1% among girls. Regarding health hazards, smoking in men was associated with increased risk of combined impaired fasting glucose/impaired glucose tolerance [hazard ratio (HR) 1.69; confidence interval (CI) 95% 1.15 - 2.48] and hypertension (HR 1.26; CI 95% 0.98 - 1.63). Moreover, men, even smoking less than 10 cigarettes per day, were at increased risk for cardiovascular diseases by HR 2.12 (CI 95% 1.14 - 3.95). For women, the risk of chronic kidney disease dramatically increased 5.74-fold (CI 95% 2.71 - 12.15) among smokers. In the whole population, smoking contributed to 7.7% of all-cause mortality with HR 1.75 (CI 95% 1.38 - 2.22). Other health aspects of tobacco smoke, including its impact on metabolic status, thyroid function, female reproductive system and life style have also been reviewed.

CONCLUSION

Considering hazards of smoking, there is the urgency for more effective preventive measures in Iran; emphasizing the need for further local studies on the hazards of smoking with special attention to women and adolescents and the independent hazards of water pipe use.

Application of survival tree analysis for exploration of potential interactions between predictors of incident chronic kidney disease: a 15-year follow-up study

Background

Chronic kidney disease (CKD) is a growing public health challenges worldwide. Various studies have investigated risk factors of incident CKD; however, a very few studies examined interaction between these risk factors. In an attempt to clarify the potential interactions between risk factors of CKD, we performed survival tree analysis.

Methods

A total of 8238 participants (46.1% men) aged > 20 years without CKD at baseline [(1999–2001) and (2002–2005)], were followed until 2014. The first occurrence of CKD, defined as the estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m², was set as the main outcome. Multivariable Cox proportional hazard (Cox PH) regression was used to identify significant independent predictors of CKD; moreover, survival tree analysis was performed to gain further insight into the potential interactions between predictors.

Results

The crude incidence rates of CKD were 20.2 and 35.2 per 1000 person-years in men and women, respectively. The Cox PH identified the main effect of significant predictors of CKD incidence in men and women. In addition, using a limited number of predictors, survival trees identified 12 and 10 subgroups among men and women, respectively, with different survival probability. Accordingly, a group of men with eGFR > 74 ml/min/1.73 m², age ≤ 46 years, low level of physical activity, waist circumference ≤ 100 cm and FPG ≤ 4.7 mmol/l had the lowest risk of CKD incidence; while men with eGFR ≤ 63.4 ml/min/1.73 m², age > 50 years had the highest risk for CKD compared to men in the lowest risk group [hazard ratio (HR), 70.68 (34.57–144.52)]. Also, a group of women aged ≤ 45 years and eGFR > 83.5 ml/min/1.73 m² had the lowest risk; while women with age > 48 years and eGFR ≤ 69 ml/min/1.73 m² had the highest risk compared to low risk group [HR 27.25 (19.88–37.34)].

Conclusion

In this post hoc analysis, we found the independent predictors of CKD using Cox PH; furthermore, by applying survival tree analysis we identified several numbers of homogeneous subgroups with different risk for incidence of CKD. Our study suggests that two methods can be used simultaneously to provide new insights for intervention programs and improve clinical decision making.