Mediating role of multimorbidity in inequality in mortality: a register study on the Danish population

BackgroundThe social inequality in mortality is due to differential incidence of several disorders and injury types, as well as differential survival. The resulting clustering and possible interaction in disadvantaged groups of several disorders make multimorbidity a potentially important component in the health divide. This study decomposes the effect of education on mortality into a direct effect, a pure indirect effect mediated by multimorbidity and a mediated interaction between education and multimorbidity.MethodsThe study uses the Danish population registers on the total Danish population aged 45–69 years. A multimorbidity index based on all somatic and psychiatric hospital contacts as well as prescribed medicines includes 22 diagnostic groups weighted together by their 5 years mortality risk as weight. The Aalen additive hazard model is used to estimate and decompose the 5 years risk difference in absolute numbers of deaths according to educational status.ResultsMost (69%–79%) of the effect is direct not involving multimorbidity, and the mediated effect is for low educated women 155 per 100 000 of which 87 is an effect of mediated interaction. For low educated men, the mediated effect is 250 per 100 000 of which 93 is mediated interaction.ConclusionMultimorbidity plays an important role in the social inequality in mortality among middle aged in Denmark and mediated interaction represents 5%–17%. As multimorbidity is a growing challenge in specialised health systems, the mediated interaction might be a relevant indicator of inequities in care of multimorbid patients.

ANALISIS SURVIVAL UNTUK DURASI PROSES KELAHIRAN MENGGUNAKAN MODEL REGRESI HAZARD ADDITIF

Survival data is the length of time until an event occurs. If the survival time is affected by other factor, it can be modeled with a regression model. The regression model for survival data is commonly based on the Cox proportional hazard model. In the Cox proportional hazard model, the covariate effect act multiplicatively on unknown baseline hazard. Alternative to the multiplicative hazard model is the additive hazard model. One of the additive hazard models is the semiparametric additive hazard model that introduced by Lin Ying in 1994. The regression coefficient estimates in this model mimic the scoring equation in the Cox model. Score equation of Cox model is the derivative of the Partial Likelihood and methods to maximize partial likelihood with Newton Raphson iterasi. Subject from this paper is describe the multiplicative and additive hazard model that applied to the duration of the birth process. The data is obtained from two different clinics,there are clinic that applies gentlebirth method while the other one no gentlebirth. From the data processing obtained the factors that affect on the duration of the birth process are baby’s weight, baby’s height and method of birth. Keywords: survival, additive hazard model, cox proportional hazard, partial likelihood, gentlebirth, duration

Regression Analysis for Outcome-Dependent Sampling Design under the Covariate-Adjusted Additive Hazards Model

This paper provides a new insight into an economical and effective sampling design method relying on the outcome-dependent sampling (ODS) design in large-scale cohort research. Firstly, the importance and originality of this paper is that it explores how to fit the covariate-adjusted additive Hazard model under the ODS design; secondly, this paper focused on estimating the distortion function through nonparametric regression and required observation of the covariate on the confounding factors of distortion; moreover, this paper further calibrated the contaminated covariates and proposed the estimators of the parameters by analyzing the calibrated covariates; finally, this paper established the large sample property and asymptotic normality of the proposed estimators and conducted many more simulations to evaluate the finite sample performance of the proposed method. Empirical research demonstrates that the results from both artificial and real data verified good performance and practicality of the proposed ODS method in this paper.

The impact of mental vulnerability on the relationship between cardiovascular disease and depression

Background. The mechanisms linking cardiovascular disease (CVD) and depression are still not established. We investigated the impact of mental vulnerability on the relationship between CVD and depression. Methods. A total of 19,856 individuals from five cohorts of random samples of the background population in Copenhagen were followed from baseline (1983–2011) until 2017 in Danish registries. Additive hazard and Cox proportional hazard models were used to analyze the effects of confounding by mental vulnerability as well as interactions between mental vulnerability and CVD on the risk of depression. Results. During follow-up, 15.3% developed CVD, while 18.1% experienced depression. A strong positive association between CVD and depression (hazard ratio: 3.60 [95% confidence intervals (CI): 3.30; 3.92]) corresponding to 35.4 (95% CI: 31.7; 39.1) additional cases per 1,000 person-years was only slightly attenuated after adjustment for mental vulnerability in addition to other confounders. Synergistic interaction between CVD and mental vulnerability was identified in the additive hazard model. Due to interaction between CVD and mental vulnerability, CVD was associated with 50.9 more cases of depression per 1,000 person-years among individuals with high mental vulnerability compared with individuals with low mental vulnerability. Conclusions. Mental vulnerability did not explain the strong relationship between CVD and depression. CVD was associated with additional cases of depression among individuals with higher mental vulnerability indicating that this group holds the greatest potential for intervention, for example, in rehabilitation settings.