Survival Analysis of Factor Affects Survival Time of Hypertension Patients

Hypertension is a major long-term health condition and a leading modifiable risk factor for cardiovascular disease and death. The aim of this study was to examine major factors that affect survival time of hypertension patients under follow-up. We considered a total of 430 random samples of hypertension patients who had been under follow up at Yekatit-12 Hospital in Ethiopia from January 2013 to January 2019. Four parametric accelerated failure time distributions: Exponential, Weibull, Lognormal and loglogistic are used to analyse survival probabilities of the patients. The Kaplan-Meierestimation method and log-rank tests were used to compare the survival experience of patients with respect to different covariates. The Weibull model is selected to best fit to the data sets. The results indicate that the baseline age of the patient, place of residence, family history of hypertension, khat intake, blood cholesterol level of the patient, hypertension disease stage, adherence to the treatment and related disease were significantly associated with survival time of hypertension patients. But factor like gender, tobacco use, alcohol use, diabetes mellitus status and fasting blood sugar were not significantly associated factors. Society and all stakeholders should be aware of the consequences of these factors which can influence the survival time of hypertension patients.


Introduction
Hypertension, sometimes called arterial hypertension, is a chronic medical condition in which the blood pressure in the arteries is elevated. This requires the heart to work harder than normal to circulate blood through the blood vessels. Blood pressure is summarized by two measurements, systolic and mechanization, population growth and ageing [3] [4].
Hypertension doubles the risk of cardiovascular diseases such as coronary heart disease, congestive heart failure, stroke, renal failure and peripheral arterial disease [5]. According to Global burden of cardiovascular disease over a fairly short period is attributable mainly due to changes in lifestyle such as diet and physical activity [6]. A meta-analysis also reported that lower values of blood pressure are associated with higher risk of cardiovascular disease and also with chronic kidney diseases [7].
Hypertension in Africa has now changed from a relative rarity to a major public health problem [8]. Current disease estimates for Sub-Saharan Africa are based on sparse data, but projections indicate increases in non-communicable diseases caused by demographic and epidemiologic transitions; however, hypertension control assumes a relatively low priority and little experience exists in implementing sustainable and successful programs. There is a wide disparity (0.4% to 43%) in the prevalence of hypertension and obesity in Sub-Saharan Africa. The detection rates in most high-income countries vary from 32% -64% while in many low-income countries, the reported detection rates are substantially lower [9] [10].
Ethiopia is a country currently prioritizing prevention of communicable and nutritional deficiency diseases. However, it experiencing double mortality burden as evidenced among adult population in Addis Ababa and communitybased cross-sectional study in urban Addis Ababa showed that the age-adjusted prevalence of high blood pressure was 31.5% among males and 28.9% among females. However, only 35.2% of the hypertension subjects were aware of their high blood pressure and only 11% were on treatment with target blood pressure attained in 25.6% [11]. A study conducted in Ethiopia in the last decade showed that the prevalence of cardiovascular diseases risk factor increased rapidly [12].
In addition, a study conducted by Awoke, et al., [13]

Variables in the Study
The explanatory (independent) variables of interest in this study include

Ethical Consideration
The Ethical clearance was checked and approved by ethical clearance committee of Arba Minch University department of Statistics and Addis Ababa Adminis-

Method of Data Analysis
Survival models are important statistical methods to describe and analyze the time-to-death events of hypertension patients. The study focused on time to event (time to death by hypertension), so the appropriate method of this particular study was survival analysis. We have used Kaplan-Meir estimator and parametric hazard model for the analysis and model building. We have also used log-rank tests for comparison of survival functions. Kaplan Meier analysis was used to study survival pattern; the KM plot, which is a step function, gives some indications about the shape of the survival distribution [16]. The figure in general shows if the pattern of one survivorship function lies above another which means the group defined by the upper curve lived longer, or had a more favourable survival experience than the group defined by the lower curve.

Parametric Regression Models
It was used for multivariate analysis to identify factors associated with death from hypertension. We applied four parametric models (Exponential, Weibull, Lognormal and Loglogistic) and the models are given by [17] [18] (Table 1).

Model Selection
Model comparison and selection are important to identify the best model that fit the data among different models. In this study, the model selection procedure was based on the deviance information criteria (DIC), Akaike information criteria (AIC), and Bayesian information criteria (BIC) [19].

Results
The statistical packages SPSS version 20 and R version 3.5.3 have been used to analyze the data. In addition, the proportion of death was varied by the alcohol consumption of the patient. The highest proportion of death was observed from a patient who consume alcohol (21.5%) whereas the lowest proportion of death (15.8%) was recorded among a patient doesn't use alcohol. Based on log-rank test result, they were significant in survival experience of the patients in different categories of gender (χ 2 = 5 with 1 df, p = 0.03), age group (χ 2 = 9.7 with 2 df, p = 0.02), khat intake (χ 2 = 9.7 with 1 df, p = 0.002), blood cholesterol (χ 2 = 5.9 with 1 df, p = 0.02), stage of hypertension (χ 2 = 59.7 with 3 df, p = 0.00), adherence (χ 2 = 12 with 1 df, p = 0.00) and related disease (χ 2 = 29.6 with 2 df, p = 0.00). But, they are not significant in survival experience of the patients in different categories of place of residence, family history of hypertension, tobacco use, alcohol use and diabetes mellitus status (α = 5%).

Results of the Parametric Regression Model
Most often the proportional hazards (PH) models are used for modeling survival data. However, when the PH assumption is violated, accelerated failure time models (parametric regression model) is an alternative approach [20] [21].   [20]. Therefore cox proportional hazard model is not appropriate to fit the data so we extend to the parametric regression models.
We applied four parametric models namely exponential, Weibull, Lognormal and Log-logistic models as a parametric distribution model of survival time T.
To select the appropriate parametric model for the hypertension patient data the common model comparison and selecting criterion, Akaike information criterion (AIC) and Bayesian information criterion were used. The model comparison analysis in Table 2 indicated that Weibull model has smallest AIC and BIC compare to other models.

Weibull Regression Model
It is essential to include statistically important and clinically relevant covariates into the model in fitting parametric regression model. We included all available covariates into the model to rank their statistical importance. This is often the case that we have no prior knowledge on which variable should be included.   The parameter estimation using finally Weibull regression model in Table 3 show that, the baseline age of the patient, place of residence, family history of hypertension, khat intake, blood cholesterol level of the patient, hypertension disease stage, adherence to the treatment and related disease were significantly associated with survival time of hypertension patients. Table 3 below indicate the parameter estimates of coefficients   Related disease is also another predictor variable related with risk of death of patients. Patient who had stroke and heart case had higher hazard rate than those who had none of related disease and the estimated hazard ratio is 2.222 and 4.780 respectively. Finally, by keeping other covariate constant the patient with high adherence has 0.495 − 1 = −0.505 or 50.5% lower hazard than patient with low adherence.

Discussion
Comparisons of survival models under different distributions of the hazard function provide the best model for fitting the specific data with appropriate inference [22]. In this study, the Weibull survival model has the smallest AIC, indicating its ability to fit the data. Previous survival studies in southwestern Ethiopia have also recognized the Weibull regression model as the best model for fitting the time until event data on HIV [23]. This finding also agrees with a study that analyzes parametric models for diabetes mellitus survival data in Addis Ababa [24].
In this study the finding regards the association between place of residence of patient and the survival time until hypertension-related death was similar with finding in Ashanti, West Africa reported that place of residence has a significant effect on hypertension patients [25]. We have also found a Family history of hypertension was a statistically significant risk factor for death in hypertension patient; this finding similar with finding of other researchers [26] [27]. Although this study did not find any association between gender and the survival time until hypertension-related death, other studies in Ethiopia and India have reported that the covariate gender doesn't have any association with hypertension [28]- [33]. Christian et al. (2013) indicated Alcohol use is an independent risk factor of hypertension and they also found Hypertension was significantly higher in individuals who take alcohol than those who did not [33].
The current study found that an age was associated with survival time of hypertension patients. These results are consistent with findings from a study in Uganda by (Wamala, et al., [34] [35] they identified khat chewing is one of the main risk factor of hypertension.

Conclusions
The objective of this study was to examine major factors that affect survival time The findings indicate that female hypertension patients had better survival probability than male hypertension patients and hypertension patients with age less than 25 year is the lowest survival probabilities when compare other age groups in the study areas. Hypertension patient with raised cholesterol level has lower survival probability as compared to that with a normal cholesterol level.
In conclusion, a significant number of patients found that lack of knowledge about behavioral risk factors of hypertension and so they need great attention.
Therefore, teaching the population or patients about the effect of behavioral risk factor of hypertension like alcohol use, tobacco use and khat intake is highly recommended and all stakeholders in synergistic approach towards awareness creation regarding hypertension, promotion of healthy lifestyle and improving health checkups among the community and early screening of those who have a family history of hypertension should be suggested. Based on the identified risk factors, appropriate interventions and implementation of community-based screening programs for early detection of hypertension and treating related disease of hypertension are recommended.