Bivariate Events of Natural Course of Chronic Diseases: An Illustration with Hypertension and Diabetes Mellitus
Date Issued
2006
Date
2006
Author(s)
Lai, Ho-Hsien
DOI
zh-TW
Abstract
Background
Diabetes Mellitus (DM) and hypertension are two common chronic diseases and may coexist in an adult. Either treating hypertension or DM as outcome, both directions in previous studies have consistent findings. In spite of these findings, it is very rare to report prevalence rate and estimate incidence rate in the same study based on data from an underlying population rather than from hypertensive or diabetic subjects. Quantifying the prevalence rate has a significant implication for clinical management of both diseases in order to reduce macro-vascular, macro-vascular complications, and deaths. Estimating incidence from general population may give a clue to onset of hypertension and DM and progression to co-morbidity. Moreover, cumulative risk for co-morbidity or incidence by different demographic features or biochemical variables may be of great help for identifying high-risk group for developing newly diagnosed hypertension or DM.
There are several potential issues that still remain elusive in investigating co-morbidities of these common chronic diseases. First, reporting the preponderance of co-morbid diseases is frequently based on prevalent cases with a cross-sectional survey. This may underestimate co-morbidity rate because those who are potential of developing co-morbidity may not be observed yet at the time of survey, which is so-called right-censored problem in survival analysis. Second, the temporal sequence between serial events, which is a thorny issue because the exact onset time of chronic disease is unknown, has been neglected and hardly addressed. Third, how life-style factors affect occurrence of serial events is also unclear.
Aim
By using the dataset of community-screening program in Matzu we carry out following tasks and try to solve up all the potential issues.
1. Estimate the prevalence, incidence, 5-year or 10-year cumulative risk for hypertension, diabetes mellitus, and comorbidity.
2. Estimate the 5-year or 10-year cumulative risks based on incidence by different biological measures.
3. Evaluate the temporal sequence between serial events of hypertension and diabetes mellitus.
4. Develop a mathematical method to evaluate the potential proportion of being comorbidity case, the temporal sequence between serial events of hypertension and diabetes mellitus, the incidence of hypertension and diabetes mellitus, and the transition probability of developing comorbidity.
5. Estimate the potential proportion of being comorbidity case, the temporal sequence between serial events of hypertension and diabetes mellitus, the incidence of hypertension and diabetes mellitus, and the transition probability of developing comorbidity by different biological measures.
Material and Methods
Study population is consisted of those who participate community-screening program in Matzu between 1996 and 2000. In the community-screening program, questionnaire, physical examination, and serological examination were performed annually. After data collection is completed, we apply basic descriptive statistics, Markov transition model, and Cox proportional hazards regression model to perform analysis. Besides, we also develop a bivariate event model to estimate two latent variables, the incidence of each single event, and the transition rate form being single event state to the state of two events. Take hypertension and diabetes mellitus for example, latent variables include the proportion of an individual with potential of developing hypertension (HTN) and diabetes mellitus (DM) and the proportion of getting hypertension first and diabetes mellitus latter among all the cases of comorbidity. For the sake of right censoring, it is unable to estimate these two latent variables directly. In order to achieve the aim we set, based on these latent variables we divided all the cases into four conditions, getting hypertension first and comorbidity latter, getting diabetes mellitus first and comorbidity latter, getting hypertension only, and getting diabetes mellitus only. Then, we constructed three three-state stationary Markov models to describe the transitional probability of every condition that mentioned above. Total likelihood function was derived, then the maximum likelihood estimates were obtained by using Newton-Raphson optimisation. 95% confidence intervals were calculated using variance estimated from the inverse Hessian matrix.
Result
The overall prevalence rate was 35% and 6% for hypertension and DM, respectively. The overall incidence rate of hypertension was 14% (13%-16%). The overall incidence rate of diabetes mellitus was 1.6%. DM and hypertension had 88 and 10 times, respectively, higher risk for developing co-morbidity compared with subjects free of DM and hypertension.
In the results of transition model, we found that after controlling for age, gender, BMI, and uric acid, DM and hypertension in previous state had 92-fold and 6-fold risk for developing co-morbidity. Similar findings are found by using Cox proportional hazards regression model. The crude hazard ratios for DM and hypertension in previous state were 2.5 and 13. After controlling for gender, obesity, smoking, and alcohol drinking, the adjusted hazard ratios for developing co-morbidity were 2-fold and 9-fold for the past history of hypertension and DM.
In the results of stochastic model, we found that around 14% (95% confidence interval (CI): 6.80%-20.75%) subjects aged 30 years and above in the Matzu cohort have potential to developing cormobidity. Among them, 77.89% subjects would develop hypertension first. The other 22.11% would have diabetes mellitus first. It yielded 86% cases could either develop single disease (either hypertension or diabetes mellitus) or none of both diseases in lifetime. The incidence of fresh hypertension is 14.71 cases per 100 person-year (95% CI: 13.03-16.39). However, the incidence of hypertension after diabetes mellitus diagnosed was much higher (0.9897, 95% CI: 0.4203-1.5590). For fresh diabetes mellitus, the annual incidence was estimated as 0.99 cases per 100 person-year (95% CI: 0.42-1.56). After hypertension, the incidence of diabetes mellitus was much higher (0.3092, 95% CI: 0.4203-1.5590).
When looking into the effect of single covariate on transition rates, we found that the estimates of p and p were quite stable. Estimates of p were between 11% and 17% when considering different covariate. Estimates of p were around 72% to 82%. We found that male had 2-fold risk (95% CI: 1.56-2.46) of incidence of fresh hypertension (p<0.05). The effects were not statistically significant on the other three transition rates. This was similar to the effects of hyperuricemia, smoking, and alcohol drinking. For obese subjects (BMI³25), they had statistically significant higher risks (RR=2.02, 95% CI: 1.60-2.54) on fresh incidence of hypertension and on the risk of developing diabetes mellitus after hypertension already diagnosed (RR=3.55, 95% CI: 1.55-8.14). Compared to those with normal value of triglyceride, cases with hypertriglycemia (³200) had statistically higher risk of both fresh incidence, 2.66-fold on hypertension (95% CI: 1.87-3.79) and 3.46-fold on diabetes mellitus (95% CI: 1.00-11.99). In this model, the effects of elevated total cholesterol (³200) were not statistically significant on all four-transition rates.
The effects of covariates on transition rates with simultaneous consideration of the effects on the potential of comorbidity (p) were similar to the results in Table 2, except the one of obesity on the incidence of diabetes mellitus after hypertension diagnosed became insignificant. We found the only significant covariate on p was hypertriglycemia (OR=2.12, 95% CI: 1.82-2.47).
Discussion
In conclusion, prevalence, incidence and cumulative risk of hypertension, DM, and co-morbidity were estimated, of which high risk group for developing hypertension, DM and co-morbidity by relevant biochemical variables were identified. It also suggests that occurrence of hypertension seems prior to the development of DM.
Finally, a statistical model was developed for modeling bivariate events of disease natural history for hypertension and DM taking susceptibility to co-morbidity and temporal order of hypertension and DM.
Subjects
糖尿病
共病症
自然病史
雙變項事件
Diabetes Mellitus
Hypertension
Comorbidity
Natural History
Bivariate Events
SDGs
Type
thesis
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