Health-related quality of life and its influencing factors in elderly people with hypertension and type 2 diabetes mellitus based on Multi-level model: take the east coastal area of China as an example

doi:10.21203/rs.3.rs-1956787/v1

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Research Article

Health-related quality of life and its influencing factors in elderly people with hypertension and type 2 diabetes mellitus based on Multi-level model: take the east coastal area of China as an example

https://doi.org/10.21203/rs.3.rs-1956787/v1

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Purpose

This study aimed to evaluate the relationship between hypertension (HTN) and type 2 diabetes mellitus (T2DM) on health-related quality of life (HRQoL) in elderly patients in the eastern coastal regions of China, which correspond to socio-demographic conditions, comorbidities and health outcomes.

Methods

From September 2021 to December 2021, 750 participants with HTN and T2DM over the age of 60 were recruited through multi-stage stratified sampling to conduct a cross-sectional study in the eastern coastal region of China. HRQoL was assessed using the EQ-5D-3L questionnaire. The multi-level model was developed to analyze the effects of patients' own factors and regional factors on HRQoL in patients with HTN and T2DM, using patients as level 1 and regions as level.

Results

The percentage of co-morbidity of HTN and T2DM in community-dwelling older adults was 14.3%, with a health utility index (0.778 ± 0.270). The multi-level model showed that the ICC was 0.797 and 0.815, respectively, which means that about 79.7% of the variance in EQ-5D health utility index was due to older adults' own factors and 20.3% of the variance was from urban level aggregation; about 81.5% of the variance in EQ-VAS scores was due to own factors and 18.5% of the variance was from urban level aggregation. Multi-level model results show that age, marital status, monthly income, number of chronic diseases, and alcohol consumption (P < 0.05) were significantly related to HrQoL in elderly patients with HTN and T2DM.

Conclusion

The HRQoL of older adults with HTN and T2DM is significantly lower than that of the general population, and emphasis should be placed on the prevention and management of older adults with the disease. Meanwhile, a central aspect to the management of HTN and T2DM is promoting a healthy lifestyle, and health-related knowledge should be promoted more, with emphasis on patients' mental health, to improve the HTN and T2DM of older adults.

Health-related quality of life (HRQoL)

Hypertension (HTN)

Type 2 diabetes mellitus(T2DM)

Multi-level model

EQ-5D Scale

With the increase in life expectancy and the intensification of population aging, there are currently a large number of people worldwide suffering from various chronic diseases, which grossly impairs their health-related quality of life(HRQoL)(1). According to the World Health Organization, after obesity, hypertension (HTN) and type 2 diabetes mellitus (T2DM) are responsible for the high number of cardiovascular events and deaths worldwide(2). Patients with HTN and T2DM had a 4-fold higher risk of developing cardiovascular disease (CVD) than the normotensive nondiabetic controls(3, 4). High blood pressure is reported in over two-thirds of patients with T2DM, and the occurrence of hypertension occurs simultaneously with the occurrence of hyperglycemia(5, 6). For example, in the San Antonio Heart Study, 85% of patients with T2DM developed HTN by the fifth decade of life, whereas 50% of those with HTN experienced impaired glucose tolerance or T2DM(7). HTN and T2DM are two important public health challenges(8).

In 2019, 11.3% of deaths among adults aged 20 to 79 years globally were attributed to diabetes(9), 14% of deaths globally to HTN(10), and more than 50% of people with diabetes mellitus (DM) had HTN(11–13). HTN and T2DM are common comorbidities. It is no coincidence that these two diseases coexist frequently in the same individual, since the pathophysiological aspects of the two diseases are the same, especially those associated with obesity and insulin resistance(14). This is also confirmed by the results of GIan Luca Colussi et al.'s analysis the simultaneous presence of HTN and T2DM in Western countries and in Japan. It emerged that 20% of HTN patients had T2DM and 50% of T2DM patients had HTN(15). Having one of these two conditions increases the risk of developing the other by a factor of 1.5-2.0 times(16).

Generally, studies focus on the effects of elevated blood pressure and blood glucose on patients' cardiac, cerebral and renal systems and metabolic aspects, but pay less attention to the effects of quality of life, while HRQoL can reflect the information and status of patients from multiple perspectives (including patients' physiological, psychological and social adaptation)(17–19), and therefore gradually becomes a predominant proxy of lifestyle outcomes, medical treatment and quality of care(1, 20). Findings obtained by evaluating HRQoL can contribute to holistic medical research as well as public health measures to minimize surges in chronic disease cases(21, 22). Among those, the EuroQol-5 Dimensions Questionnaire (EQ-5D) has become a preference-based instrument due to its simplicity and reliability in measuring HRQoL(23, 24). The EQ-5D is the most widely used health state utility instrument in health technology assessments(25) and commonly listed as the preferred instrument by international guidelines(26, 27). There are clear gaps in our understanding of the factors that may have a clear effect on HRQoL worldwide(6), hence, there is a need to further investigate this area since HRQoL has been emerging as an important outcome in HTN and T2DM treatment. Raising awareness, treatment, and control of these diseases is a major goal of the global health system.

Therefore, the objective of this study is to identify risk factors associated with HRQoL in elderly patients with HTN and T2DM among the elderly using the multi-level model, with an eye to future developments more specifically targeting chronic diseases protection. Such intervention programs represent a new therapeutic target in patients with HTN and T2DM(6).

Source of data

Based on the level of economic development and geographical location, China as a whole can be divided into three major economic regions, namely the eastern region, which is the first province to adopt the policy of coastal opening up, with a relatively high level of economic development, the central region is the second most economically developed region, and the western region is an economically underdeveloped region.. In this study, the eastern coastal areas with high economic development level are selected as the research objects(28).

This cross-sectional investigation was conducted from September 2021 to December 2021 by the public health staff in the household survey. A multi-stage stratified sampling method was selected for this study. In the first stage, three provinces of Shandong, Jiangsu and Guangdong were randomly selected in the eastern coastal area.. Secondly, a municipality was randomly selected from within each province. Qingdao in Shandong Province, Suzhou in Jiangsu Province and Guangzhou in Guangdong Province.

In the second stage, a county/district was randomly selected within the jurisdiction of each city. Qingdao was elected Jimo District, Suzhou was was elected Kunshan County and Guangzhou was was elected Yuexiu District. Next, two streets were randomly selected from each city/district. Finally, two communities were randomly selected from each street.

The questionnaire consisted of two sections. The first section included questions on demographics(e.g., sex, age), socioeconomic status (e.g., region, marital status, education, health insurance, and monthly income), health status including chronic conditions, and health-related behaviours (e.g., smoking, alcohol, sleep schedule) of the participantsand. The second part is to measure the quality of life associated with health using the EuroQol-5 dimensional scale. All investigators were trained in the questionnaire a few days before the official survey.

Inclusion criteria of participants were as follows: Age ≥ 60 years; received home and community-based services (HCBSs) for six months or more; clear consciousness and ability to communicate normally; voluntarily participated in this survey and signed an informed consent form. People with severe cognitive impairment or memory impairment were excluded. Data with missing values of key variables were removed to screen out older adults with HTN and T2DM; 750 eligible patients were included in the HRQoL study.

Quality control measures were implemented during the data acquisition process.. Since most of the respondents were adults, we used face-to-face interviews instead of filling out the questionnaire ourselves. These problems are progressive to reduce any short-term memory load..

Health-related quality of life

HRQoL measured by EQ-5D was used as the health outcome measure in this study. In this study, after extensive literature review during the questionnaire design phase, it was found that the 3-level EQ-5D scale has better reliability and is more broadly used in measuring health-related quality of life in the elderly population. And the first part comprises EQ-5D-3L, it consists of mobility (MO), self-care(SC), usual activities (UA),pain/discomfort (PD), and anxiety/depression (AD). Each dimension has three levels of response or severity (no problems, moderate problems and extreme problems). In this study, the Chinese time trade-off method conversion table was used, and the score generated by this conversion table was between − 0.149 ~ l.(29, 30). The EQ-5D index equals 1 is a binary classification analysis of the EQ-5D index equal to 1 and the EQ-5D index less than 1 when there are no problems with the 5 dimensions. The next section is the EQ-visual Analog Scale (EQ-VAS), where respondents use a VAS scale of 0-100 to describe their health. Respondents responded to their own health status, with a score of ≥ 80 as a good EQ-VAS score, and a score of < 80 as a poor EQ-VAS score.

Statistical data analysis

Means and standard deviations (SD) were presented for continuous variables, while frequency and percentage were used for categorical variables. Wilcoxon rank sum test and Kruskal-Wallis H-test were used for univariate analysis(31). The advantage of multi-level models analysis as an effective method to deal with data with hierarchical structure is that a single random error term can be decomposed into fixed and random effects at each level corresponding to the data hierarchy. This study uses a two-level model in a multi-level models for the analysis, and is formulated as follows:

$$\text{L}\text{o}\text{g}\text{i}\text{t}\left({P}_{ij}\right)=({\beta }_{o}+{\beta }_{1xij})+({\mu }_{oj}+{e}_{oij})$$

i is level 1 unit, indicating individual level, j is level 2 units, the community area where the research subject is located, ${{\beta }}_{\text{o}}$is the intercept, ${{\beta }}_{1}$is the regression coefficient of the explanatory variable χ, ${{\mu }}_{0\text{j}}$ is the random error at level 2, ${\text{e}}_{0\text{i}\text{j}}$ is the random error term, which is the random error at the level of the study subject.

Characteristics of the Participants

Socio-demographic for the participants are presented in Table 1. There were 750 eligible participants in this study, HTN participants accounted for 50.5% of the total participants (n = 661), T2DM participants accounted for 21.1% of the total participants (n = 276), co-morbidity of HTN and T2DM was 14.3% (n = 187). Generally speaking, the majority of the older adults were female (55.1%), at the age of > 80 (40.1%), married (71.5%), with primary school and below education level (62.9%), monthly income of ≤ 1000 RMB (34.6%), number of chronic diseases of ≥ 3 (48.8%), sleep time(h) of 6 ~ 8 (46.5%),never smoking (73.7%), never drinking (80.4%). Among them, 41.6% had EQ-5D health utility index equal to 1, and 48.0% had EQ-VAS scores ≥ 80.

Table 1

Factors influencing HRQoL
Variables	n(%)	EQ-5D Index = 1	χ²	P	EQ-VAS good	χ²	P
Region			61.850	< 0.001		113.533	< 0.001
Guangzhou	145(19.3)	33(10.6)			23(6.4)
Suzhou	253(33.7)	81(26.0)			103(28.6)
Qingdao	352(46.9)	198(63.5)			234(65.0)
Sex			1.352	0.245		0.108	0.742
Male	337(44.9)	148(43.9)			164(45.6)
Female	413(55.1)	164(39.7)			196(54.4)
Age(years)			50.708	< 0.001		11.091	0.004
60 ~ 70	230(30.7)	137(43.9)			129(35.8)
71 ~ 80	219(29.2)	88(28.2)			106(29.4)
> 80	301(40.1)	87(27.1)			125(34.7)
Marital status			22.672	< 0.001		4.931	0.026
Married	536(71.5)	252(80.8)			271(75.3)
Divorce/Widow	214(28.5)	60(19.2)			89(24.7)
Educational			12.724	0.005		0.934	0.817
Primary school and below	467(62.9)	175(56.6)			222(62.2)
Middle school	145(19.5)	75(24.3)			72(49.7)
Post-secondary or tertiary	107(14.4)	52(16.8)			50(14.0)
Bachelor's degree and above	23(3.1)	7(2.3)			13(3.6)
Health insurance			3.702	0.157		0.116	0.944
Basic employee health insurance	230(30.7)	104(33.3)			110(30.6)
Basic Resident Health Insurance	417(55.6)	173(55.4)			202(56.1)
Other	103(13.7)	35(11.2)			48(13.3)
Monthly income (RMB)			20.589	< 0.001		27.717	< 0.001
≤ 1000	255(34.6)	83(27.3)			116(33.1)
1001 ~ 3000	205(27.8)	106(34.9)			125(35.7)
3001 ~ 5000	190(25.8)	73(24.0)			67(19.1)
≥ 5001	86(11.7)	42(13.8)			42(12.0)
Number of chronic diseases			73.110	< 0.001		76.964	< 0.001
1	163(21.7)	103(33.0)			112(31.1)
2	221(29.5)	112(35.9)			131(36.4)
≥ 3	366(48.8)	97(31.1)			117(32.5)
Sleep time(h)			6.238	0.044		2.001	0.368
< 6	235(31.3)	88(28.2)			104(28.9)
6 ~ 8	349(46.5)	162(51.9)			172(47.8)
> 8	166(22.1)	62(19.9)			84(23.3)
Smoking			0.464	0.496		0.853	0.356
Yes	197(26.3)	86(27.6)			89(24.7)
No	553(73.7)	226(72.4)			271(75.3)
Alcohol			9.886	0.002		6.123	0.013
Yes	147(19.6)	78(25.0)			84(23.3)
No	603(80.4)	234(75.0)			276(76.7)

Health-related quality-of-life status

As shown in Table 1, region (P < 0.001), age (P < 0.001), marital status (P < 0.001), education (P = 0.005), monthly income(P < 0.001), number of chronic diseases (P < 0.001), sleep time(h)(P = 0.044), drinking (P = 0.002) were significantly different in participants of EQ-5D health utility index. Region (P < 0.001), age (P = 0.004), marital status (P = 0.026), monthly income (P < 0.001), number of chronic diseases (P < 0.001), drinking (P = 0.013) were significantly different in participants of EQ-VAS score. As shown in Fig. 1, we found the prevalence of HTN and T2DM increased with age in the elderly(32).

Estimated results of the EQ-5D health utility index

To obtain an estimate of variance between organizations (or clusters) and within, an estimate of the null model was made with the EQ-5D health utility index as the dependent variable (Table 2). The ICC is 0.797 for EQ-5D health utility index, indicating that 79.70% of the total variation exists between patients, suggesting that EQ-5D health utility index are significantly influenced by patient’s characteristics. In other words, multi-level models for EQ-5D health utility index are necessary. Based on the zero model estimation results, the EQ-5D health utility index can be estimated by multi-level binomial logistic regression model.

Table 2

The estimation results of EQ-5D health utility index null models
Variable	β	S_‾x	Wald Z-Value	P-Value	95%CI
Variable	β	S_‾x	Wald Z-Value	P-Value	Upper limit	Lower limit
Intercept	0.490	0.014	35.483	< 0.001	0.463	0.517
Residual	0.125	0.010	12.796	< 0.001	0.107	0.146

Table 3 presents the estimation results of EQ-5D health utility index, the results show that at the age of 60 ~ 70 or 71 ~ 80 is higher than 0.189 (β = 0.189,95%CI:0.100,0.279), or 0.150 (β = 0.150,95%CI:0.085,0.215) compared to at the age of > 80, monthly income of ≤ 100 or 3001 ~ 5000 is lower than 0.134 (β = 0.134,95%CI:-0.224,0.044) or 0.109 (β = 0.109,95%CI:-0.197,-0.020) compared to monthly income of ≥ 5001, number of chronic diseases of 1 or 2 is higher than 0.312 (β = 0.312,95%CI:0.225,0.400) or 0.198 (β = 0.198,95%CI:0.120,0.275)compared to number of chronic diseases of ≥ 3, drinking is lower than 0.075 (β = 0.075,95%CI:-0.134,-0.017) compared to no drinking.

Table 3

Estimation results for EQ-5D health utility index
Variable	β	S_‾x	t	P	95%CI
Variable	β	S_‾x	t	P	Upper limit	Lower limit
Fixed effects
Intercept	0.259	0.081	3.210	0.001	0.101	0.418
Age(Control group = > 80)
60 ~ 70	0.189	0.046	4.164	< 0.001	0.100	0.279
71 ~ 80	0.150	0.033	4.515	< 0.001	0.085	0.215
Marital status(Control group = Divorce/Widow)
Married	0.109	0.040	2.396	0.007	0.030	0.188
Educational(Control group = Bachelor's degree and above)
Primary school and below	0.127	0.101	1.258	0.209	-0.071	0.325
Middle school	0.180	0.103	1.739	0.082	-0.023	0.383
Post-secondary or tertiary	0.185	0.105	1.768	0.077	-0.020	0.390
Monthly income (RMB)(Control group = ≥ 5001)
≤ 1000	-0.134	0.046	-2.93	0.003	-0.224	-0.044
1001 ~ 3000	0.021	0.046	-0.455	0.649	-0.111	0.069
3001 ~ 5000	-0.109	0.045	-2.412	0.016	-0.197	-0.020
Number of chronic diseases(Control group = ≥ 3)
1	0.312	0.044	7.040	< 0.001	0.225	0.400
2	0.198	0.039	5.001	< 0.001	0.121	0.275
Sleep time(h)(Control group = > 8h)
< 6h	0.002	0.034	0.049	0.961	-0.065	0.069
6 ~ 8h	0.052	0.032	1.635	0.102	-0.010	0.114
Alcohol(Control group = No)
Yes	-0.075	0.030	-2.519	0.012	-0.134	-0.017
Random effects
Level 1 (Individual)	0.064	0.007	8.403	< 0.001	0.051	0.081
Level 2 (Region)	1.000	< 0.001

Table 4

The estimation results of EQ-VAS null models
Variable	β	S_‾x	Wald Z-Value	P-Value	95%CI
Variable	β	S_‾x	Wald Z-Value	P-Value	Upper limit	Lower limit
Intercept	0.543	0.014	38.691	< 0.001	0.515	0.570
Residual	0.124	0.010	12.555	< 0.001	0.107	0.145

Estimated results of the EQ-VAS score

The ICC is 0.815 for EQ-VAS score, indicating that 81.50% of the total variation exists between patients, in other words, multi-level models for EQ-VAS score are necessary. Based on the null model estimation results multi-level binomial logistic regression model can be estimated for EQ-VAS score (Table 2).

Table 5 presents the estimation results of EQ-VAS score, the results show that at the age of 60 ~ 70 or 71 ~ 80 is higher than 0.177(β = 0.177,95%CI:0.114,0.240) or 0.154(β = 0.154,95%CI:0.088,0.219) compared to at the age of > 80, divorce/widow is higher than 0.917(β = 0.917,95%CI:0.030,0.153) compared to married, monthly income of ≤ 100 or 3001 ~ 5000 is lower than 0.101(β = 0.101,95%CI:-0.184,0.017) or 0.097(β = 0.097,95%CI:-0.183,-0.012) compared to monthly income of ≥ 5001, number of chronic diseases of 1 or 2 is higher than 0.324(β = 0.324,95%CI:0.235,0.413) or 0.241(β = 0.221,95%CI:0.161,0.320) compared to number of chronic diseases of ≥ 3, smoking is lower than 0.090(β = 0.090,95%CI:-0.159,-0.022) compared to no drinkingdrinking is lower than 0.108(β = 0.108,95%CI:-0.207,-0.010) compared to no drinking.

Table 5

Estimation results forEQ-VAS
Variable	β	S_‾x	t	P	95%CI
Variable	β	S_‾x	t	P	Upper limit	Lower limit
Fixed effects
Intercept	0.264	0.084	3.150	0.002	0.099	0.428
Age(Control group = > 80)
60 ~ 70	0.177	0.032	5.530	< 0.001	0.114	0.240
71 ~ 80	0.154	0.033	4.620	< 0.001	0.088	0.219
Marital status(Control group = Divorce/Widow)
Married	0.917	0.312	2.939	0.003	0.030	0.153
Monthly income (RMB)(Control group = ≥ 5001)
≤ 1000	-0.101	0.427	-2.356	0.019	-0.184	-0.017
1001 ~ 3000	0.017	0.437	0.394	0.694	-0.068	0.103
3001 ~ 5000	-0.097	0.436	-2.228	0.026	-0.183	-0.012
Number of chronic diseases(Control group = ≥ 3)
1	0.324	0.045	7.146	< 0.001	0.235	0.413
2	0.241	0.041	5.939	< 0.001	0.161	0.320
Sleep time(h)(Control group = > 8h)
< 6h	-0.034	0.049	0.703	0.482	-0.131	0.061
6 ~ 8h	-0.001	0.046	0.006	0.995	-0.090	0.090
Smoking(Control group = No)
Yes	-0.090	0.035	-2.585	0.010	-0.159	-0.022
Alcohol(Control group = No)
Yes	-0.108	0.0503	2.154	0.032	-0.207	-0.010
Random effects
Level 1 (Individual)	0.656	0.008	8.253	< 0.001	0.052	0.083
Level 2 (Region)	1.000	< 0.001

The HTN and T2DM elderly has a lower level of HRQOL in comparison with other populations. Overall, The elderly had an average of EQ-5D-3L index score of 0.778, lower than the Singapore population norms (0.95)(33, 34) and Taiwan population norms (0.946) (35), USA (0.87)(36), Sri Lanka (0.85)(37), and Japan (0.877)(38, 39), but similar to the norms reported in Lebanon 0.766(40, 41). It may be due to the fact that HTN and T2DM are chronic, lifelong diseases with many complications, which can involve multiple organs and cause chronic damage or dysfunction of multiple organs(42), seriously affecting the physical and mental health of patients and reducing the subjective evaluation of HRQoL(43–45).

In this study, we judged whether HRQoL in older adults with HTN and T2DM was aggregated in groups by fitting a multi-level null model, and the results of the study showed that there was individual-level aggregation in both health utility index values and VAS scores, indicating that the variation in HRQoL was mainly due to individual factors. The results of the multi-level model showed that the HRQoL decreased with age, which is in accordance with the findings of Mannan A et al(46), and that the quality of life of patients gradually decreased with age and disease duration, which may be attributed to the gradual decrease of physiological function and social adaptation ability with age, and the HRQoL also decreased.

The HRQoL of widowed elderly patients is worse than that of married ones. Analysis may be due to the fact that compared with married elderly, the widowed elderly have an increased sense of loneliness due to the lack of spouse's companionship in life and emotion, which prominently affects the psychology adversely. The relevant government departments should focus on the widowed elderly with HTN and T2DM and carry out services such as psychological guidance and volunteer companionship to improve their HRQoL. The positive association between socio-economic status and health has been shown in Europe over decades(47–50), and previous studies in China also showed similar results(51, 52). High-income elderly have some health equity accumulation, higher compliance, and will undertake more health care expenditure for health improvement, with relatively higher health status and HRQoL.

The multi-level model established in this study showed that chronic conditions was the most significant dependent variable for predicting EQ-5D-3L index scores and reported health problems. In China, it was estimated that NCDs account for 82.5% of the total deaths(53). The higher the number of chronic diseases the worse the health status of the elderly, which is in accordance with the findings of Meloni Marco(54), the reason may be that other chronic diseases are themselves the main diseases affecting human health, which not only reduce the quality of life and well-being of patients, but also cause some direct or indirect effects on patients with HTN and diabetes. Meanwhile, diabetes is associated with an increased risk of CVD, and the presence of HN exaggerates the risk of CVD(55). Controlling comorbidities, particularly HTN, and targeted strategies to promote vascular health, may be especially important in reducing the microvascular and macrovascular complications of diabetes.

There are still some limitations in this study. First, this study is cross-sectional, and no causal conclusions can be drawn. New data will be collected at a later stage, and a longitudinal study can be conducted to elucidate the predictors of HRQoL reduction further. In addition, there is another limitation of this study which is the hierarchies advantage of HTN and T2DM was partly reduced because patients characteristics were only at age, marital status, monthly income, number of chronic diseases and more characteristics of patients. So, future research can be considered in introducing the multi-level binomial logistic model.

This study showed that HRQoL of elderly patients with HTN and T2DM was lower than that of the general population, with region, age, marital status, monthly income, number of chronic diseases, and alcohol consumption being the main influencing factors. In addition to actively assisting patients to effectively control blood pressure and blood glucose, strive to achieve target values, and improve biological indicators when carrying out community-based home interventions, we should also adopt a targeted service model from the professional side, family-based, community-wide, and demand-driven, to improve patients' knowledge of HTN and diabetes, and to alleviate patients' own indicators while also improving the quality of care as well as patient satisfaction in order to improve the quality of life of the elderly.

Patient or Public Contribution

This study was conducted on older adults with hypertension and type 2 diabetes in the community at home.

ETHICS STATEMENT

The study was approved by Zhengzhou University Life Science Ethics Committee (Code: ZZUIRB2022-07) and was conducted following the principles of the Declaration of Helsinki.

Consent for publication

All authors gave their consent for publication.

Availability of supporting data

The raw data supporting the conclusion of this article will be made available by the authors without undue reservation.

Competing interests

The authors have declared no competing interests.

FUNDING

This study was supported by the National Key R&D Program for Active Health and Ageing Science and Technology Response Key Special Project (No.2020YFC2006100); Annual Project of Henan Provincial Philosophy and Social Science Planning (No. 2020BSH017); Henan Province Special Project on Key R&D and Promotion (Science and Technology Research) (No.212102310814).

AUTHOR CONTRIBUTIONS

Le-ping WAN designed this study, participated in its implementation and coordination, and helped draft the manuscript. Guang-mei YANG performed the statistical analysis, and Hai-ying DONG and Xiao-xiao LIANG helped collect the data. Yan HE helped with the formatting of this manuscript. All authors have seen and approved the final manuscript.

ACKNOWLEDGEMENTS

We would like to thank all the teachers who collected the data. At the same time, we express our sincere gratitude to all government agencies.

Authors' information

Le-ping WAN, Guang-mei YANG, Xiao-xiao LIANG,Hai-ying DONG, Yan HE*

College of Public Health, Zhengzhou University Zhengzhou, China, 450000

*Corresponding Author:

Yan HE

College of Public Health, Zhengzhou University Zhengzhou, China, 450000

Email address:[email protected]

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No competing interests reported.

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Health-related quality of life and its influencing factors in elderly people with hypertension and type 2 diabetes mellitus based on Multi-level model: take the east coastal area of China as an example

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Abstract

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Introduction

Materials & Methods

Source of data

Health-related quality of life

Statistical data analysis

Results

Characteristics of the Participants

Health-related quality-of-life status

Estimated results of the EQ-5D health utility index

Estimated results of the EQ-VAS score

Discussion

Conclusion

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