DOI: https://doi.org/10.21203/rs.3.rs-593276/v2
Background: The self-perceived level of disability for stroke survivors in community is little known. We aimed to characterize HRQoL 1 year after stroke and to investigate how socio-demographic and stroke-related factors and medical adherence explain the self-perceived level of disability among a Korean stroke population.
Methods: This study was a multi-center and cross-sectional study. A total of 382 ischemic stroke survivors at 1 year after onset recruited from 11 university hospitals underwent a one-session assessment including: socioeconomic variables, the modified Rankin Scale (mRS), various neurological sequelae, the modifed Morisky scale (MMS), and the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) 36-items. The relationship between disability and different variables was analyzed using ordinal logistic regression.
Results: The prevalence of disability by global WHODAS 2.0 score was 62.6% (41.6% for mild; 16.0% for moderate; 5.0% for severe) in subjects. Prevalence of severe disability was higher in Participation in society (16.8%) and Getting around (11.8%) than in the other domains. Low MMS motivation was the only factor determining the significant association between all six domains of disability after adjustment. Different predictors for specific domains were age, mRS, dysarthria, trouble seeing, cognition problem and MMS-motivation for Understanding and communicating; age, recurrent stroke, mRS, hemiplegia, facial palsy, general weakness and MMS-motivation for Getting around; age, education, mRS, hemiplegia and MMS-motivation for Self-care; education, recurrent stroke, hemiplegia, dysarthria and MMS-motivation for Getting along with people; age, education, income, mRS, hemiplegia, dysarthria, MMS-knowledge and MMS-motivation for Life activities; living without spouse, mRS, hemiplegia, dysarthria, trouble seeing, cognition problem, general weakness and MMS-motivation for Participation in society.
Conclusions: Self-perceived disability by the WHODAS 2.0 had almost double the prevalence compared to hemiplegia, one of the most common neurological sequelae 1 year after stroke. Each domain of disability increased with various associated factors. Interventions promoting medical adherence of motivation seem to help high HRQoL in all domains.
Stroke is common and serious non-communicable health problem. It is the second cause of mortality [1] and the third cause of disability-adjusted life years (DALYs) [2] in the world. In Korea, the Epidemiologic Research Council of the Korean Stroke Society reported an age- and sex-standardized incidence of first ever stroke of 92.2 per 100,000 populations in 2013, an age-standardized prevalence of stroke of 1.37% in Korean adults aged over 19 years in 2014, and an age-standardized stroke mortality of 29.6 per 100,000 populations in 2015 [3]. Stroke was the third leading cause of DALYs in Korea, following diabetes mellitus and low back pain in 2012 [4].
When stroke survivors suffer from becoming disabled related with neurological sequelae, outcome assessment of acute stroke traditionally focuses on prevention of deaths, alleviation of symptoms, impairments, and restoration of function [5]. However, health related quality of life (HRQoL) measures may capture patients’ perception on disability better than traditional ways. Not only because they are multidimensional instruments which comprise functional, physical, cognitive, psychological and social elements [6] but also because the impact of limitation following stroke on well-being may differ by each patient [7]. Furthermore, they reflect health from patients’ own perspectives [8].
WHODAS 2.0 is a generic instrument of HRQoL for measuring function and disability in major life domains linked to the International Classification of Functioning, Disability and Health (ICF). It is reliable and applicable across cultures in adult populations [9–12].
In Korea, the level of disability for stroke survivors in community is little known. We aimed to characterize HRQoL 1 year after stroke using WHODAS 2.0 and to investigate how socio-demographic, stroke-related factors and medical adherence explain the self-perceived level of disability among a Korean stroke population.
This study was a multi-center and cross-sectional study conducted across the period, December 2015 - March 2016. A total of 426 participants were recruited from the neurology outpatient clinics from the 11 university hospitals designated as Regional Cardiocerebrovascular Centers (RCCs) in Korea (Daegu-Gyeongbuk, Gangwon, Jeju, Chungbuk, Gwangju-Jeonnam, Gyeongnam, Daejeon-Chungnam, Jeonbuk, Busan-Ulsan, Inchoen, and Gyeongi RCC) [13]. Participants were stroke survivors who had been admitted to one of the RCC hospitals due to acute ischemic stroke occurred 12 to 15 months before the interview and were willing to be informants. A one-on-one interview was conducted by trained nurses at the 11 hospitals using a structured questionnaire. Patients who were unable to communicate independently were excluded. Written informed consent was obtained from all participants. The study protocol was approved by the institutional review board of Kangwon National University Hospital.
Data on socio-demographic and stroke-related characteristics were collected. The common socio-demographic variables on general characteristics were sex, age, live with spouse or not, highest education qualification (elementary school /middle school /high school/college and above) and monthly household income (1 and less/1 to 2/more than 2 million Korea won; 1.2 million Korea won≒1,000 USD). And, the stroke-related variables were recurrent or first-ever stroke, modified Rankin Score (mRS), Complications after stroke (hemiplegia, dysarthria, facial palsy, trouble seeing, paresthesia, cognition problem, general weakness) [14]. The mRS is robust and the most commonly recommended functional measure in acute stroke research [5, 7, 14]. We categorized mRS into ‘normal to mild’ with score range from 0 to 2 and ‘moderate to severe’ from 3 to 5.
The Morisky Scale is self-reported measure of medication adherence. It had been originally developed to predict the adherence of outpatients to antihypertensive medications with four items in the mid-1980s [15]. Modified Morisky Scale (MMS) has 6 items measuring two domains of adherence (knowledge and motivation). Three items as ‘When you feel better do you sometimes stop taking your medicine?’, ‘Sometimes if you feel worse when you take your medicine, do you stop taking it?’, ‘Do you know the long-term benefit of taking your medicine as told to you by your doctor or pharmacist?’ are for knowledge and the other 3 items as ‘Do you ever forget to take your medicine?’, ‘Are you careless at times about taking your medicine?’, ‘Sometimes do you forget to refill your prescription medicine on time?’ are for motivation. Each item has the score of 0 or 1 and a higher score indicates high adherence, and MMS score can be categorized into ‘low’ with score range from 0 to 1 and ‘high’ from 2 to 3 for each subdomain [16].
We measured HRQoL of 12 to 15 month post-ischemic stroke patients with the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), a standardized cross-cultural measurement of disability [9]. The WHODAS 2.0 questionnaire has several forms according to number of items, administration, and respondent. We used the WHODAS 2.0, 36-items covering six domains of functioning: understanding and communicating (UAC, 6 items), getting around (GAR, 5 items), self-care (SAC, 4 items), getting along with people (GAP, 5 items), life activities (LAC, 8 items), and participation in society (PSO, 8 items) [10]. We computed 6 domain-specific scores using 36-item complex scoring. Score ranged from 0 to 100, a higher score indicates greater disability like lower QoL [10]. WHODAS 2.0 domain-specific and global scores were originally categorized as 5 grades: no problem (0–4%), mild disability (5–24%), moderate disability (25–49%), severe disability (50–95%), and extreme disability (96–100%). In fact, there were few subjects with extreme disability in this study, so the 5 groups were collapsed into 4: no, mild, moderate, and severe disability. Reliability and validity of Korean version has been established [17].
We analyzed data from 382 participants who had completed all the assessments. For descriptive purposes, absolute numbers and percentages were calculated for categorical variables and means ± SDs for continuous variable. The 6 domain-specific scores of WHODAS 2.0 were separately treated as dependent variables. As we had categories for the dependent variable that were ordered, ordinal logistic regression was used. The link function used for model fitting was logit. The evaluating overall model fit to the data was done through the Model Fitting Information. It is determined that a model exhibits good fit to the data when a significant improvement in fit of the final model containing full set of independent variables over the null model. A parallel line test confirmed that the proportional odds assumption was satisfied for every model for ordinal logistic regression. Every estimated ordinal logistic regression coefficient was transformed into odds ratio, as the exponential of a particular coefficient is an estimate of the odds ratio. Data analyses were performed using SPSS version 24.0 (SPSS Inc., Chicago, IL, USA) and p-value under 0.05 is considered as statistically significant.
Table 1 showed that the participants were relatively elderly (mean age ± SD, 65.7 ± 12.2 years). Among 382 participants, 138 (36.1%) were female. Most of the participants (272, 71.2%) were living with spouse. As for their highest academic qualification, 144 (37.7%) were elementary school, 68 (17.8%) were middle school, 105 (27.5%) were high school and 65 (17.0%) were college and above. Additionally, 148 (38.7%) had a monthly family income of less than 1,000,000 Korean won.
| Characteristics | N (%) |
|---|---|
| Age | 65.7 ± 12.2 years |
| Sex: Female | 138 (36.1%) |
| Living with spouse | 272 (71.2%) |
| Highest academic qualification | |
| Elementary school | 144 (37.7%) |
| Middle school | 68 (17.8%) |
| High school | 105 (27.5%) |
| College and above | 65 (17.0%) |
| Monthly family income (Korean won) a | |
| 1,000,000 and less | 148 (38.7%) |
| More than 1,000,000 to 2,000,000 | 93 (24.3%) |
| More than 2,000,000 | 141 (36.9%) |
| a 1.2 million Korean won ≒1,000 USD | |
The participants’ stroke-related characteristics were shown that 319 (83.5%) had experienced stroke attack only once. 332 (86.9%) counted normal to moderate level in mRS. And Hemiplegia (129, 33.8%) and Dysarthria (92, 24.1%) were the most frequent complications overall. The level of self-reported medication adherence in MMS-knowledge was higher than in MMS-motivation. 370 (96.9%) showed high level in the MMS knowledge and 331 (86.6%) in the MMS motivation (Table 2).
| Characteristics | N (%) |
|---|---|
| First-ever stroke | 319 (83.5%) |
| mRS a | |
| Normal to mild | 332 (86.9%) |
| Moderate to severe | 50 (13.1%) |
| Complication after stroke | |
| Hemiplegia | 129 (33.8%) |
| Dysarthria | 92 (24.1%) |
| Facial palsy | 13 ( 3.4%) |
| Trouble seeing | 17 ( 4.5%) |
| Paresthesia | 13 ( 3.4%) |
| Cognition problem | 10 ( 2.6%) |
| General weakness | 15 ( 3.9%) |
| MMS knowledge | |
| Low | 12 ( 3.1%) |
| High | 370 (96.9%) |
| MMS motivation | |
| Low | 51 (13.4%) |
| High | 331 (86.6%) |
| a Normal to mild mRS < 3; moderate to severe mRS ≥ 3 | |
Among 382 participants, prevalence by WHODAS 2.0 level was 37.4% for no (disability-free), 41.6% for mild, 16.0% for moderate, and 5.0% for severe disability in Global scores. The breakdown by domain also shows that prevalence decreased with severity. People with no disability was relatively common in SCA (63.6%) and GAP (51.6%), whereas the prevalence for severe disability was higher in PSO (16.8%) and GAR (11.8%) than in the other domains of WHODAS 2.0 (Fig. 1).
Table 3 showed associations between different variables and disability in domain-specific WHODAS 2.0 scores. The aORs obtained from ordinal logistic regression models for different variables represent disability in the index group compared with that in the reference group. The results for domain-specific scores were adjusted for the 5 demographic, 9 stroke-related and 2 medication adherence variables.
| Variable | UAC | GAR | SCA | |||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| aOR | (95% CI) | P | aOR | (95% CI) | P | aOR | (95% CI) | P | ||||||||||||||||||
| Age (years) | 1.03 | ( | 1.01 | - | 1.05 | ) a | 0.010 | 1.06 | ( | 1.04 | - | 1.09 | ) | < 0.001 | 1.07 | ( | 1.04 | - | 1.10 | ) | < 0.001 | |||||
| Sex: female | 1.21 | ( | 0.77 | - | 1.90 | ) | 0.404 | 1.25 | ( | 0.79 | - | 1.97 | ) | 0.339 | 0.86 | ( | 0.50 | - | 1.48 | ) | 0.582 | |||||
| Living without spouse | 0.97 | ( | 0.61 | - | 1.56 | ) | 0.915 | 1.05 | ( | 0.66 | - | 1.69 | ) | 0.826 | 1.44 | ( | 0.84 | - | 2.47 | ) | 0.181 | |||||
| Highest academic qualification | ||||||||||||||||||||||||||
| Elementary school | 1.44 | ( | 0.68 | - | 3.06 | ) | 0.344 | 0.80 | ( | 0.38 | - | 1.70 | ) | 0.565 | 1.30 | ( | 0.50 | - | 3.34 | ) | 0.590 | |||||
| Middle school | 2.07 | ( | 0.96 | - | 4.45 | ) | 0.063 | 0.91 | ( | 0.42 | - | 1.94 | ) | 0.799 | 2.66 | ( | 1.02 | - | 6.92 | ) | 0.045 | |||||
| High school | 1.32 | ( | 0.66 | - | 2.63 | ) | 0.434 | 0.84 | ( | 0.43 | - | 1.66 | ) | 0.617 | 2.92 | ( | 1.20 | - | 7.12 | ) | 0.018 | |||||
| College and above | 1 | 1 | 1 | |||||||||||||||||||||||
| Monthly family income (10,000won) | ||||||||||||||||||||||||||
| 100 and less | 0.73 | ( | 0.43 | - | 1.24 | ) | 0.243 | 1.10 | ( | 0.63 | - | 1.87 | ) | 0.733 | 1.58 | ( | 0.86 | - | 2.93 | ) | 0.143 | |||||
| 100 to 200 | 1.30 | ( | 0.77 | - | 2.18 | ) | 0.039 | 1.10 | ( | 0.65 | - | 1.86 | ) | 0.996 | 1.25 | ( | 0.69 | - | 2.30 | ) | 0.479 | |||||
| More than 200 | 1 | 1 | 1 | |||||||||||||||||||||||
| Recurrent stroke | 1.22 | ( | 0.71 | - | 2.11 | ) | 0.465 | 1.88 | ( | 1.08 | - | 3.26 | ) | 0.024 | 1.42 | ( | 0.78 | - | 2.59 | ) | 0.253 | |||||
| mRSb | ||||||||||||||||||||||||||
| Normal to mild | 1 | 1 | 1 | |||||||||||||||||||||||
| Moderate to severe | 4.04 | ( | 2.06 | - | 7.93 | ) | < 0.001 | 8.27 | ( | 4.03 | - | 16.96 | ) | < 0.001 | 11.60 | ( | 5.50 | - | 24.46 | ) | < 0.001 | |||||
| Complication after stroke | ||||||||||||||||||||||||||
| Hemiplegia | 1.41 | ( | 0.87 | - | 2.28 | ) | 0.159 | 3.86 | ( | 2.37 | - | 6.27 | ) | < 0.001 | 5.32 | ( | 3.06 | - | 9.28 | ) | < 0.001 | |||||
| Dysarthria | 1.88 | ( | 1.17 | - | 3.03 | ) | 0.010 | 1.32 | ( | 0.81 | - | 2.15 | ) | 0.263 | 1.16 | ( | 0.67 | - | 1.98 | ) | 0.599 | |||||
| Facial palsy | 2.76 | ( | 0.95 | - | 8.02 | ) | 0.063 | 4.85 | ( | 1.55 | - | 15.21 | ) | 0.007 | 1.70 | ( | 0.53 | - | 5.42 | ) | 0.372 | |||||
| Trouble seeing | 2.86 | ( | 1.11 | - | 7.38 | ) | 0.030 | 1.44 | ( | 0.52 | - | 4.00 | ) | 0.480 | 1.97 | ( | 0.66 | - | 5.84 | ) | 0.222 | |||||
| Paresthesia | 0.86 | ( | 0.26 | - | 2.79 | ) | 0.800 | 2.31 | ( | 0.76 | - | 7.02 | ) | 0.140 | 2.00 | ( | 0.48 | - | 8.23 | ) | 0.339 | |||||
| Cognition problem | 5.59 | ( | 1.61 | - | 19.38 | ) | 0.007 | 0.65 | ( | 0.18 | - | 2.32 | ) | 0.506 | 1.30 | ( | 0.33 | - | 5.15 | ) | 0.706 | |||||
| General weakness | 2.46 | ( | 0.88 | - | 6.88 | ) | 0.085 | 3.19 | ( | 1.14 | - | 8.93 | ) | 0.027 | 0.98 | ( | 0.24 | - | 3.93 | ) | 0.972 | |||||
| MMS knowledgec | ||||||||||||||||||||||||||
| Low | 2.15 | ( | 0.68 | - | 6.75 | ) | 0.191 | 2.24 | ( | 0.72 | - | 7.00 | ) | 0.164 | 2.93 | ( | 0.87 | - | 9.86 | ) | 0.082 | |||||
| High | 1 | 1 | 1 | |||||||||||||||||||||||
| MMS motivationc | ||||||||||||||||||||||||||
| Low | 3.12 | ( | 1.75 | - | 5.55 | ) | < 0.001 | 3.22 | ( | 1.78 | - | 5.80 | ) | < 0.001 | 2.88 | ( | 1.52 | - | 5.46 | ) | 0.001 | |||||
| High | 1 | 1 | . | 1 | . | |||||||||||||||||||||
| UAC = Understanding and communicating; GAR = Getting around; SCA = Self-care; GAP = Getting along with people; LAC = Life activities; PSO = Participation in society; MMS = Modified Morisky Scale. a Adjusted odds ratios (95% confidence interval) from ordinal logistic regression models were estimated using age, sex, living without spouse, education level, family income level, recurrent stroke, mRS, hemiplegia, dysarthria, facial palsy, trouble seeing, paresthesia, cognition problem, general weakness, MMS knowledge and MMS motivation. b Normal to mild mRS < 3; moderate to severe mRS ≥ 3. c Low MMS knowledge (or motivation) < 2; high MMS knowledge (or motivation) ≥ 2. | ||||||||||||||||||||||||||
| Variable | GAP | LAC | PSO | |||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| aOR | (95% CI) | P | aOR | (95% CI) | P | aOR | (95% CI) | P | ||||||||||||||||||
| Age (years) | 1.02 | ( | 1.00 | - | 1.04 | ) | 0.051 | 1.05 | ( | 1.03 | - | 1.07 | ) | < 0.001 | 1.00 | ( | 0.98 | - | 1.02 | ) | 0.979 | |||||
| Sex: female | 1.34 | ( | 0.85 | - | 2.11 | ) | 0.208 | 1.42 | ( | 0.89 | - | 2.29 | ) | 0.146 | 1.13 | ( | 0.73 | - | 1.76 | ) | 0.589 | |||||
| Living without spouse | 0.70 | ( | 0.43 | - | 1.13 | ) | 0.141 | 1.04 | ( | 0.64 | - | 1.70 | ) | 0.871 | 1.76 | ( | 1.11 | - | 2.80 | ) | 0.017 | |||||
| Highest academic qualification | ||||||||||||||||||||||||||
| Elementary school | 1.59 | ( | 0.74 | - | 3.40 | ) | 0.235 | 1.28 | ( | 0.57 | - | 2.86 | ) | 0.543 | 1.07 | ( | 0.52 | - | 2.21 | ) | 0.846 | |||||
| Middle school | 2.17 | ( | 1.01 | - | 4.67 | ) | 0.048 | 2.23 | ( | 1.00 | - | 5.01 | ) | 0.051 | 1.24 | ( | 0.60 | - | 2.58 | ) | 0.565 | |||||
| High school | 1.58 | ( | 0.80 | - | 3.14 | ) | 0.188 | 1.70 | ( | 0.82 | - | 3.54 | ) | 0.155 | 1.22 | ( | 0.65 | - | 2.30 | ) | 0.538 | |||||
| College and above | 1 | 1 | 1 | |||||||||||||||||||||||
| Monthly family income (10,000won) | ||||||||||||||||||||||||||
| 100 and less | 1.34 | ( | 0.78 | - | 2.29 | ) | 0.292 | 1.77 | ( | 1.01 | - | 3.11 | ) | 0.048 | 1.16 | ( | 0.69 | - | 1.97 | ) | 0.574 | |||||
| 100 to 200 | 1.20 | ( | 0.70 | - | 2.04 | ) | 0.694 | 1.70 | ( | 0.98 | - | 2.96 | ) | 0.899 | 1.08 | ( | 0.64 | - | 1.83 | ) | 0.799 | |||||
| More than 200 | 1 | 1 | 1 | |||||||||||||||||||||||
| Recurrent stroke | 1.72 | ( | 1.00 | - | 2.94 | ) | 0.049 | 1.30 | ( | 0.74 | - | 2.28 | ) | 0.367 | 1.73 | ( | 1.00 | - | 3.01 | ) | 0.050 | |||||
| mRSb | ||||||||||||||||||||||||||
| Normal to mild | 1 | 1 | 1 | |||||||||||||||||||||||
| Moderate to severe | 1.03 | ( | 0.53 | - | 2.00 | ) | 0.924 | 10.17 | ( | 4.84 | - | 21.35 | ) | < 0.001 | 12.48 | ( | 5.77 | - | 27.00 | ) | < 0.001 | |||||
| Complication after stroke | ||||||||||||||||||||||||||
| Hemiplegia | 2.72 | ( | 1.68 | - | 4.42 | ) | < 0.001 | 6.23 | ( | 3.74 | - | 10.38 | ) | < 0.001 | 3.87 | ( | 2.38 | - | 6.28 | ) | < 0.001 | |||||
| Dysarthria | 1.82 | ( | 1.13 | - | 2.94 | ) | 0.015 | 1.87 | ( | 1.14 | - | 3.06 | ) | 0.013 | 1.94 | ( | 1.19 | - | 3.18 | ) | 0.008 | |||||
| Facial palsy | 1.56 | ( | 0.53 | - | 4.62 | ) | 0.418 | 2.36 | ( | 0.77 | - | 7.20 | ) | 0.131 | 1.94 | ( | 0.62 | - | 6.04 | ) | 0.253 | |||||
| Trouble seeing | 1.61 | ( | 0.61 | - | 4.26 | ) | 0.334 | 2.67 | ( | 0.97 | - | 7.39 | ) | 0.058 | 5.45 | ( | 1.93 | - | 15.41 | ) | 0.001 | |||||
| Paresthesia | 1.51 | ( | 0.48 | - | 4.78 | ) | 0.485 | 1.17 | ( | 0.32 | - | 4.25 | ) | 0.810 | 1.53 | ( | 0.51 | - | 4.58 | ) | 0.447 | |||||
| Cognition problem | 1.93 | ( | 0.56 | - | 6.63 | ) | 0.298 | 2.68 | ( | 0.77 | - | 9.31 | ) | 0.122 | 6.06 | ( | 1.62 | - | 22.58 | ) | 0.007 | |||||
| General weakness | 2.28 | ( | 0.81 | - | 6.43 | ) | 0.120 | 1.19 | ( | 0.37 | - | 3.84 | ) | 0.777 | 3.88 | ( | 1.38 | - | 10.92 | ) | 0.010 | |||||
| MMS knowledge | ||||||||||||||||||||||||||
| Low | 2.23 | ( | 0.72 | - | 6.93 | ) | 0.164 | 4.35 | ( | 1.31 | - | 14.44 | ) | 0.016 | 2.19 | ( | 0.65 | - | 7.37 | ) | 0.207 | |||||
| High | 1 | 1 | 1 | |||||||||||||||||||||||
| MMS motivation | ||||||||||||||||||||||||||
| Low | 3.83 | ( | 2.16 | - | 6.82 | ) | < 0.001 | 3.21 | ( | 1.76 | - | 5.82 | ) | < 0.001 | 2.59 | ( | 1.42 | - | 4.70 | ) | 0.002 | |||||
| High | 1 | . | 1 | . | 1 | |||||||||||||||||||||
| UAC = Understanding and communicating; GAR = Getting around; SCA = Self-care; GAP = Getting along with people; LAC = Life activities; PSO = Participation in society; MMS = Modified Morisky Scale. a Adjusted odds ratios (95% confidence interval) from ordinal logistic regression models were estimated using age, sex, living without spouse, education level, family income level, recurrent stroke, mRS, hemiplegia, dysarthria, facial palsy, trouble seeing, paresthesia, cognition problem, general weakness, MMS knowledge and MMS motivation. b Normal to mild mRS < 3; moderate to severe mRS ≥ 3. c Low MMS knowledge (or motivation) < 2; high MMS knowledge (or motivation) ≥ 2. | ||||||||||||||||||||||||||
Age, mRS, dysarthria, trouble seeing, cognition problem and MMS-motivation were significant positive predictors of the disability of UAC in the model. For every one-year increase on age, the odds of being in more severe category on UAC was 1.03 times higher (p = 0.01). This indicated that a participant aged older was more likely to indicate greater disability of UAC. The odds of being in more severe level on UAC was 4.04 times higher for those who had the level of moderate to severe as compared to those who had the level of normal to mild in mRS (p < 0.001). And the odds of being in more severe level on UAC were 1.88, 2.86 and 5.59 times higher when a participant had the complication in dysarthria, trouble seeing and cognition problem, respectively (p = 0.01; 0.03; 0.007). In addition, the odds of being in more severe level on UAC was 3.12 times higher for those who showed low level of adherence to medication as compared to those who showed high in MMS-motivation (p < 0.001).
Age, recurrent stroke, mRS, hemiplegia, facial palsy, general weakness and MMS-motivation were significant positive predictors of the disability of GAR in the model. For every one-year increase on age, the odds of being in more severe category on GAR was 1.06 times higher (p < 0.001). This indicated that a participant aged older was more likely to indicate greater disability of GAR. The odds of being in more severe level on GAR was 1.88 times higher for those who experienced recurrent stroke as compared to those who had experienced stroke attack only once (p = 0.024). The odds of being in more severe level on GAR was 8.27 times higher for those who had the level of moderate to severe as compared to those who had the level of normal to mild in mRS (p < 0.001). And the odds of being in a higher level on GAR were 3.86, 4.85 and 3.19 times higher when a participant had the complication in hemiplegia, facial palsy, and general weakness, respectively (p < 0.001; 0.007; 0.027). In addition, the odds of being in more severe level on GAR was 3.22 times higher for those who showed low level of adherence to medication as compared to those who showed high in MMS-motivation (p < 0.001).
Age, highest academic qualification, mRS, hemiplegia and MMS-motivation were significant positive predictors of the disability of SCA in the model. For every one-year increase on age, the odds of being in more severe category on SCA was 1.07 times higher (p < 0.001). This indicated that a participant aged older was more likely to indicate greater disability of SCA. The odds of being in more severe level on SCA were 2.92 and 2.66 times higher for those who had their highest academic qualification as high and middle school, respectively as compared to those who had college and above (p = 0.018; 0.045). The odds of being in more severe level on SCA was 11.6 times higher for those who had the level of moderate to severe as compared to those who had the level of normal to mild in mRS (p < 0.001). And the odds of being in more severe level on SCA was 5.32 times higher when a participant had the complication in hemiplegia (p < 0.001). In addition, the odds of being in more severe level on SCA was 2.88 times higher for those who showed low level of adherence to medication as compared to those who showed high in MMS-motivation (p = 0.001).
Highest academic qualification, recurrent stroke, hemiplegia, dysarthria and MMS-motivation were significant positive predictors of the disability of GAP in the model. The odds of being in more severe level on GAP was 2.17 times higher for those who had their highest academic qualification as middle school as compared to those who had college and above (p = 0.048). The odds of being in more severe level on GAP was 1.72 times higher for those who experienced recurrent stroke as compared to those who had experienced stroke attack only once (p = 0.049). And the odds of being in more severe level on GAP were 2.72 and 1.82 times higher when a participant had the complication in hemiplegia, and dysarthria, respectively (p < 0.001; 0.015). In addition, the odds of being in more severe level on GAP was 3.83 times higher for those who showed low level of adherence to medication as compared to those who showed high in MMS-motivation (p < 0.001).
Age, highest academic qualification, monthly family income, mRS, hemiplegia, dysarthria, MMS-knowledge and MMS-motivation were significant positive predictors of the disability of LAC in the model. For every one-year increase on age, the odds of being in more severe category on LAC was 1.05 times higher (p < 0.001). This indicated that a participant aged older was more likely to indicate greater disability of LAC. The odds of being in more severe level on LAC was 2.23 times higher for those who had their highest academic qualification as middle school as compared to those who had college and above (p = 0.051). The odds of being in more severe level on LAC was 1.77 times higher for those who had their monthly family income as one million and less Korean won as compared to those who had more than two million and less Korean won (p = 0.048). The odds of being in more severe level on LAC was 10.17 times higher for those who had the level of moderate to severe as compared to those who had the level of normal to mild in mRS (p < 0.001). And the odds of being in more severe level on LAC were 6.23 and 1.87 times higher when a participant had the complication in hemiplegia, and dysarthria, respectively (p < 0.001; 0.013). In addition, the odds of being in more severe level on LAC was 4.35 and 3.21 times higher for those who showed low level of adherence to medication as compared to those who showed high in MMS-knowledge and MMS-motivation, respectively (p = 0.016; <0.001).
Living without spouse, mRS, hemiplegia, dysarthria, trouble seeing, cognition problem, general weakness and MMS-motivation were significant positive predictors of the disability of PSO in the model. For living without spouse, the odds of being in more severe category on PSO was 1.76 times higher (p = 0.017). The odds of being in more severe level on PSO was 12.48 times higher for those who had the level of moderate to severe as compared to those who had the level of normal to mild in mRS (p < 0.001). And the odds of being in more severe level on PSO were 3.87, 1.94, 5.45, 6.06 and 3.88 times higher when a participant had the complication in hemiplegia, dysarthria, trouble seeing, cognition problem and general weakness, respectively (p < 0.001; 0.008; 0.001; 0.007; 0.010). In addition, the odds of being in more severe level on PSO was 2.59 times higher for those who showed low level of adherence to medication as compared to those who showed high in MMS-motivation (p = 0.002).
To our knowledge, this is the first detailed and nationwide disability prevalence survey on ischemic stroke patients at 1 year after onset in Korea. The study shows that prevalence of disability based on the WHODAS 2.0 is 62.6% which is almost double compared to hemiplegia (33.8%), one of the most common neurological sequelae 1 year after stroke. And the prevalence of severe disability (the WHODAS 2.0 of 50 ~ 100%) is higher in PSO (16.8%) and GAR (11.8%) than in the other domains. It also demonstrates that each domain of disability increases with various associated factors. Particularly, age, recurrent stroke, moderate to severe mRS, hemiplegia and dysarthria are generally related to different domains of disability and low MMS motivation is the only modifiable factor determining the significant association between all six domains of disability after adjustment.
Concerning the personal background, age is associated with disability like as previous studies using WHODAS 2.0 [18–20]. Greater disability tends to increase as age goes higher. The elderly are more vulnerable to age-related comorbidity related with physical health problems [21]. However, even though the adjusted odds of being in a higher category on each domain except SCA is higher (aOR of 1.13 ~ 1.42) for female compared to male, these sex-related differences in WHODAS disability measurements are not significant. A Korean study previously has reported that male elderly stroke patients seem to be more vulnerable to self-care because of Korean tradition of the passive domestic role of male [19]. The Framingham study has reported that female with ischemic stroke is not functionally more disabled than male [21].
PSO is particularly limited by the most variables such as living without spouse, recurrent stroke, moderate to severe mRS, hemiplegia, dysarthria, trouble seeing, cognition problem, general weakness, and low MMS motivation. However, both LAC and GAP are associated only with hemiplegia and dysarthria among seven variables of neurological sequelae. This indicates PSO is not only about getting along with people either not only doing daily life. A prior study considers PSO as most problematic and important because this domain has involved the usage of complex skills and navigation in daily life [20].
Each neurological sequela is associated with different domains of WHODAS 2.0. For example, hemiplegia is associated with five domains except UAC; dysarthria with UAC, GAP, LAC, and PSO; trouble seeing with UAC and PSO; and general weakness with GAR and PSO. Therefore, a tailored support can be shaped such as home visiting, a comprehensive type for hemiplegia, and companion going out, a simpler type for general weakness. It would be reasonable to manage these supports according to periodically assessed HRQoL.
It is of interest and importance that low MMS motivation is significantly associated with all six domains of disability after adjustment (OR of 2.59 ~ 3.83) because this variable is modifiable and essential to prevention from repeating event. Medication adherence is usually known as the proportion of days covered (PDC), the percentage of medication actually taken of the prescribed doses [22], at 1 year after stroke. The Epidemiologic Research Council of the Korean Stroke Society reports a much lower adherence compared to a previous study from the US [23] (75% vs. 91% for lipid-lowering drugs, 74% vs. 91% for antidiabetic drugs, and 82% vs. 92% for antihypertensive drugs) [3]. Moreover, unlike MMS knowledge, MMS motivation is also associated with the adherence to lifestyle modification for risk reduction [24]. Such evidences imply that there is a substantial room of improvement on HRQoL for stroke survivors. It is necessary for stroke survivors to provide with interventions to improve MMS motivation by the specific methods such as a tailored education, computer-based education, mobile phone reminders.
This study has several limitations. Our participants are regarded as persons from higher socioeconomic status in Korean context, as it is likely that the affluent have regular outpatient follow-up at a particular university hospital. Thus, it is possible overall participants demonstrated mild deficits as well as better level of adherence to their medication compared with stroke survivors in the general population. There is also a possibility of selection bias from excluding the stroke survivor 1 year after event due to difficulties in the interview in spite that we tried to ensure stroke survivors with eligibility in the study. In addition, the WHODAS 2.0 covers mainly the activities and participation domains of the ICF, so there has been a need to be addressed for bodily impairments and environmental factors [9]. However, this study choose several bodily impairments related factors to be investigated such as hemiplegia, dysarthria, facial palsy and so on.
Self-perceived disability by the WHODAS 2.0 had almost double the prevalence compared to hemiplegia, one of the most common neurological sequelae 1 year after stroke. Each domain of disability increased with various associated factors. Interventions promoting medical adherence of motivation seem to help high HRQoL in all domains.
Authors’ contributions
All authors were responsible for the study hypothesis and the analytical methodology. HJL, KK, HKP, GWK, JK, BGK, YHL, HSJ, HL, WKL, and SK played a central role in collecting data from each center. HJL, JM, YKP and JKS performed the statistical analyses. The draft of the manuscript was written by HJL and JKS. All authors contributed to the discussion as well as the process of review and approved the final manuscript.
Funding
This work was supported by the Ministry of Health and Welfare of Korea.
Availability of data and materials
The datasets generated and/or analyzed during the current study are not publicly available because an informed consent was not obtained from the participants during enrollment, but are available from the corresponding author at [email protected] on reasonable request.
Ethics approval and consent to participate
The study was approved by the institutional review board of Kangwon National University Hospital.
Consent for publication
Not applicable.
Competing interests
The authors of this paper declare no conflicts of interest.
Author details
aDepartment of Preventive Medicine, Gangwon Regional Cardiocerebrovascular Center, Kangwon National University Hospital, Chuncheon, Republic of Korea. bDepartment of Preventive Medicine¸ College of Medicine, Jeju National University. Jeju Regional Cardiocerebrovascular Center, Jeju National University Hospital, Jeju, Republic of Korea. cDepartment of Preventive Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea. dDepartment of Health Policy and Management, School of Medicine, Jeju National University, Jeju, Republic of Korea. eDepartment of Health Informatics and Management, College of Medicine, Chungbuk National University, Cheong-ju, Republic of Korea. f Department of Preventive Medicine, School of Medicine, Chonnam National University, Gwangju, Republic of Korea. gDepartment of Neurology, Gyeongnam Regional Cardiocerebrovascular Center, Gyeongsang National University Hospital, Jinju, Republic of Korea. hBusan Regional Cardiocerebrovascular Center, Dong-A University Hospital, Busan, Republic of Korea. iDepartment of Preventive Medicine, Wonkwang Medical Science, Wonkwang University School of Medicine. Jeonbuk Regional Cardiocerebrovascular Center, Wonkwang University Hospital, Iksan, Republic of Korea. jDepartment of Neurology, Daejeon-Chungnam Regional Cardiocerebrovascular Center, Chungnam National University Hospital, Daejeon, Republic of Korea. kSeoul National University Bundang Hospital, Gyeonggi, Republic of Korea. lDepartment of Prevention and Management, Inha University Hospital, School of Medicine, Inha University, Incheon, Republic of Korea. mKangwon National University School of Medicine, Chuncheon, Republic of Korea. nPrevention and Management Center, Ulsan Regional Cardiocerebrovascular Center, Ulsan University Hospital, Ulsan, Republic of Korea