Potentially Preventable Hospital Readmissions After Patients’ First Stroke —A National Population-Based Study In Taiwan


 Readmission is an important indicator of the quality of care. The purpose of this study was to explore the probabilities and predictors of 30-day and 1-year potentially preventable hospital readmission (PPR) after a patient’s first stroke. We used claims data from the National Health Insurance (NHI) from 2010 to 2018. Multinomial logistic regression was used to assess the predictors of 30-day and 1-year PPR. A total of 41,921 discharged stroke patients was identified. We found that hospital readmission rates were 15.48% within 30-days and 47.25% within 1-year. The PPR and non-PPR were 9.84% (4,123) and 5.65% (2,367) within 30-days, and 30.65% (12,849) and 16.60% (6,959) within 1-year, respectively. The factors of older patients, type of stroke, shorter length of stay, higher Charlson Comorbidity Index (CCI), higher stroke severity index (SSI), hospital level, hospital ownership, and urbanization level were associated significantly with the 30-day PPR. In addition, the factors of gender, hospitalization year, and monthly income were associated significantly with 1-year PPR. The results showed that better discharge planning and post-discharge follow-up programs could reduce PPR substantially. Also, implementing a post-acute care program for stroke patients has helped reduce the long-term PPR in Taiwan.

the study proposal (VGHIRB No. 2015-05-006BC#4). The database contained no identi able personal information; hence waiver of informed content was granted. We con rm that all experiments were performed in accordance with relevant guidelines and regulations.

Sample Selection
We included patients hospitalized for their rst-ever stroke (ICD-9-CM 430-437) between 2010 and 2018 who were examined within 30 days with computed tomography (CT) or magnetic resonance imaging (MRI). We excluded patients with a stroke diagnosis before the index date, those who died during hospitalization, discharged themselves voluntarily, were transferred, had fewer than three outpatient visits within one year after discharge, had no insurance record, and were younger than 18-years-old. The nal sample comprised 41,921 patients. The sample selection procedure is shown in Fig.  1.

Causes of Readmission
Preventable readmissions were de ned according to the Agency for Healthcare Research and Quality (AHRQ) Prevention Quality Indicators (PQIs) 8, 16 , which include chronic lung condition indicators (chronic obstructive pulmonary disease and adult asthma), diabetes-related preventable conditions (short-and long-term complications, and uncontrolled diabetes), cardiovascular-related indicators (hypertension, congestive heart failure, and angina without procedure), and acute condition indicators (dehydration, bacterial pneumonia, and urinary tract infection) ( Table 1). Non-PPR was de ned as readmission after initial admission with a stroke diagnosis where the aforementioned diseases were not diagnosed at the time of readmission. Patients who were not readmitted to the hospital were de ned as having no readmission. The comorbidities with PPR and non-PPR after discharge post-stroke were calculated according to 29 diagnosed diseases (Table 2), which include any primary and secondary diagnosed conditions in outpatient or inpatient data during the period between the rst admission for stroke and readmission post-discharge. The items included in the Stroke Severity Index (SSI) essentially re ect the management of stroke-related complications, and are generally correlated with stroke severity and other accompanying neurological de cits. We extracted the above claims information from the inpatient claims database at rst admission for stroke and then computed each patient's SSI. Following a previous study, patients were categorized as having mild (SSI ≤ 5), moderate (SSI 5 to ≤12), or severe (SSI > 12) stroke 29 .

Data Analysis
Descriptive statistics were used to summarize all of the covariates considered in this study, in which categorical variables were analyzed using Pearson's Chi-squared test, and continuous variables (LOS) were analyzed using the t-test. In this study, separate models were built to examine the covariates associated with readmission status within 30-days and 1-year. Multinomial logistic regression (MLR) was performed to determine the association between related factors and readmissions. Three levels were de ned for the dependent variable, readmission status: PPR patients, non-PPR patients, and no-readmission patients. Among them, no readmission was set as the reference level. The MLR results were presented as odds ratios (ORs), 95% con dence intervals (95% CIs), and p-values. Statistical signi cance was set at p < 0.05. All statistical analyses were performed with SAS software v. 9.4 (SAS Institute Inc., Cary, NC, U.S.A.).

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,921 discharged stroke patients in total were identi ed during the study period. Table 3 shows the summary statistics of all covariates for the PPR, non-PPR, and non-readmission groups. Of these patients, 6,490 (15.48%) were readmitted within 30-days, and 19,808 (47.25%) were readmitted within 1-year. Among them, the readmission rates for PPR and non-PPR were 4,123 (9.84%) and 2,367 (5.65%) within 30-days, and 12,849 (30.65%) and 6,959 (16.60%) within 1-year, respectively.    In addition to these covariates, male gender, hospitalization year, monthly income, and treatment at a hospital in the central area of Taiwan, also affected readmission signi cantly. Moreover, direct trends were discernable for age, CCI, SSI, and urbanization for PPR within 1-year, and age and SSI for PPR within 30-days. Further, Figure 2 shows the forest plot of the odds ratios and 95% con dence intervals for factors associated with 30-day and 1year PPR.

Discussion
Studies have found that the rates of hospital readmissions after a stroke ranged from 6.5-24.3% within 30-days 5 and 31-49% within 1-year 11 .
However, not all readmissions are considered "potentially preventable" 30 . A review paper reported that preventable readmissions ranged from 14-23% within 30-days and from 48-59% within 1-year based upon older patients or general medical patients 15,31 . Another study estimated that the 1-year cumulative risks of readmission for ischemic stroke patients in Taiwan were 34.1%, 44.7%, and 62.9% for patients with mild, moderate, or severe stroke, respectively 32 . In this study, we determined that hospital readmission rates were 15.48% within 30-days and 47.25% within 1-year; the PPRs based upon the PQI de nition were 9.84% within 30-days and 30.65% within 1-year using population-based data in Taiwan 30,34 .
The extant literature has reported that certain patient characteristics, such as age and socioeconomic status, were potential factors associated with readmission after stroke 5,8,15 . Our study found the same effect of age, but patients with the highest and lowest monthly income had a signi cantly higher rate of readmissions than those with the median income. This may indicate inequalities in healthcare and additional investigation is necessary to determine the reasons 35 .
The severity of stroke upon the rst admission was also a signi cant predictor of 28-day readmission in Australia 5 . Further, CCI was found to be associated with the 30-day PPR after stroke discharge 30 . In this study, we identi ed a direct, positive relation between age, CCI, SSI, and long-term PPR.
In cases of long-term PPR, the increase in these factors was associated with increasing readmission. These ndings were similar to a 234 hospitalbased study in Florida, which found that PPR was related to the severity of illness and older age. In addition, their results showed that increased severity of the disease and time between admission and readmission increased readmission rates 14 .
After adjusting for other variables, regional hospitals showed a higher risk of PPR compared to medical centers and district hospitals. The effect of hospital-level on short-and long-term readmissions was consistent with those of previous studies 32,36 . We assume that medical centers provide a better quality of inpatient care 32 , and suggest that regional hospitals' policymakers give more attention to the quality of patient care. In addition, the fact that district hospitals had lower PPR than regional hospitals may be attributable to the implementation of the Post-Acute Care (PAC) program in Taiwan described in the next paragraph. The district hospitals received more PAC patients, which led to a decreasing readmission rate.
Our results showed that the hospitals' urbanization level was related signi cantly to both short-and long-term PPR; the most urbanized area had the lowest readmission rate compared to the least urbanized area. One study suggested that this may be related to the poor quality of care in rural areas 37 .
Most discharged stroke patients still need to receive follow-up healthcare at home or in a skilled nursing or inpatient rehabilitation facility; however, those resources may not be allocated su ciently in rural areas compared to urban areas 38 . As a result, the quality of post-discharge care in rural areas may be poorer than that in urban areas and have led to a higher readmission rate.
Our study demonstrated further that, compared to no-readmission patients, a one-day increase in LOS was associated signi cantly with 0.97 times the risk of 30-day PPR. However, a one-day increase in LOS was associated signi cantly with 1.01 times the risk of 1-year PPR. Hence, LOS may have different implications for short-and long-term PPR. This nding is consistent with that in Bjerkreim's study 11 . LOS' short-term effect on PPR may be explained by incomplete treatment during the index hospitalization 14 , and suggests the need for a better quality of care and discharge planning. On the other hand, LOS' long-term effect on PPR may be related to the severity of the stroke or comorbidities 10,39,40 , and suggests the need to improve the continuity of follow-up care.
The comorbidities associated with PPR diagnosed most frequently in our study were hypertension without complications, diabetes, and congestive heart failure. Previous reports have indicated that patients who were readmitted either early or later seemed to have higher frequencies of hypertension, atrial brillation, cerebrovascular disease, and diabetes as prior comorbidity conditions 10,11 . To decrease the risk of short-term PPR after discharge, our results showed that older patients, stroke type (ICH), CCI level of 4-6 and 7+, either moderate or severe SSI, and patients treated at regional, public or private, and hospitals in less urbanized areas are the groups most likely to experience a rst-ever stroke, which suggest that adequate discharge planning must be provided for the rst month after these patients are discharged. Although a previous study indicated that readmission reduction initiatives might not be highly effective for patients who are socioeconomically disadvantaged 41 , we found that more attention should be given to median-income patients to decrease readmission rates.
An important nding was that the ORs of long-term PPR vs. no readmission showed a decreasing trend. We believe that this is attributable to Taiwan's implementation in 2014 of the national health insurance post-acute care (PAC) program for rst-ever stroke patients. Patients who qualify for the PAC can receive intensive rehabilitation and integrated care within the treatment period. The PAC plan proposes to improve the incentives and review of discharge care for stroke patients in these hospitals.
PPR events may be avoided and healthcare costs reduced by improving the quality of care during the index inpatient stay and the period immediately following discharge. As a consequence, our study suggests that speci c groups of patients should be targeted for PPR intervention.

Limitations
This study has certain limitations. First, it was a retrospective cohort study with data derived from Taiwan's National Health Insurance (NHI) claims database. Consequently, data on certain important factors, such as patient behavioral characteristics, the process of care, and health-related quality of life could not be collected 23 . Nonetheless, compared to a hospital chart review database, the NHI claims database provides a population-based sample from a wide range of hospitals, as well as longitudinal follow-up information on readmission post-stroke. Second, the diagnosis codes' accuracy was uncertain. Therefore, the results may be limited to patients who are hospitalized with a primary discharge diagnosis of stroke in Taiwan 36 .

Conclusion
We recommend that hospital managers provide better discharge planning and post-discharge follow-up programs for these patients before and after discharge, as the combination is likely to reduce the number of PPR substantially. Figure 1 Flow chart of the data processing Forest plot displaying Odds ratios and 95% con dence intervals for 30-day and 1-year PPR after multinomial logistic regression