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Research article | Open | Open Peer Review | Published: 29 March 2015
Predicting readmission of heart failure patients using automated follow-up calls
Shelby Inouye1,
Vasileios Bouras2,
Eric Shouldis3,
Adam Johnstone3,
Zachary Silverzweig2 &
Pallav Kosuri4
BMC Medical Informatics and Decision Makingvolume 15, Article number: 22 (2015) | Download Citation
Readmission rates for patients with heart failure (HF) remain high. Many efforts to identify patients at high risk for readmission focus on patient demographics or on measures taken in the hospital. We evaluated a method for risk assessment that depends on patient self-report following discharge from the hospital.
In this study, we investigated whether automated calls could be used to identify patients who are at a higher risk of readmission within 30 days. An automated multi-call follow-up program was deployed with 1095 discharged HF patients. During each call, the patient reported his or her general health status. Patients were grouped by the trend of their responses over the two calls, and their unadjusted 30-day readmission rates were compared. Pearson's chi-square test was used to evaluate whether readmission risk was independent of response trend.
Of the 1095 patients participating in the program, 837 (76%) responded to the general status question in at least one of the calls and 515 (47%) patients responded to the general status question in both calls. Out of the 89 patients exhibiting a negative response trend, 37% were readmitted. By contrast, the 97 patients showing a positive trend and the 329 patients showing a neutral trend were readmitted at rates of 16% and 14% respectively. The dependence of readmission on trend group was statistically significant (P < 0.0001).
Patients at an elevated risk of readmission can be identified based on the trend of their responses to automated follow-up calls. This presents a simple method for risk stratification based on patient self-assessment.
An estimated 5.1 million people in the United States suffer from heart failure, and approximately 550,000 new diagnoses are made each year [1,2]. Although notable improvements have been made in the treatment of patients diagnosed with heart failure (HF), the national average readmission rate remains stagnant, with approximately one in four patients readmitted within 30 days of discharge [3]. In addition to the excessive trauma this may cause for the patient, readmissions can also place a large financial burden on the hospital. In FY2013 alone, Centers for Medicare and Medicaid Services penalized 2,200 U.S. hospitals a combined $280 million [4].
While it may not be possible to determine the exact proportion of preventable readmissions, evidence shows that comprehensive discharge planning and early follow-up can reduce the likelihood of readmission in HF patients [5,6]. The American Heart Association has advocated for post-discharge follow-up and has published a set of guidelines for post-discharge telephone calls [7]. However, due to the high volume of discharged patients, it is likely necessary to perform targeted interventions based on risk stratification. Prior studies have identified demographic and clinical patient data, such as marital status, insurance status, and comorbidities, as predictive factors for readmission [8-15]. While these models may provide considerable value, they tend to omit the potentially significant component of the patient's self-reported general condition. One efficient method for obtaining patient information post-discharge is through the use of automated calling. Automated calls have been used in many studies to monitor patients and attempt to minimize readmission [16,17]. However, despite the often unique insight provided into a patient's condition, patient response data have yet to be used as an effective means for risk stratification.
In this study, we investigated whether automated calls could be used to identify patients with HF who were at a higher risk of readmission within 30 days of hospital discharge. Our analysis showed that for this category of patients, self-assessment could provide a simple and efficient means for risk stratification.
Study population and eligibility
The study population was comprised of individuals enrolled in an automated post-discharge follow-up call program. The program was initiated at Charleston Area Medical Center (CAMC) in December of 2010 with the purpose of improving quality of care and patient outcomes. All enrolled patients for this study were discharged from CAMC in Charleston, West Virginia between December 2010 and September 2012. Individuals eligible for the call program were over 18 years of age, English-speaking, had a valid phone number, and had been admitted with a diagnosis of HF.
The automated call program was used to deliver information to CAMC clinicians regarding the patient's condition following discharge. A third party executed CAMC's Business Association Agreement prior to providing any automated call services. The third party was in full compliance with all HIPAA standards, rules and regulations. The study was performed using data acquired from a program that was implemented for the purpose of improving care management services at CAMC. Therefore, there was no requirement for external institutional review board approval [18].
Follow-up call design and protocol
The call script was generated via a collaborative effort between the third party (CipherHealth LLC, New York) and physicians at CAMC and JFK Medical Center in Edison, New Jersey. Follow-up questions were formulated based on best practice guidelines published by the American Heart Association, and the American College of Cardiology [7,19].
Upon discharge from the hospital, patient information was stored in the third party database. The call program consisted of two automated phone calls: patients received the first call within 48 hours of discharge and the second call seven days later. No calls were made on weekends, so if a patient was scheduled to be called during the weekend, his/her call was transferred to Monday. Therefore, patients who were discharged on Thursday or Friday, received their first call (two days after discharge) on Monday instead of Saturday or Sunday, respectively. Patients who were discharged during the weekend received their second call (nine days after discharge) on Monday after the following weekend instead of Saturday or Sunday respectively. Patients input their responses using a touch-tone phone. On the first call, four questions related to general health status, medications, follow-up appointments, and weight gain were asked. On the second call, the same inquiries were made, and a fifth question regarding maintenance of a low-sodium diet was included.
We hypothesized that responses to the general health status would provide predictive information about a patient's readmission risk. The general status question reads, "How are you feeling compared to when you were discharged from the hospital?" Possible responses were 1-better, 2-about the same, 3-worse, or 4-much worse. A trend was generated based on patients' responses to the general status question on the second call as compared to the first. Patients who responded more positively on the second call than on the first were considered to have a positive trend. Patients who responded more negatively on the second call than on the first were considered to have a negative trend. We hypothesized that patients showing a negative trend would be more likely to be readmitted than those with a positive or neutral trend.
Several measures were taken to maximize compliance. Patients were notified by the nursing staff of the approximate time and date of the calls. In addition, the automated calls were made using a phone number from the hospital, and the voice talent reflected the accent of the region. In the event of a missed call, the patient received a voicemail explaining that another attempt would be made in the near future. Up to four call attempts would be made on the scheduled call day.
Data collection and statistical analysis
Patient response data were delivered via automatic reports. Prior to analysis, all identifying data such as name, date of birth, and medical record number were removed. For the trend analysis, patients were assigned a value of "positive", "neutral", or "negative" based on the trend of their answers. Readmissions within 30 days of discharge were recorded and unadjusted readmission rates were calculated for each trend group. Pearson's chi-square test of independence was used to assess whether the unadjusted readmission rates were independent of response trend group.
Out of the 1095 HF patients selected for the study, 837 patients (76%) responded to the general status question in at least one call, and 515 patients (47%) responded to the general status question in both calls (Figure 1). A total of 244 patients (22%) were readmitted within 30 days of discharge from the hospital, which is consistent with the nationwide average rate of readmission [20]. The outcomes for different patient groups are summarized in Table 1. The rate of readmissions among patients who answered the general status question at least once was 21% as compared to 27% for those who did not answer the question. This difference was found to be statistically significant with P = 0.03.
Patient inclusion flow chart.
Table 1 Readmission rates by patient group
Of the 515 patients who completed both follow-up calls, 89 exhibited a negative response trend, 329 exhibited a neutral trend, and 97 exhibited a positive trend. Among patients with a negative trend, the readmission rate was 37%. Among patients with positive or neutral trends, the readmission rates were 16% and 14%, respectively. With a P value less than 0.0001, trend group was found to be a significant predictor of readmission rate.
Further analysis revealed a relationship between readmission probability and the patient's self-assessed status in the second follow-up call (P < 0.0001). 622 patients answered the general health status question in the second call. Of those patients, 412 responded feeling better, 152 responded feeling the same, and 58 responded feeling worse or much worse. The readmission rates for patients feeling better, same, and worse/much worse were 13%, 24%, and 43%, respectively. Results from the first call also revealed a difference in readmission rate among the groups, however with less significance (P = 0.03).
Predicting readmission
Our study found that patients who responded to two automated follow-up calls could be stratified by readmission risk based on their self-assessments of health in two automated phone calls. Patients who displayed a self-reported decline in condition following discharge were more than twice as likely to be readmitted as those who reported a neutral or improved condition over the two phone calls.
Useful risk information was also obtained from the second call alone. Patients who responded negatively were readmitted almost three times as frequently as patients who responded positively or neutrally. These two methods could be complementary, since these latter high-risk patients were not necessarily represented in the trend group analysis (these patients may not have answered the first call). Furthermore, these patients may have responded negatively on both calls and thus have been included in the neutral trend group.
This study takes a new approach to risk stratification. Many preceding efforts have been made to stratify patients based on medical records and data obtained during the hospital stay. For instance, Krumholz et al. reviewed 2,176 patients in 18 hospitals and derived a model comprising four independent predictors, which included hospitalization in the prior year, medical history of HF, medical history of diabetes mellitus, and serum creatinine levels at discharge [9]. Philbin and DiSalvo accessed a data set including 42,731 patients in 236 hospitals and derived a risk score based on 11 variables, including black race/ethnicity, primary insurance of Medicare or Medicaid, medical history of ischemic heart disease, the use of telemetry during hospitalization, etc [10]. Although successful within the scope of their studies, models such as these have been shown to lack consistency when compared to other studies. In a review of statistical models for predicting HF readmission, 117 studies were examined and it was discovered that few characteristics were consistently associated with readmission [8]. With regard to risk stratification, no studies to date have demonstrated strong model discrimination for readmission [14,21].
We speculate that a risk model that includes dynamic patient-reported data, such as the data recorded in this study, may help strengthen discrimination.
Preventing readmission
With the objective of improving healthcare quality and reducing costs, the United States government has increasingly encouraged hospitals to reduce preventable readmissions. In 2009, Medicare began publicly reporting 30-day risk-standardized readmission rates for HF, acute myocardial infarction, and pneumonia [22]. Although quality of inpatient care is an important factor associated with early readmission, there is also evidence for the efficacy of reaching out to high-risk patients following discharge [16,23]. A randomized controlled study showed that patients with HF who received telephone care post discharge had 84% lower HF-related readmission charges (P < 0.05) than the usual care group. This result suggests that follow-up care may lead to reductions in readmissions, emergency visits, and cost of care [24]. More recently, a Cochrane review of 30 peer-reviewed randomized controlled trials found that telemonitoring and structured telephone support decreased the rate of hospitalization in patients with HF [16].
Studies have also presented contrary evidence. Following the Cochrane review, a 2012 review of studies involving remote monitoring of patients with HF showed inconsistent results with regard to outcome improvement [17]. Of importance, however, were the inclusion criteria for the studies analyzed in the referenced reviews. In the Cochrane review, a program was classified as being "structured telephone support" if the remote care were delivered using simply a telephone, and a program was considered "telemonitoring" if there were digital transmission of physiologic or other non-invasive data [16]. This is contrasted with the 2012 review in which data from more invasive means such as implanted devices were included [17]. It is possible that certain interventional studies reported high readmission rates due to increased anxiety or even increased complications in patients using self-monitoring devices. The mixed results may also reflect the dependence on quality of follow-up.
Two large trials were recently published showing no significant difference in readmission for patients with heart failure participating in a telemonitoring program as compared to the control group [25,26]. In a study by Chaudhry, telemonitoring was performed using a telephone-based interactive-response system. Information was collected from discharged heart failure patients regarding symptoms and weight, and responses were reviewed by clinicians. Patients who triggered variances in their responses received follow-up care. While it was found that telemonitoring plus clinician intervention did not significantly improve readmission rates, the authors of that study pointed out that other telemonitoring studies may have yielded positive results due to an especially motivated follow-up staff [25,27]. Based on the varied success of such studies, it appears that the specific type of intervention plays a significant role in successfully preventing readmission.
By identifying high-risk patients, the method presented in this study can be used to more efficiently direct resources for follow-up care. In addition, such targeting could be of value when comparing the efficacy of various follow-up strategies.
We observed a difference in readmission rates between patients who adhered to the call program and patients who did not. Readmission risk was higher for non-adherent patients, which raises a concern about behavioral differences between these two patient groups. For instance, it is possible that patients who complied with the program were more inclined to follow their discharge instructions and therefore had a lower likelihood of readmission. It is also possible, however, that some of the non-adherent patients experienced very early readmission. Since dates of readmission were unavailable, we were not able to investigate this further. Regardless, it may be useful to identify characteristics associated with non-adherent patients since they displayed a higher rate of readmission. Our data showed that non-adherent patients tended to be younger than the patients who responded to calls. This might indicate that older patients consider post-discharge instructions more seriously. On the other hand, we found that the readmission rate did not differ significantly between different age groups, indicating that age difference alone could not explain the higher readmission rate of non-adherent patients.
Another important limitation is the lack of response to the first call. Of the 1095 patients called, 365 (33%) patients either failed to answer the first call or did not respond to the general status question on the first call. Studies show that 32% of 30-day readmissions in heart failure patients occur within the first seven days post-discharge [28]. Since the first call went out within 48 hours of discharge and the second call followed seven days later, a potentially significant number of patients at risk for readmission could not be identified. However, 107 of the 365 who failed to respond to the question on the first call responded on the second call, bringing the total number of completely non-adherent patients down to 258. Addressing this limitation, it is likely that stronger predictability could be attained by increasing the overall compliance of the program. This could be achieved by providing better information to the patients or by decreasing the number of questions asked in the phone calls. It is important to note that no tangible incentives were offered to patients to complete the automated calls, so the adherence rate should be representative of the general patient population.
This study focused on predicting readmissions post-discharge, and so we did not consider other factors that might contribute to risk of readmission. A future study could incorporate demographic information, health histories, and other relevant characteristics of automated call respondents to investigate which factors contribute to readmission risk.
The approach of dynamic self-report may present a potential advantage over risk stratification based on demographic or hospital data alone. Since it captures those who report a decline in condition regardless of their demographic profiles and health histories, self-assessment may identify individuals who are overlooked with alternative strategies. Furthermore, the method presented here can with advantage be used in situations when a patient's health history and clinical information is unavailable or unreliable. Nevertheless, in most cases, an integrated approach that uses several sources of data would likely prove advantageous.
Hospitals and patients both stand to benefit from efficient post-discharge care. In particular, readmission rates can potentially be lowered through targeted interventions. An important step towards this goal is finding methods that can reliably stratify patients according to their readmission risk. In this study, we have described a simple method, using self-assessment through automated phone calls, to identify patients with HF who are at a high risk of readmission. Future studies may lead to a more integrated approach, whereby patient history and demographic data are utilized to further improve the accuracy of risk stratification. We conclude that automated phone calls present an effective initial means for hospitals to identify and engage in targeted follow-up of high-risk HF patients.
Hospital Outcomes of Care Measures - National Average. 2013.
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Krumholz HM, Amatruda J, Smith GL, Mattera JA, Roumanis SA, Radford MJ, et al. Randomized trial of an education and support intervention to prevent readmission of patients with heart failure. J Am Coll Cardiol. 2002;39:83–9.
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We thank Alex Hejnosz for his helpful guidance and feedback.
Keck School of Medicine of USC, Los Angeles, CA, USA
Shelby Inouye
CipherHealth, New York, NY, USA
Vasileios Bouras
& Zachary Silverzweig
Department of Internal Medicine, Charleston Area Medical Center, Charleston, WV, USA
Eric Shouldis
& Adam Johnstone
Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA, 02138, USA
Pallav Kosuri
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Correspondence to Pallav Kosuri.
Vasileios Bouras, Zachary Silverzweig, and Shelby Inouye are currently or were formerly employed by CipherHealth, LLC. The other authors declare no competing interest. The authors declare that their affiliation did not influence the interpretation of the results.
SI wrote the manuscript and assisted with data analysis and interpretation. VB led data analysis and interpretation and assisted with writing of the manuscript. ES coordinated data collection, implemented the automated call program in the hospital, and provided revisions to the manuscript. AJ assisted with data collection and implementation of the automated call program. ZS conceived of the study and participated in interpretation of the results. PK directed the team with regard to data analysis and writing the manuscript. All authors read and approved the final manuscript.
Post-discharge follow-up
Readmission prediction
Associated Content
eHealth/ telehealth/ mobile health systems


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Home US News Trump holds back-to-back rallies in Georgia, Tennessee
Trump holds back-to-back rallies in Georgia, Tennessee
By Holly Rosenkrantz CBS News November 4, 2018, 5:40 PM Trump heads to Tennessee for rally following event in Georgia
President Trump arrives for a "Make America Great Again" campaign rally at Middle Georgia Regional Airport on Nov. 4, 2018 in Macon, Georgia.
President Trump is heading to Tennessee for a rally with Vice President Mike Pence on behalf of Marsha Blackburn, the Republican candidate for Senate.
He held a rally in Georgia Sunday afternoon to stump for Republican gubernatorial candidate Brian Kemp, the Georgia secretary of state whose race is one of the most closely watched of the midterm election cycle. Stacey Abrams, Kemp's Democratic opponent, would be the first African-American woman elected governor in the U.S. The race has featured repeated allegations of voter suppression by Kemp's office, which oversees state elections.
The president focused much of his remarks on his own record, and criticized Abrams as "extreme far-left." Kemp, he said, is a "great leader." He pressed voters to focus on his support for Kemp, not Abrams' recent high-profile support from Oprah Winfrey.
Earlier, Kemp's office said it had launched an investigation into the Georgia Democratic Party over "a failed attempt to hack the state's voter registration system." The head of the state Democratic Party categorically denied the accusation, saying the "scurrilous claims" are "100 percent false." The president did not mention the issue at his Georgia rally.
How to watch the Trump rally in Tennessee:
Before leaving the White House for the Georgia rally, Mr. Trump said he had no knowledge of the hacking accusations. He said he thought Kemp will be a "fantastic governor of Georgia."
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Measuring parent proxy-reported quality of life of 11 rare diseases in children in Zhejiang, China
Qisheng Gao ORCID: orcid.org/0000-0002-8347-25771,
Shanshan Wang2,
Jianping Ren2 &
Xin Wen3
It has become increasingly important to measure the health-related quality of life (HRQoL) of rare diseases in children and adolescents in recent decades. Much attention has been paid to investigate the HROoL of a specific rare disease by self-report in previous studies. This study aimed to evaluate and compare the HROoL of 11 rare diseases in Chinese children by parent proxy-report, to explore the factors associated with HROoL of patients, and to understand the problems of most concern.
A total of 651 children aged from 2 to 18 were enrolled from the Children's Hospital Affiliated Zhejiang University in 2018. Their parents completed the parent proxy-report version of the Pediatric Quality of Life Inventory™ 4.0 (PedsQL™ 4.0). Independent samples t-test, one-way ANOVA, or Kruskal–Wallis H test was used to compare HROoL scores between groups. Multilevel linear regression models with random intercept were applied to analyze the relationship between socioeconomic variables and both the total score and subdomain scores.
The total PedsQL scores of Patent ductus arteriosus (PDA), Infantile agranulocytosis, Autoimmune thrombocytopenia (ITP), Polysyndactyly, Hirschsprung disease, Cleft lip and palate, Tetralogy of fallot, Myasthenia gravis, Guillain–barre syndrome, Glycogen storage disease, and Langerhans cell histiocytosis children were 79.65 ± 5.46, 95.88 ± 3.48, 71.39 ± 3.27, 91.77 ± 6.35, 76.18 ± 6.92, 96.33 ± 4.22, 77.85 ± 8.90, 95.99 ± 3.31, 85.77 ± 4.56, 82.97 ± 4.13 and 77.6 ± 5.15, respectively. Age was significantly associated with physical functioning, school functioning, and psychosocial health scores. The household registration place was significantly related to the total score. The most urgent desire of patients was to reduce the overall medical costs.
This study showed that patients with PDA had the lowest physical functioning score, while patients with ITP scored the lowest in the emotional functioning, social functioning, school functioning, psychosocial health, and total scores. Incentive policies should be further adopted to improve orphan drug availability and reduce the economic burden of rare diseases.
Rare diseases, also known as "orphan diseases", refer to diseases with a low prevalence but which are seriously debilitating or even life-threatening [1]. There is no universal definition of rare diseases worldwide. Different countries or regions have distinct definitions depending on disease incidence or prevalence, the severity of the disease, and the existence of adequate treatments or drugs. The average prevalence threshold used to define rare diseases across different organizations within individual jurisdictions ranges from 5 to 76 cases/100,000 people, with a global average prevalence threshold of 40 cases/100,000 people [2]. In China, rare diseases have not been officially defined until now. In 2010, experts in the seminar held by the Chinese Society of Genetic Medicine of the Chinese Medical Association reached a consensus that a rare disease was defined as the prevalence of less than 1/500,000 or neonatal morbidity of less than 1/10,000. It is estimated to exceed 16.8 million rare disease patients in China with a population of 1.4 billion according to this prevalence [3]. There are an estimated 6000–8000 rare diseases worldwide [4], 75% of rare diseases affect children, and 30% of rare diseases patients die before the age of 5 [5]. In 2018, the National Health Commission of China and other 4 government departments jointly formulated and published China's First List of Rare Diseases, including 121 rare diseases [6]. The onset age of 43 rare diseases are during an infant and child's stage, such as Albinism, Angelman syndrome, Arginase deficiency and so on. Most rare diseases are the result of small genetic changes and can severely impair physical, emotional, and mental abilities. These disabilities can decrease the quality of life considerably and cause a tremendous burden on the affected families and health care systems [7]. In recent decades, it has become increasingly important to measure the health-related quality of life (HRQoL) of rare diseases in children and adolescents. Generic instruments and disease-specific instruments may be applied to measure HRQoL in children and adolescents with the same rare diseases [8]. The generic instruments can measure HRQoL domains across diseases and can be used in different populations [9]. Among the generic instruments, the Pediatric Quality of Life Inventory™ (PedsQL™) is one of the widely used instruments in young people [10]. To evaluate patients' HRQoL, many previous studies randomly selected healthy controls subjects for comparisons [11,12,13,14]. However, there is a paucity in the literature documenting differences in HRQoL among different rare diseases.
The main purpose of this study was to evaluate and compare HRQoL by surveying the parents of children with 11 different rare diseases between the ages of 2 and 18 by using the PedsQL™ instrument which can assess the domains outlined by WHO [15], to identify the association between potentially confounding factors on the HRQoL summary scores, and to understand the issues that patients are most concerned about.
Participants and procedures
The study was a cross-sectional, observational study performed in 2018 in the Children's Hospital affiliated to Zhejiang University. The hospital we investigated is the highest-level children's hospital in the region, which can reduce misdiagnosis and missed diagnosis to some extent. Besides, the diagnosis of patients was confirmed by the doctor, and the screened cases were all hospitalized cases, so the accuracy of diagnosis was relatively high. We signed a confidentiality agreement with the hospital to ensure that the patient's information will be kept strictly confidential. Patients diagnosed with rare diseases included in the List of Rare Diseases in the European Union during the period 2013–2017 were recruited from the hospital [16]. The criteria for inclusion were as follows: (1) The age of patients was between 2 and 18 years old; (2) The patients were diagnosed with no other diseases severely affecting the quality of life; (3) The parents were capable of understanding and expressing normally; (4) Consent of parents. The exclusion criteria were as follows: (1) Patients were younger than 2 years or older than 18 years at the time of interview; (2) Patients had other unrelated serious diseases. The parents were informed of the purpose, significance, and main content of the investigation orally before the telephone interview, and asked for their consent. The investigators were trained before the interview by the project manager to be very familiar with the questionnaire and use the normative expression. Besides, the investigators were responsible for ensuring that there were no missing data or logical errors in the questionnaire.
The Pediatric Quality of Life Inventory™ 4.0 (PedsQL™ 4.0), which includes parallel child self-report (age range 5–18 years) and parent proxy-report (age range 2–18 years), is a reliable, valid and sensitive instrument for widely used to assess HRQoL in the health and patient populations [17, 18]. While child self-report should be considered the gold standard for measuring HRQoL, in some cases, due to mental or cognitive impairment, or the lack of instruments for specific diseases, children may not be able to complete the questionnaire directly, and parent proxy-report may be a strategy for assessing children or adolescents' HRQoL [19, 20]. The items for child self-report and parent proxy-report are basically consistent, except that they differ in the developmentally appropriate language and the first or third person tense [17, 21]. Previous empirical studies have confirmed the validity of parental reports [22, 23]. Besides, it is generally the parents' perceptions of their children's HRQoL that influence healthcare utilization [24, 25]. In this study, the Chinese parent proxy-report version of the PedsQL™ 4.0 which has been validated in Chinese children was applied to evaluate children's quality of life [26,27,28,29]. This scale consists of 23 items, which are divided into 4 dimensions: Physical functioning (8 items), Emotional functioning (5 items), Social functioning (5 items) and School functioning (5 items). The latter 3 dimensions can also be united, called psychosocial health. Each item was scored using a 5-point response scale, of which 0 = never a problem, 1 = almost never a problem, 2 = sometime a problem, 3 = often a problem, 4 = almost always a problem. The items were reverse-scored and linearly converted to a score of 0–100 (0 = 100, 1 = 75, 2 = 50, 3 = 25, 4 = 0) with higher scores indicated better HRQoL. The scale scores were computed as a sum of the items divided by the number of items answered. If more than 50% of the items in the scale were missing, the scale score would not be calculated. The physical, emotional, social, school, psychosocial, and total scores were used in this study. In addition to PedsQL™ 4.0, all parents were required to complete a brief questionnaire covering the children's socio-demographic characteristics, such as gender, age group, household registration place, household type, and monthly family income per capita.
The choice proportions of the reimbursement rate of medical insurance and the most urgent desires
Categorical data were presented as counts and percentages. Quantitative data were described by mean and standard deviation. The distribution of the HRQoL scores was tested for normality using the Shapiro–Wilk test. Comparisons between two groups were made using independent t-test. When there were more than two groups, the differences were evaluated by one-way ANOVA and Kruskal–Wallis H test according to the homogeneity of variance.
Multiple comparisons were made by Dunn's post hoc test or least significant difference test (LSD). The multilevel linear regression models with random intercept were applied to analyze the relationship between socioeconomic variables and total score and subdomain scores. Initially, a model was estimated with the intercept only to estimate the proportion of variance due to the diseases concerning the individuals. This model served as the basis for evaluating the reduction in the variance of other models studied. After this, individual socioeconomic variables were tested. The two-tailed P < 0.05 was considered to be statistically significant. Data were processed and analyzed using R 3.6.1 for windows.
Participants' characteristics
A total of 651 children's parents completed the questionnaire. The socio-demographic characteristics of their children were presented in Table 1. The most frequently presenting rare disease was ITP (18.4%). Among the children, 373 (57.3%) were male, 310 (47.6%) aged between 2 to 4 years, 474 (72.8%) came from urban areas, and 288 (44.2%) of the monthly family income per capita less than 5000 CNY. These children came from 9 cities in Zhejiang province. They were 350 (53.8%) from Hangzhou, 105 (16.1%) from Ningbo, 67 (10.3%) from Shaoxing, 25 (3.8%) from Wenzhou, 51 (7.8%) from Jinhua, 10 (1.5%) from Lishui, 29 (4.5%) from Quzhou, 11 (1.7%) from Taizhou and 3 (0.5%) from Zhoushan. Due to the discrepancy of sample size in different cities, except the provincial capital city of Hangzhou, the other 8 cities were integrated into a group.
Table 1 Socio-demographic characteristics of the total participants
PedsQL scores of children
Univariate analysis of categorical variables relative to PedsQL total score and subdomain scores were summarized in Table 2. There were significant differences in the total scale and each subscale in different rare diseases, household registration place, and monthly family income per capita (all P < 0.05).The physical functioning score of children with Patent ductus arteriosus (PDA) was significantly lower than that of children with Infantile agranulocytosis, Polysyndactyly, Hirschsprung disease, Cleft lip and palate, and Myasthenia gravis (all P < 0.05). The emotional functioning, school functioning and total scores of ITP were significantly lower than those of other rare diseases except Langerhans cell histiocytosis (all P < 0.05). The social functioning score of ITP was significantly lower than that of other rare diseases except Hirschsprung disease (all P < 0.001), and the psychosocial health score of ITP was significantly lower than that of other rare diseases (all P < 0.001). Females reported significantly higher scores than male in the physical functioning score (t = − 3.29, P = 0.001) and total score (t = − 2.43, P = 0.015). The 2–4 year group was significantly higher than the 8–12 year group in the emotional functioning score (t = 2.917, P = 0.004). The 2–4 year group was significantly higher than the 5–7 year group (Z = 2.979, P = 0.017) and the 8–12 year group (Z = 5.512, P < 0.001) in the school functioning score. The 8–12 year group was significantly lower than other age groups in the psychosocial health score (all P < 0.05). The 2–4 year group was significantly higher than the 8–12 year group (Z = 3.436, P = 0.004) in the total score. The scores of subscale and total scale of children with household registration place in Hangzhou were higher than those of children with household registration place in other cities (all P < 0.001). Children in urban areas reported significantly higher scores than children in rural areas in physical functioning score (t = 3.31, P = 0.001), school functioning score (t = 3.24, P = 0.001),psychosocial health score (t = 2.45, P = 0.015) and total score (t = 3.05, P = 0.002). The total score and each subdomain score of the children with family monthly income per capita over 10,000 CNY were significantly higher than those of the low-income groups (all P < 0.05).
Table 2 Comparisons of PedsQL scores reported by parents
Factors associated with HRQoL
The multilevel linear regression models were shown in Table 3. The physical functioning score of 8–12-year-old children was higher than that of 2–4-year-old children (P < 0.001). The score of school functioning decreased with the increase of children's age (P < 0.05). The psychosocial health score of 8–12-year-old children was lower than that of 2–4-year-old children (P = 0.019). The total score of children whose household registration place was in Hangzhou was higher than that of children in other cities (P = 0.027).
Table 3 Multilevel regression coefficients and P value between variables and PedsQL scores
Reimbursement rates and the most urgent desires
The reimbursement rates of medical insurance for rare diseases were set to four grades: 0–20%, 20–40%, 40–60%, 60–80%, ≥ 80%. The proportion of patient who chose these four levels were 47.2%, 30.1%, 17.2%, 4.9% and 0.6%, respectively. The choice proportions of the most urgent desires were 28.2%,21.1%,18.3%,15.5%,14.4%, and 2.5% for a decrease in overall medical costs, improve diagnosis and treatment techniques, increase reimbursement rate, improve drug availability, service organizations can provide nursing services, not be discriminated at school, respectively (Fig. 1).
In the current study, we used the parent proxy-report version of the PedsQL™ 4.0 instrument to assess and compare the quality of life of patients with 11 rare diseases. Many studies proved that there was no statistically significant difference between child self-report and parent-proxy report of the PedsQL™ 4.0 scales, and moderate to good concordance was found between the child and parent-proxy scores [8, 15, 30,31,32,33]. However, other studies showed inconsistencies between parent-proxy and child self-report of HRQoL, especially in the subjective domains such as emotional and social functioning [23, 34,35,36]. Collins et al. pointed out that parent-proxy questionnaires may potentially reduce the accuracy of children's experience [37]. Most authors agree that parent-proxy report should be used as a supplement to child self-report as a secondary outcome measure [15].
The present study demonstrated that PDA had the lowest score in the physical functioning, ITP scored the lowest in the emotional functioning, social functioning, school functioning, psychosocial health and total score. ITP is an acquired autoimmune disease associated with some symptoms, such as spontaneous bruising, mucosal bleeding, epistaxis, and even fatal bleeding events [32]. The low quality of life of ITP may be associated with incessant worries of unpredictable bleeding, fears of invasive procedures, and risk of splenectomy. Additionally, the serious side effects of corticosteroid therapy and daily life restrictions may worsen it [38, 39]. Sood et al. summarized that the average scores of physical functioning, psychosocial health and total score measured by parent proxy-report version of PedsQL™ 4.0 for 11–18 year patients with Hirschsprung disease were 89.17 (SD = 14.40), 82.53 (SD = 17.61) and 84.84 (SD = 14.91), respectively [8], which were higher the results revealed by the current study and Collins [37] who reported that the psychosocial health score of children with Hirschsprung disease aged between 2 and 10 years (mean = 76.0) was significantly less than the healthy control group (mean = 81.2). Kwon et al. reported that the average scores of physical functioning, emotional functioning, social functioning, school functioning and total score measured by parent proxy-report version of PedsQL™ 4.0 of patients with Repaired tetralogy of fallot from 8.4 to 18.7 years old were 78.00 (SD = 19.30),76.00 (SD = 17.60),73.00 (SD = 22.40), 73.50 (SD = 17.90) and 77.40 (SD = 15.00), respectively [40], which were significantly lower than the normal group except the emotional functioning score, and the total score was very close to the present study. Collett et al. found that the scores of physical functioning and psychosocial health of children with Orofacial Clefts were 85.68 (SD = 17.57) and 81.03 (SD = 15.20), which were lower than the results of this study, and negligible differences were found in psychosocial outcomes between children with and without orofacial clefts [41]. Storch et al. reported that the average scores of physical functioning, emotional functioning, social functioning, school functioning, psychosocial health and total score measured by parent proxy-report version of PedsQL™ 4.0 of patients aged from 3 to 25 years with Glycogen storage disease Type I were 76.45 (SD = 19.63), 76.93 (SD = 19.24),74.23 (SD = 20.33), 71.92 (SD = 18.28), 74.36 (SD = 15.07) and 74.88 (SD = 15.64), respectively, which were all lower than that of the current study except for the physical functioning score [14], and the healthy control sample was significantly higher than the GSD sample in these domains.
HRQoL is a broad multidimensional concept influenced by numerous factors. The results of multilevel linear regression showed that children at the age of 8–12 reported higher physical functioning scores and less psychosocial health scores than children who at the age of 2–4. Although there was no significant difference among the 13–18 age group, 5–7 age group and 2–4 age group, the regression coefficients indicated that the score of psychosocial health decreased with age. Collins et al. reported a similar result that psychosocial functioning in the children with Hirschsprung disease was negatively affected by increasing age [37]. Peer teasing has been shown to be a strong predictor of self-concept and parent-reported behavior problems [42]. Collett et al.found that many pre-school children with Orofacial clefts are quite resilient, despite the potential for social stigmatization. However, as children develop and peer groups become more important, this situation may become more difficult to maintain [41]. This study also showed that increasing age negatively affected school functioning scores. Miatton et al.revealed that children with Tetralogy of fallot showed significantly lower scores on the school performances than healthy peers [43]. Roodbol et al. reported that many children were held back in class or dropped out of school after developing Guillain-Barré syndrome [44]. Khalil found that age at the time of surgery was negatively affecting school functioning (β = − 0.907) in children with Hirschsprung's disease [45]. The results of the multivariate analysis show that gender was not an influencing factor of HRQol. Though Mills et al. found that girls with Hirschsprung's disease had higher scores across all HRQol scales, the difference was not significant [33]. Many previous studies also indicated that gender did not have a significant effect on aspects of the global HRQol in children with Hirschsprung's disease [8, 37, 45]. The total score of children whose household registration place was in Hangzhou was significantly higher than that in other cities, which is likely to be the provincial capital city with the highest per capita GDP, representing a higher socio-economic status, where children have access to better medical services and education, and develop positive coping mechanisms to cope with adversity. Moreover, Damiano et al. reported that in families with higher income, patients with Cleft lip and palate had better physical, psychosocial, and total health scores [46].
A 2016 study investigated 1771 patients covered with 142 rare diseases in China. According to the study, 66% of the patients had been misdiagnosed before, and the most serious problem in the course of treatment was high medical cost (32.07%).Other problems were few types of drugs and rehabilitation (15.81%), poor treatment effect (12.25%), low reimbursement rate (12.14%), poor accessibility to health service (11.80%), respectively [47]. Among the 121 rare diseases listed in China's First List of Rare Diseases, 9 rare diseases have no medication, and the related medications of 13 rare diseases are excluded from the coverage of medical insurance [48]. Another study revealed similar results to the present study that the reimbursement rate of medical insurance was between 10 to 50%, and 80% of patients did not have commercial insurance [49]. Therefore, the Chinese government should strengthen its support to carry out fundamental research on the treatment and drugs of rare diseases, and formulate and implement incentive policies for the production or importation of orphan drugs. Moreover, it is recommended to include more rare disease drugs in medical insurance and raise the reimbursement rate to enhance orphan drug availability and reduce the financial burden of rare diseases.
Some limitations of the study should be considered. First, we did not inquire about the age at onset of symptoms or age at diagnosis. Although most of the literature did not take the age at onset of symptoms or age at diagnosis as an influencing factor, we still believe that this is one of the limitations of our study. Second, the research team did not confirm the diagnosis through a manual chart review, and there may be misdiagnosis. Third, since there were only 18 subjects in the 13–18 age group, this may be underrepresented in our sample. Fourth, according to the information we collected, among the 11 rare diseases included in the study, only PDA, Polysyndactyly, Cleft lip and palate can be cured by surgery, and the rest are treated with symptomatic support or no treatment. There is no denying that different treatments and prognosis will affect the quality of life. If patients with rare diseases can be cured by surgery, their quality of life will theoretically be improved in the long run. For most rare diseases that lack specific treatment, the improvement of quality of life by general supportive treatment requires further scientific evaluation. However, since we did not investigate information about treatment and prognosis, we are unable to provide evidence of the effect of treatment on the quality of life of rare diseases, which will be the direction of our future efforts.
This study revealed that the HRQoL of children with ITP was relatively lower compared with other rare diseases. We should pay more attention to the children's mental health and school functioning with age. Patients still face a lot of problems when seeking treatment, such as a lack of effective medicine and unable to afford medical expenses. Therefore, further measures should be adopted to improve the efficiency of diagnosis and the effect of treatment, to improve the availability of health services, and to alleviate the economic burden.
Please contact author for data requests.
PedsQL™:
Pediatric quality of life inventory™
LSD:
Least significant difference
ITP:
Autoimmune thrombocytopenia
PDA:
GSD:
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We would like to thank all participants for their time.
This study was supported by Zhejiang Province Public Welfare Technology Application Research Project (CN), Grant No. 2015C33145.
Department of Public Health, Hangzhou Medical College, No. 481 Binwen Road, Hangzhou, 310053, Zhejiang Province, China
Qisheng Gao
Department of Health Management, School of Medicine, Hangzhou Normal University, No. 2318 Yuhangtang Road, Hangzhou, 311121, Zhejiang Province, China
Shanshan Wang & Jianping Ren
Center for Medical Science, Technology and Education of Zhejiang Province, No, 60 Hefang Street, Hangzhou, 310006, Zhejiang Province, China
Xin Wen
Shanshan Wang
Jianping Ren
XW and JR participated in the design of the study. SW collected and processed the data. QG performed the statistical analysis and drafted the manuscript. All authors read and approved the final manuscript.
Correspondence to Xin Wen.
The respondents were anonymous, voluntary and consent for participation. All study procedures were approved by the Ethics Committee of Hangzhou Medical College.
Gao, Q., Wang, S., Ren, J. et al. Measuring parent proxy-reported quality of life of 11 rare diseases in children in Zhejiang, China. Health Qual Life Outcomes 18, 372 (2020). https://doi.org/10.1186/s12955-020-01572-0


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namespace content {

// NativeFileSystemPermissionGrant implementation that returns a fixed value as
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// RequestPermission will immediately call the callback, leaving the status
// unchanged.
class CONTENT_EXPORT FixedNativeFileSystemPermissionGrant
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 protected:
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#endif  // CONTENT_BROWSER_NATIVE_FILE_SYSTEM_FIXED_NATIVE_FILE_SYSTEM_PERMISSION_GRANT_H_

redpajama_set_name: RedPajamaGithub


text:
package org.assertj.core.internal.conditions;

import static org.assertj.core.error.ShouldBe.shouldBe;
import static org.assertj.core.test.TestData.someInfo;
import static org.assertj.core.test.TestFailures.failBecauseExpectedAssertionErrorWasNotThrown;

import static org.mockito.Mockito.verify;

import org.assertj.core.api.AssertionInfo;
import org.assertj.core.api.Condition;
import org.assertj.core.internal.Conditions;
import org.assertj.core.internal.ConditionsBaseTest;
import org.junit.Test;

/**
 * Tests for <code>{@link Conditions#assertIs(AssertionInfo, Object, Condition)}</code>.
 *
 * @author Alex Ruiz
 * @author Yvonne Wang
 * @author Joel Costigliola
 */
public class Conditions_assertIs_Test extends ConditionsBaseTest {

  @Test
  public void should_throw_error_if_Condition_is_null() {
    thrown.expectNullPointerException("The condition to evaluate should not be null");
    conditions.assertIs(someInfo(), actual, null);
  }

  @Test
  public void should_pass_if_Condition_is_met() {
    condition.shouldMatch(true);
    conditions.assertIs(someInfo(), actual, condition);
  }

  @Test
  public void should_fail_if_Condition_is_not_met() {
    condition.shouldMatch(false);
    AssertionInfo info = someInfo();
    try {
      conditions.assertIs(info, actual, condition);
    } catch (AssertionError e) {
      verify(failures).failure(info, shouldBe(actual, condition));
      return;
    }
    failBecauseExpectedAssertionErrorWasNotThrown();
  }
}


text:
❯English Premier League ❯ Marco Silva says Tom Davies needs to improve at Everton
Marco Silva says Tom Davies needs to improve at Everton
Big things are expected of Tom Davies who came through the youth ranks at the club and made his senior debut for the Toffees in 2015.
The 20-year-old has made six appearances for the Toffees this season, including five starts, and is thriving well under the tutelage of Marco Silva.
The Everton boss has said to the club's official website that Davies is working hard in training, but he still needs to improve.
The Portuguese boss has been impressed by Davies' attitude in training, and even handed the youngster the captaincy on occasion.
Davies played in Everton's final match before the international break in their 2-1 win over Leicester, where he played till the 71st minute and picked up a yellow card.
However, Silva insists that Davies still needs to improve if he wants to cement his starting role at Everton.
"For me, [the captaincy] is not the most important thing," Silva said to the club's official website. "Tom performed well [against Leicester] because he is working hard.
"He feels the support from the technical staff and his teammates which they give to him. He is a boy – 20 years old – and he needs to improve.
"But he will improve and we are here to support him."
Davies is a highly rated young talent, and it seems he is liked by the Everton boss. He remains one of the brightest prospects in Everton's team, and Silva is getting the best out of him.


text:
{% extends "threebot/worker/detail_base.html" %}
{% load staticfiles %}
{% load sekizai_tags %}
{% load threebot_tags %}

{% block inner_content %}

Log History {% if logs %}(latest {{ logs|length }}){% endif %}

    {% if logs %}
    {% for log in logs %}

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    {% endfor %}
    {% else %}{# no logs #}

Sorry, No Logs found.

    {% endif %}

{% endblock %}