Dataset Information

Impact of patient characteristics and treatment procedures on hospitalization cost and length of stay in Japanese patients with influenza: A structural equation modelling approach.

ABSTRACT:

Objectives

Little is known about the economic burden of influenza-related hospitalizations in Japan. This study sought to identify the factors that contribute to the total healthcare costs (THCs) associated with hospitalizations due to influenza in the Japanese population.

Study design

A retrospective cross-sectional database analysis study.

Methods

A structural equation modelling approach was used to analyse a nationwide Japanese hospital claims data. This study included inpatients with at least 1 confirmed diagnosis of influenza and with a hospital stay of at least 2 days, who were admitted between April 2014 and March 2015.

Results

A total of 5261 Japanese inpatients with a diagnosis of influenza were included in the final analysis. The elderly (≥65 years) and the young (≤15 years) comprised more than 85% of patients. The average length of stay (LOS) was 12.5 days, and the mean THC was 5402 US dollars (US$) per hospitalization. One additional hospital day increased the THC by 314 US$. Intensive care unit hospitalizations were linked to higher costs (+4957 US$) compared to regular hospitalizations. The biggest procedure-related cost drivers, which were also impacted by LOS, were blood transfusions (+6477 US$), tube feedings (+3501 US$) and dialysis (+2992 US$).

Conclusions

In Japan, the economic burden due to influenza-related hospitalizations for both children and the elderly is considerable and is further impacted by associated comorbidities, diagnostic tests and procedures that prolong the LOS.

SUBMITTER: Sruamsiri R

PROVIDER: S-EPMC5705683 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Impact of patient characteristics and treatment procedures on hospitalization cost and length of stay in Japanese patients with influenza: A structural equation modelling approach.

Sruamsiri Rosarin R Ferchichi Sameh S Jamotte Aurélien A Toumi Mondher M Kubo Hiroshi H Mahlich Jörg J

Influenza and other respiratory viruses 20171027 6

<h4>Objectives</h4>Little is known about the economic burden of influenza-related hospitalizations in Japan. This study sought to identify the factors that contribute to the total healthcare costs (THCs) associated with hospitalizations due to influenza in the Japanese population.<h4>Study design</h4>A retrospective cross-sectional database analysis study.<h4>Methods</h4>A structural equation modelling approach was used to analyse a nationwide Japanese hospital claims data. This study included i ...[more]

PMID: 28987034

Similar Datasets

Project description:BackgroundCommunity-acquired pneumonia is a common cause of patient hospitalization, and its burden on health care systems is increasing in aging societies. In this study, we aimed to investigate the factors that affect hospitalization costs in community-acquired pneumonia patients while considering the intermediate influence of patient length of stay.MethodsUsing a multi-institutional administrative claims database, we analyzed 30,041 patients hospitalized for community-acquired pneumonia who had been discharged between April 1, 2012 and September 30, 2013 from 289 acute care hospitals in Japan. Possible factors associated with hospitalization costs were investigated using structural equation modeling with length of stay as an intermediate variable. We calculated the direct, indirect (through length of stay), and total effects of the candidate factors on hospitalization costs in the model. Lastly, we calculated the ratio of indirect effects to direct effects for each factor.ResultsThe structural equation model showed that higher disease severities (using A-DROP, Barthel Index, and Charlson Comorbidity Index scores), use of mechanical ventilation, and tube feeding were associated with higher hospitalization costs, regardless of the intermediate influence of length of stay. The severity factors were also associated with longer length of stay durations. The ratio of indirect effects to direct effects on total hospitalization costs showed that the former was greater than the latter in the factors, except in the use of mechanical ventilation.ConclusionsOur structural equation modeling analysis indicated that patient profiles and procedures impacted on hospitalization costs both directly and indirectly. Furthermore, the profiles were generally shown to have greater indirect effects (through length of stay) on hospitalization costs than direct effects. These findings may be useful in supporting the more appropriate distribution of health care resources.

Project description:Background Surgical site complications (SSCs) can have serious and life-threatening consequences for patients; however, their frequency and impact on healthcare utilization across surgical procedures, particularly for non-infectious SSCs, are unknown. This study examined incidence of overall SSCs and non-infectious SSCs in patients undergoing open surgical procedures in the United States and their effect on length of stay (LOS) and costs. Methods This retrospective study utilizing 2019–2020 data from Medicare and Premier Health Database identified patients with SSCs during hospitalization or within 90 days of discharge. Propensity score matching was used to calculate incremental LOS and costs attributable to SSCs. Mean LOS and costs attributable to SSCs for the index admission, readmissions, and outpatient visits were summed by procedure and Charlson Comorbidity Index score to estimate the overall impact of an SSC on LOS and costs across healthcare settings. Results Overall and non-infectious SSC rates were 7.3 % and 5.3 % respectively for 2,696,986 Medicare and 6.7 % and 5.0 % for 1,846,254 Premier open surgeries. Total incremental LOS and cost per SSC were 7.8 days and $15,339 for Medicare patients and 6.2 days and $17,196 for Premier patients. Incremental LOS and cost attributable to non-infectious SSCs were 6.5 days and $12,703 and 5.2 days and $14,477 for Medicare and Premier patients respectively. Conclusions This study utilizing two large national databases provides strong evidence that SSCs, particularly non-infectious SSCs, are not uncommon in open surgeries and result in increased healthcare utilization and costs. These findings demonstrate the need for increased adoption of evidence-based interventions that can reduce SSC rates. Highlights • Overall and non-infectious SSC rates were ≈7% and ≈5% in this retrospective analysis of Medicare and Premier data.• Total incremental LOS and cost per SSC/non-infectious SSC were 7.8/6.5 days and $15,339/$12,703 for Medicare patients.• Total incremental LOS and cost per SSC/non-infectious SSC were 6.2/5.2 days and $17,196/$14,477 for Premier patients.• SSCs, particularly non-infectious SSCs, are not uncommon in open procedures and result in increased LOS and costs.• Increased adoption of evidence-based interventions that reduce SSCs is needed to improve care delivery and outcomes.

Project description:Background contextLength-of-hospital stay (LOS) after lumbar spine surgery (LSS) can be affected by many factors. However, few studies have evaluated predictors of LOS, and all have used limited number of variables as predictors.PurposeThe purpose of the study was to identify presurgical, surgical, and postsurgical predictors of LOS after LSS.Study design/settingRetrospective review of consecutive patients who had LSS at the University of Kansas Hospital from October 2008 to April, 2012.Patient sampleFive hundred ninety-three patients underwent LSS consisting of laminotomy, laminectomy, or arthrodesis.Outcome measuresDependent variableLOS. Multiple presurgical, surgical, and postsurgical variables were extracted from the patients' medical records and considered as possible predictors (independent variables) of LOS.MethodsPotential predictors that were significantly correlated with LOS were used as indicators to construct three latent factors presurgical, surgical, and postsurgical, which were in turn used to predict LOS in a structural equation model.ResultsThe average LOS was 4.01±2.73 days. The presurgical factor was indicated by age (61.97±14.49 years), previous level of function (60.5% were totally independent), previous hemoglobin level (13.70±1.36 mg/dL), and use of assistive devices (60% were assistive device users). The surgical factor was indicated by severity of illness (50.2% had minor disease severity), presence of complications (1.9%), and stay in an intensive care unit (4.0%). The postsurgical factor was indicated by postsurgical walking distance (166.43±175.75 ft), level of assistance during walking (5.18±0.81 out of 7 points), balance scores (6.18±1.82 out of 10 points), and bed mobility and transfer dependency scores (9.81±1.99 out of 14 points). These three latent factors explained 47% of variation in LOS.ConclusionsPostsurgical factors predicted the highest variation in LOS in comparison with presurgical and surgical factors and should be taken into consideration for discharge planning. Postsurgical factors are related to the patient's function, modifiable with rehabilitation, and can be improved to shorten LOS. Inclusion of more reliable and standardized presurgical variables could improve the predictability of the model.

Project description:AimAppraise the clinical features and influencing factors of the hospitalization times and length of stay in bipolar disorder (BD) patients.MethodsThis is a multicenter, observational, cohort study of patients diagnosed of type I or type II bipolar disorder. Five hundred twenty outpatients in seven hospitals from six cities in China were recruited from February 2013 to June 2014 and followed up using a continuous sampling pattern. The research included a retrospective period of 12 months and the prospective period of 9 months. The demographic and clinical features of the patients were collected. The influencing factors that could affect the length of stay (number of days spent in the hospital in the prospective period) were analyzed by poisson's regression and the hospitalization times (times of hospitalization in the prospective and retrospective period) was analyzed by general linear model. The selected variables included gender, age, years of education, occupational status, residence status, family history of mental disease, comorbid substance abuse, comorbid anxiety disorder, times of suicide (total suicide times that occurred in the retrospective and prospective period), polarity of the first mood episode, and BD type(I/II).ResultsPoisson's regression analysis showed that suicide times [Incidence Rate Ratio (IRR) = 1.20, p < 0.001], use of antipsychotic (IRR = 0.62, p = 0.011), and use of antidepressant (IRR = 0.56, p < 0.001) were correlated to more hospitalization times. Linear regression analysis showed that BD type II (β = 0.28, p = 0.005) and unemployment (β = 0.16, p = 0.039) which might mean longer duration of depression and poor function were correlated to longer length of stay. However, patients who experienced more suicide times (β = -0.21, p = 0.007) tended to have a shorter length of stay.ConclusionOverall, better management of the depressive episode and functional rehabilitation may help to reduce the length of stay. BD patients with more hospitalization times were characterized by higher risk of suicide and complex polypharmacy. Patients at high risk of suicide tended to have inadequate therapy and poor compliance, which should be assessed and treated adequately during hospitalization.Clinical trial registrationwww.ClinicalTrials.gov, Identifier: NCT01770704.

Dataset Information

Impact of patient characteristics and treatment procedures on hospitalization cost and length of stay in Japanese patients with influenza: A structural equation modelling approach.

Objectives

Study design

Methods

Results

Conclusions

Publications

Impact of patient characteristics and treatment procedures on hospitalization cost and length of stay in Japanese patients with influenza: A structural equation modelling approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets