Dataset Information

Trading quality for quantity? Evidence from patient level data in China.

ABSTRACT: In China, overcrowding at hospitals increases the workload of medical staff, which may negatively impact the quality of medical services. This study empirically examined the impact of hospital admissions on the quality of healthcare services in Chinese hospitals. Specifically, we estimated the impact of the number of hospital admissions per day on a patient's length of stay (LOS) and hospital mortality rate using both ordinary least squares (OLS) and instrumental variable (IV) methods. To deal with potential endogeneity problems and accurately identify the impact of medical staff configuration on medical quality, the daily air quality index was selected as the IV. Furthermore, we examined the differential effects of hospital admissions on the quality of care across different hospital tiers. We used the data from a random sample of 10% of inpatients from a city in China, covering the period from January 2014 to June 2019. Our final regression analysis included a sample of 167 disease types (as per the ICD-10 classification list) and 862,722 patient cases from 517 hospitals. According to our results, the LOS decreased and hospital mortality rate increased with an increasing number of admissions. Using the IV method, for every additional hospital admission, there was a 6.22% (p < 0.01) decrease in LOS and a 1.86% (p < 0.01) increase in hospital mortality. The impact of healthcare staffing levels on the quality of care varied between different hospital tiers. The quality of care in secondary hospitals was most affected by the number of admissions, with the average decrease of 18.60% (p < 0.05) in LOS and the increase of 6.05% (p < 0.01) in hospital mortality for every additional hospital admission in our sample. The findings suggested that the supply of medical services in China should be increased and a hierarchical diagnosis and treatment system should be actively promoted.

SUBMITTER: Song J

PROVIDER: S-EPMC8445449 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Estimating and projecting population trends using population viability analysis (PVA) are central to identifying species at risk of extinction and for informing conservation management strategies. Models for PVA generally fall within two categories, scalar (count-based) or matrix (demographic). Model structure, process error, measurement error, and time series length all have known impacts in population risk assessments, but their combined impact has not been thoroughly investigated. We tested the ability of scalar and matrix PVA models to predict percent decline over a ten-year interval, selected to coincide with the IUCN Red List criterion A.3, using data simulated for a hypothetical, short-lived organism with a simple life-history and for a threatened snail, Tasmaphena lamproides. PVA performance was assessed across different time series lengths, population growth rates, and levels of process and measurement error. We found that the magnitude of effects of measurement error, process error, and time series length, and interactions between these, depended on context. We found that high process and measurement error reduced the reliability of both models in predicted percent decline. Both sources of error contributed strongly to biased predictions, with process error tending to contribute to the spread of predictions more than measurement error. Increasing time series length improved precision and reduced bias of predicted population trends, but gains substantially diminished for time series lengths greater than 10-15 years. The simple parameterization scheme we employed contributed strongly to bias in matrix model predictions when both process and measurement error were high, causing scalar models to exhibit similar or greater precision and lower bias than matrix models. Our study provides evidence that, for short-lived species with structured but simple life histories, short time series and simple models can be sufficient for reasonably reliable conservation decision-making, and may be preferable for population projections when unbiased estimates of vital rates cannot be obtained.

Project description:IntroductionSome cardiovascular devices are licensed based on limited evidence, potentially exposing patients to devices that are not safe or effective. Research is needed to ascertain if the same is true of other types of medical devices. Knee arthroplasty is a widely-used surgical procedure yet implant failures are not uncommon. The purpose of this study was to characterize available evidence on the safety and effectiveness of knee implants.MethodsA review of primary studies included in health technology assessments (HTA) on total (TKA) and unicompartmental knee arthroplasty (UKA) was conducted. MEDLINE, EMBASE, CINAHL, Cochrane Library and Biotechnology & BioEngineering Abstracts were searched from 2005 to 2014, plus journal tables of contents and 32 HTA web sites. Patients were aged 18 and older who underwent primary TKA or UKA assessed in cohort or randomized controlled studies. Summary statistics were used to report study characteristics.ResultsA total of 265 eligible primary studies published between 1986 and 2014 involving 59,217 patients were identified in 10 HTAs (2 low, 7 moderate, 1 high risk of bias). Most evaluated TKA (198, 74.5%). The quality of evidence in primary studies was limited. Most studies were industry-funded (23.8%) or offered no declaration of funding or conflict of interest (44.9%); based on uncontrolled single cohorts (58.5%), enrolled fewer than 100 patients (66.4%), and followed patients for 2 years or less (UKA: single cohort 29.8%, comparative cohort 16.7%, randomized trial 25.0%; TKA: single cohort 25.0%, comparative cohort 31.4%, randomized trial 48.6%). Furthermore, most devices were evaluated in only one study (55.3% TKA implants, 61.1% UKA implants).ConclusionsPatients, physicians, hospitals and payers rely on poor-quality evidence to support decisions about knee implants. Further research is needed to explore how decisions about the use of devices are currently made, and how the evidence base for device safety and effectiveness can be strengthened.

Project description:BackgroundRecently, the quality of clinical trials conducted in China has made considerable progress. However, clinical trials conducted in China still fall below the global average standard. The aim of this systematic review was to assess studies that investigated the quality of clinical trials conducted in China, summarize the issues, and provide suggestions for conducting high-quality clinical trials in China.MethodsWe comprehensively searched studies that investigated the quality of clinical trials conducted in China in the following databases from inception to December 1, 2021: National Knowledge Infrastructure, the Chinese Science and Technology Periodical Database, WanFang Data, China Biology Medicine, PubMed, and Embase. We then analyzed the issues in clinical trial registration, ethics review, implementation, and reporting. SPSS 25.0 software was used for data analysis. The data synthesis was conducted using summary statistics and a narrative format.ResultsA total of 90 studies were analyzed, there were 50 studies with 0-5 citation counts (55.56%), 18 studies with 5-10 citation counts (20%), 9 studies with 10-15 citation counts (10%), and 13 studies with more than 15 citation counts (14.44%). Eight (8.89%) studies were conducted by a supervision department, 38 (42.22%) by organizations with GCP qualification, and 44 (48.89%) by third parties. Additionally, there were some problems in the ethical review process of clinical trials, clinical trial registration process, clinical trial implementation process, and clinical trial reporting process.ConclusionsThe current study shows that the quality problems of clinical trials in China still exist. The reported problems are related to the process of clinical trials, including ethical review, registration, implementation, reporting. Due to the limited quantity and quality of included studies, the conclusions of this study need to be verified by high-quality studies.Review registrationNot registerated in PROSPERO.

Dataset Information

Trading quality for quantity? Evidence from patient level data in China.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets