The hospital community benefit standards program and health reform

This article suggests that the community hospital can be an important key to health reform at the local level; that community benefit guidelines are acceptable to hospitals and community leaders in a 49-site national demonstration program; and that these guidelines can prove useful for communities in moving toward health reform. Types of community involvement by hospitals are categorized, and examples of each type are developed. Community benefit programs can be a promising approach to effectively respond at the local level to the problems of poor health status, lack of access to care, and increasing health care costs. Addressing financing of care without attention to changes in the delivery system will not lead to effective health reform.     

Association between non-profit hospital community benefit spending and health outcomes

Objective

To determine if greater non-profit hospital spending for community benefits is associated with better health outcomes in the county where they are located.

Data Sources and Study Setting

Community benefit data from IRS Form 990/Schedule H was linked to health outcome data from Area Health Resource Files, Map the Meal Gap, and Medicare claims from the Center for Medicare and Medicaid Services at the county level. Counties with at least one non-profit hospital in the United States from 2015 to 2019 (N = 5469 across the 5 years) were included.

Study Design

We ran multiple regressions on community benefit expenditures linked with the number of health professionals, food insecurity, and adherence to diabetes and hypertension medication for each county.

Data Collection

The three outcomes were chosen based on prior studies of community benefit and a recent survey sent to 12 health care executives across four regions of the U.S. Data on community benefit expenditures and health outcomes were aggregated at the county level.

Principal Findings

Average hospital community benefit spending in 2019 was $63.6 million per county ($255 per capita). Multivariable regression results did not demonstrate significant associations of total community benefit spending with food insecurity or medication adherence for diabetes. Statistically significant associations with the number of health professionals per 1000 (coefficient, 12.10; SE, 0.32; p < 0.001) and medication adherence for hypertension (marginal effect, 0.27; SE, 0.09; p = 0.003) were identified, but both would require very large increases in community benefit spending to meaningfully improve outcomes.

Conclusions

Despite varying levels of non-profit hospital community benefit investment across counties, higher community benefit expenditures are not associated with an improvement in the selected health outcomes at the county level. Hospitals can use this information to reassess community benefit strategies, while federal, state, and local governments can use these findings to redefine the measures of community benefit they use to monitor and grant tax exemption.     

Association between non‐profit hospital community benefit spending and health outcomes

ObjectiveTo determine if greater non‐profit hospital spending for community benefits is associated with better health outcomes in the county where they are located.Data Sources and Study SettingCommunity benefit data from IRS Form 990/Schedule H was linked to health outcome data from Area Health Resource Files, Map the Meal Gap, and Medicare claims from the Center for Medicare and Medicaid Services at the county level. Counties with at least one non‐profit hospital in the United States from 2015 to 2019 (N = 5469 across the 5 years) were included.Study DesignWe ran multiple regressions on community benefit expenditures linked with the number of health professionals, food insecurity, and adherence to diabetes and hypertension medication for each county.Data CollectionThe three outcomes were chosen based on prior studies of community benefit and a recent survey sent to 12 health care executives across four regions of the U.S. Data on community benefit expenditures and health outcomes were aggregated at the county level.Principal FindingsAverage hospital community benefit spending in 2019 was $63.6 million per county ($255 per capita). Multivariable regression results did not demonstrate significant associations of total community benefit spending with food insecurity or medication adherence for diabetes. Statistically significant associations with the number of health professionals per 1000 (coefficient, 12.10; SE, 0.32; p < 0.001) and medication adherence for hypertension (marginal effect, 0.27; SE, 0.09; p = 0.003) were identified, but both would require very large increases in community benefit spending to meaningfully improve outcomes.ConclusionsDespite varying levels of non‐profit hospital community benefit investment across counties, higher community benefit expenditures are not associated with an improvement in the selected health outcomes at the county level. Hospitals can use this information to reassess community benefit strategies, while federal, state, and local governments can use these findings to redefine the measures of community benefit they use to monitor and grant tax exemption.     

The increased concentration of health plan markets can benefit consumers through lower hospital prices

The long-term trend of consolidation among US health plans has raised providers' concerns that the concentration of health plan markets can depress their prices. Although our study confirmed that, it also revealed a more complex picture. First, we found that 64 percent of hospitals operate in markets where health plans are not very concentrated, and only 7 percent are in markets that are dominated by a few health plans. Second, we found that in most markets, hospital market concentration exceeds health plan concentration. Third, our study confirmed earlier studies showing that greater hospital market concentration leads to higher hospital prices. Fourth, we found that hospital prices in the most concentrated health plan markets are approximately 12 percent lower than in more competitive health plan markets. Overall, our results show that more concentrated health plan markets can counteract the price-increasing effects of concentrated hospital markets, and that-contrary to conventional wisdom-increased health plan concentration benefits consumers through lower hospital prices as long as health plan markets remain competitive. Our findings also suggest that consumers would benefit from policies that maintained competition in hospital markets or that would restore competition to hospital markets that are uncompetitive.     

Effect of population and health care environment on hospital utilization.

A path analytic test of a causal model linking a county population's demographic and socioeconomic characteristics, the way its hospital services are delivered, and the health care resources available to it with its rate of short-term general hospital utilization is performed using data from 56 New York State counties. The results generally support the model and point to the central importance of an area's hospital bed supply for an understanding of its hospitalization rates. The path analysis reveals the patterns of direct and indirect effects of population and health care environment variables on hospitalization rates and supports the contention that health care environment characteristics intervene between population and hospitalization. The practical implications of these results for those in the health care field are discussed.     

医院实施HIS后的综合效益评估

对二炮医院实施信息化工程后的社会效益、经济效益、技术效益指标与实施前进行比较，找出两阶段的差异。运用加权指数法和两样本t检验的统计学方法，对我院“十五”实施HIS系统后，技术效益、医疗工作量、工作效率和经济效益等指标的变化进行分析。因此，单病人就诊效率比实施HIS前明显提高（P〈0．01）；甲级病案率由85．78％上升至98．83％；综合效益指数由0.9576上升至1．0718。根据分析评价结果，对医院的信息化管理提出了建议和措施。     

某医院体检人群糖尿病预测模型研究

目的 探讨logistic回归和随机森林在体检人群糖尿病患病风险预测中的应用。 方法 选择2006年1月-2015年12月在北京航天总医院体检中心参加体检的非糖尿病者11 769例次,随机选取70%样本,以性别、年龄、BMI、吸烟史、饮酒史、高血压既往史、高血压家族史、糖尿病家族史、收缩压、舒张压、空腹血糖、总胆固醇、甘油三酯、脂肪肝等14个因素作为自变量,以5年内是否罹患糖尿病作为因变量,基于logistic回归和随机森林分别建立糖尿病预测模型。将预测模型应用于剩余30%样本,根据所得受试者工作特征曲线的曲线下面积(AUC)评价模型的预测效果。 结果 Logistic回归预测模型和随机森林预测模型的AUC分别为0.912(95%CI:0.898~0.927)和0.919(95%CI:0.906~0.932),在最佳临界点,Logistic回归预测模型的灵敏度和特异度分别为80.8%和87.3%,随机森林预测模型的灵敏度和特异度分别为84.1%和85.3%。 结论 Logistic回归预测模型和随机森林预测模型对体检人群的糖尿病患病风险均具有良好的预测能力。     

Glove-induced dermal and respiratory symptoms among health care workers in one finnish hospital

To evaluate the prevalence of skin and respiratory symptoms associated with the use of protective gloves in health care workers, an inquiry study was carried out on 534 hospital employees who used protective latex or vinyl gloves on a daily basis at work. The prevalence of skin disorders related to the usage of gloves was 56%. Rhinorrhea or nasal congestion was present in 13% of the workers who used powdered disposable gloves. The prevalence of both skin and respiratory symptoms was significantly higher among the workers who used gloves > 2 hours a day (p < 0.001). The skin disorders were more common in young employees. The findings indicate that most of the symptoms were caused by irritation or an immediate, IgE-mediated allergy. We conclude that there is a positive correlation between the duration of daily glove usage at work and the skin and respiratory symptoms. In order to reduce skin disorders associated with the daily use of gloves, it is necessary to develop safer materials in the glove manufacturing process. © 1995 Wiley-Liss, Inc.     

对公立医院公益性的几点思考

公立医院要坚持公益性这早已经成为共识，现在之所以强调这个问题，是因为大家觉得这些年医改使医院偏离了公益性的方向。所以，有必要对公立医院公益性的现状、存在的问题与原因、改进的途径与办法等，作认真地分析与研究。笔者试就其中的几个问题谈点看法。     

Getting accredited for population health

The inclusion of population health in the accreditation process is an important new direction in the Canadian healthcare system. While quality improvement is a concept familiar to most clinicians and administrators, the inclusion of population health may raise some questions: What is "population health?"; Should healthcare organizations be responsible for population health?; If so, what could they and should they be doing about it?; How would a healthcare organization achieve accreditation in population health?