Why do outcomes of CABG care vary between urban and rural areas in Taiwan? A perspective from quality of care

Can Urban-Rural Patterns of Hospital Selection Be Changed Using a Report Card Program? A Nationwide Observational Study

Background: Guiding patients to choose high-quality healthcare providers helps ensure that patients receive excellent care and helps reduce health disparities among patients of different socioeconomic backgrounds. The purpose of this study was to examine and compare the effect of implementing a report-card program on the patterns of hospital selection in patients from different socioeconomic subgroups. Patients undergoing total knee replacement (TKR) surgery were used as the sample population. Methods: A patient-level, retrospective, observational and cross-sectional study design was conducted. Taiwan National Health Insurance claims data were used and all patients in this database who had received TKR between April 2007–March 2008 (prior to report-card program implementation) and between April 2009–March 2010 (after program implementation) were included. Those patients who were under 18 years of age or who lacked area-of-residence or National Health Insurance premium information were excluded. Travelling distance to the hospital and level of hospital performance were used to evaluate the effect of the report-card program. Results: A total of 32,821 patients were included in this study. The results showed that patterns of hospital selection varied based on the socioeconomic characteristics of patients. In terms of travelling distance and hospital selection, the performance of urban and higher income patients was shorter and better, respectively, than their rural and lower-income peers both before and after report-card-program implementation. Moreover, although the results of multivariate analysis showed that the urban-rural difference in travelling distance enlarged (by 4.75 km) after implementation of the report-card program, this increase was shown to not be significantly related to this program. Furthermore, the results revealed that implementation of the report-card program did not significantly affect the urban-rural difference in terms of level of hospital performance. Conclusions: A successful report-card program should ensure that patients in all socioeconomic groups obtain comprehensive information. However, the results of this study indicate that those in higher socioeconomic subgroups attained more benefits from the program than their lower-subgroup peers. Ensuring that all have equal opportunity to access high-quality healthcare providers may therefore be the next issue that needs to be addressed and resolved.

Are PCI Service Volumes Associated with 30-Day Mortality? A Population-Based Study from Taiwan

The volume-outcome relationship has been discussed for over 30 years; however, the findings are inconsistent. This might be due to the heterogeneity of service volume definitions and categorization methods. This study takes percutaneous coronary intervention (PCI) as an example to examine whether the service volume was associated with PCI 30-day mortality, given different service volume definitions and categorization methods. A population-based, cross-sectional multilevel study was conducted. Two definitions of physician and hospital volume were used: (1) the cumulative PCI volume in a previous year before each PCI; (2) the cumulative PCI volume within the study period. The volume was further treated in three ways: (1) a categorical variable based on the American Heart Association’s recommendation; (2) a semi-data-driven categorical variable based on k-means clustering algorithm; and (3) a data-driven categorical variable based on the Generalized Additive Model. The results showed that, after adjusting the patient-, physician-, and hospital-level covariates, physician volume was associated inversely with PCI 30-day mortality, but hospital volume was not, no matter which definitions and categorization methods of service volume were applied. Physician volume is negatively associated with PCI 30-day mortality, but the results might vary because of definition and categorization method.

Intra-hospital correlations among 30-day mortality rates in 18 different clinical and surgical settings

Objective

To examine whether a correlation exists in hospitals among 30-day mortality rates for different types of hospitalizations.

Design

Cross-sectional study of hospital care based on publically available Italian data from the National Outcome Evaluation Program Edition 2015 of the Italian Agency for Regional Health Services.

Setting and Participants

Patients hospitalized with a diagnosis of congestive heart failure, acute myocardial infarction, chronic renal failure, chronic obstructive pulmonary disease exacerbation, femoral neck fracture, ischemic stroke and non-variceal upper gastrointestinal bleeding, or those who underwent isolated cardiac valve procedure, isolated coronary artery bypass graft surgery, non-ruptured abdominal aortic aneurysm repair and interventions for the following tumors: colon, kidney, brain, lung, stomach, rectal, liver or pancreatic cancer.

Main Outcome Measures

Condition-specific 30-day crude and risk-adjusted mortality rates.

Results

A total of 808 280 admissions were reported from 844 institutions (median of 4 conditions evaluated per hospital; interquartile range 2-8). Volumes and outcome varied by clinical and surgical conditions across hospitals. Out of 153 pairs of different conditions, 41 were statistically significant in terms of concordance with crude mortality rates and 44 for their adjusted values. The hospital mean percentile rank for 30-day mortality, a composite measure that summarized the multiple indicators, increased significantly alongside number of conditions per hospital with a significant reduction of mortality when most of the studied conditions were treated in the same hospital.

Conclusions

The variability in 30-day mortality rates at hospital level and the correlation between risk mortality rates suggest that there may be common hospital-wide factors influencing short-term mortality.

Relationship between Early Physician Follow-Up and 30-Day Readmission after Acute Myocardial Infarction and Heart Failure

Background

Thirty-day readmission rates after acute myocardial infarction (AMI) and heart failure are important patient outcome metrics. Early post-discharge physician follow-up has been promoted as a method of reducing 30-day readmission rates. However, the relationships between early post-discharge follow-up and 30-day readmission for AMI and heart failure are inconclusive. We used nationwide population-based data to examine associations between 7-day physician follow-up and 30-day readmission, and further associations of 7-day same physician (during the index hospitalization and at follow-up) and cardiologist follow-up with 30-day readmission for non-ST-segment-elevation myocardial infarction (NSTEMI) or heart failure.

Methods

We analyzed all patients 18 years or older with NSTEMI and heart failure and discharged from hospitals in 2010 in Taiwan through Taiwan’s National Health Insurance Research Database. Cox proportional hazard models with robust sandwich variance estimates and propensity score weighting were performed after adjustment for patient and hospital characteristics to test associations between 7-day physician follow-up and 30-day readmission.

Results

The study population for NSTEMI and heart failure included 5,008 and 13,577 patients, respectively. Early physician follow-up was associated with a lower hazard ratio of readmission compared with no early physician follow-up for patients with NSTEMI (hazard ratio [HR], 0.47; 95% confidence interval [CI], 0.39–0.57), and for patients with heart failure (HR, 0.54; 95% CI, 0.48–0.60). Same physician follow-up was associated with a reduced hazard ratio of readmission compared with different physician follow-up for patients with NSTEMI (HR, 0.56; 95% CI, 0.48–0.65), and for patients with heart failure (HR, 0.69; 95% CI, 0.62–0.76).

Conclusions

For each condition, patients who have an outpatient visit with a physician within 7 days of discharge have a lower risk of 30-day readmission. Moreover, patients who have an outpatient visit with the same physician within 7 days of discharge have a much lower risk of 30-day readmission.

Measuring inequality in physician distributions using spatially adjusted Gini coefficients

Objective

To measure inequality in physician distributions using Gini coefficient and spatially adjusted Gini coefficients.

Design

Measurements were based on the distribution of physician data from the Taiwan National Health Insurance Research Database (NHIRD) and population data from the Ministry of the Interior in Taiwan.

Settings

The distribution of population and physicians in Taiwan from 2001 to 2010.

Participants

This study considered 35 000 physicians who are registered in Taiwan.

Main Outcome Measures

To calculate the Gini coefficient and spatially adjusted Gini coefficients in Taiwan from 2001 to 2010.

Results

The Gini coefficient for each year, from 2001 to 2010, ranged from 0.5128 to 0.4692, while the spatially adjusted Gini coefficients based on travel time and travel distance ranged, respectively, from 0.4324 to 0.4066 and from 0.4408 to 0.4178. We found that, in each year, irrespective of the type of spatial adjustment, the spatially adjusted Gini coefficient was smaller than the Gini coefficient itself. Our empirical findings support that the Gini coefficient may overestimate the maldistribution of physicians.

Conclusions

Our simulations demonstrate that increasing the number of physicians in medium-sized cities (such as capitals of counties or provinces), and/or improving the transportation time between medium-sized cities and rural areas, could be feasible solutions to mitigate the problem of geographical maldistribution of physicians.

Patterns of Reasons for Taiwanese Adults' Health Information-Seeking Efforts: A Latent Class Analysis Approach

The aims of this study were to use latent class analysis (LCA) to identify subgroups of adults in Taiwan based on their reasons for seeking health information and to explore predictors of subgroup membership. A questionnaire survey of 752 adults from 25 communities in Taiwan was conducted. LCA was used to identify distinct classes of participants; latent class regression was performed to identify factors predicting latent class membership. Three classes emerged through LCA. The Health-Improving Group (50.40%) reported high probabilities of reasons relevant to improving their or someone else's health but low probabilities of reasons relevant to patient-provider interaction. The Active Group (32.98%) showed high probabilities of almost all of the reasons for seeking health information. The Passive Group (16.62%) showed low probabilities across all of the reasons. Compared to the Health-Improving Group, the Active Group was significantly more likely to have higher education and perceive higher information-seeking self-efficacy. The individuals in the Passive Group were significantly more likely to be male, be younger, have lower health literacy, and have fewer years of education than those in the Health-Improving Group. This LCA approach can provide important information on how communication strategies should be applied to different population subgroups.

Do the Preferences of Healthcare Provider Selection Vary among Rural and Urban Patients with Different Income and Cause Different Outcome?

Background

Equal access to healthcare facilities and high-level quality of care are important strategies to eliminate the disparity in outcome of care. However, the existing literature regarding how urban or rural dwelling patients with different income level select healthcare providers is insufficient. The purposes of this study were to examine whether differences of healthcare provider selection exist among urban and rural coronary artery bypass surgery (CABG) patients with different income level. If so, we further investigated the associated impact on mortality.

Methods

A retrospective, multilevel study design was conducted using claims data from 2007–2011 Taiwan’s Universal Health Insurance Scheme. Healthcare providers’ performance and patients’ travelling distance to hospitals were used to define the patterns of healthcare provider selection. Baron and Kenny’s procedures for mediation effect were conducted.

Results

There were 10,108 CABG surgeries included in this study. The results showed that urban dwelling and higher income patients were prone to receive care from better-performance providers. The travelling distances of urban dwelling patients was 15 KM shorter, especially when they received better-performance provider’s care. The results also showed that the difference of healthcare provider selection and mortality rate existed between rural and urban dwelling patients with different income levels. After the procedure of mediation effect testing, the results showed that the healthcare provider selection partially mediated the relationships between patients’ residential areas with different income levels and 30-day mortality.

Conclusion

Preferences of healthcare provider selection vary among rural and urban patients with different income, and such differences partially mediated the outcome of care. Health authorities should pay attention to this issue, and propose appropriate solutions to eliminate the disparity in outcome of CABG care.