Risk of Cardiovascular Events and Death—Does Insurance Matter?

BACKGROUND: Many Americans lack health insurance. Despite good evidence that lack of insurance compromises access to care, few prospective studies examine its relationship to health outcomes. OBJECTIVE: To determine the relationship between insurance and cardiovascular outcomes and the relationship between insurance and selected process measures. DESIGN AND PARTICIPANTS: We used data from 15,792 participants in the Atherosclerosis Risk in Communities Study, a prospective cohort study. Participants were enrolled in 1987–1989 and returned for follow-up visits every 3 years, for a total of 4 visits. MAIN OUTCOME MEASURES: We estimated the hazard of myocardial infarction, stroke, and death associated with insurance status using Cox proportional hazard modeling. We used generalized estimating equations to examine the association between insurance status and risk of (1) reporting no routine physical examinations, (2) being unaware of a personal cardiovascular risk condition, and (3) inadequate control of cardiovascular risk conditions. RESULTS: Persons without insurance had higher rates of stroke (adjusted hazard ratio, 95% CI 1.22–2.22) and death (adjusted hazard ratio 1.26, 95% CI 1.03–1.53), but not myocardial infarction, than those who were insured. The uninsured were less likely to report routine physical examinations (adjusted risk ratio 1.13, 95% CI 1.08–1.18); more likely to be unaware of hypertension (adjusted risk ratio 1.12, 95% CI 1.00–1.25) and hyperlipidemia (adjusted risk ratio 1.11, 95% CI 1.03–1.19); and more likely to have poor blood pressure control (adjusted risk ratio 1.23, 95% CI 1.08–1.39). CONCLUSIONS: Lack of health insurance is associated with increased rates of stroke and death and with less awareness and control of cardiovascular risk conditions. Health insurance may improve cardiovascular risk factor awareness, control and outcomes

Risk-adjusted cesarean section rates for the assessment of physician performance in Taiwan: a population-based study

BACKGROUND: Over the past decade, about one-third of all births nationwide in Taiwan were delivered by cesarean section (CS). Previous studies in the US and Europe have documented the need for risk adjustment for fairer comparisons among providers. In this study, we set out to determine the impact that adjustment for patient-specific risk factors has on CS among different physicians in Taiwan. METHODS: There were 172,511 live births which occurred in either hospitals or obstetrics/gynecology clinics between 1 January and 31 December 2003, and for whom birth certificate data could be linked with National Health Insurance (NHI) claims data, available as the sample for this study. Physicians were divided into four equivalent groups based upon the quartile distribution of their crude (actual) CS rates. Stepwise logistic regressions were conducted to develop a predictive model and to determine the expected (risk-adjusted) CS rate and 95% confidence interval (CI) for each physician. The actual rates were then compared with the expected CS rates to see the proportion of physicians whose actual rates were below, within, or above the predicted CI in each quartile. RESULTS: The proportion of physicians whose CS rates were above the predicted CI increased as the quartile moved to the higher level. However, more than half of the physicians whose actual rates were higher than the predicted CI were not in the highest quartile. Conversely, there were some physicians (40 of 258 physicians) in the highest quartile who were actually providing obstetric care that was appropriate to the risk. When a stricter standard was applied to the assessment of physician performance by excluding physicians in quartile 4 for predicting CS rates, as many as 60% of physicians were found to have higher CS rates than the predicted CI, and indeed, the CS rates of no physicians in either quartile 3 or quartile 4 were below the predicted CI. CONCLUSION: Overall, our study found that the comparison of unadjusted CS rates might not provide a valid reflection of the quality of obstetric care delivered by physicians, and may ultimately lead to biased judgments by purchasers. Our study has also shown that when we changed the standard of quality assessment, the evaluation results also changed

Measuring and explaining mortality in Dutch hospitals; The Hospital Standardized Mortality Rate between 2003 and 2005

Background. Indicators of hospital quality, such as hospital standardized mortality ratios (HSMR), have been used increasingly to assess and improve hospital quality. Our aim has been to describe and explain variation in new HSMRs for the Netherlands. Methods. HSMRs were estimated using data from the complete population of discharged patients during 2003 to 2005. We used binary logistic regression to indirectly standardize for differences in case-mix. Out of a total of 101 hospitals 89 hospitals remained in our explanatory analysis. In this analysis we explored the association between HSMRs and determinants that can and cannot be influenced by hospitals. For this analysis we used a two-level hierarchical linear regression model to explain variation in yearly HSMRs. Results. The average HSMR decreased yearly with more than eight

Incorporating health care quality into health antitrust law

<p>Abstract</p> <p>Background</p> <p>Antitrust authorities treat price as a proxy for hospital quality since health care quality is difficult to observe. As the ability to measure quality improved, more research became necessary to investigate the relationship between hospital market power and patient outcomes. This paper examines the impact of hospital competition on the quality of care as measured by the risk-adjusted mortality rates with the hospital as the unit of analysis. The study separately examines the effect of competition on non-profit hospitals.</p> <p>Methods</p> <p>We use California Office of Statewide Health Planning and Development (OSHPD) data from 1997 through 2002. Empirical model is a cross-sectional study of 373 hospitals. Regression analysis is used to estimate the relationship between Coronary Artery Bypass Graft (CABG) risk-adjusted mortality rates and hospital competition.</p> <p>Results</p> <p>Regression results show lower risk-adjusted mortality rates in the presence of a more competitive environment. This result holds for all alternative hospital market definitions. Non-profit hospitals do not have better patient outcomes than investor-owned hospitals. However, they tend to provide better quality in less competitive environments. CABG volume did not have a significant effect on patient outcomes.</p> <p>Conclusion</p> <p>Quality should be incorporated into the antitrust analysis. When mergers lead to higher prices and lower quality, thus lower social welfare, the antitrust challenge of hospital mergers is warranted. The impact of lower hospital competition on quality of care delivered by non-profit hospitals is ambiguous.</p

On Being the Right Size: The Impact of Population Size and Stochastic Effects on the Evolution of Drug Resistance in Hospitals and the Community

The evolution of drug resistant bacteria is a severe public health problem, both in hospitals and in the community. Currently, some countries aim at concentrating highly specialized services in large hospitals in order to improve patient outcomes. Emergent resistant strains often originate in health care facilities, but it is unknown to what extent hospital size affects resistance evolution and the resulting spillover of hospital-associated pathogens to the community. We used two published datasets from the US and Ireland to investigate the effects of hospital size and controlled for several confounders such as antimicrobial usage, sampling frequency, mortality, disinfection and length of stay. The proportion of patients acquiring both sensitive and resistant infections in a hospital strongly correlated with hospital size. Moreover, we observe the same pattern for both the percentage of resistant infections and the increase of hospital-acquired infections over time. One interpretation of this pattern is that chance effects in small hospitals impede the spread of drug-resistance. To investigate to what extent the size distribution of hospitals can directly affect the prevalence of antibiotic resistance, we use a stochastic epidemiological model describing the spread of drug resistance in a hospital setting as well as the interaction between one or several hospitals and the community. We show that the level of drug resistance typically increases with population size: In small hospitals chance effects cause large fluctuations in pathogen population size or even extinctions, both of which impede the acquisition and spread of drug resistance. Finally, we show that indirect transmission via environmental reservoirs can reduce the effect of hospital size because the slow turnover in the environment can prevent extinction of resistant strains. This implies that reducing environmental transmission is especially important in small hospitals, because such a reduction not only reduces overall transmission but might also facilitate the extinction of resistant strains. Overall, our study shows that the distribution of hospital sizes is a crucial factor for the spread of drug resistance

Personal and Societal Health Quality Lost to Tuberculosis

BACKGROUND: In developed countries, tuberculosis is considered a disease with little loss of Quality-Adjusted Life Years (QALYs). Tuberculosis treatment is predominantly ambulatory and death from tuberculosis is rare. Research has shown that there are chronic pulmonary sequelae in a majority of patients who have completed treatment for pulmonary tuberculosis (PTB). This and other health effects of tuberculosis have not been considered in QALY calculations. Consequently both the burden of tuberculosis on the individual and the value of tuberculosis prevention to society are underestimated. We estimated QALYs lost to pulmonary TB patients from all known sources, and estimated health loss to prevalent TB disease. METHODOLOGY/PRINCIPAL FINDINGS: We calculated values for health during illness and treatment, pulmonary impairment after tuberculosis (PIAT), death rates, years-of-life-lost to death, and normal population health. We then compared the lifetime expected QALYs for a cohort of tuberculosis patients with that expected for comparison populations with latent tuberculosis infection and without tuberculosis infection. Persons with culture-confirmed tuberculosis accrued fewer lifetime QALYs than those without tuberculosis. Acute tuberculosis morbidity cost 0.046 QALYs (4% of total) per individual. Chronic morbidity accounted for an average of 0.96 QALYs (78% of total). Mortality accounted for 0.22 QALYs lost (18% of total). The net benefit to society of averting one case of PTB was about 1.4 QALYs. CONCLUSIONS/SIGNIFICANCE: Tuberculosis, a preventable disease, results in QALYs lost owing to illness, impairment, and death. The majority of QALYs lost from tuberculosis resulted from impairment after microbiologic cure. Successful TB prevention efforts yield more health quality than previously thought and should be given high priority by health policy makers. (Refer to Abstracto S1 for Spanish language abstract)

Risk adjustment for inter-hospital comparison of primary cesarean section rates: need, validity and parsimony

BACKGROUND: Cesarean section rates is often used as an indicator of quality of care in maternity hospitals. The assumption is that lower rates reflect in developed countries more appropriate clinical practice and general better performances. Hospitals are thus often ranked on the basis of caesarean section rates. The aim of this study is to assess whether the adjustment for clinical and sociodemographic variables of the mother and the fetus is necessary for inter-hospital comparisons of cesarean section (c-section) rates and to assess whether a risk adjustment model based on a limited number of variables could be identified and used. METHODS: Discharge abstracts of labouring women without prior cesarean were linked with abstracts of newborns discharged from 29 hospitals of the Emilia-Romagna Region (Italy) from 2003 to 2004. Adjusted ORs of cesarean by hospital were estimated by using two logistic regression models: 1) a full model including the potential confounders selected by a backward procedure; 2) a parsimonious model including only actual confounders identified by the "change-in-estimate" procedure. Hospital rankings, based on ORs were examined. RESULTS: 24 risk factors for c-section were included in the full model and 7 (marital status, maternal age, infant weight, fetopelvic disproportion, eclampsia or pre-eclampsia, placenta previa/abruptio placentae, malposition/malpresentation) in the parsimonious model. Hospital ranking using the adjusted ORs from both models was different from that obtained using the crude ORs. The correlation between the rankings of the two models was 0.92. The crude ORs were smaller than ORs adjusted by both models, with the parsimonious ones producing more precise estimates. CONCLUSION: Risk adjustment is necessary to compare hospital c-section rates, it shows differences in rankings and highlights inappropriateness of some hospitals. By adjusting for only actual confounders valid and more precise estimates could be obtained