P.Re.Val.E.: outcome research program for the evaluation of health care quality in Lazio, Italy

<p>Abstract</p> <p>Background</p> <p>P.Re.Val.E. is the most comprehensive comparative evaluation program of healthcare outcomes in Lazio, an Italian region, and the first Italian study to make health provider performance data available to the public.</p> <p>The aim of this study is to describe the P.Re.Val.E. and the impact of releasing performance data to the public.</p> <p>Methods</p> <p>P.Re.Val.E. included 54 outcome/process indicators encompassing many different clinical areas. Crude and adjusted rates were estimated for the 2006-2009 period. Multivariate regression models and direct standardization procedures were used to control for potential confounding due to individual characteristics. Variable life-adjusted display charts were developed, and 2008-2009 results were compared with those from 2006-2007.</p> <p>Results</p> <p>Results of 54 outcome indicators were published online at <url>http://www.epidemiologia.lazio.it/prevale10/index.php</url>.</p> <p>Public disclosure of the indicators' results caused mixed reactions but finally promoted discussion and refinement of some indicators.</p> <p>Based on the P.Re.Val.E. experience, the Italian National Agency for Regional Health Services has launched a National Outcome Program aimed at systematically comparing outcomes in hospitals and local health units in Italy.</p> <p>Conclusions</p> <p>P.Re.Val.E. highlighted aspects of patient care that merit further investigation and monitoring to improve healthcare services and equity.</p

Risk adjustment models for interhospital comparison of CS rates using Robson's ten group classification system and other socio-demographic and clinical variables

BACKGROUND: Caesarean section (CS) rate is a quality of health care indicator frequently used at national and international level . The aim of this study was to assess whether adjustment for Robson's Ten Group Classification System (TGCS), and clinical and socio-demographic of the mother and the fetus is necessary for inter-hospital comparisons of CS rates. METHODS: The study population includes 64,423 deliveries in Emilia-Romagna between January 1, 2003 and December 31, 2004, classified according to theTGCS. Poisson regression was used to estimate crude and adjusted hospital relative risks of CS compared to a reference category. Analyses were carried out in the overall population and separately according to the Robson groups (groups I, II, III, IV and V-X combined). Adjusted relative risks (RR) of CS were estimated using two risk-adjustment models; the first (M1) including the TGCS group as the only adjustment factor; the second (M2) including in addition demographic and clinical confounders identified using a stepwise selection procedure. Percentage variations between crude and adjusted RRs by hospital were calculated to evaluate the confounding effect of covariates. RESULTS: The percentage variations from crude to adjusted RR proved to be similar in M1 and M2 model. However, stratified analyses by Robson's classification groups showed that residual confounding for clinical and demographic variables was present in groups I (nulliparous, single, cephalic, [greater than or equal to]37 weeks, spontaneous labour) and III (multiparous, excluding previous CS, single, cephalic, [greater than or equal to]37 weeks, spontaneous labour) and IV (multiparous, excluding previous CS, single, cephalic, [greater than or equal to]37 weeks, induced or CS before labour) and to a minor extent in groups II (nulliparous, single, cephalic, [greater than or equal to]37 weeks, induced or CS before labour) and IV (multiparous, excluding previous CS, single, cephalic, [greater than or equal to]37 weeks, induced or CS before labour). CONCLUSIONS: The TGCS classification is useful for inter-hospital comparison of CS section rates, but residual confounding is present in the TGCS strata

Indicators of breast cancer severity and appropriateness of surgery based on hospital administrative data in the Lazio Region, Italy

BACKGROUND: Administrative data can serve as an easily available source for epidemiological and evaluation studies. The aim of this study is to evaluate the use of hospital administrative data to determine breast cancer severity and the appropriateness of surgical treatment. METHODS: the study population consisted of 398 patients randomly selected from a cohort of women hospitalized for first-time breast cancer surgery in the Lazio Region, Italy. Tumor severity was defined in three different ways: 1) tumor size; 2) clinical stage (TNM); 3) severity indicator based on HIS data (SI). Sensitivity, specificity, and positive predictive value (PPV) of the severity indicator in evaluating appropriateness of surgery were calculated. The accuracy of HIS data was measured using Kappa statistic. RESULTS: Most of 387 cases were classified as T1 and T2 (tumor size), more than 70% were in stage I or II and the SI classified 60% of cases in medium-low category. Variation from guidelines indications identified under and over treatments. The accuracy of the SI to predict under-treatment was relatively good (58% of all procedures classified as under-treatment using pT where also classified as such using SI), and even greater predicting over-treatment (88.2% of all procedures classified as over treatment using pT where also classified as such using SI). Agreement between clinical chart and hospital discharge reports was K = 0.35. CONCLUSION: Our findings suggest that administrative data need to be used with caution when evaluating surgical appropriateness, mainly because of the limited ability of SI to predict tumor size and the questionable quality of HIS data as observed in other studies

Mortality and morbidity among people living close to incinerators: a cohort study based on dispersion modeling for exposure assessment

<p>Abstract</p> <p>Background</p> <p>Several studies have been conducted on the possible health effects for people living close to incinerators and well-conducted reviews are available. Nevertheless, several uncertainties limit the overall interpretation of the findings. We evaluated the health effects of emissions from two incinerators in a pilot cohort study.</p> <p>Methods</p> <p>The study area was defined as the 3.5 km radius around two incinerators located near Forlì (Italy). People who were residents in 1/1/1990, or subsequently became residents up to 31/12/2003, were enrolled in a longitudinal study (31,347 individuals). All the addresses were geocoded. Follow-up continued until 31/12/2003 by linking the mortality register, cancer registry and hospital admissions databases. Atmospheric Dispersion Model System (ADMS) software was used for exposure assessment; modelled concentration maps of heavy metals (annual average) were considered the indicators of exposure to atmospheric pollution from the incinerators, while concentration maps of nitrogen dioxide (NO₂) were considered for exposure to other pollution sources. Age and area-based socioeconomic status adjusted rate ratios and 95% Confidence Intervals were estimated with Poisson regression, using the lowest exposure category to heavy metals as reference.</p> <p>Results</p> <p>The mortality and morbidity experience of the whole cohort did not differ from the regional population. In the internal analysis, no association between pollution exposure from the incinerators and all-cause and cause-specific mortality outcomes was observed in men, with the exception of colon cancer. Exposure to the incinerators was associated with cancer mortality among women, in particular for all cancer sites (RR for the highest exposure level = 1.47, 95% CI: 1.09, 1.99), stomach, colon, liver and breast cancer. No clear trend was detected for cancer incidence. No association was found for hospitalizations related to major diseases. NO₂levels, as a proxy from other pollution sources (traffic in particular), did not exert an important confounding role.</p> <p>Conclusions</p> <p>No increased risk of mortality and morbidity was found in the entire area. The internal analysis of the cohort based on dispersion modeling found excesses of mortality for some cancer types in the highest exposure categories, especially in women. The interpretation of the findings is limited given the pilot nature of the study.</p

Modelling the impact of improving screening and treatment of chronic hepatitis C virus infection on future hepatocellular carcinoma rates and liver-related mortality.

BACKGROUND: The societal, clinical and economic burden imposed by the complications of chronic hepatitis C virus (HCV) infection - including cirrhosis and hepatocellular carcinoma (HCC) - is expected to increase over the coming decades. However, new therapies may improve sustained virological response (SVR) rates and shorten treatment duration. This study aimed to estimate the future burden of HCV-related disease in England if current management strategies remain the same and the impact of increasing diagnosis and treatment of HCV as new therapies become available. METHODS: A previously published model was adapted for England using published literature and government reports, and validated through an iterative process of three meetings of HCV experts. The impact of increasing diagnosis and treatment of HCV as new therapies become available was modelled and compared to the base-case scenario of continuing current management strategies. To assess the 'best case' clinical benefit of new therapies, the number of patients treated was increased by a total of 115% by 2018. RESULTS: In the base-case scenario, total viraemic (HCV RNA-positive) cases of HCV in England will decrease from 144,000 in 2013 to 76,300 in 2030. However, due to the slow progression of chronic HCV, the number of individuals with cirrhosis, decompensated cirrhosis and HCC will continue to increase over this period. The model suggests that the 'best case' substantially reduces HCV-related hepatic disease and HCV-related liver mortality by 2020 compared to the base-case scenario. The number of HCV-related HCC cases would decrease 50% by 2020 and the number progressing from infection to decompensated cirrhosis would decline by 65%. Therefore, compared to projections of current practices, increasing treatment numbers by 115% by 2018 would reduce HCV-related mortality by 50% by 2020. CONCLUSIONS: This analysis suggests that with current treatment practices the number of patients developing HCV-related cirrhosis, decompensated cirrhosis and HCC will increase substantially, with HCV-related liver deaths likely to double by 2030. However, increasing diagnosis and treatment rates could optimise the reduction in the burden of disease produced by the new therapies, potentially halving HCV-related liver mortality and HCV-related HCC by 2020