Temporal and geographic heterogeneity of the association between socioeconomic position and hospitalisation in Italy: an income based indicator

<p>Abstract</p> <p>Background</p> <p>The inverse association between socioeconomic position (SEP) and health has been extensively explored in Italy; however few studies have been carried out on the relationship between income inequalities and health status or health services utilisation, particularly at a local level.</p> <p>The objective of this study is to test the association between the demand for hospital care and a small area indicator based on income in four Italian cities, over a four-year period (1997-2000), in the adult population.</p> <p>Methods</p> <p>Census Block (median 260 residents) Median per capita Income (CBMI) was computed through record linkage between 1998 national tax and local population registries in the cities of Rome, Turin, Milan and Bologna (total population approximately 5.5 million). CBMI was linked to acute hospital discharges among residents, based on patient's residence.</p> <p>Age-standardized gender-specific hospitalisation rates were computed by CBMI quintiles (first quintile indicating lowest income), overall, and by city and year. Heterogeneity of the association between income level and hospitalisation was analysed through a Poisson model.</p> <p>Results</p> <p>We found an inverse association between small area income level and hospitalisation rates, which decreased continuously from 153 per 1000 inhabitants in the first quintile to 107 per 1000 inhabitants in the fifth quintile. Income differences in hospitalisation were confirmed in each city and year. However, the magnitude of the association and the absolute level of hospitalisation rates were quite different in each city and tended to slightly decrease over time in all cities considered, except Bologna.</p> <p>Conclusion</p> <p>Our study confirms an inverse association between income level and the use of hospitalization in four Italian cities, using a small area economic indicator, based on population tax data. Further analysis of the association between income and cause-specific hospitalization rates will allow to better understand the capability of the Italian National Health System to compel with socio-economic inequalities in health needs.</p> <p>Furthermore the SEP indicator we propose can represent a contribution to the improvement of tools for monitoring inequalities in health and in health services utilization.</p

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods. MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions. ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models' prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR. ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Income level and chronic ambulatory care sensitive conditions in adults: a multicity population-based study in Italy

<p>Abstract</p> <p>Background</p> <p>A relationship between quality of primary health care and preventable hospitalizations has been described in the US, especially among the elderly. In Europe, there has been a recent increase in the evaluation of Ambulatory Care Sensitive Conditions (ACSC) as an indicator of health care quality, but evidence is still limited. The aim of this study was to determine whether income level is associated with higher hospitalization rates for ACSC in adults in a country with universal health care coverage.</p> <p>Methods</p> <p>From the hospital registries in four Italian cities (Turin, Milan, Bologna, Rome), we identified 9384 hospital admissions for six chronic conditions (diabetes, hypertension, congestive heart failure, angina pectoris, chronic obstructive pulmonary disease, and asthma) among 20-64 year-olds in 2000. Case definition was based on the ICD-9-CM coding algorithm suggested by the Agency for Health Research and Quality - <it>Prevention Quality Indicators</it>. An area-based (census block) income index was used for each individual. All hospitalization rates were directly standardised for gender and age using the Italian population. Poisson regression analysis was performed to assess the relationship between income level (quintiles) and hospitalization rates (RR, 95% CI) separately for the selected conditions controlling for age, gender and city of residence.</p> <p>Results</p> <p>Overall, the ACSC age-standardized rate was 26.1 per 10.000 inhabitants. All conditions showed a statistically significant socioeconomic gradient, with low income people being more likely to be hospitalized than their well off counterparts. The association was particularly strong for chronic obstructive pulmonary disease (level V low income vs. level I high income RR = 4.23 95%CI 3.37-5.31) and for congestive heart failure (RR = 3.78, 95% CI = 3.09-4.62). With the exception of asthma, males were more vulnerable to ACSC hospitalizations than females. The risks were higher among 45-64 year olds than in younger people.</p> <p>Conclusions</p> <p>The socioeconomic gradient in ACSC hospitalization rates confirms the gap in health status between social groups in our country. Insufficient or ineffective primary care is suggested as a plausible additional factor aggravating inequality. This finding highlights the need for improving outpatient care programmes to reduce the excess of unnecessary hospitalizations among poor people.</p

Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

IntroductionArtificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer (NSCLC) using eXplainable AI (XAI) Machine Learning (ML) methods.MethodsWe prospectively collected real-world data from patients with an advanced NSCLC condition receiving immune-checkpoint inhibitors (ICIs) either as a single agent or in combination with chemotherapy. With regards to six different outcomes - Disease Control Rate (DCR), Objective Response Rate (ORR), 6 and 24-month Overall Survival (OS6 and OS24), 3-months Progression-Free Survival (PFS3) and Time to Treatment Failure (TTF3) - we evaluated five different classification ML models: CatBoost (CB), Logistic Regression (LR), Neural Network (NN), Random Forest (RF) and Support Vector Machine (SVM). We used the Shapley Additive Explanation (SHAP) values to explain model predictions.ResultsOf 480 patients included in the study 407 received immunotherapy and 73 chemo- and immunotherapy. From all the ML models, CB performed the best for OS6 and TTF3, (accuracy 0.83 and 0.81, respectively). CB and LR reached accuracy of 0.75 and 0.73 for the outcome DCR. SHAP for CB demonstrated that the feature that strongly influences models’ prediction for all three outcomes was Neutrophil to Lymphocyte Ratio (NLR). Performance Status (ECOG-PS) was an important feature for the outcomes OS6 and TTF3, while PD-L1, Line of IO and chemo-immunotherapy appeared to be more important in predicting DCR.ConclusionsIn this study we developed a ML algorithm based on real-world data, explained by SHAP techniques, and able to accurately predict the efficacy of immunotherapy in sets of NSCLC patients

Socioeconomic inequalities in coronary heart disease in Italy: A multilevel population-based study

This longitudinal study evaluates the role of individual and contextual socioeconomic determinants in the socioeconomic inequalities in incidence and mortality for coronary events in Turin, Italy, using hierarchical models. All residents aged 35-74 at the start of 1997 were included in the study population. We considered as outcomes all incident cases and deaths that occurred in the study population in the period 1997-2002. The socioeconomic indicators were educational level, job status and median income per census tract. A neighbourhood deprivation index was also used, which combines, in an aggregated measure, a series of poor individual socioeconomic conditions. The analyses were performed using hierarchical Poisson models, with individuals (n=523,755) considered as level I units and neighbourhoods (n=23) as level II units. Among men, we observed an inverse gradient in incidence by educational level and an excess risk for persons who were not actively employed. More marked excesses were found for mortality (RR: 1.63; 95% CI: 1.05-2.55, for unemployed persons compared to employed persons). Among women, greater socioeconomic differences were observed for both incidence and mortality; all of the individual indicators contributed to these differences. The differentials in mortality were particularly great for the retired and for housewives (RR: 1.98; 95% CI: 1.40-2.81). Slight excesses in incidence were observed among men for the most deprived areas. The results of this study reveal that job status is the most important individual factor explaining socioeconomic inequalities for coronary events, whereas context seems to play a marginal role.Coronary events Socioeconomic inequalities Contextual effects Hierarchical models Italy Gender

Professional development of university staff in an international affairs division in Japan

Background: Previous studies have shown that mortality inequalities are smaller in Italy than in most European countries. This may be due to the weak association between socioeconomic status and smoking in Italy. However, most published studies were based on data from a single city in northern Italy (Turin). In this study, we aimed to assess the size of mortality inequalities in Italy as a whole, their geographical pattern of variation within Italy, and the contribution of smoking to these inequalities. Methods: Participants in the National Health Interview Survey 1999-2000 were followed up for mortality until 31 December 2007. Using Cox regression, we computed the age-adjusted relative index of inequality (RII) for allcause mortality with and without controlling for smoking status and intensity. Education was used as an indicator of socioeconomic status. Results: A

Lifetime income and old age mortality risk in Italy over two decades

<p>BACKGROUND</p><p>The evidence on the shape and trend of the relationship between (lifetime) income and old age mortality is scarce and mixed both for North American and European countries. Nationwide evidence for Italy does not exist yet.</p><p>OBJECTIVE</p><p>We investigate the shape and evolution of the association between lifetime income and old age mortality risk, referred to as the income-old age mortality gradient, for males in the 1980s and the 1990s.</p><p>METHODS</p><p>We use data drawn from an administrative pension archive and proxy individual lifetime income with pension income. We use non-standard Cox proportional hazard models, in which the positions and number of the knots in the spline function for income are determined by the data.</p><p>RESULTS</p><p>The income-old age mortality gradient is negative but weak across most of the income distribution. Its shape shows two kink points situated almost at the same percentiles of the income distribution during the 1980s and the 1990s. The widening of the gradient over time is largely explained by regional differences in mortality and income.</p><p>CONCLUSIONS</p><p>Our findings show that mortality risk decreases with income. Once regional differences are controlled for, the relative difference in mortality risk between high and low-income individuals in Italy is rather stable over time.</p>