Multimorbidity in middle-aged women and COVID-19: binary data clustering for unsupervised binning of rare multimorbidity features and predictive modeling.

BACKGROUND: Multimorbidity is typically associated with deficient health-related quality of life in mid-life, and the likelihood of developing multimorbidity in women is elevated. We address the issue of data sparsity in non-prevalent features by clustering the binary data of various rare medical conditions in a cohort of middle-aged women. This study aims to enhance understanding of how multimorbidity affects COVID-19 severity by clustering rare medical conditions and combining them with prevalent features for predictive modeling. The insights gained can guide the development of targeted interventions and improved management strategies for individuals with multiple health conditions. METHODS: The study focuses on a cohort of 4477 female patients, (aged 45-60) in Piedmont, Italy, and utilizes their multimorbidity data prior to the COVID-19 pandemic from their medical history from 2015 to 2019. The COVID-19 severity is determined by the hospitalization status of the patients from February to May 2020. Each patient profile in the dataset is depicted as a binary vector, where each feature denotes the presence or absence of a specific multimorbidity condition. By clustering the sparse medical data, newly engineered features are generated as a bin of features, and they are combined with the prevalent features for COVID-19 severity predictive modeling. RESULTS: From sparse data consisting of 174 input features, we have created a low-dimensional feature matrix of 17 features. Machine Learning algorithms are applied to the reduced sparsity-free data to predict the Covid-19 hospital admission outcome. The performance obtained for the corresponding models are as follows: Logistic Regression (accuracy 0.72, AUC 0.77, F1-score 0.69), Linear Discriminant Analysis (accuracy 0.7, AUC 0.77, F1-score 0.67), and Ada Boost (accuracy 0.7, AUC 0.77, F1-score 0.68). CONCLUSION: Mapping higher-dimensional data to a low-dimensional space can result in information loss, but reducing sparsity can be beneficial for Machine Learning modeling due to improved predictive ability. In this study, we addressed the issue of data sparsity in electronic health records and created a model that incorporates both prevalent and rare medical conditions, leading to more accurate and effective predictive modeling. The identification of complex associations between multimorbidity and the severity of COVID-19 highlights potential areas of focus for future research, including long COVID and intervention efforts.

Equity in the recovery of elective and oncological surgery volumes after the COVID-19 lockdown: a multicentre cohort study in Italy.

BACKGROUND: The COVID-19 pandemic has had, and still has, a profound impact on national health systems, altering trajectories of care and exacerbating existing inequalities in health. Postponement of surgeries and cancellation of elective surgical procedures have been reported worldwide. In Italy, the lock-down measures following the COVID-19 pandemic caused cancellations of surgical procedures and important backlogs; little is known about potential social inequalities in the recovery process that occurred during the post-lockdown period. This study aims at evaluating whether all population social strata benefited equally from the surgical volumes' recovery in four large Italian regions. METHODS: This multicentre cohort study covers a population of approximately 11 million people. To assess if social inequalities exist in the recovery of eight indicators of elective and oncological surgery, we estimated Risk Ratios (RR) through Poisson models, comparing the incidence proportions of events recorded during COVID-19 (2020-21) with those in pre-pandemic years (2018-19) for each pandemic period and educational level. RESULTS: Compared to 2018-19, volumes of elective surgery showed a U-shape with the most significant drops during the second wave or the vaccination phase. The recovery was socially unequal. At the end of 2021, incidence proportions among highly educated people generally exceeded the expected ones; RRs were 1.31 (95%CI 1.21-1.42), 1.24 (95%CI 1.17-1.23), 1.17 (95%CI 1.08-1.26) for knee and hip replacement and prostatic surgery, respectively. Among low educated patients, RR remained always < 1. Oncological surgery indicators showed a similar social gradient. Whereas volumes were preserved among the highly educated, the low educated were still lagging behind at the end of 2021. CONCLUSIONS: Surgical procedures generally returned to pre-pandemic levels but the low educated experienced the slowest recovery. An equity-oriented appraisal of trends in healthcare provision should be included in pandemic preparedness plans, to ensure that social inequalities are promptly recognised and tackled.

Health status assessment of a population of asylum seekers in Northern Italy.

BACKGROUND: Since 2011 Italy has faced an extraordinary increase in migrants arrivals, mainly from the Mediterranean route, one of the world's most dangerous journeys. The purpose of the present article is to provide a comprehensive picture of the migrants' health status in the "T. Fenoglio" centre, Settimo Torinese (Turin, Italy). METHODS: A retrospective cross-sectional study was conducted using data collected from June 2016 to May 2018 on adult migrants (over 18 years old) from Africa, Middle East and South East Asia (Bangladesh, Cambodia, India, Nepal). Data was collected through the migrants' medical records. Descriptive statistics were performed on socio-demographic variables. The diagnosed diseases were anonymously registered and classified according to the International Classification of Primary Care (ICPC-2). Conditional Inference Trees were used to perform a descriptive analysis of the sample and to detect the covariates with the strongest association with the variables Disease on arrival, Disease after arrival, ICPC on arrival and ICPC after arrival. RESULTS: Analyzed observations were 9 857. 81.8% were men, median age was 23 (Interquartile range: 20.0-27.4). 70.3% of the sample came from Sub-Saharan Africa. 2 365 individuals (24%) arrived at the centre with at least one disease. On arrival, skin (27.71%), respiratory (14.46%), digestive (14.73%) and generic diseases (20.88%) were the most frequent. During the stay respiratory diseases were the most common (25.70%). The highest probability of arriving with a disease occurred in 2018 and during the period September-November 2016, in particular for people from the Horn of Africa. During this period and also in the first half of 2017, skin diseases were the most reported. In seasons with lower prevalence of diseases on arrival the most common disease code was generic for both men and women (usually fever or trauma). CONCLUSIONS: This study provides information on the diverse diseases that affect the asylum seekers population. In our sample, the Horn of Africa was the most troubled area of arrival, with severe conditions frequently reported regarding skin diseases, in particular scabies. 2018 was the most critical year, especially for migrants from the Horn of Africa and Sub-Saharan Africa. During the stay at the camp, the prevalence of respiratory diseases increased. However, skin diseases remained the main issue for people from the Horn of Africa. Overall, the most reported diseases in the sample were dermatological, respiratory, digestive and generic diseases, both on arrival and during the stay. A better understanding of the health status of asylum seekers is an important factor to determine a more efficient reception and integration process and a better allocation of economic resources in the context of migrants' health care.

Validation of an Algorithm to Detect Multiple Sclerosis Cases in Administrative Health Databases in Piedmont (Italy): An Application to the Estimate of Prevalence by Age and Urbanization Level.

INTRODUCTION: Italy is considered a high-risk country for multiple sclerosis (MS). Exploiting electronic health archives (EHAs) is highly useful to continuously monitoring the prevalence of the disease, as well as the care delivered to patients and its outcomes. The aim of this study was to validate an EHA-based algorithm to identify MS patients, suitable for epidemiological purposes, and to estimate MS prevalence in Piedmont (North Italy). METHODS: MS cases were identified, in the period between January 1, 2012 and December 31, 2017, linking data from 4 different sources: hospital discharges, drug prescriptions, exemptions from co-payment to health care, and long-term care facilities. Sensitivity of the algorithm was tested through record linkage with a cohort of 656 neurologist-confirmed MS cases; specificity was tested with a cohort of 2,966,293 residents presumably not affected by MS. Undercount was estimated by a capture-recapture method. We calculated crude, and age- and gender-specific prevalence. We also calculated age-adjusted prevalence by level of urbanization of the municipality of residence. RESULTS: On December 31, 2017, the algorithm identified 8,850 MS cases. Sensitivity was 95.9%, specificity was 99.97%, and the estimated completeness of ascertainment was 91.9%. The overall prevalence, adjusted for undercount, was 152 per 100,000 among men and 286 among women; it increased with increasing age and reached its peak value in the 45- to 54-year class, followed by a progressive reduction. The age-adjusted prevalence of residents in cities was 15% higher than in those living in the countryside. DISCUSSION/CONCLUSION: We validated an algorithm based on EHAs to identify cases of MS for epidemiological use. The prevalence of MS, adjusted for undercount, was among the highest in Italy. We also found that the prevalence was higher in highly urbanized areas.

Hepatic fibrosis of any origin in a large population of type 2 diabetes patients.

BACKGROUND AND AIMS: Excess morbidity and mortality from chronic liver disease in type 2 diabetes (T2DM) is recognized; however, the clinical features associated with liver fibrosis (LF) of any origin are poorly known. Metabolic status and/or coexisting complications over time may play a role. METHODS AND RESULTS: We interrogated the database of the diabetes unit network of Piedmont (Italy) (71,285 T2DM patients) and calculated a fibrosis-4 score (FIB-4) from data recorded between 2006 and 2019. Comorbidities were obtained by linkage with hospital data. The study population was subdivided by aetiology of LF (alcoholic, viral, metabolic). Associations between upper level of FIB-4 and demographic and clinical variables were evaluated separately for each group using robust Poisson models and presented as prevalence ratios. Nearly one-quarter (24%) of T2DM patients had some form of LF: viral (0.44%) and alcoholic (0.53%) forms were far less frequent than metabolic ones (22.7%). Only 1 out of 5 of these patients had a history of known cirrhosis. Age, male sex, duration of diabetes, coronary disease, hyperuricemia, renal failure, and features of liver failure (e.g., lower body-mass index, lipid and HbA1c levels) were positively associated with metabolic LF. More intensive treatments with insulin and segretagogue emerged as a significant predictive indicators of LF of metabolic origin. CONCLUSION: A sizeable proportion of T2DM patients has some degree of LF, mainly of metabolic origin and often undiagnosed. There is a need to clarify whether the link between insulin therapy and advanced LF is causal or not.

Therapy With Agents Acting on the Renin-Angiotensin System and Risk of Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

Exposure to agents acting on the renin-angiotensin system was not associated with a risk increase of COVID-19 infection in 2 Italian matched case-control studies, 1 nested in hypertensive patients and the other in patients with cardiovascular diseases or diabetes.

Educational inequalities in the prevalence and outcomes of diabetes in the Emilian Longitudinal Study.

BACKGROUND AND AIM: Studies carried out in Italy in the last decades reported an effect modification in the association between socioeconomic position and diabetes outcomes, and the disease integrated care approach has been suggested as an explanatory factor. Whether this is true in Emilia-Romagna region in recent years is unknown and the aim of this study is to describe the role of educational level both on diabetes prevalence and health outcomes among the adult population with and without diabetes enrolled in the Emilian Longitudinal Study. METHODS AND RESULTS: Inequalities in diabetes prevalence were evaluated through standardised estimates and prevalence ratios by educational level and inequalities in outcomes through standardised hospitalisation and mortality ratios and rate ratios by educational level. The lower the education the greater the diabetes prevalence; such differences were larger among women and younger age groups. Diabetes conferred a higher risk of hospitalisation and mortality; those outcomes also presented a social gradient with the less educated bearing the higher risk. However, educational differences were slightly stronger among the disease-free subjects, especially in the case of mortality. In both genders, inequalities tended to disappear with age. CONCLUSION: This study confirms that diabetes increases the risk of unfavourable outcomes, but does not increase social inequalities in outcomes as might be expected. Similarly to what has been previously shown, it is likely that the protective effect of diabetes on the negative health effects of the low social position is attributable to the disease integrated care approach.

[Epidemiology in support of intervention priorities: the case of diabetes in Turin (Piedmont Region, Northern Italy)]. / L'epidemiologia a supporto delle priorità di intervento: il caso del diabete a Torino.

OBJECTIVES: to describe the epidemiology of diabetes within the city of Turin (Piedmont Region, Northern Italy) and to present the process initiated by the city network of diabetes care for the improvement of prevention and treatment of the disease. DESIGN: ecological study based on administrative database. SETTING AND PARTICIPANTS: residents in Turin from 2016 to 2018. MAIN OUTCOME MEASURES: incidence and prevalence of diabetes, percentage of glycosylated haemoglobin testing, and case-fatality. RESULTS: in the considered three-year period (2016-2018), the cumulative incidence of diabetes was 11.5 x1,000; as of 31.12.2018 the prevalence was 5.9%. 77% had performed at least one measurement of glycated haemoglobin during the previous year, and the case-fatality was 12.6% in the three-year period. The standardized prevalence per statistical zone varied from a minimum of 2% (95%CI 1.2-3.3) to a maximum of 10.2% (95%CI 9.1-11.4). The highest values were recorded in the most deprived city areas. The geographical distribution of incidence, varying between 5.1 x1,000 (95%CI 2.7-10.0) e 19.4 x1,000 (95%CI 15.8-24.0), reproduces the geography of prevalence, as well as the percentage of measurement of glycated haemoglobin, while the variability of the fatality rate is more modest without an obvious geographic pattern. CONCLUSIONS: diabetes occurs most frequently in the most deprived areas of the city, but the response of the health care system is adequate and equitable. Sharing of these results with the city health authorities and the diabetologists has led to identify as a priority interventions for the reduction of unhealthy behaviours, and for the improvements of patient care pathway, starting form the most disadvantaged areas of the city. A process of listening and involvement of all actors potentially interested in the prevention and treatment of diabetes has been started.

Prevention, education and counselling: the worldwide role of the community pharmacist as an epidemiological sentinel of headaches.

Headache disorders are the third among the worldwide causes of disability, measured in years of life lost to disability. Given the pharmacies' importance in general in headache patient and, in particular in migraine patient management, various studies have been carried out in recent years dealing with this issue. Indeed, in 2014, our research group first analysed publications on a number of studies conducted worldwide. As five years have passed since our first analysis of the literature and having carried out a number of specific studies in Italy since 2014, we wish to analyse once again the studies carried out globally on this topic to evaluate how the situation has evolved in the meantime. The key words used for the bibliographic search were "community pharmacy" and "headache"; we considered articles published between 2014 and 2018. The selected studies regarded Sweden USA, Belgium, Ireland, Jordan and Ethiopia. From the analysis of the international research papers, it is evident that, despite the time that has passed since the previous analyses and the general agreement that pharmacists find themselves in an ideal position to offer adequate levels of counselling to headache patients, the knowledge of pharmacists is not yet sufficient. Clearly, there is a strong need to develop training programmes specifically focused on this subject. Regarding Italy, a national study, commenced in 2016, was designed as a cross-sectional survey employing face-to-face interviews between pharmacist and patient using a questionnaire drawn up by experts in compliance with best practice from scientific literature. Six hundred ten pharmacists followed a specific training course; 4425 questionnaires were correctly completed. The use of pharmacies as epidemiological sentinels, given their capillarity and daily contact with the local population in Italy, enabled us to obtain an epidemiological snapshot closer to the real-life situation compared to specialist headache centres. Over the course of this study, data on headaches were gathered in Italian pharmacies with the highest levels of numerosity in the world.

A Systematic Review of Case-Identification Algorithms Based on Italian Healthcare Administrative Databases for Two Relevant Diseases of the Endocrine System: Diabetes Mellitus and Thyroid Disorders.

BACKGROUND: diabetes mellitus (DM) and thyroid disorders (TDs) are two of the most prevalent and relevant endocrine disorders worldwide, and determining their occurrence and their follow-up pathways is essential. In Italy, due to the presence of a universal health care system, administrative data can be effectively used to determine these measurements. DM is an ideal model for surveillance with administrative data, due to its specific pharmacologic treatment, high rate of hospitalization, and specific care units. The identification of TDs, conversely, is more challenging: they are less frequently managed in a hospital setting, and even if the treatment is highly specific, subclinical forms often do not need any pharmacological treatment. OBJECTIVES: to identify and to describe all DM and TD caseidentification algorithms by means of Italian Healthcare Administrative Databases (HADs), through the review of papers published in the past 10 years. METHODS: this study is part of a project that systematically reviewed case-identification algorithms for 18 acute and chronic conditions by means of HADs in Italy. PubMed was searched for original articles, published between 2007 and 2017, in Italian or English. The search string consisted of a combination of free text and MeSH terms with a common part that focused on HADs and a disease-specific part. All identified papers were screened by two independent reviewers. Pertinent papers were classified according to the objective for which the algorithm had been used, and only articles that used algorithms for "primary objectives" (I disease occurrence; II population/cohort selection; III outcome identification) were considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, follow-back periods, and age ranges applied by the algorithms have been reported. Further information on specific objective(s), accuracy measures, sensitivity analyses and the contribution of each HAD, have also been recorded. Algorithms were divided between those identifying type 2/not specified DM and type 1 DM, and those created to identify hypo- and hyperthyroidism. RESULTS: of the 780 articles identified for DM, 77 were included and a further 14 papers were added by screening the references. For TD, 65 articles were identified through the search string and 5 of them were included. Of the selected articles, 64% and 80% were published after 2014 for DM and TD, respectively, and 33% (for DM) and 20% (for TD) used multicentric national or international data. Forty original algorithms for DM (29 for type 2 DM/not-specified DM, and 11 for type 1 DM) and 9 for TD (6 for hypo- and 3 for hyperthyroidism) were extracted. In 6 algorithms, specific selections were made so as not to include gestational diabetes. With regard to type 2 DM, the most commonly used sources were the drug prescription database (DPD, 27 cases), hospital discharge record database (HDD, 23 cases), and exemption from healthcare co-payment database (ECD, 19 cases). Other sources were the ambulatory care services database (ACD), birth register, and mortality record database (MRD). Among the 11 algorithms to identify type 1 DM, 9 used DPD, 7 ECD, and 6 HDD; in one case ACD codes were added, and all 11 algorithms but one was applied to a population of young people (always <35 years old). With regard to TDs, 2 algorithms from one paper for hypo and hyperthyroidism relied on DPD as the only source, the other 7 original algorithms combined DPD with HDD (5 cases), ECD (3 cases), and ACD (1 case). One paper identified autoimmune/iodine deficiency hypothyroidism by subtracting iatrogenic hypothyroidism cases (identified through records of previous procedures from HDD and ACD) from the whole hypothyroid population (identified through DPD). External validation was performed for two algorithms for DM and none for TD. The first algorithm for DM was obtained through HDD only and its sensitivity ranged from 61% to 70%, the second had a sensitivity of 71%. CONCLUSION: Italian literature on the use of administrative healthcare data for case identification of diabetes is vast; the proposed algorithms are quite similar to one another, and the differences between them are rarely accompanied by clinical justification. On the contrary, the literature concerning thyroid disorders is relatively poor. Further validations of the proposed algorithms, as well as their further implementation, are needed.

A Systematic Review of Case-Identification Algorithms for 18 Conditions Based on Italian Healthcare Administrative Databases: A Study Protocol.

BACKGROUND: there has been a long-standing, consistent use worldwide of Healthcare Administrative Databases (HADs) for epidemiological purposes, especially to identify acute and chronic health conditions. These databases are able to reflect health-related conditions at a population level through disease-specific case-identification algorithms that combine information coded in multiple HADs. In Italy, in the past 10 years, HAD-based case-identification algorithms have experienced a constant increase, with a significant extension of the spectrum of identifiable diseases. Besides estimating incidence and/or prevalence of diseases, these algorithms have been used to enroll cohorts, monitor quality of care, assess the effect of environmental exposure, and identify health outcomes in analytic studies. Despite the rapid increase in the use of case-identification algorithms, information on their accuracy and misclassification rate is currently unavailable for most conditions. OBJECTIVES: to define a protocol to systematically review algorithms used in Italy in the past 10 years for the identification of several chronic and acute diseases, providing an accessible overview to future users in the Italian and international context. METHODS: PubMed will be searched for original research articles, published between 2007 and 2017, in Italian or English. The search string consists of a combination of free text and MeSH terms with a common part on HADs and a disease-specific part. All identified papers will be screened for eligibility by two independent reviewers. All articles that used/defined an algorithm for the identification of each disease of interest using Italian HADs will be included. Algorithms with exclusive use of death certificates, pathology register, general practitioner or pediatrician data will be excluded. Pertinent papers will be classified according to the objective for which the algorithm was used, and only articles that used algorithms with "primary objectives" (I disease occurrence; II population/cohort selection; III outcome identification) will be considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, follow-back periods, and age ranges applied by the algorithms will be collected. Further information on specific accuracy measures from external validations, sensitivity analyses, and the contribution of each source will be recorded. This protocol will be applied for 16 different systematic reviews concerning eighteen diseases (Hypothyroidism, Hyperthyroidism, Diabetes mellitus, Type 1 diabetes mellitus, Acute myocardial infarction, Ischemic heart disease, Stroke, Hypertension, Heart failure, Congenital heart anomalies, Parkinson's disease, Multiple sclerosis, Epilepsy, Chronic obstructive pulmonary disease, Asthma, Inflammatory bowel disease, Celiac disease, Chronic kidney failure). CONCLUSION: this protocol defines a standardized approach to extensively examine and compare all experiences of case identification algorithms in Italy, on the 18 abovementioned diseases. The methodology proposed may be applied to other systematic reviews concerning diseases not included in this project, as well as other settings, including international ones. Considering the increasing availability of healthcare data, developing standard criteria to describe and update characteristics of published algorithms would be of great use to enhance awareness in the choice of algorithms and provide a greater comparability of results.

Community pharmacies as epidemiological sentinels of headache: first experience in Italy.

Migraine is a disabling neurovascular syndrome which affects 12-15% of the global population and it represents the third cause in years lived with disability in both males and females aged 15-49 years. Among migraineurs, the symptomatic drug abuse may be a risk factor in the development of medication overuse headache (MOH). Detecting cases of MOH is not straightforward; community pharmacists may, therefore, be in a strategic position to identify individuals who self-medicate, particularly with respect to prevent the development of MOH. In 2014, our group published the results of a survey conducted in Piedmont, Italy, on the patterns of use and dispensing of drugs in patients requesting assistance from pharmacists for relief of a migraine attack. We decided, now, to expand the scope of the model to a national level. The study is based on cross-sectional face-to-face interviews using questionnaires, presented in this paper, consisting of a first part regarding the socio-economic situation and a second part which aimed to classify the disease and any excessive use of drugs. Of the 610 pharmacists trained with an online course, 446 gathered a total of 4425 correctly compiled questionnaires. The participation of community pharmacies has highlighted various criticalities especially of an organisational nature; however, it also revealed the power of this method as a means of gathering epidemiological data with a capillarity which few other methods can match. The objective was also to identify each territory's requirements and facilitate the decision-making process in terms of understanding what patients/citizens actually require.

[Assessment of an algorithm to identify paediatric-onset celiac disease cases through administrative healthcare databases]. / Valutazione di un algoritmo per l'identificazione dei casi di celiachia a insorgenza pediatrica utilizzando fonti sanitarie correnti.

OBJECTIVES: to assess the role of four administrative healthcare databases (pathology reports, copayment exemptions, hospital discharge records, gluten-free food prescriptions) for the identification of possible paediatric cases of celiac disease. DESIGN: population-based observational study with record linkage of administrative healthcare databases. SETTING AND PARTICIPANT S: children born alive in the Friuli Venezia Giulia Region (Northern Italy) to resident mothers in the years 1989-2012, identified using the regional Medical Birth Register. MAIN OUTCOME MEASURES: we defined possible celiac disease as having at least one of the following, from 2002 onward: 1. a pathology report of intestinal villous atrophy; 2. a copayment exemption for celiac disease; 3. a hospital discharge record with ICD-9-CM code of celiac disease; 4. a gluten-free food prescription. We evaluated the proportion of subjects identified by each archive and by combinations of archives, and examined the temporal relationship of the different sources in cases identified by more than one source. RESULT S: out of 962 possible cases of celiac disease, 660 (68.6%) had a pathology report, 714 (74.2%) a copayment exemption, 667 (69.3%) a hospital discharge record, and 636 (66.1%) a gluten-free food prescription. The four sources coexisted in 42.2% of subjects, whereas 30.2% were identified by two or three sources and 27.6% by a single source (16.9% by pathology reports, 4.2% by hospital discharge records, 3.9% by copayment exemptions, and 2.6% by gluten-free food prescriptions). Excluding pathology reports, 70.6% of cases were identified by at least two sources. A definition based on copayment exemptions and discharge records traced 80.5% of the 962 possible cases of celiac disease; whereas a definition based on copayment exemptions, discharge records, and gluten-free food prescriptions traced 83.1% of those cases. The temporal relationship of the different sources was compatible with the typical diagnostic pathway of subjects with celiac disease. CONCLUSIONS: the four sources were only partially consistent. A relevant proportion of all possible cases of paediatric celiac disease were identified exclusively by pathology reports.

Geographic and socioeconomic differences in access to revascularization following acute myocardial infarction.

BACKGROUND: Geographic and socioeconomic barriers may hinder fair access to healthcare. This study assesses geographic and socioeconomic disparities in access to reperfusion procedures in acute myocardial infarction (AMI) patients residing in Piedmont (Italy). METHODS: Coronary Care Units (CCUs) were geocoded with a geographic information system (GIS) and the shortest drive time from CCUs to patients' residence was computed and categorized as 0 to <20, 20 to <40 and ≥40 min. Using data on AMI emergency hospitalizations in 2004-2012, we employed a log-binomial regression model to evaluate the relation between drive time and use of Percutaneous Transluminal Coronary Angioplasty (PTCA) occurring within 2 days after a hospitalization for an episode of AMI, and whether this relation varied depending on the period of hospitalization. RESULTS: A total of 29% of all cases with a diagnosis of AMI (n = 66 097), were revascularized within 2 days from the index admission. The further AMI patients lived from CCUs, the less likely they were to receive revascularization: compared with distance <20 min, RRs were respectively 0.84 [95% CI 0.80-0.88] and 0.78 [95% CI 0.71-0.86]. Findings also showed that less educated people had a lower relative risk of being revascularized compared to more educated people (RR = 0.78; 95% CI = 0.74-0.82). Both inequalities have reduced in recent years. CONCLUSION: This study provides evidence of reduced geographical and socioeconomic differences in revascularization use over time. Geography and socioeconomic status should not determine the type of treatment received for life-threatening conditions such as AMI.

[Contribution of amenable mortality to the decrease of overall mortality in Piedmont Region (Northern Italy), 1980-2011]. / Il contributo della mortalità amenable alla diminuzione della mortalità generale in Piemonte dal 1980 al 2011.

OBJECTIVES: to describe overall and amenable mortality trends over the last 30 years in the Local Health Authorities (LHAs) of Piedmont Region (Northern Italy). By comparing these trends, it is possible to analyse intraregional variability in the performance of the healthcare system. DESIGN: descriptive study. SETTING AND PARTICIPANTS: mortality data from the Italian National Institute of Statistics (Istat) for the population between 0 and 74 years resident in Piedmont Region for the period 1980-2011. MAIN OUTCOME MEASURES: overall and amenable age-standardised death rates, by gender and health unit; ratio of the differences in amenable and in all-cause mortality (standardised rate difference - SRD: SRDamenable/SRDall-cause) over the observation period. RESULTS: between 1980 and 2011, overall mortality in Piedmont has decreased from 425.8 x100,000 to 205.5 x100.000 among women, and from 891.6 x100,000 to 390.7 x100,000 among men. The rate of amenable mortality on overall mortality decreased from 40% to 32% among women, and from 33% to 21% among men. Furthermore, amenable mortality contributed to 48% of the overall mortality reduction among women and to 35% among men. Regional results show heterogeneity between health units. This heterogeneity decreased over the three decades and was higher in men than in women. CONCLUSION: although Piedmont is one of the Italian Regions with the highest amenable mortality rate, a considerable decrease of its contribution to the overall mortality was seen in the last three decades. This improvement was not equally among LHAs, and substantial intraregional differences are still present, probably due to different timing and way of introduction of healthcare innovations for prevention and care for amenable to healthcare diseases. The proportion of amenable mortality on overall mortality is much higher among women than men, and it probably depends on the diseases considered in the definition itself.

Is the choice of the statistical model relevant in the cost estimation of patients with chronic diseases? An empirical approach by the Piedmont Diabetes Registry.

BACKGROUND: Chronic diseases impose large economic burdens. Cost analysis is not straightforward, particularly when the goal is to relate costs to specific patterns of covariates, and to compare costs between diseased and healthy populations. Using different statistical methods this study describes the impact on results and conclusions of analyzing health care costs in a population with diabetes. METHODS: Direct health care costs of people living in Turin were estimated from administrative databases of the Regional Health System. Patients with diabetes were identified through the Piedmont Diabetes Registry. The effect of diabetes on mean annual expenditure was analyzed using the following multivariable models: 1) an ordinary least squares regression (OLS); 2) a lognormal linear regression model; 3) a generalized linear model (GLM) with gamma distribution. Presence of zero cost observation was handled by means of a two part model. RESULTS: The OLS provides the effect of covariates in terms of absolute additive costs due to the presence of diabetes (€ 1,832). Lognormal and GLM provide relative estimates of the effect: the cost for diabetes would be six fold that for non diabetes patients calculated with the lognormal. The same data give a 2.6-fold increase if calculated with the GLM. Different methods provide quite different estimated costs for patients with and without diabetes, and different costs ratios between them, ranging from 3.2 to 5.6. CONCLUSIONS: Costs estimates of a chronic disease vary considerably depending on the statistical method employed; therefore a careful choice of methods to analyze data is required before inferring results.

A Multimorbidity Analysis of Hospitalized Patients With COVID-19 in Northwest Italy: Longitudinal Study Using Evolutionary Machine Learning and Health Administrative Data.

BACKGROUND: Multimorbidity is a significant public health concern, characterized by the coexistence and interaction of multiple preexisting medical conditions. This complex condition has been associated with an increased risk of COVID-19. Individuals with multimorbidity who contract COVID-19 often face a significant reduction in life expectancy. The postpandemic period has also highlighted an increase in frailty, emphasizing the importance of integrating existing multimorbidity details into epidemiological risk assessments. Managing clinical data that include medical histories presents significant challenges, particularly due to the sparsity of data arising from the rarity of multimorbidity conditions. Also, the complex enumeration of combinatorial multimorbidity features introduces challenges associated with combinatorial explosions. OBJECTIVE: This study aims to assess the severity of COVID-19 in individuals with multiple medical conditions, considering their demographic characteristics such as age and sex. We propose an evolutionary machine learning model designed to handle sparsity, analyzing preexisting multimorbidity profiles of patients hospitalized with COVID-19 based on their medical history. Our objective is to identify the optimal set of multimorbidity feature combinations strongly associated with COVID-19 severity. We also apply the Apriori algorithm to these evolutionarily derived predictive feature combinations to identify those with high support. METHODS: We used data from 3 administrative sources in Piedmont, Italy, involving 12,793 individuals aged 45-74 years who tested positive for COVID-19 between February and May 2020. From their 5-year pre-COVID-19 medical histories, we extracted multimorbidity features, including drug prescriptions, disease diagnoses, sex, and age. Focusing on COVID-19 hospitalization, we segmented the data into 4 cohorts based on age and sex. Addressing data imbalance through random resampling, we compared various machine learning algorithms to identify the optimal classification model for our evolutionary approach. Using 5-fold cross-validation, we evaluated each model's performance. Our evolutionary algorithm, utilizing a deep learning classifier, generated prediction-based fitness scores to pinpoint multimorbidity combinations associated with COVID-19 hospitalization risk. Eventually, the Apriori algorithm was applied to identify frequent combinations with high support. RESULTS: We identified multimorbidity predictors associated with COVID-19 hospitalization, indicating more severe COVID-19 outcomes. Frequently occurring morbidity features in the final evolved combinations were age>53, R03BA (glucocorticoid inhalants), and N03AX (other antiepileptics) in cohort 1; A10BA (biguanide or metformin) and N02BE (anilides) in cohort 2; N02AX (other opioids) and M04AA (preparations inhibiting uric acid production) in cohort 3; and G04CA (Alpha-adrenoreceptor antagonists) in cohort 4. CONCLUSIONS: When combined with other multimorbidity features, even less prevalent medical conditions show associations with the outcome. This study provides insights beyond COVID-19, demonstrating how repurposed administrative data can be adapted and contribute to enhanced risk assessment for vulnerable populations.

Hospitalisation for herpes zoster in people with and without diabetes: A 10-year-observational study.

AIMS: This study explores the association between Herpes Zoster (HZ) hospitalizations and diabetes in Piedmont, Italy from 2010 to 2019. Focusing on the burden of HZ hospitalizations in diabetic and non-diabetic groups, it aims to identify risk factors in diabetics to enhance prevention strategies. METHODS: In a two-phase study, we first compared age-standardized HZ hospitalization rates between diabetic and non-diabetic individuals from 2010 to 2019. We then examined hospitalization risk factors for HZ within a diabetic patient cohort managed by regional diabetes clinics. RESULTS: Of 3,423 HZ hospitalizations in 2010-2019, 17.9 % (613 cases) were diabetic patients, who exhibited higher hospitalization rates (15.9 to 6.0 per 100,000) compared to non-diabetese individuals. Among diabetics subjects risk factors for HZ hospitalization included age over 65, obesity (BMI > 30), and poor glycemic control (HbA1c > 8.0 %). These patients had a 40 % increased rehospitalization risk and a 25 % higher risk of severe complications, such as stroke and myocardial infarction, post-HZ. CONCLUSIONS: Diabetes markedly increases HZ hospitalization rates, rehospitalization, and complication risks. These findings underscore the need for preventive strategies, especially improved glycemic control among high-risk diabetic patients, to inform public health policies and clinical practices aimed at mitigating HZ's impact on this population.