dalla prevenzione alla continuità delle cure atti del ix congresso nazionale sismec

322 Pages·2012·6.65 MB·Italian

Atti

LA STATISTICA A SUPPORTO DELLA SALUTE:
DALLA PREVENZIONE ALLA CONTINUITÀ DELLE CURE

ATTI DEL IX CONGRESSO NAZIONALE SISMEC

Segreteria di rediazione: Anna Bossi, Elena Spada, WebMarketingMedia
Edizione: Marzo 2018
A cura di: Consiglio Direttivo SISMEC
Copyright: SISMEC
ISBN 978-88-943456-0-5

Si ingraziano Bristol-Myers Squibb e Novartis per il contributo non condizionato alla realizzazione
degli Atti

I
Atti

Comitato Scientifico
Elia Biganzoli (Università degli Studi di Milano)
Flavia Carle (Università Politecnica delle Marche)
Giovanni Corrao (Università degli Studi di Milano-Bicocca)
Ivan Cortinovis (Università degli Studi di Milano)
Adriano Decarli (Università degli Studi di Milano)
Monica Ferraroni (Università degli Studi di Milano)
Ciro Gallo (Università degli Studi della Campania "Luigi Vanvitelli")
Carlo La Vecchia (Università degli Studi di Milano)
Umberto Genovese (Università degli Studi di Milano)
Francesco Masedu (Università degli Studi dell'Aquila)
Rocco Micciolo (Università di Trento)
Silvano Milani (Università degli Studi di Milano)
Antonella Piga (Università degli Studi di Milano)
Patrizio Pasqualetti (AFaR-Associazione Fatebenefratelli per la Ricerca - Roma)
Paolo Trerotoli (Università degli Studi di Bari)
Maria Grazia Valsecchi (Università degli Studi di Milano-Bicocca)
Anna Zolin (Università degli Studi di Milano)

Consiglio Direttivo
Franco Cavallo (Presidente)
Anna Bossi (Presidente eletto)
Simona Villani (Segretario)
Rosaria Gesuita (Tesoriere)
Giulia Barbati
Lucia Simoni
Simone Accordini
Paolo Chiodini

Comitato Organizzatore
Anna Bossi
Ivan Cortinovis
Valeria Edefonti
Elena Spada
Anna Zolin

Segreteria Organizzativa
WebMarketingMedia
webmarketingmedia.it
LECCO - MONZA - BERGAMO - BARI – VARSAVIA
II
Atti

Gli articoli pubblicati su prestigiose riviste scientifiche (per un pubblico di esperti) e le notizie riportate dai
media (per un pubblico più vasto) ci informano, quasi giornalmente, sulle conseguenze per la salute,
dell'invecchiamento, di errati stili di vita, della ricomparsa di malattie che si ritenevano eradicate, delle
strategie di prevenzione e dell’uso di nuovi farmaci.
Spesso però le informazioni riportate sono contrastanti, di non facile interpretazione o provengono da analisi
non corrette, da studi mal programmati o male interpretati.
Infatti, le tecnologie dell’informazione rendono disponibili, facilmente e in tempo reale, i risultati della ricerca
biomedica e la “medicina basata sulle evidenze” (EBM) insegna come utilizzare appropriatamente questi
risultati nella pratica corrente. Tuttavia, come riportato dal Prof. Vettore (Presidente emerito della Società
Italiana di Pedagogia Medica) sul Bollettino d'Informazione sui Farmaci dell’AIFA - … non bisogna farsi
prendere da “deliri di onnipotenza”: i risultati scientifici non sono sempre corretti, e soprattutto non sono mai
certi, completi e definitivi, ma solo probabili e provvisori; e poi nella clinica anche la migliore delle
conoscenze scientifiche va filtrata attraverso l’esperienza del medico e le aspettative del paziente, perché
l’EBM – come diceva Sackett – non è un “libro di ricette da cucina”.
Prendere decisioni razionali, e corrette, in ambito sanitario richiede quindi non solo competenza clinica, ma
anche capacità di usare metodi adeguati a valutare l'efficacia degli interventi che si attuano per la salute dei
singoli e della collettività.
Inoltre, perché tutti capiscano la razionalità dei risultati delle ricerche scientifiche e li accolgano, è di
importanza cruciale che i mezzi di comunicazione (specializzati, o di massa) forniscano informazioni corrette
e comprensibili sugli effetti che gli stili di vita, le condizioni ambientali, nonché le strategie di prevenzione,
diagnosi, cura e riabilitazione hanno sulla salute di ciascuno.
In questo ambito, il metodo statistico riveste un ruolo indispensabile sia nell'assicurare l'attendibilità delle
statistiche sanitarie correnti, sia nel pianificare studi osservazionali e sperimentazioni cliniche eticamente
accettabili, sia infine nell'interpretare i risultati e disseminarli in modo chiaro e non distorto.
Il IX Congresso Nazionale SISMEC intende contribuire a consolidare il rapporto di collaborazione tra
ricercatori clinici e statistici per migliorare la salute di tutti.
Con questo intento, l’argomento principale della prima giornata sarà relativo all’impatto dello stile di vita
sulla salute e quello della seconda, alle nuove strategie terapeutiche e alla valutazione del danno e
dell’accertamento della colpa nel caso di “errori” nella cura. Infine, nella terza giornata, sarà discusso come
poter monitorare la continuità delle cure quando sono coinvolti molteplici interlocutori e differenti modalità
assistenziali.
Con l’augurio che la partecipazione attiva dei Soci della Società e dei ricercatori e professionisti della salute,
interessati ad approfondire e discutere l’applicazione del metodo biostatistico ed epidemiologico per
arricchire le conoscenze e promuovere la salute, possa far germogliare e crescere alberi robusti…

III
Atti

Continua a piantare i tuoi semi, perché non saprai mai quali cresceranno – forse lo faranno tutti
Albert Einstein

IV
Atti - Epidemiologia Generale e Clinica

EPIDEMIOLOGIA GENERALE E CLINICA

1
Atti - Epidemiologia Generale e Clinica
CARDIOVASCULAR DISEASE RISK ESTIMATION IN THE WORKING POPULATION:
DISCRIMINATION ABILITY OF LIFESTYLE RISK FACTORS AND JOB-RELATED CONDITIONS

Veronesi Giovanni1, Gianfagna Francesco1,2, Borchini Rossana3, Grassi Guido4, Iacoviello Licia1,2, Cesana
Giancarlo4, Tayoun Patrick5, Ferrario Marco Mario1,3

1. Research Centre in Epidemiology and Preventive Medicine, Department of Medicine and Surgery, University of
Insubria, Varese, Italy.
2. IRCCS Neuromed, Pozzilli, Italy
3. Occupational Medicine Unit, Varese Hospital, Varese, Italy
4. Department of Medicine, University of Milano-Bicocca, Monza, Italy. 5. School of Medicine, University of Insubria,
Varese, Italy

Introduction
Lifestyle and job-related (LS&JR) conditions are recognized risk factors for cardiovascular disease (CVD),
but their prognostic utility remains to be established in prospective studies. We investigated the
discrimination ability at 10 years of LS&JR risk factors in a Northern Italian working male population.

Methods
N=2532 men, 35-64 years, free of CVD and employed at the time of recruitment (1989-1996) in either the
MONICA-Brianza and PAMELA (3 population-based surveys) or the SEMM (1 factory-based survey)
studies, were available for the analyses. At baseline, the following LS&JR conditions were ascertained:
measured height and weight; self-reported smoking (current vs. non-current) and alcohol intake (drinks/day;
less than 1, 1-3, 4-5 and 6 or more); job strain (high vs. non-high; Job Content Questionnaire); physical
activity (PA) at work (low, intermediate and intense, according to sample tertiles) and doing sport
(minutes/week of moderate or intense activity) from the Baecke questionnaire. Workers were followed-up to
the first occurrence of coronary event, acute revascularizations, or ischemic stroke, fatal and non-fatal. A
10-year risk estimation model was developed using LS&JR risk factors satisfying the Akaike Information
Criterion for the selection of candidate predictors, and contrasted to a standard risk score including blood
lipids, blood pressure, smoking and diabetes. Model discrimination was estimated by the Area Under the
ROC-Curve (AUC), in the overall sample and among workers at “low” risk and therefore not qualifying for
preventive actions according to European guidelines [1].

Results
During 14 years of median follow-up, we observed n=162 events (10-year risk: 4.3%). Body mass index was
not associated with the endpoint. The following risk factors met the AI Criterion and entered into the LS&JR
model: smoking (Hazard Ratio=2.49, p<.0001); alcohol intake (less than 1 drink/day: HR=1.52, 95%CI 1.03-
2.23; 6+ drinks/day: HR=1.81, 1.11-2.95; 3df p=0.07); job strain (HR=1.39, p=0.06); combined sport and
occupational PA (5df p=0.02), as the HRs for sport PA changed between workers at low (HR=0.42) and
intense (HR=1.55) occupational PA (interaction test p=0.001). The model was well-calibrated (Gronnesby-
Borgan chi-square statistic 7.7, p=0.6) and its discrimination ability (AUC=0.750, bootstrapped 95% CI:
0.702-0.780) did not differ from the standard model (AUC=0.749) in the overall sample. The AUC for the
LS&JR model was 0.743 among “low” risk workers (1832, with 91 events). Of these, 38% could have been
selected for preventive action based on their estimated LS&JR risk; 1 every 16 was a CVD case.

2
Atti - Epidemiologia Generale e Clinica
Conclusions
In our working male population, lifestyle and job-related conditions had the same discriminant ability than
clinical and biological risk factors in identifying future cardiovascular events, and they may improve
stratification of the overwhelming majority of workers classified at low risk by standard scores. A LS&JR risk
score may increase feasibility and lower costs of CVD screening at the workplace.

References
[1] Piepoli MF, Hoes AW, Agewall S, et al. European Guidelines on cardiovascular disease prevention
in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other
Societies on Cardiovascular Disease Prevention in Clinical Practice. Eur Heart J 2016;37:2315-81.

3
Atti - Epidemiologia Generale e Clinica
SEDENTARINESS AND EDUCATION CONTRIBUTE SIGNIFICANTLY TO SOCIOECONOMIC
INEQUALITIES IN NON-COMMUNICABLE DISEASES

Matranga Domenica1, Bono Filippa2

1. Dipartimento di Scienze per la Promozione della Salute e Materno-Infantile “G. D’Alessandro”, Università degli
Studi di Palermo
2. Dipartimento di Scienze Economiche, Aziendali e Statistiche, Università degli Studi di Palermo

Introduction
In Europe, the main non-communicable diseases (NCDs), including diabetes, cardiovascular diseases,
cancer, chronic respiratory diseases and mental disorders, all together account for an estimated 86% of the
deaths and 77% of the disease burden. Of the six WHO regions, Europe is the most affected by NCDs [1].
The detection and control of physiological and behavioral risk factors (BRFs) remain the essential preventive
strategy to counteract not only the average population’s exposure to the main NCDs, but also socioeconomic
inequalities, which are related to chronic diseases.
The scope of this work is to investigate socioeconomic inequalities among the European elderly in NCDs
and BRFs for NCDs, namely tobacco consumption, obesity, unhealthy nutrition and physical inactivity,
between 2004 and 2015.

Methods
Data are drawn from the Survey of Health, Ageing and Retirement in Europe, which is a panel database of
microdata on health, socioeconomic status and social and family networks of people aged 50 years and
over, covering most of the European Union [2]. From waves 1 and 6, release 6, information has been
obtained about ten European countries (Austria, Germania, Svezia, Spagna, Italia, Francia, Danimarca,
Grecia, Svizzera e Belgio), for a total of 25016 people for year 2004 and 43916 people for year 2015.
Socioeconomic inequalities are measured by means of Wastgaff‘s concentration index and people have
been ranked form poorest to richest according to both income and wealth (C) [3-5]. The number of NCDs is
predicted through negative binomial regression model, with socioeconomic, physical and behavioral
covariates. The predicted number of NCDs is used to estimate the concentration curve and to find the
contributions (CO) of determinants to socioeconomic inequalities in NCDs. In order to estimate change over
time in socioeconomic inequalities in NCDs, the Oaxaca decomposition is used to discriminate how much
of this variation is due to change in elasticity and how much is due to changes in inequality of determinants.

Results
Among European elderly people, the number of chronic diseases is significantly associated to all SES
determinants and BRF’s both in 2004 and 2015. The inequality in the number of NCDs disfavor the poorer
in both years, but the effect is decreasing from 2004 (C=-0.191) to 2015 (C=-0.161). This inequality can be
mostly attributed to sedentariness and education in both years, even if the role of these determinants is
exchanged between 2004 (CO =-0.021, CO =-0.014) and 2015 (CO =-0.013, CO =-
education sedentariness education bmi
0.016). In 2015, inequalities in all determinants disfavor the poorer in both years. Among SES determinants,
the most concentrated in both years are education (C =0.055 in 2004 and C =0.053 in 2015)
education education
and marital status (C =-0.055 in 2004 and C =-0.045 in 2015). Among BRFs, the most concentrated
marital marital
is sedentariness in both years (C =-0.221 in 2004 and C =-0.211 in 2015) (Figure 1).
sedentariness sedentariness

4
Atti - Epidemiologia Generale e Clinica
1.00 1.00
0.90 0.90
0.80 0.80
elbairav emoctuo fo % evitalumuC 000000......234567000000 elbairav emoctuo fo % evitalumuC 000000......234567000000
0.10 0.10
0.000.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0.000.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Cumulative % of population, ranked from poorest to richest Cumulative % of population, ranked from poorest to richest
2004 2015
Figure 1. Concentration curve for the number of NCDs among European elderly, years 2004 and 2015

Conclusions
The effect of sedentariness in our study has been found increased in the study period and is the most
unequally distributed to the disadvantage of poor people. Other studies addressing the association between
socioeconomic status (SES) and sedentariness have shown that poor living conditions and primary
education are associated with sedentariness in old age. Among SES determinants, we have found education
as the main determinant of both the average number of NCDs and of socioeconomic inequalities in NCDs.
The inverse relationship between higher education and the number of NCDs can be explained because
educated people have major knowledge about risk behaviors, preventive care and medical treatments and
have major access to health care services and use them more efficiently. Wealth more than income has
been shown as an important socioeconomic determinant of both NCDs and inequalities in NCDs.
Sedentariness, tobacco consumption and unhealthy nutrition not only contribute to determine the burden of
NCDs for European countries but they have been found among the most important determinants of
socioeconomic inequalities of NCDs. These are exacerbated by education and wealth. Effective actions to
reduce NCDs inequalities include programs to enhance education and economic development and healthy
lifestyle promotion.

References
[1] World Health Organization. Non communicable diseases country profiles. Available from:
http://www.who.int/nmh/publications/ncd-profiles-2014/en/ [Accessed March 14, 2017].
[2] Börsch-Supan A, Brandt M, Hunkler C, et al. Data Resource Profile: The Survey of Health, Ageing
and Retirement in Europe (SHARE). Int Journal Epidemiol 2013;42:992-1001.
[3] Wagstaff A, Paci P, van Doorslaer E. On the measurement of inequalities in health. Soc Sci Med
1991;33:545-57.
[4] Wagstaff A. Socioeconomic inequalities in child mortality: comparisons across nine developing
countries. Bulletin of the World Health Organization 2000;78(1):19-29.
[5] Kakwani N, Wagstaff A, Van Doorlsaer E. Socioeconomic inequalities in health: Measurement,
computation and statistical inference. Journal of Econometrics 1997;77(1):87-104.

5