Monitoring Migrants’ Health Risk Factors for Noncommunicable Diseases

Noncommunicable diseases (NCDs) have become the first cause of morbidity and mortality around the world. These have been targeted by most governments because they are associated with well-known risk factors and modifiable behaviors. Migrants present, as any population subgroup, peculiarities with regard to NCDs and, more relevantly, need specific information on associated risk factors to appropriately target policies and interventions. The country of origin, assimilation process, and many other migrant health aspects well studied in the literature can be related to migrants’ health risk factors. In most countries, existing sources of information are not sufficient or should be revised, and new sources of data should be found. Existing survey systems can meet organizational difficulties in changing their questionnaires; moreover, the number of changes in the adopted questionnaire should be limited for the sake of brevity to avoid excessive burden on respondents. Nevertheless, a limited number of additional variables can offer a lot of information on migrant health. Migrant status, country of origin, time of arrival should be included in any survey concerned about migrant health. These, along with information on other Social Determinants of Health and access to health services, can offer fundamental information to better understand migrants’ health and its evolution as they live in their host countries. Migrants are often characterized by a better health status, in comparison with the native population, which typically is lost over the years. Public health and health promotion could have a relevant role in modifying, for the better, this evolution, but this action must be supported by timely and reliable information

¿Is a global surveillance still valid and feasible?

More and more health problem are becoming global. Also with regard to NCDs, the words “global epidemic” (e.g. for obesity, luck of physical activity, etc.) are often used. Recent years have seen also emerging global solutions (interventions, programs, approaches) for these problems. But, quite often, the global approach lacks global information. Certainly to find ways of comparison is not an easy task, and the perfect comparable data constitutes in most cases a myth; still, a lot can be done. Investments are needed more on the analysis and data linkage front, more than on that of data collection: new and old sources of data are more available when, instead, quite often we lack resources and capacity to analyze them and to make this information useful for public health action and health promotion.

Life expectancy drop in 2020. Estimates based on Human Mortality Database

In many countries of the world, COVID–19 pandemic has led to exceptional changes in mortality trends. Some studies have tried to quantify the effects of Covid-19 in terms of a reduction in life expectancy at birth in 2020. However, these estimates might need to be updated now that, in most countries, the mortality data for the whole year are available. We used data from the Human Mortality Database (HMD) Short-Term Mortality Fluctuations (STMF) data series to estimate life expectancy in 2020 for several countries. The changes estimated using these data and the appropriate methodology seem to be more pessimistic than those that have been proposed so far: life expectancy dropped in the Russia by 2.16 years, 1.85 in USA, and 1.27 in England and Wales. The differences among countries are substantial: many countries (e.g. Denmark, Island, Norway, New Zealand, South Korea) saw a rather limited drop in life expectancy or have even seen an increase in life expectancy

Suitable statistical approaches for novel policies: spatial clusters of childcare’s services in Veneto, Italy

[EN] More and more often, policymakers face complex problems that require suitable information obtainable only from the "intelligence of data." This can be obtained by analyzing several data sets (many of high dimension) and adopting suitable, often "sophisticated," statistical models. Here we deal with policies for affordable and quality childcare, essential to balance work and family life, increase labor market participation, promote gender equality, and fight against fertility decline. Understanding the complex dynamics of demand and supply of childcare services is challenging due to the nature of the data: high-dimensional, complex, and heterogeneous nationwide. Considering the Italian case, this complexity and heterogeneity are partially due to the lack of governance at the regional level leading to immediate and effective new policies challenging. This paper aims to analyze the multidimensional aspect of the supply-demand of childcare services combination in the Veneto Italian region using a novel statistical approach and an innovative dataset. We apply the regionalization approach (a clustering method with spatial constraints) to give an immediate picture of childcare services' supply and demand variability. Our empirical findings confirm how the Veneto region is described by many "sub-regional models," providing a preliminary attempt to demonstrate how socio-demographic factors drive these patterns.Andreella, A.; Campostrini, S. (2023). Suitable statistical approaches for novel policies: spatial clusters of childcare’s services in Veneto, Italy. Editorial Universitat Politècnica de València. 205-212. https://doi.org/10.4995/CARMA2023.2023.1643920521

Determinants and spatio-temporal changes in morbidity curves of Italian population

Understanding the mechanisms that drive the changes in morbidity curve is of paramount importance to improving life quality and designing interventions and policies. In this study, we analyze data from the Italian behavioral risk factor surveillance system (PASSI) and propose a pseudo-panel approach to study the spatio-temporal changes in Italian local health authorities (ASLs). We develop a Bayesian logistic hierarchical model, in which unit-specific covariates (e.g. age, sex, socio-economic status, etc) explain the observed variations by means of regression coefficients varying in space (ASLs) and time (cohorts). We leverage a state space formulation of the model where temporal changes are driven by correlated impulses and the degree of correlation is determined by weighting available external information at the ASL level (e.g. region, social habits, pollution, etc). By using out-of-sample predictive inference, we show how our method outperform other standard approaches, allowing for interpretable results that highlight how different social and environmental factors influence the shape of the morbidity curves

Informative Sources for the Evaluation of the University Education Effectiveness in Italy

Summary. The evaluation of the effectiveness of a study programme refers to its outcomes, and may be measured with the level of satisfaction of the programme objectives. In an educational programme, we can recognize three macro objectives referred to students: i) the formation of specific competences; ii) general competences and individual cultural development; iii) capability in finding a suitable job. The measurement of these components is based on the construction and analysis of several indicators from surveys on teaching assessment, or on placement, or on teachers and employers, and, too, on linkage between databases

A preface to the keynote and accepted abstracts of the Joint 9th World Alliance for Risk Factor Surveillance (WARFS) and 12th Americas' Network for Chronic Disease Surveillance (AMNET) 2015 Conference, November 18th-20th, 2015, St. John's, Antigua

It is a pleasure to introduce the abstracts arising from the Joint World Alliance for Risk Factor Surveillance (WARFS) and Americas' Network for Chronic Disease Surveillance (AMNET) conference held in Antigua during November 18th-20th, 2015. This was a global conference on risk factor surveillance for public health. This conference was particularly notable because it combined three organizations concerned with public health. First, the host of the conference, the American University of Antigua College of Medicine; second, the World Alliance of Risk Factor Surveillance, a group affiliated with the Paris-based International Union for Health Promotion and Education (IUHPE); and third, Americas' Network for Chronic Disease Surveillance. Thus it was subtitled the Joint 9th WARFS Global Conference and 12th AMNET Conference 2015. Both surveillance groups WARFS and AMNET had their historical origins at the U.S. Centers for Disease Control and Prevention (CDC). Over the yeas, since the first global meeting on behavioral risk-factor surveillance held in the Atlanta, GA USA area, these two institutions have developed and held meetings focusing on issues in risk factorsurveillance. Historically there have been keen discussions, keynotes, presentations, and posters on such topics as the theory of surveillance, the incorporation of social factors into the surveillance, the ever arising methodological and technical challenges, and the global importance of good data to inform decision making and policy taking with regard to major public health issues. The Antigua conference touched on all these issues and the abstracts within this volume represent this diversity of topics. Of particular note was the chance for a small Caribbean country to be a part of this global discussion and it illustrates that the importance of risk factor surveillance is not confined just to larger economically highly developed nations. It is also notable because the previous WARFS-IUHPE Conference in 2013 was held in Beijing, China. Nonetheless, despite the obvious difference in venue and country size, the discussions were just as globally relevant and pertinent to today's global concerns. Therefore, as the editors of this special issue, we are pleased to present the abstracts that represent the 2015 Antigua Conference

The use of a chronic disease and risk factor surveillance system to determine the age, period and cohort effects on the prevalence of obesity and diabetes in South Australian adults--2003-2013

Age, period and cohort (APC) analyses, using representative, population-based descriptive data, provide additional understanding behind increased prevalence rates

Epidemiology of chronic respiratory diseases and associated factors in the adult Italian population

Detailed epidemiology of Chronic Respiratory Diseases (CRDs) and of their risk and protective factors is needed to plan preventive interventions to reduce the burden of CRDs on population health. This study determines the prevalence of doctor-diagnosed CRDs and its associated factors in the adult Italian population

Health Estimates Using Survey Raked-Weighting Techniques in an Australian Population Health Surveillance System

A challenge for population health surveillance systems using telephone methodologies is to maintain representative estimates as response rates decrease. Raked weighting, rather than conventional poststratification methodologies, has been developed to improve representativeness of estimates produced from telephone-based surveillance systems by incorporating a wider range of sociodemographic variables using an iterative proportional fitting process. This study examines this alternative weighting methodology with the monthly South Australian population health surveillance system report of randomly selected people of all ages in 2013 (n = 7,193) using computer-assisted telephone interviewing. Poststratification weighting used age groups, sex, and area of residence. Raked weights included an additional 6 variables: dwelling status, number of people in household, country of birth, marital status, educational level, and highest employment status. Most prevalence estimates (e.g., diabetes and asthma) did not change when raked weights were applied. Estimates that changed by at least 2 percentage points (e.g., tobacco smoking and mental health conditions) were associated with socioeconomic circumstances, such as dwelling status, which were included in the raked-weighting methodology. Raking methodology has overcome, to some extent, nonresponse bias associated with the sampling methodology by incorporating lower socioeconomic groups and those who are routinely not participating in population surveys into the weighting formula