Stefano Campostrini

Ten-year trends in major lifestyle risk factors using an ongoing population surveillance system in Australia

BackgroundUnderstanding how risk factors (tobacco, alcohol, physical inactivity, unhealthy diet, high blood pressure, and high cholesterol) change over time is a critical aim of public health. The associations across the social gradient over time are important considerations. Risk factor surveillance systems have a part to play in understanding the epidemiological distribution of the risk factors so as to improve preventive measures and design public health interventions for reducing the burden of disease.MethodsRepresentative, cross-sectional data were collected in South Australia using telephone interviews, conducted on a minimum of 600 randomly selected people (of all ages) each month. Data were collected from January 2004 to December 2013. Unadjusted prevalence over time, the relative percentage change over the 10 years, and the absolute change of the risk factors with sex, age group, and socio-economic status (SES) estimates are presented.ResultsIn total 55,548 adults (?18 years) were interviewed (mean age = 47.8 years, 48.8% male). Decreases were apparent for insufficient physical activity, inadequate fruit and vegetables, smoking, and soft drink consumption of ≥500 ml/day. Increases were found over the 10 years for obesity, high cholesterol, diabetes, and for those with no risk factors. Apparent differences were noticeable by different sex, age, and SES categories. While increases in physical activity and fruit and vegetable consumption and decreases in smoking prevalence and multiple risk factors are to be expected in 2020-2021, the prevalence of obesity, high blood pressure, high cholesterol, and diabetes are expected to increase.ConclusionsPublic health efforts in increasing the proportion of the population undertaking appropriate risk factor behavior are showing signs of success, with data from 2004 to 2013 showing encouraging trends. Deriving comparable trends over time by key demographics and SES variables provides evidence for policymakers and health planners to encourage interventions aimed at preventing chronic disease.

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.

Social determinants effects from the Italian risk factor surveillance system PASSI

ObjectivesTo offer examples on how risk factor surveillance systems can help in providing useful information on social determinants effects and health inequalities.MethodsThe Italian risk factor surveillance system (PASSI) collects monthly information from most of the Italian Local Health Units (over 85% of the Italian population is covered) on major health-related behaviours together with information on health practices, attitudes and opinions. Multivariate analysis of associations with possible indicators of social determinants collected by the system, offers important indications on the value that the system has in providing useful information on the effects of social determinants.ResultsSocial determinants, although measured through very simple indicators, have major influence on health outcomes (in the example here, depression), geographical disparities in health (efficacy of smoking ban), and access to preventive services (pap test in our analysis).ConclusionsRisk factor surveillance can offer valuable information for monitoring social determinants effects and inequalities, and, when considering data over time, for evaluating the gross impact of future interventions and policies aimed at reducing them.

Social determinants and surveillance in the new Millennium

Surveillance has been a common practice in Public Health (Teutsch and Churchill 2000; Lee et al. 2010; Croft et al. 2009), although, until recent decades, it has been applied mainly to the infectious diseases. Now, that Non communicable diseases (NCD’s) are the major cause of morbidity and mortality globally, much attention has been paid to NCD surveillance and, more specifically, to the surveillance of NCD related risk factors. This is often called Behavioral Risk Factor Surveillance (BRFS, McQueen and Puska 2003; Campostrini and McQueen, 2005) to emphasize the importance of risk factors, although, in the practice, BRFS practice covers a wide range of public health related matters (e.g., from vaccination to service access to demographic data). The major challenge Public Health is facing globally at the beginning of this new millennium is that of ‘‘closing the gap’’ (CSDH 2008), working for reducing health disparities within and between countries. Particularly national and local public health systems face the challenge of adopting suitable interventions and policies to reduce inequalities caused by Social Determinants (SD; Marmot 2009). If health outcomes are derived mainly by the agency of key behavioral risk factors, and SD the ‘‘causes of the causes’’ of such risk factors (Marmot 2005), it is dramatically important for decision makers charged with reducing health inequalities to have information both on the causes (risk factors) and the causes of the causes (SD). In search for what is termed ‘‘evidence-based public health’’ (McQueen 2001), quite often public-health professionals and more specifically health-promotion practitioners have struggled to find suitable information for evaluating the effectiveness of their work. Surveillance data and particularly BRFS data are an important source (Campostrini and McQueen 2005; Campostrini 2007; Minardi et al., this issue) for evidence that can be used to plan, monitor, and evaluate interventions or policies aimed to reduce the effect of SD on health disparities. The discussion about how to best measure the SD is still open, and much research is needed to agree on how to effectively measure SD to better understand the mechanisms by which generally (but not always) the ‘‘poorer’’ are the more unhealthy. With guidance from the papers published in this issue, we would like to note briefly some particular issues that show why the BRFS approach is a unique source for information on SD and health. As we see in the work of Pfoertner et al. (this issue), there is always something that we can know better about the relationship between poverty and health that is quite often shadowed by conventional measurements. The need to consider a wide range of aspects, not only economical, but also social and cultural (Abel 2008) is quite globally acknowledged, still the ‘‘how to (measure)’’ is under discussion. S. Campostrini and T. Abel edited the special issue ‘‘Monitoring social determinants of health’’.

The clinical and economic efficacy of HAART: a shift from inpatient medical to outpatient pharmaceutical care for HIV/AIDS patients in Northeastern Italy

This study describes epidemiological, clinical and economic impact of the HIV epidemic in Italy prior to and after the introduction of HAART. A prospective, observational, multi-center design was applied using data collected on an AIDS cohort from 1994 and updated data from a comparable cohort in 1998. Mortality and direct medical costs were measured in 251 AIDS patients in 1994 and 77 AIDS patients in 1998. A considerable difference was observed in mortality (33.9% in 1994 vs. 3.9% in 1998). The numbers of hospital admissions were 1.9 in 1994 and 0.8 in 1998; average length of stay was 31.4 days in 1994 and 12.6 days in 1998. The cost per patient per year was 17,250 Euros in 1994 and 11,465 Euros in 1998. The comparison of two cohorts between time periods has enabled changes in costs and outcomes to be linked to the introduction of HAART in 1997. In conclusion, after the introduction of HAART hospital-based provision shifted from an inpatient-based to an outpatient-based service with major focus on pharmaceutical care.

Surveillance Systems and Data Analysis: Continuously Collected Behavioural Data

Many public health initiatives are concerned with making behavioural changes happen at a population level (e.g., community-wide initiatives to change sexual practices to reduce the threat of HIV infection, or physical activity and nutrition campaigns to reduce the number of people who are overweight). The need for a surveillance system to monitor and track changes at this level is clear. In this chapter I discuss how data analysis should contribute to such a system and help make it responsive to the needs of those who use behavioural surveillance data.

Pre-Survey Text Messages (SMS) Improve Participation Rate in an Australian Mobile Telephone Survey: An Experimental Study

Mobile telephone numbers are increasingly being included in household surveys samples. As approach letters cannot be sent because many do not have address details, alternatives approaches have been considered. This study assesses the effectiveness of sending a short message service (SMS) to a random sample of mobile telephone numbers to increase response rates. A simple random sample of 9000 Australian mobile telephone numbers: 4500 were randomly assigned to be sent a pre-notification SMS, and the remaining 4500 did not have a SMS sent. Adults aged 18 years and over, and currently in paid employment, were eligible to participate. American Association for Public Opinion Research formulas were used to calculated response cooperation and refusal rates. Response and cooperation rate were higher for the SMS groups (12.4% and 28.6% respectively) than the group with no SMS (7.7% and 16.0%). Refusal rates were lower for the SMS group (27.3%) than the group with no SMS (35.9%). When asked, 85.8% of the pre-notification group indicated they remembered receiving a SMS about the study. Sending a pre-notification SMS is effective in improving participation in population-based surveys. Response rates were increased by 60% and cooperation rates by 79%.

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

Background Age, period and cohort (APC) analyses, using representative, population-based descriptive data, provide additional understanding behind increased prevalence rates. Methods Data on obesity and diabetes from the South Australian (SA) monthly chronic disease and risk factor surveillance system from July 2002 to December 2013 (n = 59,025) were used. Age was the self-reported age of the respondent at the time of the interview. Period was the year of the interview and cohort was age subtracted from the survey year. Cohort years were 1905 to 1995. All variables were treated as continuous. The age-sex standardised prevalence for obesity and diabetes was calculated using the Australia 2011 census. The APC models were constructed with ‘‘apcfit’’ in Stata. Results The age-sex standardised prevalence of obesity and diabetes increased in 2002-2013 from 18.6% to 24.1% and from 6.2% to 7.9%. The peak age for obesity was approximately 70 years with a steady increasing rate from 20 to 70 years of age. The peak age for diabetes was approximately 80 years. There were strong cohort effects and no period effects for both obesity and diabetes. The magnitude of the cohort effect is much more pronounced for obesity than for diabetes. Conclusion The APC analyses showed a higher than expected peak age for both obesity and diabetes, strong cohort effects with an acceleration of risk after 1960s for obesity and after 1940s for diabetes, and no period effects. By simultaneously considering the effects of age, period and cohort we have provided additional evidence for effective public health interventions.

Demographic trends in alcohol use: The value of a surveillance system

OBJECTIVE To determine trends in alcohol consumption in South Australia. METHODS Data collection from 2003 to 2011. Time series trends overall and by age, sex, education level, and income by proportion of drinkers, mean number of drinks, drinking less than one day, drinking on six or more days per week, lifetime alcohol risk and injury risk. RESULTS An overall decline in the proportion of alcohol drinkers, an increase in the overall proportion of adults drinking alcohol less than one day per week. No overall change in mean number of drinks consumed per day but with differences by demographic groups. CONCLUSION This study presents multiple consumption-related variables over time and has highlighted important demographic variations in alcohol consumption.

Health promotion and surveillance: the establishment of an IUHPE global working group

Following a series of international meetings on behavioral monitoring and surveillance, in 2007 the Italian Ministry of Health (Ministerio della Salute) and the Institute for Health (Istituto Superiore della Sanità) hosted the 5th International Conference on Behavioral Risk Factor Surveillance (BRFS) in Rome. A key focus of the conference was on how current surveillance systems could be applied to the field of health promotion, particularly in building the evidence base for health promotion practice. As a result of these discussions, the World Alliance for Risk Factor Surveillance (WARFS), an IUHPE Global Working Group, was formed to work toward providing knowledge and expertise in surveillance as a tool for advancing health promotion. For those IUHPE members interested in participation, this article provides an overview on the strategic direction of WARFS and the newly formed sub-working groups. (Global Health Promotion, 2009; 16(4): pp. 58—60)