Chronic illness, life style and emotional health in adolescence: results of a cross-sectional survey on the health of 15-20-year-olds in Switzerland.

The objective was to evaluate the prevalence of chronic conditions (CC) in adolescents in Switzerland; to describe their behaviour (leisure, sexuality, risk taking behaviour) and to compare them to those in adolescents who do not have CC in order to evaluate the impact of those conditions on their well-being. The data were obtained from the Swiss Multicentre Adolescent Survey on Health, targeting a sample of 9268 in-school adolescents aged 15 to 20 years, who answered a self-administered questionnaire. Some 11.4% of girls and 9.6% of boys declared themselves carriers of a CC. Of girls suffering from a CC, 25% (versus 13% of non carriers; P=0.007) and 38% of boys (versus 25%; P=0.002) proclaimed not to wear a seatbelt whilst driving. Of CC girls, 6.3% (versus 2.7%; P=0.000) reported within the last 12 months to have driven whilst drunk. Of the girls, 43% (versus 36%; P=0.004) and 47% (versus 39%; P=0.001) were cigarette smokers. Over 32% of boys (versus 27%; P=0.02) reported having ever used cannabis and 17% of girls (versus 13%; P=0.013) and 43% of boys (versus 36%; P=0.002) admitted drinking alcohol. The burden of their illness had important psychological consequences: 7.7% of girls (versus 3.4%; P=0.000) and 4.9% of boys (versus 2.0%; P=0.000) had attempted suicide during the previous 12 months. CONCLUSION: experimental behaviours are not rarer in adolescents with a chronic condition and might be explained by a need to test their limits both in terms of consumption and behaviour. Prevention and specific attention from the health caring team is necessary.

Measures of health and disease in Africa: are current methods giving us useful information about trends in cardiovascular diseases?

An enormous burst of interest in the public health burden from chronic disease in Africa has emerged as a consequence of efforts to estimate global population health. Detailed estimates are now published for Africa as a whole and each country on the continent. These data have formed the basis for warnings about sharp increases in cardiovascular disease (CVD) in the coming decades. In this essay we briefly examine the trajectory of social development on the continent and its consequences for the epidemiology of CVD and potential control strategies. Since full vital registration has only been implemented in segments of South Africa and the island nations of Seychelles and Mauritius - formally part of WHO-AFRO - mortality data are extremely limited. Numerous sample surveys have been conducted but they often lack standardization or objective measures of health status. Trend data are even less informative. However, using the best quality data available, age-standardized trends in CVD are downward, and in the case of stroke, sharply so. While acknowledging that the extremely limited available data cannot be used as the basis for inference to the continent, we raise the concern that general estimates based on imputation to fill in the missing mortality tables may be even more misleading. No immediate remedies to this problem can be identified, however bilateral collaborative efforts to strength local educational institutions and governmental agencies rank as the highest priority for near term development.

L'apport de CoLaus en santé publique [What CoLaus has brought to public health].

In order to fulfil their duties, Public health authorities have to rely on valid and recent data on the health status of populations. The CoLaus study helped to update our knowledge regarding the prevalence and management of the main cardiovascular risk factors in the Lausanne population. The results indicate that cardiovascular risk factor management is suboptimal and can still be improved, namely that specific population subgroups could benefit from targeted prevention measures. The CoLaus study also allowed to simulate the effect of different preventive strategies, thus enabling to choose the most (cost) effective ones, an important issue taking into account the current health budget restrictions.

Self-selection and risk selection on the Swiss health insurance market

Abstract This thesis presents three empirical studies in the field of health insurance in Switzerland. First we investigate the link between health insurance coverage and health care expenditures. We use claims data for over 60 000 adult individuals covered by a major Swiss Health Insurance Fund, followed for four years; the data show a strong positive correlation between coverage and expenditures. Two methods are developed and estimated in order to separate selection effects (due to individual choice of coverage) and incentive effects ("ex post moral hazard"). The first method uses the comparison between inpatient and outpatient expenditures to identify both effects and we conclude that both selection and incentive effects are significantly present in our data. The second method is based on a structural model of joint demand of health care and health insurance and makes the most of the change in the marginal cost of health care to identify selection and incentive effects. We conclude that the correlation between insurance coverage and health care expenditures may be decomposed into the two effects: 75% may be attributed to selection, and 25 % to incentive effects. Moreover, we estimate that a decrease in the coinsurance rate from 100% to 10% increases the marginal demand for health care by about 90% and from 100% to 0% by about 150%. Secondly, having shown that selection and incentive effects exist in the Swiss health insurance market, we present the consequence of this result in the context of risk adjustment. We show that if individuals choose their insurance coverage in function of their health status (selection effect), the optimal compensations should be function of the se- lection and incentive effects. Therefore, a risk adjustment mechanism which ignores these effects, as it is the case presently in Switzerland, will miss his main goal to eliminate incentives for sickness funds to select risks. Using a simplified model, we show that the optimal compensations have to take into account the distribution of risks through the insurance plans in case of self-selection in order to avoid incentives to select risks.Then, we apply our propositions to Swiss data and propose a simple econometric procedure to control for self-selection in the estimation of the risk adjustment formula in order to compute the optimal compensations.

Negative and positive effects of trade on health.

Comment on: Blouin C, Chopra M, van der Hoeven R.Trade and social determinants of health. Lancet. 2009;373(9662):502-7. PMID: 19167058.

Physical activity level and health-related quality of life in the general adult population: a systematic review.

OBJECTIVE: Little is known regarding health-related quality of life and its relation with physical activity level in the general population. Our primary objective was to systematically review data examining this relationship. METHODS: We systematically searched MEDLINE, EMBASE, CINAHL, and PsycINFO for health-related quality of life and physical activity related keywords in titles, abstracts, or indexing fields. RESULTS: From 1426 retrieved references, 55 citations were judged to require further evaluation. Fourteen studies were retained for data extraction and analysis; seven were cross-sectional studies, two were cohort studies, four were randomized controlled trials and one used a combined cross sectional and longitudinal design. Thirteen different methods of physical activity assessment were used. Most health-related quality of life instruments related to the Medical Outcome Study SF-36 questionnaire. Cross-sectional studies showed a consistently positive association between self-reported physical activity and health-related quality of life. The largest cross-sectional study reported an adjusted odds ratio of "having 14 or more unhealthy days" during the previous month to be 0.40 (95% Confidence Interval 0.36-0.45) for those meeting recommended levels of physical activity compared to inactive subjects. Cohort studies and randomized controlled trials tended to show a positive effect of physical activity on health-related quality of life, but similar to the cross-sectional studies, had methodological limitations. CONCLUSION: Cross-sectional data showed a consistently positive association between physical activity level and health-related quality of life. Limited evidence from randomized controlled trials and cohort studies precludes a definitive statement about the nature of this association.

Health care renunciation for economic reasons in Switzerland.

BACKGROUND: Most societies elaborate ways to contain increasing health care expenditures. In Switzerland out of pocket payments and cuts in the catalogue of reimbursed services are used as cost-containment measures. The aims of the study were to estimate the extent of health care renunciation for economic reasons and to identify associated factors. METHODS: A population-based cross-sectional survey (2008-2009) of a representative sample in the Canton of Geneva, Switzerland. Health care underuse, income level categories (<CHF 3000/month, 3000-4999, 5000-6999, 7000-9499, 9500-13 000, >13 000), education, occupation, insurance status and cardiovascular comorbidities were collected using self-rated questionnaires. RESULTS: 765 men and 814 women aged 35-74 years participated. 14.5% (229/1579) (95%CI 12.7-16.2) renounced health care for economic reasons. Among those who renounced (N = 229), 74% renounced dental care, 37% physician consultation (22% specialist, 15% general practitioner), 26% health devices, 13% medication, and 5% surgery. Income was negatively correlated with renouncement (r = -0.18, p <.0001). Each decrease in income level category provided a 48% increased risk of renouncing health care for economic reasons (OR 1.48, 1.31-1.65). This association remained when dental care was excluded from the definition of health care renunciation. CONCLUSIONS: In a region of Switzerland with a high cost of living, such as Geneva, socioeconomic status may influence the use of the health care system, and renunciation for economic reasons was not uncommon. More than 30% of the lowest income group renounced health care for economical reasons in the previous year. Health care underuse and renunciation may worsen the health status of a substantial part of society.

Project Newsletter Iowa Public Health Tracking, July 2013

The IDPH Public Health Tracking Program is an excellent way to collect data related to various indicators for Iowans, but those looking for national statistics or data from other states may want to check out the Health Indicators Warehouse (healthindicators.gov). Run and maintained by the CDC’s National Center for Health Statistics, this site is a centralized source for national, state, and county data for a wide variety of indicators. The data is available to the public, and can be accessed either through the tables and charts directly on the website, or indicators can be downloaded to use in a spreadsheet. Once on the site, users are able to search for their desired data either by topic or geographic region. Filters can then be applied to the chosen field to narrow down the user’s search and obtain the preferred statistics. In addition, users are also able to search for indicators derived from state and federal health indicator initiatives: County Health Rankings, Community Health Status Indicators, Healthy People 2020, and CMS Community Indicators. The warehouse provides an overview of each indicator after the user has made their selection. This overview includes information on how the data was calculated and what characteristics are being represented. For example, percent of binge drinking adults is prefaced in the overview that data was based on the question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you have [5 for men, 4 for women] or more drinks on an occasion?" Data is viewable either in the basic table format, chart format, or for some indicators it is possible to view it in terms of a national map. The Health Indicators Warehouse updates indicators as data becomes available, but the collection of years varies amongst the indicators. Nonetheless, this site is a useful resource to anyone looking for comparative indicators throughout the nation or is interested in one of the hundreds of indicators housed by the site. For more information or to check out what the warehouse has to offer visit: http://healthindicators.gov/

Project Newsletter Iowa Public Health Tracking, August 2013

The IDPH Public Health Tracking Program is an excellent way to collect data related to various indicators for Iowans, but those looking for national statistics or data from other states may want to check out the Health Indicators Warehouse (healthindicators.gov). Run and maintained by the CDC’s National Center for Health Statistics, this site is a centralized source for national, state, and county data for a wide variety of indicators. The data is available to the public, and can be accessed either through the tables and charts directly on the website, or indicators can be downloaded to use in a spreadsheet. Once on the site, users are able to search for their desired data either by topic or geographic region. Filters can then be applied to the chosen field to narrow down the user’s search and obtain the preferred statistics. In addition, users are also able to search for indicators derived from state and federal health indicator initiatives: County Health Rankings, Community Health Status Indicators, Healthy People 2020, and CMS Community Indicators. The warehouse provides an overview of each indicator after the user has made their selection. This overview includes information on how the data was calculated and what characteristics are being represented. For example, percent of binge drinking adults is prefaced in the overview that data was based on the question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you have [5 for men, 4 for women] or more drinks on an occasion?" Data is viewable either in the basic table format, chart format, or for some indicators it is possible to view it in terms of a national map. The Health Indicators Warehouse updates indicators as data becomes available, but the collection of years varies amongst the indicators. Nonetheless, this site is a useful resource to anyone looking for comparative indicators throughout the nation or is interested in one of the hundreds of indicators housed by the site. For more information or to check out what the warehouse has to offer visit: http://healthindicators.gov/

Project Newsletter Iowa Public Health Tracking, September 2013

The IDPH Public Health Tracking Program is an excellent way to collect data related to various indicators for Iowans, but those looking for national statistics or data from other states may want to check out the Health Indicators Warehouse (healthindicators.gov). Run and maintained by the CDC’s National Center for Health Statistics, this site is a centralized source for national, state, and county data for a wide variety of indicators. The data is available to the public, and can be accessed either through the tables and charts directly on the website, or indicators can be downloaded to use in a spreadsheet. Once on the site, users are able to search for their desired data either by topic or geographic region. Filters can then be applied to the chosen field to narrow down the user’s search and obtain the preferred statistics. In addition, users are also able to search for indicators derived from state and federal health indicator initiatives: County Health Rankings, Community Health Status Indicators, Healthy People 2020, and CMS Community Indicators. The warehouse provides an overview of each indicator after the user has made their selection. This overview includes information on how the data was calculated and what characteristics are being represented. For example, percent of binge drinking adults is prefaced in the overview that data was based on the question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you have [5 for men, 4 for women] or more drinks on an occasion?" Data is viewable either in the basic table format, chart format, or for some indicators it is possible to view it in terms of a national map. The Health Indicators Warehouse updates indicators as data becomes available, but the collection of years varies amongst the indicators. Nonetheless, this site is a useful resource to anyone looking for comparative indicators throughout the nation or is interested in one of the hundreds of indicators housed by the site. For more information or to check out what the warehouse has to offer visit: http://healthindicators.gov/

Project Newsletter Iowa Public Health Tracking, October 2013

The IDPH Public Health Tracking Program is an excellent way to collect data related to various indicators for Iowans, but those looking for national statistics or data from other states may want to check out the Health Indicators Warehouse (healthindicators.gov). Run and maintained by the CDC’s National Center for Health Statistics, this site is a centralized source for national, state, and county data for a wide variety of indicators. The data is available to the public, and can be accessed either through the tables and charts directly on the website, or indicators can be downloaded to use in a spreadsheet. Once on the site, users are able to search for their desired data either by topic or geographic region. Filters can then be applied to the chosen field to narrow down the user’s search and obtain the preferred statistics. In addition, users are also able to search for indicators derived from state and federal health indicator initiatives: County Health Rankings, Community Health Status Indicators, Healthy People 2020, and CMS Community Indicators. The warehouse provides an overview of each indicator after the user has made their selection. This overview includes information on how the data was calculated and what characteristics are being represented. For example, percent of binge drinking adults is prefaced in the overview that data was based on the question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you have [5 for men, 4 for women] or more drinks on an occasion?" Data is viewable either in the basic table format, chart format, or for some indicators it is possible to view it in terms of a national map. The Health Indicators Warehouse updates indicators as data becomes available, but the collection of years varies amongst the indicators. Nonetheless, this site is a useful resource to anyone looking for comparative indicators throughout the nation or is interested in one of the hundreds of indicators housed by the site. For more information or to check out what the warehouse has to offer visit: http://healthindicators.gov/

Project Newsletter Iowa Public Health Tracking, October 2013

The IDPH Public Health Tracking Program is an excellent way to collect data related to various indicators for Iowans, but those looking for national statistics or data from other states may want to check out the Health Indicators Warehouse (healthindicators.gov). Run and maintained by the CDC’s National Center for Health Statistics, this site is a centralized source for national, state, and county data for a wide variety of indicators. The data is available to the public, and can be accessed either through the tables and charts directly on the website, or indicators can be downloaded to use in a spreadsheet. Once on the site, users are able to search for their desired data either by topic or geographic region. Filters can then be applied to the chosen field to narrow down the user’s search and obtain the preferred statistics. In addition, users are also able to search for indicators derived from state and federal health indicator initiatives: County Health Rankings, Community Health Status Indicators, Healthy People 2020, and CMS Community Indicators. The warehouse provides an overview of each indicator after the user has made their selection. This overview includes information on how the data was calculated and what characteristics are being represented. For example, percent of binge drinking adults is prefaced in the overview that data was based on the question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you have [5 for men, 4 for women] or more drinks on an occasion?" Data is viewable either in the basic table format, chart format, or for some indicators it is possible to view it in terms of a national map. The Health Indicators Warehouse updates indicators as data becomes available, but the collection of years varies amongst the indicators. Nonetheless, this site is a useful resource to anyone looking for comparative indicators throughout the nation or is interested in one of the hundreds of indicators housed by the site. For more information or to check out what the warehouse has to offer visit: http://healthindicators.gov/

Project Newsletter Iowa Public Health Tracking, November 2013

The IDPH Public Health Tracking Program is an excellent way to collect data related to various indicators for Iowans, but those looking for national statistics or data from other states may want to check out the Health Indicators Warehouse (healthindicators.gov). Run and maintained by the CDC’s National Center for Health Statistics, this site is a centralized source for national, state, and county data for a wide variety of indicators. The data is available to the public, and can be accessed either through the tables and charts directly on the website, or indicators can be downloaded to use in a spreadsheet. Once on the site, users are able to search for their desired data either by topic or geographic region. Filters can then be applied to the chosen field to narrow down the user’s search and obtain the preferred statistics. In addition, users are also able to search for indicators derived from state and federal health indicator initiatives: County Health Rankings, Community Health Status Indicators, Healthy People 2020, and CMS Community Indicators. The warehouse provides an overview of each indicator after the user has made their selection. This overview includes information on how the data was calculated and what characteristics are being represented. For example, percent of binge drinking adults is prefaced in the overview that data was based on the question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you have [5 for men, 4 for women] or more drinks on an occasion?" Data is viewable either in the basic table format, chart format, or for some indicators it is possible to view it in terms of a national map. The Health Indicators Warehouse updates indicators as data becomes available, but the collection of years varies amongst the indicators. Nonetheless, this site is a useful resource to anyone looking for comparative indicators throughout the nation or is interested in one of the hundreds of indicators housed by the site. For more information or to check out what the warehouse has to offer visit: http://healthindicators.gov/

Project Newsletter Iowa Public Health Tracking, December 2013

The IDPH Public Health Tracking Program is an excellent way to collect data related to various indicators for Iowans, but those looking for national statistics or data from other states may want to check out the Health Indicators Warehouse (healthindicators.gov). Run and maintained by the CDC’s National Center for Health Statistics, this site is a centralized source for national, state, and county data for a wide variety of indicators. The data is available to the public, and can be accessed either through the tables and charts directly on the website, or indicators can be downloaded to use in a spreadsheet. Once on the site, users are able to search for their desired data either by topic or geographic region. Filters can then be applied to the chosen field to narrow down the user’s search and obtain the preferred statistics. In addition, users are also able to search for indicators derived from state and federal health indicator initiatives: County Health Rankings, Community Health Status Indicators, Healthy People 2020, and CMS Community Indicators. The warehouse provides an overview of each indicator after the user has made their selection. This overview includes information on how the data was calculated and what characteristics are being represented. For example, percent of binge drinking adults is prefaced in the overview that data was based on the question: “Considering all types of alcoholic beverages, how many times during the past 30 days did you have [5 for men, 4 for women] or more drinks on an occasion?" Data is viewable either in the basic table format, chart format, or for some indicators it is possible to view it in terms of a national map. The Health Indicators Warehouse updates indicators as data becomes available, but the collection of years varies amongst the indicators. Nonetheless, this site is a useful resource to anyone looking for comparative indicators throughout the nation or is interested in one of the hundreds of indicators housed by the site. For more information or to check out what the warehouse has to offer visit: http://healthindicators.gov/

Nonlinear analysis of human physical activity patterns in health and disease.

The reliable and objective assessment of chronic disease state has been and still is a very significant challenge in clinical medicine. An essential feature of human behavior related to the health status, the functional capacity, and the quality of life is the physical activity during daily life. A common way to assess physical activity is to measure the quantity of body movement. Since human activity is controlled by various factors both extrinsic and intrinsic to the body, quantitative parameters only provide a partial assessment and do not allow for a clear distinction between normal and abnormal activity. In this paper, we propose a methodology for the analysis of human activity pattern based on the definition of different physical activity time series with the appropriate analysis methods. The temporal pattern of postures, movements, and transitions between postures was quantified using fractal analysis and symbolic dynamics statistics. The derived nonlinear metrics were able to discriminate patterns of daily activity generated from healthy and chronic pain states.