Adherence to dietary recommendations for Swedish adults across categories of greenhouse gas emissions from food

Objective To explore associations between diet-related greenhouse gas emissions (GHGE), nutrient intakes and adherence to the Nordic Nutrition Recommendations among Swedish adults. Design Diet was assessed by 4d food records in the Swedish National Dietary Survey. GHGE was estimated by linking all foods to carbon dioxide equivalents, using data from life cycle assessment studies. Participants were categorized into quartiles of energy-adjusted GHGE and differences between GHGE groups regarding nutrient intakes and adherence to nutrient recommendations were explored. Setting Sweden. Subjects Women (n 840) and men (n 627) aged 18-80 years. Results Differences in nutrient intakes and adherence to nutrient recommendations between GHGE groups were generally small. The dietary intake of participants with the lowest emissions was more in line with recommendations regarding protein, carbohydrates, dietary fibre and vitamin D, but further from recommendations regarding added sugar, compared with the highest GHGE group. The overall adherence to recommendations was found to be better among participants with lower emissions compared with higher emissions. Among women, 27 % in the lowest GHGE group adhered to at least twenty-three recommendations compared with only 12 % in the highest emission group. For men, the corresponding figures were 17 and 10 %, respectively. Conclusions The study compared nutrient intakes as well as adherence to dietary recommendations for diets with different levels of GHGE from a national dietary survey. We found that participants with low-emission diets, despite higher intake of added sugar, adhered to a larger number of dietary recommendations than those with high emissions

Relative immaturity and ADHD : findings from nationwide registers, parent- and self-reports

BACKGROUND: We addressed if immaturity relative to peers reflected in birth month increases the likelihood of ADHD diagnosis and treatment. METHODS: We linked nationwide Patient and Prescribed Drug Registers and used prospective cohort and nested case-control designs to study 6-69 year-old individuals in Sweden from July 2005 to December 2009 (Cohort 1). Cohort 1 included 56,263 individuals diagnosed with ADHD or ever used prescribed ADHD-specific medication. Complementary population-representative cohorts provided DSM-IV ADHD symptom ratings; parent-reported for 10,760 9-year-old twins born 1995-2000 from the CATSS study (Cohort 2) and self-reported for 6,970 adult twins age 20-47 years born 1959-1970 from the STAGE study (Cohort 3). We calculated odds ratios (OR:s) for ADHD across age for individuals born in November/December compared to January/February (Cohort 1). ADHD symptoms in Cohorts 2 and 3 were studied as a function of calendar birth month. RESULTS: ADHD diagnoses and medication treatment were both significantly more common in individuals born in November/December versus January/February; peaking at ages 6 (OR: 1.8; 95% CI: 1.5-2.2) and 7 years (OR: 1.6; 95% CI: 1.3-1.8) in the Patient and Prescribed Drug Registers, respectively. We found no corresponding differences in parent- or self-reported ADHD symptoms by calendar birth month. CONCLUSION: Relative immaturity compared to class mates might contribute to ADHD diagnosis and pharmacotherapy despite absence of parallel findings in reported ADHD symptom loads by relative immaturity. Increased clinical awareness of this phenomenon may be warranted to decrease risk for imprecise diagnostics and treatment. We speculate that flexibility regarding age at school start according to individual maturity could reduce developmentally inappropriate demands on children and improve the precision of ADHD diagnostic practice and pharmacological treatment.Swedish Research Council (2010-3184)Karolinska Institutet Center of Neurodevelopmental Disorders (KIND)Accepte

Workplace health promotion to facilitate physical activity among office workers in Sweden

Office workers spend most of their working time being sedentary, contributing to a sedentary lifestyle that increases the risk of developing disease and disability. A gradual decline in cardiorespiratory fitness among adults, along with increased rate of non-communicable diseases across developed countries, makes the workplace an important opportunity for promoting healthy behaviors. This study aimed to investigate: how office companies in Sweden organize and provide workplace health promotion services related to physical activity; the companies' vision for providing workplace health promotion; and potential facilitators and barriers. Nine informants from eight companies participated in the study, and both qualitative and quantitative data were collected by semi-structured interviews. Informants were selected through purposive sampling in collaboration with eight companies in the office market, including companies that own and develop office buildings, shared workspaces, interior design, sustainable solutions, or consult on issues related to the office sector. The framework method was used to analyze the data in a flexible and systematic way. The results showed that workplace health promotion is implemented to maintain employee health, productivity, and employee branding. Also, a significant number of financial resources, organizational support and office space are devoted to workplace health promotion. Convenience and easy access to storage and fitness facilities are key facilitators. In conclusion, this study highlights the importance of employees' engagement in developing and improving workplace health promotion and addressing work-life balance constraints that hinder a healthy lifestyle. Removing barriers on an organizational level may improve the usage of workplace health promotion related to physical activity among office employees

Shared genetic origin of asthma, hay fever and eczema elucidates allergic disease biology

Asthma, hay fever (or allergic rhinitis) and eczema (or atopic dermatitis) often coexist in the same individuals, partly because of a shared genetic origin. To identify shared risk variants, we performed a genome-wide association study (GWAS; n = 360,838) of a broad allergic disease phenotype that considers the presence of any one of these three diseases. We identified 136 independent risk variants (P < 3 × 10-8), including 73 not previously reported, which implicate 132 nearby genes in allergic disease pathophysiology. Disease-specific effects were detected for only six variants, confirming that most represent shared risk factors. Tissue-specific heritability and biological process enrichment analyses suggest that shared risk variants influence lymphocyte-mediated immunity. Six target genes provide an opportunity for drug repositioning, while for 36 genes CpG methylation was found to influence transcription independently of genetic effects. Asthma, hay fever and eczema partly coexist because they share many genetic risk variants that dysregulate the expression of immune-related genes

Classification models for high-dimensional data with sparsity patterns

Abstract Today&apos;s high-throughput data collection devices, e.g. spectrometers and gene chips, create information in abundance. However, this poses serious statistical challenges, as the number of features is usually much larger than the number of observed units. Further, in this highdimensional setting, only a small fraction of the features are likely to be informative for any specific project. In this thesis, three different approaches to the two-class supervised classification in this high-dimensional, low sample setting are considered. There are classifiers that are known to mitigate the issues of high-dimensionality, e.g. distance-based classifiers such as Naive Bayes. However, these classifiers are often computationally intensive and therefore less time-consuming for discrete data. Hence, continuous features are often transformed into discrete features. In the first paper, a discretization algorithm suitable for high-dimensional data is suggested and compared with other discretization approaches. Further, the effect of discretization on misclassification probability in highdimensional setting is evaluated. Linear classifiers are more stable which motivate adjusting the linear discriminant procedure to high-dimensional setting. In the second paper, a two-stage estimation procedure of the inverse covariance matrix, applying Lasso-based regularization and Cuthill-McKee ordering is suggested. The estimation gives a block-diagonal approximation of the covariance matrix which in turn leads to an additive classifier. In the third paper, an asymptotic framework that represents sparse and weak block models is derived and a technique for block-wise feature selection is proposed. Probabilistic classifiers have the advantage of providing the probability of membership in each class for new observations rather than simply assigning to a class. In the fourth paper, a method is developed for constructing a Bayesian predictive classifier. Given the blockdiagonal covariance matrix, the resulting Bayesian predictive and marginal classifier provides an efficient solution to the high-dimensional problem by splitting it into smaller tractable problems. The relevance and benefits of the proposed methods are illustrated using both simulated and real data

Classification models for high-dimensional data with sparsity patterns

Today's high-throughput data collection devices, e.g. spectrometers and gene chips, create information in abundance. However, this poses serious statistical challenges, as the number of features is usually much larger than the number of observed units.  Further, in this high-dimensional setting, only a small fraction of the features are likely to be informative for any specific project. In this thesis, three different approaches to the two-class supervised classification in this high-dimensional, low sample setting are considered. There are classifiers that are known to mitigate the issues of high-dimensionality, e.g. distance-based classifiers such as Naive Bayes. However, these classifiers are often computationally intensive and therefore less time-consuming for discrete data. Hence, continuous features are often transformed into discrete features. In the first paper, a discretization algorithm suitable for high-dimensional data is suggested and compared with other discretization approaches. Further, the effect of discretization on misclassification probability in high-dimensional setting is evaluated.   Linear classifiers are more stable which motivate adjusting the linear discriminant procedure to high-dimensional setting. In the second paper, a two-stage estimation procedure of the inverse covariance matrix, applying Lasso-based regularization and Cuthill-McKee ordering is suggested. The estimation gives a block-diagonal approximation of the covariance matrix which in turn leads to an additive classifier. In the third paper, an asymptotic framework that represents sparse and weak block models is derived and a technique for block-wise feature selection is proposed.      Probabilistic classifiers have the advantage of providing the probability of membership in each class for new observations rather than simply assigning to a class. In the fourth paper, a method is developed for constructing a Bayesian predictive classifier. Given the block-diagonal covariance matrix, the resulting Bayesian predictive and marginal classifier provides an efficient solution to the high-dimensional problem by splitting it into smaller tractable problems. The relevance and benefits of the proposed methods are illustrated using both simulated and real data.Med dagens teknik, till exempel spektrometer och genchips, alstras data i stora mängder. Detta överflöd av data är inte bara till fördel utan orsakar även vissa problem, vanligtvis är antalet variabler (p) betydligt fler än antalet observation (n). Detta ger så kallat högdimensionella data vilket kräver nya statistiska metoder, då de traditionella metoderna är utvecklade för den omvända situationen (p&lt;n).  Dessutom är det vanligtvis väldigt få av alla dessa variabler som är relevanta för något givet projekt och styrkan på informationen hos de relevanta variablerna är ofta svag. Därav brukar denna typ av data benämnas som gles och svag (sparse and weak). Vanligtvis brukar identifiering av de relevanta variablerna liknas vid att hitta en nål i en höstack. Denna avhandling tar upp tre olika sätt att klassificera i denna typ av högdimensionella data.  Där klassificera innebär, att genom ha tillgång till ett dataset med både förklaringsvariabler och en utfallsvariabel, lära en funktion eller algoritm hur den skall kunna förutspå utfallsvariabeln baserat på endast förklaringsvariablerna. Den typ av riktiga data som används i avhandlingen är microarrays, det är cellprov som visar aktivitet hos generna i cellen. Målet med klassificeringen är att med hjälp av variationen i aktivitet hos de tusentals gener (förklaringsvariablerna) avgöra huruvida cellprovet kommer från cancervävnad eller normalvävnad (utfallsvariabeln). Det finns klassificeringsmetoder som kan hantera högdimensionella data men dessa är ofta beräkningsintensiva, därav fungera de ofta bättre för diskreta data. Genom att transformera kontinuerliga variabler till diskreta (diskretisera) kan beräkningstiden reduceras och göra klassificeringen mer effektiv. I avhandlingen studeras huruvida av diskretisering påverkar klassificeringens prediceringsnoggrannhet och en mycket effektiv diskretiseringsmetod för högdimensionella data föreslås. Linjära klassificeringsmetoder har fördelen att vara stabila. Nackdelen är att de kräver en inverterbar kovariansmatris och vilket kovariansmatrisen inte är för högdimensionella data. I avhandlingen föreslås ett sätt att skatta inversen för glesa kovariansmatriser med blockdiagonalmatris. Denna matris har dessutom fördelen att det leder till additiv klassificering vilket möjliggör att välja hela block av relevanta variabler. I avhandlingen presenteras även en metod för att identifiera och välja ut blocken. Det finns också probabilistiska klassificeringsmetoder som har fördelen att ge sannolikheten att tillhöra vardera av de möjliga utfallen för en observation, inte som de flesta andra klassificeringsmetoder som bara predicerar utfallet. I avhandlingen förslås en sådan Bayesiansk metod, givet den blockdiagonala matrisen och normalfördelade utfallsklasser. De i avhandlingen förslagna metodernas relevans och fördelar är visade genom att tillämpa dem på simulerade och riktiga högdimensionella data.    

Active Commuting and Healthy Behavior among Adolescents in Neighborhoods with Varying Socioeconomic Status : The NESLA Study

(1) Background: The World Health Organization recommends active commuting as a source of physical activity. Active commuting is determined by various factors, including the socioeconomic status (SES) of families and neighborhoods, distance to schools, perceived neighborhood safety, lifestyles, and availability of walkways and biking paths. This study aimed to assess factors associated with modes of transportation to and from school among adolescents aged 16–19 living in a middle-sized city in Sweden. (2) Method: Three hundred and fourteen students, of whom 55% were females, from schools in the city of Västerås participated in the study. Printed as well as web-based self-administered questionnaires were used to collect the data. (3) Results: Adolescents living in high SES neighborhoods were 80% more likely to bike or walk to school (OR = 1.80; CI: 1.01, 3.20) than adolescents living in low SES neighborhoods. Furthermore, active commuting was associated with higher consumption of fruits and vegetables (OR = 1.77; CI: 1.05, 2.97) and less consumption of junk foods (OR = 0.43; CI: 0.26, 0.71), as compared to passive commuting. (4) Conclusions: Active commuting is a cost-effective and sustainable source of regular physical activity and should be encouraged at a societal level.

Early-Life Factors and Risk of Parkinson's Disease: A Register-Based Cohort Study.

Parkinson's disease (PD) may take decades to develop and early life exposures such as infection may be important. However, few epidemiological studies have evaluated early life risk factors in relation to PD risk. We therefore examined such associations in a prospective analysis of 3 545 612 individuals born in Sweden between 1932 and 1970 without PD on January 1, 2002. Incident PD cases were identified using the Swedish Patient Register during 2002-2010. Information on sibship size, number of older and younger siblings, multiple births, parental age, birth month and season was obtained from the Swedish Multi-Generation Register. Monthly data on national burden of influenza-like illness during 1932-1970 were obtained from the Swedish Public Health Agency. Hazard ratios with 95% confidence intervals (CI) were estimated using Cox proportional hazards regression. During the follow-up, 8779 incident PD cases were identified. As expected, older age, male sex, parental occupation as farmers, and family history of PD were associated with higher PD risk. Overall, early life factors, including flu burden in the year of birth, were not associated with PD risk, although we did find a lower PD risk among participants with older siblings than those without (HR = 0.93, 95%CI: 0.89, 0.98). Our study therefore provided little support for important etiological contributions of early life factors to the PD risk late in life. The finding of a lower PD risk among individuals with older siblings will need confirmation and further investigation

Workplace health promotion to facilitate physical activity among office workers in Sweden

Office workers spend most of their working time being sedentary, contributing to a sedentary lifestyle that increases the risk of developing disease and disability. A gradual decline in cardiorespiratory fitness among adults, along with increased rate of non-communicable diseases across developed countries, makes the workplace an important opportunity for promoting healthy behaviors. This study aimed to investigate: how office companies in Sweden organize and provide workplace health promotion services related to physical activity; the companies vision for providing workplace health promotion; and potential facilitators and barriers. Nine informants from eight companies participated in the study, and both qualitative and quantitative data were collected by semi-structured interviews. Informants were selected through purposive sampling in collaboration with eight companies in the office market, including companies that own and develop office buildings, shared workspaces, interior design, sustainable solutions, or consult on issues related to the office sector. The framework method was used to analyze the data in a flexible and systematic way. The results showed that workplace health promotion is implemented to maintain employee health, productivity, and employee branding. Also, a significant number of financial resources, organizational support and office space are devoted to workplace health promotion. Convenience and easy access to storage and fitness facilities are key facilitators. In conclusion, this study highlights the importance of employees engagement in developing and improving workplace health promotion and addressing work-life balance constraints that hinder a healthy lifestyle. Removing barriers on an organizational level may improve the usage of workplace health promotion related to physical activity among office employees