Flowchart for identifying stomach adenocarcinoma cases.

<p>In total 105 stomach cancers (ICD-7 code 151) which occurred at least 5 years after blood sample collection were identified during follow-up. Among these, 27 were excluded due to either missing serum samples or the fact that the cancers were found incidentally at autopsy. For the remaining 78 cases, medical records related to cancer diagnosis for 68 cases were successfully retrieved. After review, 9 cases were deemed to be tumors other than stomach adenocarcinoma. Among the remaining 59 stomach adenocarcinoma cases, 15 stomach adenocarcinoma cases were determined to be cardia adenocarcinoma and 41 as non-cardia stomach adenocarcinoma. In 3 stomach adenocarcinoma cases it was not possible to determine the exact origin in the stomach.</p

<i>H. pylori</i> -CSAs and CagA seropositivity and the risk of stomach adenocarcinoma overall, cardia site and non-cardia site.

<p>* Odds ratios (ORs) were derived from conditional logistic regression models.</p

Characteristics of the stomach adenocarcinoma cases and their matched controls.

<p>* Defined as serum pepsinogen I<25 ug/l or pepsinogen I:II ratio <3 at time of initial serum collection.</p

Evaluation of an Internet-Based Monitoring System for Influenza-Like Illness in Sweden

<div><p>To complement traditional influenza surveillance with data on disease occurrence not only among care-seeking individuals, the Swedish Institute for Communicable Disease Control (SMI) has tested an Internet-based monitoring system (IMS) with self-recruited volunteers submitting weekly on-line reports about their health in the preceding week, upon weekly reminders. We evaluated IMS acceptability and to which extent participants represented the Swedish population. We also studied the agreement of data on influenza-like illness (ILI) occurrence from IMS with data from a previously evaluated population-based system (PBS) with an actively recruited random sample of the population who spontaneously report disease onsets in real-time via telephone/Internet, and with traditional general practitioner based sentinel and virological influenza surveillance, in the 2011–2012 and 2012–2013 influenza seasons. We assessed acceptability by calculating the participation proportion in an invited IMS-sample and the weekly reporting proportion of enrolled self-recruited IMS participants. We compared distributions of socio-demographic indicators of self-recruited IMS participants to the general Swedish population using chi-square tests. Finally, we assessed the agreement of weekly incidence proportions (%) of ILI in IMS and PBS with cross-correlation analyses. Among 2,511 invited persons, 166 (6.6%) agreed to participate in the IMS. In each season, 2,552 and 2,486 self-recruited persons participated in the IMS respectively. The weekly reporting proportion among self-recruited participants decreased from 87% to 23% (2011–2012) and 82% to 45% (2012–2013). Women, highly educated, and middle-aged persons were overrepresented among self-recruited IMS participants (p<0.01). IMS (invited and self-recruited) and PBS weekly incidence proportions correlated strongest when no lags were applied (r = 0.71 and r = 0.69, p<0.05). This evaluation revealed socio-demographic misrepresentation and limited compliance among the self-recruited IMS participants. Yet, IMS offered a reasonable representation of the temporal ILI pattern in the community overall during the 2011–2012 and 2012–2013 influenza seasons and could be a simple tool for collecting community-based ILI data.</p></div

Physical activity, sleep and risk of respiratory infections: A Swedish cohort study

<div><p>Objectives</p><p>Previous studies found higher levels of physical activity to be protective against infections and that short and long sleep negatively affects the immune response. However, these relationships remain debatable. We aimed to investigate if physical activity and sleep habits affect incidence of upper respiratory tract infections (URTI) in a prospective cohort study.</p><p>Methods</p><p>A total of 2,038 adults aged 25–64 years served as a random sample of the gainfully employed population of an industrial town in Sweden. Physical activity and sleep habits were estimated through self-reported questionnaires. Physical activity was expressed as metabolic energy turnover hours per day. Sleep was assessed as number of hours slept per night and its perceived quality. URTI outcome was prospectively self-reported during a 9-month follow-up period. Associations of physical activity and sleep with URTI were estimated using hurdle regression models adjusted for potential confounders.</p><p>Results</p><p>During 1,583 person-years 1,597 URTI occurred, resulting in an incidence of 1.01 infections/person-year (95% CI 0.96–1.06). The fitted regression models did not provide support for an association with physical activity or sleep habits. Factors positively associated with experiencing URTI were having children ≤ 6 years, female gender, higher education and treatment for allergy, asthma or lung cancer. Having children ≤ 6 years and female gender were related to a higher number of URTI among those experiencing URTI.</p><p>Conclusions</p><p>We did not find any association between physical activity, sleep duration or sleep quality and the occurrence of upper respiratory tract infections in adult Swedish population.</p></div

Summary of system components of the IMS and PBS during the influenza seasons 2011–2012 and 2012–2013.

<p>Summary of system components of the IMS and PBS during the influenza seasons 2011–2012 and 2012–2013.</p

The weekly number of reports and reporting proportion among self-recruited and invited IMS participants during the 2011–2012 and 2012–2013 influenza seasons.

<p>*Active as defined in the Methods section.</p><p>**Number of reports per participant.</p

Distribution of socio-demographic characteristics among self-recruited and invited IMS participants during the 2011–2012 and 2012–2013 influenza seasons and the corresponding distribution of the general Swedish population 2011 and 2012.

<p>*Chi square goodness of fit test participants vs. Swedish population.</p><p>**Participants who contributed with at least one <i>active</i> report. For definition of active reports, see Methods section.</p><p>***Among participants 16–95+ year old.</p><p>****Including children in age group 0–15 yrs.</p

Bland-Altman plots 2011–2012 and 2012–2013.

<p>The upper graph shows a Bland-Altman plot of data from the 2011–2012 season and the lower graph shows a Bland-Altman plot of data from the 2012–2013 season. The black dots represents the differences of the weekly incidence proportions between the IMS and PBS (y) by the average of the IMS and PBS weekly incidence proportions (x). The thick blue line represents a simple linear regression model of the differences on the averages, while the thin blue lines represent the respective 95% limits of agreement. The limits of agreement for the difference between the IMS and PBS can be calculated from the equation in the bottom of the graphs, when their average is known. With the equations at the top of the graphs one system’s incidence proportions can be transformed to the other.</p

Multivariable logistic regression modelling of the association between background factors and risk of false negative reporting (i.e. no report through the population-based, event-driven surveillance system when the reference method – one-week recall questionnaires – signals onset of disease) in 2008, n = 396.

a<p>Education is the guardians’ highest education if child.</p>b<p>Low & Middle/low≤226,810; Middle = 226,811–340,466; Middle/high = 340,467–473,903; High≥473,904 in SEK in 2006.</p