Search

Military deployment increases the risk of exposure to potentially traumatising events as well as the risk of post-traumatic stress disorder and other mental illnesses. This may have adverse consequences for the family of the deployed, including negatively affecting the well-being of children. Well-being among children of formerly deployed military personnel has mainly been studied in the United States. Research from Europe and especially Scandinavian countries is scarce, and no knowledge of the well-being of children of formerly deployed military Danish personnel exists. Since 1992, Denmark has deployed more than 30,000 soldiers and other personnel to international conflicts/warzones, and at least 14% were parents when deployed. Exploiting survey data, we examined well-being among a representative sample of 1590 children (age 11 and 15) of formerly deployed Danish fathers, compared with a representative sample of 4722 children (age 11 and 15) from the background population (controls). Well-being was examined through children’s subjective well-being measured by the Strengths and Difficulties Questionnaire (SDQ), as well as through their perception of their family, self-evaluated school outcomes and relationships with friends. The analyses show that children of formerly deployed fathers, in general, are doing as well as children of the background population; however, the 11-year-old children of formerly deployed fathers are more challenged compared with children of the background population, both for subjective well-being and in having more conflicts with their parents.

Purpose: Evidence exists of an association between pre-morbid lower cognitive ability and higher risk of hospitalization for depressive disorder in civilian cohorts. The purpose of this study was to examine the relationship of cognitive ability at conscription with post-deployment depression and the influence of (1) baseline factors: age, gender, and pre-deployment educational level, (2) deployment-related factors: e.g., war-zone stress and social support, and (3) co-morbid PTSD. Methods: An observational cohort study linking conscription board registry data with post-deployment self-report data. The study population consisted of Danish Army military personnel deployed to different war zones from 1997 to 2015. The association between cognitive ability at conscription and post-deployment depression was analyzed using repeated-measure logistic regression models. Results: Study population totaled 9716 with a total of 13,371 deployments. Low-level cognitive ability at conscription was found to be weakly associated with post-deployment probable depression after adjustment for more important risk factors like gender, education, and deployment-related factors [odds ratio (OR) 0.93, 95% confidence interval (CI) 0.88–0.99]. The co-occurrence rate with PTSD was nearly 60%. When adding co-morbid PTSD as an independent variable, the association between cognitive ability and probable depression became insignificant, OR 0.95, CI 0.89–1.02. Conclusions: Low cognitive ability at conscription is a risk factor for depression among returning military personnel, but unimportant compared to gender, education, and deployment-related factors. Part of this effect may be related to co-morbid PTSD. Use of cognitive ability score as an isolated selection tool cannot be recommended because of low predictive performance.

BACKGROUND: Many register studies make use of information about permanent nursing home residents. Statistics Denmark (StatD) identifies nursing home residents by two different indirect methods, one based on reports from the municipalities regarding home care in taken place in a nursing home, and the other based on an algorithm created by StatD. The aim of the present study was to validate StatD's nursing home register using dedicated administrative municipality records on individual nursing home residents as gold standard.METHODS: In total, ten Danish municipalities were selected. Within each Danish Region, we randomly selected one municipality reporting to Stat D (Method 1) and one not reporting where instead an algorithm created by StatD was used to discover nursing home residents (Method 2). Method 1 means that municipalities reported to Stat D whether home care has taken place in a nursing home or in a private home. Method 2 is based on an algorithm created by Stat D for the municipalities where Method 1 is not applicable. Our gold standard was the information from the local administrative system in all ten selected municipalities. Each municipality provided a list with all individuals > 65 years living in a nursing home on January 1st, 2013 as well as the central personal number. This was compared to the list of individuals >65 living in nursing home facilities in the same ten municipalities on January 1st, 2013 retrieved from StatD.RESULTS: According to the data received directly from the municipalities, which was used as our gold Standard 3821 individuals were identified as nursing home residents. The StatD register identified 6,141 individuals as residents. Additionally, 556 of the individuals identified by the municipalities were not identified in the StatD register. Overall sensitivity for the ten municipalities in the StatD nursing home register was 0.85 (95% CI 0.84-0.87) and the PPV was 0.53 (95% CI 0.52-0.54). The municipalities for which nursing home status was based on the StatD algorithm (method 2) had a sensitivity of 0.84 (95% CI 0.82-0.86) and PPV of 0.48 (95% CI 0.46-0.50). Both slightly lower than the reporting municipalities (method 1) where the sensitivity was 0.87(95% CI 0.85-0.88) and the PPV was 0.57 (95% CI 0.56-0.59). Additionally, the sensitivity and PPV of the Stat D register varied heavily among the ten municipalities from 0.51 (95% CI 0.43-0.59) to 0.96 (95% CI 0.95-0.98) and PPV correspondingly, from 0.14 (95% CI: 0.11-0.17) to 0.73 (95% CI 0.69-0.77).CONCLUSIONS: The overall PPV of StatD nursing home register was low and differences between municipalities existed. Even in countries with extensive nation-wide registers, validating studies should be conducted for outcomes based on these registers.

Background Diabetic patients’ lifestyle adaptations to improve glycaemic control are not always followed by improvements in self-rated general health (SRH). The perceived impact of diabetes on patients’ daily lives may influence changes in their SRH. This paper examines the association of illness severity, treatment, behavioural, and coping-related factors with changes in SRH from diagnosis of type 2 diabetes until one year later, in a population-based sample of 599 patients aged 40 years or over who were treated in general practice. Methods Change in SRH was estimated by a cumulative probit model with the inclusion of covariates related to SRH (e.g. illness severity at diagnosis, behaviour, treatment, and the perceived impact of diabetes on patients’ daily lives one year later). Results At diagnosis, 11.6% of patients reported very good, 35.1% good, 44.6% fair and 8.5% poor SRH. Physical inactivity, many diabetes-related symptoms, and cardiovascular disease were related to lower SRH ratings. On average SRH improved by 0.46 (95% CI: 0.37; 0.55) during the first year after diagnosis without inclusion of covariates. Mental and practical illness burden was the only factor associated with change in SRH, independent of patients’ diabetes severity and medical treatment (p = 0.03, multivariate analysis). Compared to otherwise similar patients without illness burden, increase in SRH was marginally smaller among patients who expressed minor illness burden, but much smaller among patients with more pronounced illness burden. Conclusions Much as one would expect, many patients increased their SRH during the first year after diabetes diagnosis. This increase in SRH was not associated with indicators of illness severity or factors reflecting socio-demographic circumstances, but patients experiencing illness burden had a smaller increase than those who reported no illness burden. We suggest that during the diabetes consultation, general practitioners explore further how patients manage their illness burden. We further suggest that diabetes guidelines extend their current focus on clinical and social aspects of diabetes to include questions on patient’s perceived illness burden and SRH. Electronic supplementary material The online version of this article (doi:10.1186/s12889-015-1790-6) contains supplementary material, which is available to authorized users.

Background The Inuit in Greenland have a high average consumption of marine species and are highly exposed to methylmercury, which in other studies has been related to hypertension. Data on the relation between methylmercury and hypertension is limited, especially in populations subjected to a high exposure of methylmercury. We examined the relation between whole blood mercury and blood pressure (BP) in Inuit in Greenland. Methods A cross-sectional population-based study among adult Inuit in Greenland was performed in 2005–2009. Information on socio-demography, lifestyle, BP, blood samples and clinical measurements was obtained – the latter after overnight fasting. BP was measured according to standardized guidelines. Whole blood mercury concentration was used as a marker of exposure. The analyses were restricted to Inuit aged 30–69 years with four Greenlandic grandparents (N = 1,861). Multivariate regression analyses with inclusion of confounders were done separately for men and women with the omission of participants receiving anti-hypertensive drugs, except for logistic regression analyses of the relation between mercury and presence of hypertension (yes/no). Results The mean whole blood mercury level was 20.5 μg/L among men and 14.7 μg/L among women. In multivariate analyses adjusted for confounders, diastolic BP decreased with increasing mercury concentration. In men diastolic BP decreased significantly for each four-fold increase in mercury concentration (Beta = −0.04, standard error = 0.01, p = 0.001), while no relation between mercury and diastolic BP was found among women. For systolic BP, a similar non-statistically significant result was seen only for men (Beta = −0.02, standard error = 0.01, p = 0.06). A relation between mercury and hypertension was only found in men; the odds ratio for hypertension was 0.99 (95% CI: 0.98-0.99). No relation between quintiles of mercury and hypertension was found. The relationship between mercury and BP parameters may be non-linear: In analyses of quintiles of mercury the overall effect of mercury on BP parameters was only statistically significant for diastolic BP among men (Wald test, p = 0.01), however pairwise comparisons showed that some quintiles were not statistically different. This result is supported by LOESS modelling. Conclusions No adverse associations between whole blood mercury and blood pressure were found. With increasing whole blood mercury concentrations, diastolic BP and the risk of hypertension decreased among men in the study: this may be explained by confounding by exercise or unknown factors.