Trends and projections in fall death in the Netherlands from 1990 to 2045.

BACKGROUND: Increasing life expectancy in high-income countries has been linked to a rise in fall mortality. In the Netherlands, mortality rates from falls have increased gradually from the 1950s, with some indication of stabilisation in the 1990s. For population health and clinical practice, it is important to foresee the future fall mortality trajectories. METHODS: A graphical approach was used to explore trends in mortality by age, calendar period and cohorts born in the periods of 1915-1945. Population data and the numbers of people with accidental fall fatality as underlying cause of death from 1990 to 2021 were derived from Statistics Netherlands. Age-standardised mortality rates of unintentional falls per 100 000 population were calculated by year and sex. A log-linear model was used to examine the separate effects of age, period and cohort on the trend in mortality and to produce estimates of future numbers of fall deaths until 2045. RESULTS: While the total population increased by 17% between 1990 and 2021, absolute numbers of fall-related deaths rose by 230% (from 1584 to 5234), which was 251% (an increase of 576 deaths in 1990 to 2021 deaths in 2020) for men and 219% (from 1008 to 3213) for women. Age-standardised figures were higher for women than men and increased more over time. In 2020, 79% of those with death due to falls were over the age of 80, and 35% were 90 years or older. From 2020 to 2045, the observed and projected numbers of fall deaths were 2021 and 7073 for men (250% increase) and 3213 and 12 575 for women (291% increase). CONCLUSION: Mortality due to falls has increased in the past decades and will continue to rise sharply, mainly caused by growing numbers of older adults, especially those in their 80s and 90s. Contributing risk factors are well known, implementation of preventive measures is a much needed next step. An effective approach to managing elderly people after falls is warranted to reduce crowding in the emergency care and reduce unnecessary long hospital stays.

[A sudden onset of groin pain]. / Plotselinge pijn in de lies.

We describe an 11-year-old boy with sudden onset of right groin pain which occurred during soccer. He was diagnosed with an avulsion fracture of the lesser trochanter, a hyperextension and rotation trauma due to traction on the iliopsoas tendon. The treatment was conservative and full function returned.

E-bike and classic bicycle-related traumatic brain injuries presenting to the emergency department.

BACKGROUND: E-bike usage is increasingly popular and concerns about e-bike-related injuries and safety have risen as more injured e-bikers attend the emergency department (ED). Traumatic brain injury (TBI) is the main cause of severe morbidity and mortality in bicycle-related accidents. This study compares the frequency and severity of TBI after an accident with an e-bike or classic bicycle among patients treated in the ED. METHODS: This was a prospective cohort study of patients with bicycle-related injuries attending the ED of a level 1 trauma centre in the Netherlands between June 2016 and May 2017. The primary outcomes were frequency and severity of TBI (defined by the Abbreviated Injury Scale head score ≥1). Injury Severity Score, surgical intervention, hospitalisation and 30-day mortality were secondary outcomes. Independent risk factors for TBI were identified with multiple logistic regression. RESULTS: We included 834 patients, of whom there were 379 e-bike and 455 classic bicycle users. The frequency of TBI was not significantly different between the e-bike and classic bicycle group (respectively, n=56, 15% vs n=73, 16%; p=0.61). After adjusting for age, gender, velocity, anticoagulation use and alcohol intoxication the OR for TBI with an e-bike compared with classic bicycle was 0.90 (95% CI 0.56 to 1.45). Independent of type of bicycle, TBI was more likely if velocity was 26-45 km/hour, OR 8.14 (95% CI 2.36 to 28.08), the patient was highly alcohol intoxicated, OR 7.02 (95% CI 2.88 to 17.08) or used anticoagulants, OR 2.18 (95% CI 1.20 to 3.97). TBI severity was similar in both groups (p=0.65): eight e-bike and seven classic bicycle accident victims had serious TBI. CONCLUSION: The frequency and severity of TBI among patients treated for bicycle-related injuries at our ED was similar for e-bike and classic bicycle users. Velocity, alcohol intoxication and anticoagulant use were the main determinants of the risk of head injury regardless of type of bicycle used.

Timely assessment of cardiovascular risk after preeclampsia.

Evaluation of: Cusimano MC, Pudwell J, Roddy M, Chan-Kyung JC, Smith GN. The maternal health clinic: an initiative for cardiovascular identification in women with pregnancy-related complications. Am. J. Obstet. Gynecol. 438, e1 (2014). Cardiovascular risk management, for men and women alike, is a preventative means to detect individuals' running an elevated risk of myocardial disorders, stroke and metabolic syndrome. Because age is an important factor in the risk assessment, especially young women almost always are classified in the low-risk category and therefore do not qualify for preventive treatment. A history of preeclampsia identifies women who have underlying cardiovascular risk factors. Approximately 6-8% of all pregnancies are complicated by hypertensive disorders, about 2% ends in preeclampsia. For that very reason, the Maternal Health Clinic at Kingston General Hospital in Kingston, Canada, was established to provide postpartum cardiovascular risk counseling or follow-up for women with the pregnancy-related complications. The outcomes were significant: 17% of the young target population with an average age of 33 years met criteria of metabolic syndrome and 85% revealed elevated lifetime cardiovascular disease risk. These figures are to be compared with control results of women with uncomplicated pregnancies: 7% metabolic syndrome and 46% non-optimal risk. It is concluded that the clinic may serve as a prolific and effective primary prevention strategy.

[Predicting chance of disease: calculation using prediction rules]. / Ziektekansen voorspellen: rekenen met predictieregels.

A prediction rule is a statistical model that can be used to predict the presence or absence of a disease based on a limited number of tests or predictive factors. One of the mathematical methods used to formulate prediction rules is a logistic regression analysis of patient data. The discriminatory power of a model is visualizable using box-whisker plots and ROC curves; calibration plots show the match between the predicted chance and the observed frequency of a disease. These graphs are used to assess whether a model adequately reproduces reality. On publication of prediction rules it is important that the regression function is written out and that the chances of a disease on the basis of diagnostic scores are displayed in a histogram. For the practical significance of the model, it is also important to know how often the predicted low, medium or high probabilities of a disease do actually occur in comparison with the advance chance of occurrence.