Introduction Healthcare expenditure, a common input used in health systems efficiency analyses is affected by population age structure. However, while age structure is usually considered to adjust health system outputs, health expenditure and other inputs are seldom adjusted. We propose methods for adjusting Health Expenditure per Capita (HEpC) for population age structure on health system efficiency analyses and assess the goodness-of-fit, correlation, reliability and disagreement of different approaches. Methods We performed a worldwide (188 countries) cross-sectional study of efficiency in 2015, using a stochastic frontier analysis. As single outputs, healthy life expectancy (HALE) at birth and at 65 years-old were considered in different models. We developed five models using as inputs: (1) HEpC (unadjusted); (2) age-adjusted HEpC; (3) HEpC and the proportion of 0–14, 15–64 and 65 + years-old; (4) HEpC and 5-year age-groups; and (5) HEpC ageing index. Akaike and Bayesian information criteria, Spearman’s rank correlation, intraclass correlation coefficient and information-based measure of disagreement were computed. Results Models 1 and 2 showed the highest correlation (0.981 and 0.986 for HALE at birth and HALE at 65 years-old, respectively) and reliability (0.986 and 0.988) and the lowest disagreement (0.011 and 0.014). Model 2, with age-adjusted HEpC, presented the lowest information criteria values. Conclusions Despite different models showing good correlation and reliability and low disagreement, there was important variability when age structure is considered that cannot be disregarded. The age-adjusted HE model provided the best goodness-of-fit and was the closest option to the current standard.

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Obsessive-compulsive disorder (OCD) is a chronic psychiatric disorder characterized by obsessions and compulsions. It affects about 2.5% of people throughout their life and usually emerges in infancy/adolescence or early adulthood.Despite high levels of suffering and disability, high comorbidity rates, and low treatment response rates, suicidal behavior associated with this disorder was traditionally considered a rare phenomenon. However, recent studies recognize a significant risk of suicidal behavior in obsessive-compulsive patients.As a result, we describe a clinical case of attempted suicide in an obsessive-compulsive patient and discuss risk factors that have been considered predictive of suicide in OCD.

Objectives: Information on the effectiveness of COVID-19 contact tracing is lacking. We proposed 2 measures for evaluating the effectiveness of contact tracing and applied them in a public health unit in northern Portugal. Methods: This retrospective cohort study included the contacts of people with COVID-19 diagnosed July 1–September 15, 2020. We examined 2 measures: (1) number needed to quarantine (NNQ), as the number of quarantine person-days needed to prevent 1 potential infectious person-day; and (2) proportion of prevented infectious days by quarantine (PPID), as the number of potential infectious days prevented by quarantine divided by all infectious days. We assessed these measures by sociodemographic characteristics, types of contacts, and intervention timings (ie, time between diagnosis or symptom onset and intervention). We considered 3 scenarios for infectiousness periods: 10 days before to 10 days after symptom onset, 3 days before to 3 days after symptom onset, and 2 days before to 10 days after symptom onset. Results: We found an NNQ of 19.8-41.8 person-days and a PPID of 19.7%-38.2%, depending on the infectiousness period scenario. Effectiveness was higher among cohabitants and symptomatic contacts than among social or asymptomatic contacts. NNQ and PPID changed by intervention timings: the effectiveness of contact tracing decreased with time from diagnosis to quarantine of contacts and with time from symptom onset of the index case to contacts’ quarantine. Conclusions: These proposed measures of contact tracing effectiveness of communicable diseases can be important for decision making and prioritizing contact tracing when resources are scarce. They are also useful measures for communication with the general population, policy makers, and clinicians because they are easy to understand and use to assess the impact of health interventions.