The COVID-19 pandemic has sparked unprecedented public health and social measures (PHSM) by national and local governments, including border restrictions, school closures, mandatory facemask use and stay at home orders. Quantifying the effectiveness of these interventions in reducing disease transmission is key to rational policy making in response to the current and future pandemics. In order to estimate the effectiveness of these interventions, detailed descriptions of their timelines, scale and scope are needed. The Health Intervention Tracking for COVID-19 (HIT-COVID) is a curated and standardized global database that catalogues the implementation and relaxation of COVID-19 related PHSM. With a team of over 200 volunteer contributors, we assembled policy timelines for a range of key PHSM aimed at reducing COVID-19 risk for the national and first administrative levels (e.g. provinces and states) globally, including details such as the degree of implementation and targeted populations. We continue to maintain and adapt this database to the changing COVID-19 landscape so it can serve as a resource for researchers and policymakers alike.
BackgroundWe review procurement and pricing transparency practices for pharmaceutical products. We specifically focus on Brazil and examine its approach to increasing pricing transparency, with the aim of determining the level of effectiveness in lower prices using a tool (Banco de Preços em Saúde, BPS) that only reveals purchase prices as compared to other tools (in other countries) that establish a greater degree of price transparency.MethodsA general report of Preços em Saúde (BPS) and Sistema Integrado de Administração de Serviços Gerais (SIASG) pricing data was created for 25 drugs that met specific criteria. To explore the linear time trend of each of the drugs, separate regression models were fitted for each drug, resulting in a total of 19 models. Each model controlled for the state variable and the interaction between state and time, in order to accommodate expected heterogeneity in the data. Additionally, the models controlled for procurement quantities and the effect they have on the unit price. Secondary analysis using mixed effects models was also carried out to account for the impact that institutions and suppliers may have upon the unit price. Adjusting for these predictor variables (procurement quantities, supplier, purchasing institution) was important to determine the sole effect that time has had on unit prices. A total of 2 x 19 = 38 models were estimated to explore the overall effect of time on changes in unit price. All statistical analyses were performed using the R statistical software, while the linear mixed effects models were fitted using the lme4 R package.ResultsThe findings from our analysis suggest that there is no pattern of consistent price decreases within the two Brazilian states during the five-year period for which the prices were analyzed.ConclusionsWhile the BPS does allow for an increase in transparency and information on drug purchase prices in Brazil, it has not shown to lead to consistent reductions in drug purchase prices for some of the most widely used medicines. This is indicative of a limited model for addressing the challenges in pharmaceutical procurement and puts into question the value of tools used globally to improve transparency in pharmaceutical pricing.Electronic supplementary materialThe online version of this article (doi:10.1186/s12992-015-0118-8) contains supplementary material, which is available to authorized users.
Background In light of overall increasing healthcare expenditures, it is mandatory to study determinants of future costs in chronic diseases. This study reports the first longitudinal results on healthcare utilization and associated costs from the German chronic obstructive pulmonary disease (COPD) cohort COSYCONET. Material and methods Based on self-reported data of 1904 patients with COPD who attended the baseline and 18-month follow-up visits, direct costs were calculated for the 12 months preceding both examinations. Direct costs at follow-up were regressed on baseline disease severity and other co-variables to identify determinants of future costs. Change score models were developed to identify predictors of cost increases over 18 months. As possible predictors, models included GOLD grade, age, sex, education, smoking status, body mass index, comorbidity, years since COPD diagnosis, presence of symptoms, and exacerbation history. Results Inflation-adjusted mean annual direct costs increased by 5% (n.s., €6,739 to €7,091) between the two visits. Annual future costs were significantly higher in baseline GOLD grades 2, 3, and 4 (factors 1.24, 95%-confidence interval [1.07–1.43], 1.27 [1.09–1.48], 1.57 [1.27–1.93]). A history of moderate or severe exacerbations within 12 months, a comorbidity count >3, and the presence of dyspnea and underweight were significant predictors of cost increase (estimates ranging between + €887 and + €3,679, all p <0.05). Conclusions Higher GOLD grade, comorbidity burden, dyspnea and moderate or severe exacerbations were determinants of elevated future costs and cost increases in COPD. In addition we identified underweight as independent risk factor for an increase in direct healthcare costs over time.
As ongoing trials study the safety of an active surveillance strategy for low-risk ductal carcinoma in situ (DCIS), there is a need to explain why particular choices regarding treatment strategies are made by eligible women as well as their oncologists, what factors enter the decision process, and how much each factor affects their choice. To measure preferences for treatment and surveillance strategies, women with newly-diagnosed, primary low-risk DCIS enrolled in the Dutch CONTROL DCIS Registration and LORD trial, and oncologists participating in the Dutch Health Professionals Study were invited to complete a discrete choice experiment (DCE). The relative importance of treatment strategy-related attributes (locoregional intervention, 10-year risk of ipsilateral invasive breast cancer (iIBC), and follow-up interval) were discerned using conditional logit models. A total of n = 172 patients and n = 30 oncologists completed the DCE. Patient respondents had very strong preferences for an active surveillance strategy with no surgery, irrespective of the 10-year risk of iIBC. Extensiveness of the locoregional treatment was consistently shown to be an important factor for patients and oncologists in deciding upon treatment strategies. Risk of iIBC was least important to patients and most important to oncologists. There was a stronger inclination toward a twice-yearly follow-up for both groups compared to annual follow-up.
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