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The Federal District in Brazil has about 2.9 million inhabitants and the public health system is focused on medical specialties, with one university hospital and twenty regional hospitals. This ecosystem is favorable for fostering health technology assessment (HTA) to improve the efficiency and effectiveness of health care. The objective was to identify institutions that could form a HTA network to support decision-oriented evidence in the public health system.
Stakeholders from the hospitals and training/research institutions in the Federal District were surveyed. An online questionnaire (Google Docs) was developed to identify the potential and capacity of institutions to analyze or produce clinical and economic evidence. Two HTA seminars were held to spread knowledge about HTA and to encourage stakeholders to complete the survey.
The questionnaire response rate was thirty-five percent (25/70). Fifteen institutions were cited by the respondents as having the potential to build a HTA network. Twelve of the institutions produced rapid reviews and clinical guidelines, but only three of these had an organized priority setting process or produced assessments at the request of the hospital manager. The challenges identified were training and willingness of decision makers to organize HTA units in the hospitals.
An executive group was created which defined a strategy to support the implementation of HTA units as part of the HTA National Network (REBRATS). A regulation proposal was also created to encourage decision makers to activate a HTA network in the Federal District.
The Brazilian Household Food Insecurity Measurement Scale (EBIA) has eight general/adult items applied in all households and six additional items exclusively asked in households with children and/or adolescents (HHCA). Continuing an investigation programme on the adequacy of model-based cut-off points for EBIA, the present study aims to: (i) explore the capacity of properly stratifying HHCA according to food insecurity (FI) severity level by applying only the eight ‘generic’ items; and (ii) compare it against the fourteen-item scale.
Latent class factor analysis (LCFA) models were applied to the answers to the eight general/adult items to identify latent groups corresponding to FI levels and optimal group-separating cut-off points. Analyses involved a thorough classification agreement evaluation and were performed at the national level and by macro-regions.
Data derived from the cross-sectional Brazilian National Household Sample Survey of 2013.
A nationally representative sample of 116 543 households.
In all households and investigated domains, LCFA detected four distinct household food (in)security groups (food security and three levels of severity of FI) and the same set of cut-off points (1/2, 4/5 and 6/7). Misclassification in the aggregate data was 0·66 % in adult-only households and 1·06 % in HHCA. Comparison of the scale reduced to eight items with the ‘original’ fourteen-item scale demonstrated consistency in the classification. In HHCA, the agreement between both classifications was 96·2 %.
Results indicate the eight ‘generic’ items in HHCA can be reliably used when it is not possible to apply the fourteen-item scale.
To analyze the impact of the International Nosocomial Infection Control Consortium (INICC) Multidimensional Approach (IMA) and the INICC Surveillance Online System (ISOS) on central line-associated bloodstream infection (CLABSI) rates in 14 intensive care units (ICUs) in Argentina from January 2014 to April 2017.
This prospective, pre–post surveillance study of 3,940 ICU patients was conducted in 11 hospitals in 5 cities in Argentina. During our baseline evaluation, we performed outcome and process surveillance of CLABSI applying Centers for Disease Control and Prevention/National Health Safety Network (CDC/NHSN) definitions. During the intervention, we implemented the IMA through ISOS: (1) a bundle of infection prevention practice interventions, (2) education, (3) outcome surveillance, (4) process surveillance, (5) feedback on CLABSI rates and consequences, and (6) performance feedback of process surveillance. Bivariate and multivariate regression analyses were performed using a logistic regression model to estimate the effect of the intervention on the CLABSI rate.
During the baseline period, 5,118 CL days and 49 CLABSIs were recorded, for a rate of 9.6 CLABSIs per 1,000 central-line (CL) days. During the intervention, 15,659 CL days and 68 CLABSIs were recorded, for a rate of 4.1 CLABSIs per 1,000 CL days. The CLABSI rate was reduced by 57% (incidence density rate: 0.43; 95% confidence interval, 0.34–0.6; P<.001).
Implementing IMA through ISOS was associated with a significant reduction in the CLABSI rate in ICUs in Argentina.
Infect Control Hosp Epidemiol 2018;39:445–451
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