Geographical variations in the benefit of applying a prioritization system for cataract surgery in different regions of Spain

<p>Abstract</p> <p>Background</p> <p>In Spain, there are substantial variations in the utilization of health resources among regions. Because the need for surgery differs in patients with appropriate surgical indication, introducing a prioritization system might be beneficial. Our objective was to assess geographical variations in the impact of applying a prioritization system in patients on the waiting list for cataract surgery in different regions of Spain by using a discrete-event simulation model.</p> <p>Methods</p> <p>A discrete-event simulation model to evaluate demand and waiting time for cataract surgery was constructed. The model was reproduced and validated in five regions of Spain and was fed administrative data (population census, surgery rates, waiting list information) and data from research studies (incidence of cataract). The benefit of introducing a prioritization system was contrasted with the usual first-in, first-out (FIFO) discipline. The prioritization system included clinical, functional and social criteria. Priority scores ranged between 0 and 100, with greater values indicating higher priority. The measure of results was the waiting time weighted by the priority score of each patient who had passed through the waiting list. Benefit was calculated as the difference in time weighted by priority score between operating according to waiting time or to priority.</p> <p>Results</p> <p>The mean waiting time for patients undergoing surgery according to the FIFO discipline varied from 1.97 months (95% CI 1.85; 2.09) in the Basque Country to 10.02 months (95% CI 9.91; 10.12) in the Canary Islands. When the prioritization system was applied, the mean waiting time was reduced to a minimum of 0.73 months weighted by priority score (95% CI 0.68; 0.78) in the Basque Country and a maximum of 5.63 months (95% CI 5.57; 5.69) in the Canary Islands. The waiting time weighted by priority score saved by the prioritization system varied from 1.12 months (95% CI 1.07; 1.16) in Andalusia to 2.73 months (95% CI 2.67; 2.80) in Aragon.</p> <p>Conclusion</p> <p>The prioritization system reduced the impact of the variations found among the regions studied, thus improving equity. Prioritization allocates the available resources within each region more efficiently and reduces the waiting time of patients with greater need. Prioritization was more beneficial than allocating surgery by waiting time alone.</p

Force of tuberculosis infection among adolescents in a high HIV and TB prevalence community: a cross-sectional observation study

BACKGROUND: Understanding of the transmission dynamics of tuberculosis (TB) in high TB and HIV prevalent settings is required in order to develop effective intervention strategies for TB control. However, there are little data assessing incidence of TB infection in adolescents in these settings. METHODS: We performed a tuberculin skin test (TST) and HIV survey among secondary school learners in a high HIV and TB prevalence community. TST responses to purified protein derivative RT23 were read after 3 days. HIV-infection was assessed using Orasure(R) collection device and ELISA testing. The results of the HIV-uninfected participants were combined with those from previous surveys among primary school learners in the same community, and force of TB infection was calculated by age. RESULTS: The age of 820 secondary school participants ranged from 13 to 22 years. 159 participants had participated in the primary school surveys. At a 10 mm cut-off, prevalence of TB infection among HIV-uninfected and first time participants, was 54% (n = 334/620). HIV prevalence was 5% (n = 40/816). HIV infection was not significantly associated with TST positivity (p = 0.07). In the combined survey dataset, TB prevalence was 45% (n = 645/1451), and was associated with increasing age and male gender. Force of infection increased with age, from 3% to 7.3% in adolescents [greater than or equal to]20 years of age. CONCLUSIONS: We show a high force of infection among adolescents, positively associated with increasing age. We postulate this is due to increased social contact with infectious TB cases. Control of the TB epidemic in this setting will require reducing the force of infection

Indicators of "Healthy Aging" in older women (65-69 years of age). A data-mining approach based on prediction of long-term survival

<p>Abstract</p> <p>Background</p> <p>Prediction of long-term survival in healthy adults requires recognition of features that serve as early indicators of successful aging. The aims of this study were to identify predictors of long-term survival in older women and to develop a multivariable model based upon longitudinal data from the Study of Osteoporotic Fractures (SOF).</p> <p>Methods</p> <p>We considered only the youngest subjects (<it>n </it>= 4,097) enrolled in the SOF cohort (65 to 69 years of age) and excluded older SOF subjects more likely to exhibit a "frail" phenotype. A total of 377 phenotypic measures were screened to determine which were of most value for prediction of long-term (19-year) survival. Prognostic capacity of individual predictors, and combinations of predictors, was evaluated using a cross-validation criterion with prediction accuracy assessed according to time-specific AUC statistics.</p> <p>Results</p> <p>Visual contrast sensitivity score was among the top 5 individual predictors relative to all 377 variables evaluated (mean AUC = 0.570). A 13-variable model with strong predictive performance was generated using a forward search strategy (mean AUC = 0.673). Variables within this model included a measure of physical function, smoking and diabetes status, self-reported health, contrast sensitivity, and functional status indices reflecting cumulative number of daily living impairments (HR ≥ 0.879 or RH ≤ 1.131; P < 0.001). We evaluated this model and show that it predicts long-term survival among subjects assigned differing causes of death (e.g., cancer, cardiovascular disease; P < 0.01). For an average follow-up time of 20 years, output from the model was associated with multiple outcomes among survivors, such as tests of cognitive function, geriatric depression, number of daily living impairments and grip strength (P < 0.03).</p> <p>Conclusions</p> <p>The multivariate model we developed characterizes a "healthy aging" phenotype based upon an integration of measures that together reflect multiple dimensions of an aging adult (65-69 years of age). Age-sensitive components of this model may be of value as biomarkers in human studies that evaluate anti-aging interventions. Our methodology could be applied to data from other longitudinal cohorts to generalize these findings, identify additional predictors of long-term survival, and to further develop the "healthy aging" concept.</p