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GBD 2016 SDG Collaborators. Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016. Lancet 2017; 390: 1423–59—In figure 8B of this Article (published Online First on Sept 12, 2017), the number of indicator targets has been changed from 1 to 9 for Turkmenistan, from 0 to 1 for Afghanistan, and from 1 to 2 for Yemen. Ettore Beghi, Neeraj Bhala, Hélène Carabin, Raimundas Lunevicius, Donald H Silberberg, and Caitlyn Steiner have been added to the list of GBD 2016 SDG Collaborators. Their affiliations, along with the affiliation of Soumya Swaminathan, have been added to the Affiliations section. These corrections have been made to the online version as of Sept 18, 2017, and the printed Article is correct.

GBD 2016 SDG Collaborators. Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016. Lancet 2017; 390: 1423–59—The full-text version of this Article has been updated so that the list of authors is displayed in the correct order, in line with the pdf version, rather than in alphabetical order. This correction has been made to the online version as of Oct 12, 2017.

IMPORTANCE: Cancer is the second leading cause of death worldwide. Current estimates on the burden of cancer are needed for cancer control planning. OBJECTIVE: To estimate mortality, incidence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 32 cancers in 195 countries and territories from 1990 to 2015. EVIDENCE REVIEW: Cancer mortality was estimated using vital registration system data, cancer registry incidence data (transformed to mortality estimates using separately estimated mortality to incidence [MI] ratios), and verbal autopsy data. Cancer incidence was calculated by dividing mortality estimates through the modeled MI ratios. To calculate cancer prevalence, MI ratios were used to model survival. To calculate YLDs, prevalence estimates were multiplied by disability weights. The YLLs were estimated by multiplying age-specific cancer deaths by the reference life expectancy. DALYs were estimated as the sum of YLDs and YLLs. A sociodemographic index (SDI) was created for each location based on income per capita, educational attainment, and fertility. Countries were categorized by SDI quintiles to summarize results. FINDINGS: In 2015, there were 17.5 million cancer cases worldwide and 8.7 million deaths. Between 2005 and 2015, cancer cases increased by 33%, with population aging contributing 16%, population growth 13%, and changes in age-specific rates contributing 4%. For men, the most common cancer globally was prostate cancer (1.6 million cases). Tracheal, bronchus, and lung cancer was the leading cause of cancer deaths and DALYs in men (1.2 million deaths and 25.9 million DALYs). For women, the most common cancer was breast cancer (2.4 million cases). Breast cancer was also the leading cause of cancer deaths and DALYs for women (523 000 deaths and 15.1 million DALYs). Overall, cancer caused 208.3 million DALYs worldwide in 2015 for both sexes combined. Between 2005 and 2015, age-standardized incidence rates for all cancers combined increased in 174 of 195 countries or territories. Age-standardized death rates (ASDRs) for all cancers combined decreased within that timeframe in 140 of 195 countries or territories. Countries with an increase in the ASDR due to all cancers were largely located on the African continent. Of all cancers, deaths between 2005 and 2015 decreased significantly for Hodgkin lymphoma (-6.1% [95% uncertainty interval (UI), -10.6% to -1.3%]). The number of deaths also decreased for esophageal cancer, stomach cancer, and chronic myeloid leukemia, although these results were not statistically significant. CONCLUSION AND RELEVANCE: As part of the epidemiological transition, cancer incidence is expected to increase in the future, further straining limited health care resources. Appropriate allocation of resources for cancer prevention, early diagnosis, and curative and palliative care requires detailed knowledge of the local burden of cancer. The GBD 2015 study results demonstrate that progress is possible in the war against cancer. However, the major findings also highlight an unmet need for cancer prevention efforts, including tobacco control, vaccination, and the promotion of physical activity and a healthy diet.

Objective: To identify the lifecourse model that best describes the association between social class and adult oral health. Methods: Data from 10,217 participants of the 1958 National Child Development Study were used. Social class at ages 7, 16 and 33 years were chosen to represent socioeconomic conditions during childhood, adolescence and adulthood, respectively. Two subjective oral health indicators (lifetime and past-year prevalence of persistent trouble with gums or mouth) were measured at age 33. The critical period, accumulation and social trajectories models were tested in logistic regression models and the most appropriate lifecourse model was identified using the structured modelling approach. Results: The critical period model showed that only adulthood social class was significantly associated with oral health. For the accumulation model, a monotonic gradient was found between the number of periods in manual social class and oral health; and four out of eight social trajectories were found to be distinctive. Finally, the social trajectories model was not significantly different from the saturated model indicating that it provided a good fit to the data. Conclusion: This study shows the social trajectories model was the most appropriate, in terms of model fit, to describe the association between social class and oral health.

Introducción: Los sistemas de soporte a la toma de decisiones clínicas utilizan características individuales del paciente para generar recomendaciones a los clínicos. Objetivo: Evaluar el impacto de una herramienta de ajuste de fármacos en insuficiencia renal como sistema de soporte en la toma de decisiones clínicas en cuanto al grado de aceptación de las intervenciones y el tiempo invertido por el farmacéutico. Método: Estudio cuasi-experimental del tipo antes y después realizado en dos hospitales comarcales. La intervención consistía en la incorporación de una alerta automatizada de función renal en la orden médica. Antes de la intervención (2007) se monitorizaron 40 fármacos. Se revisaron las analíticas de pacientes cuyo tratamiento contenía alguno de ellos. En caso de función renal alterada, se insertaba una recomendación de ajuste en la orden médica. Si el médico aceptaba, se consideraba éxito. El tiempo medio empleado fue 1 minuto/analítica consultada y 3 minutos/recomendación. En 2008 se incorporó un sistema de recomendación automática de ajuste según función renal de 100 fármacos con mensajes emergentes. En una fase posterior (2009) se evaluó y comparó el número de intervenciones y el porcentaje de éxito con la herramienta. Resultados: Fase previa: Se validaron 28.234 Ordenes Médicas Electrónicas, correspondientes a un promedio de 205 pacientes hospitalizados/día, y se revisaron 4.035 analíticas. Se realizaron 121 intervenciones farmacéuticas (0,43% del total de órdenes médicas). Se obtuvo éxito en el 33,06% de las intervenciones. El tiempo invertido por el farmacéutico en consultar analíticas y realizar recomendaciones fue 73,3 horas (67,25 horas correspondían a pacientes sin alteración de la función renal y en los que no se realizó ninguna intervención). Fase posterior: Se validaron 26.584 Ordenes Médicas Electrónicas, correspondientes a un promedio de 193 pacientes hospitalizados/día, y se realizaron 1.737 intervenciones automatizadas (6,53% del total de ordenes médicas), de las cuales se aceptaron 65,69% (éxito). Conclusiones: La implantación de sistemas de soporte a la toma de decisiones clínicas, permite ampliar los pacientes y fármacos monitorizados, optimizando el tiempo invertido por el farmacéutico. La aparición simultánea de la alerta durante la prescripción puede haber contribuido al mayor porcentaje de éxito observado. Introduction: Support systems in clinical decision-making use individual characteristics of the patient to generate recommendations to the clinician. Objective: To assess the impact of a tool for adjusting drug dosing in renal failure as a support system in clinical decision-making regarding the level of acceptance of the interventions as well as the time invested by the pharmacist. Method: Non-randomized, prospective and hospital interventional study comparing pre- and post-implementation phases of an automated renal function alert system, carried out at two county hospitals. Forty drugs were monitored before the intervention (2007). The blood work of the patients receiving any of these drugs was reviewed. In case of impaired renal function, an adjustment recommendation was inserted in the medical prescription. If the physician accepted it, it was rated as success. The average time was 1 minute per blood work reviewed and 3 minutes per recommendation. An automated adjustment recommendation system according to renal function with alert pop-ups was implemented in 2008 for 100 drugs. Later (2009), the number of interventions and the success rate for this tool were assessed and compared. Results: Pre-implementation phase: 28,234 electronic medical prescriptions corresponding to a mean number of 205 hospitalized patients/day were validated and 4,035 blood works were reviewed. One hundred and twenty-one pharmaceutical interventions (0.43% of the medical prescriptions) were inserted. A success rate of 33.06% of the interventions was obtained. The time invested by the pharmacist for consulting the blood works and making the recommendations was 73.3 hours (67.25 hours corresponding to patients without renal function impairment and in whom no intervention was made). Post-implementation phase: 26,584 electronic medical orders corresponding to 193 hospitalized patients/day were validated and 1,737 automated interventions were performed (6.53% of total medical orders), of which 65.69% were accepted (success). Conclusions: The implementation of clinical decision-making support systems allows extending the number of patients and drugs monitored, optimizing the time invested by the pharmacist. Simultaneous occurrence of an alert during prescription may have contributed to the greater success rate observed.

Introducción: Los sistemas de soporte a la toma de decisiones clínicas utilizan características individuales del paciente para generar recomendaciones a los clínicos. Objetivo: Evaluar el impacto de una herramienta de ajuste de fármacos en insuficiencia renal como sistema de soporte en la toma de decisiones clínicas en cuanto al grado de aceptación de las intervenciones y el tiempo invertido por el farmacéutico. Método: Estudio cuasi-experimental del tipo antes y después realizado en dos hospitales comarcales. La intervención consistía en la incorporación de una alerta automatizada de función renal en la orden médica. Antes de la intervención (2007) se monitorizaron 40 fármacos. Se revisaron las analíticas de pacientes cuyo tratamiento contenía alguno de ellos. En caso de función renal alterada, se insertaba una recomendación de ajuste en la orden médica. Si el médico aceptaba, se consideraba éxito. El tiempo medio empleado fue 1 minuto/analítica consultada y 3 minutos/recomendación. En 2008 se incorporó un sistema de recomendación automática de ajuste según función renal de 100 fármacos con mensajes emergentes. En una fase posterior (2009) se evaluó y comparó el número de intervenciones y el porcentaje de éxito con la herramienta. Resultados: Fase previa: Se validaron 28.234 Ordenes Médicas Electrónicas, correspondientes a un promedio de 205 pacientes hospitalizados/día, y se revisaron 4.035 analíticas. Se realizaron 121 intervenciones farmacéuticas (0,43% del total de órdenes médicas). Se obtuvo éxito en el 33,06% de las intervenciones. El tiempo invertido por el farmacéutico en consultar analíticas y realizar recomendaciones fue 73,3 horas (67,25 horas correspondían a pacientes sin alteración de la función renal y en los que no se realizó ninguna intervención). Fase posterior: Se validaron 26.584 Ordenes Médicas Electrónicas, correspondientes a un promedio de 193 pacientes hospitalizados/día, y se realizaron 1.737 intervenciones automatizadas (6,53% del total de ordenes médicas), de las cuales se aceptaron 65,69% (éxito). Conclusiones: La implantación de sistemas de soporte a la toma de decisiones clínicas, permite ampliar los pacientes y fármacos monitorizados, optimizando el tiempo invertido por el farmacéutico. La aparición simultánea de la alerta durante la prescripción puede haber contribuido al mayor porcentaje de éxito observado.

The lower permanent incisor tooth width sum has been proposed as the best predictor for the tooth width sums of the unerupted canine and premolars (SPCP) for populations from different ethnic origins. Only two previous studies have refuted it. The purpose of the present study was to determine which sum or combination of sums of permanent tooth widths presented the best prediction capability for the SPCP in a Peruvian sample, to calculate a specific linear regression equation for this population, and to evaluate the clinical significance. A total of 150 children with complete permanent dentitions were selected. Fifty more children were used as a validation sample for the application of a multiple linear regression equation (MLRE). They did not present clinically visible dental caries or proximal restorations and no active or previous orthodontic treatment. Their dental casts were measured to 0.1 mm with a sliding caliper with a Vernier scale. Three-way analysis of variance, Pearson Correlation Test, Fisher Z values and a MLRE were used for the statistical analysis. The combination of the sums of permanent upper and lower central incisors and upper first molars was the best predictor for the SPCP in this sample. A MLRE was calculated including sex and arch as additional predictor variables. The MLRE determination coefficient was 60% with a standard error of 0.8 mm. This new MLRE underestimates (less than 1 mm discrepancy) the actual SPCP in only 7% of the cases on the basis of a validation sample. (Angle Orthod 2005;75: 202–207.)

Discrepancies in tooth width ratios could affect the excellence in the finishing of orthodontic cases. This study compares tooth width ratios in crowded and noncrowded dental arches. Tooth widths were measured from 143 dental casts (40 crowded and 33 spaced in male individuals and 43 crowded and 27 spaced in female individuals). Simultaneous crowded or spaced arches were selected. Tooth width measurements were made with a sliding caliper with a Vernier scale neared 0.1 mm. Inter- (0.990) and intraexaminer (0.993) reliability with mean differences of 0.03 mm (−0.03; 0.07) and 0.01 mm (−0.04; 0.05), respectively, were attained. Bolton analysis was applied to calculate the tooth width ratios (anterior and total). Descriptive statistics, Student's t-test, and analysis of variance were applied. There was no significant difference between the anterior and total tooth width ratios according to sex (P > .05). Both anterior and total ratios were significantly greater in subjects with crowding (P = .003 and P = .026, respectively), but no statistical difference by arch type in anterior and total tooth mass excess was found (P > .05). Differences among subjects with noncrowded and crowded dentitions were of 0.39 and 0.51 mm for the excess of anterior and total upper tooth mass, respectively, with respect to lower mass excess. Although the anterior and overall ratios and the differences between the upper and lower tooth width sums are greater in subjects with crowding, no clinically significant difference is observed (less than one mm).

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