BackgroundPrevious studies showed inconsistent results on the association of smoking with all-cause dementia and vascular dementia (VaD), and are limited by inclusion of a small number of studies and unexplained heterogeneity. Our review aimed to assess the risk of all-cause dementia, Alzheimer’s disease (AD) and VaD associated with smoking, and to identify potential effect modifiers.Methods and FindingsThe PubMed, Embase, Cochrane Library and Psychinfo databases were searched to identify studies that provided risk estimates on smoking and incidence of dementia. A random-effects model was used to yield pooled results. Thirty-seven studies were included. Compared with never smokers, current smokers showed an increased risk of all-cause dementia (risk ratio (RR) 1.30, 95% confidence interval (CI) 1.18–1.45), AD (RR 1.40, 95% CI 1.13–1.73) and VaD (RR 1.38, 95% CI 1.15–1.66). For all-cause dementia, the risk increased by 34% for every 20 cigarettes per day (RR 1.34, 95% CI 1.25–1.43). Former smokers did not show an increased risk of all-cause dementia (RR 1.01, 95% CI 0.96–1.06), AD (RR 1.04, 95% CI 0.96–1.13) and VaD (RR 0.97, 95% CI 0.83–1.13). Subgroup analyses indicated that (1) the significantly increased risk of AD from current smoking was seen only in apolipoprotein E ε4 noncarriers; (2) current smokers aged 65 to 75 years at baseline showed increased risk of all-cause dementia and AD compared to those aged over 75 or under 65 years; and (3) sex, race, study location and diagnostic criteria difference in risk of dementia was not found.ConclusionsSmokers show an increased risk of dementia, and smoking cessation decreases the risk to that of never smokers. The increased risk of AD from smoking is more pronounced in apolipoprotein E ε4 noncarriers. Survival bias and competing risk reduce the risk of dementia from smoking at extreme age.

Several quantitative RBA methods are available that could be used to help lessen concern over subjective drug assessments and to help guide authorities toward more objective and transparent decision-making. When evaluating a new drug therapy, we recommend the use of multiple RBA approaches across different therapeutic indications and treatment populations in order to bound the risk-benefit profile.

Pilot results indicate that the QOPI process provides a rapid and objective measurement of practice quality that allows comparisons among practices and over time. It also provides a mechanism for measuring concordance with published guidelines. Most importantly, it provides a tool for practice self-examination that can promote excellence in cancer care.

IMPORTANCE Antipsychotics are used increasingly in youth for nonpsychotic and off-label indications, but cardiometabolic adverse effects and (especially) type 2 diabetes mellitus (T2DM) risk have raised additional concern. OBJECTIVE To assess T2DM risk associated with antipsychotic treatment in youth. DATA SOURCES Systematic literature search of PubMed and PsycINFO without language restrictions from database inception until May 4, 2015. Data analyses were performed in July 2015, and additional analyses were added in November 2015. STUDY SELECTION Longitudinal studies reporting on T2DM incidence in youth 2 to 24 years old exposed to antipsychotics for at least 3 months. DATA EXTRACTION AND SYNTHESIS Two independent investigators extracted study-level data for a random-effects meta-analysis and meta-regression of T2DM risk. MAIN OUTCOMES AND MEASURES The coprimary outcomes were study-defined T2DM, expressed as cumulative T2DM risk or as T2DM incidence rate per patient-years. Secondary outcomes included the comparison of the coprimary outcomes in antipsychotic-treated youth with psychiatric controls not receiving antipsychotics or with healthy controls RESULTS Thirteen studies were included in the meta-analysis, including 185 105 youth exposed to antipsychotics and 310 438 patient-years. The mean (SD) age of patients was 14.1 (2.1) years, and 59.5% were male. The mean (SD) follow-up was 1.7 (2.3) years. Among them, 7 studies included psychiatric controls (1 342 121 patients and 2 071 135 patient-years), and 8 studies included healthy controls (298 803 patients and 463 084 patient-years). Antipsychotic-exposed youth had a cumulative T2DM risk of 5.72 (95% CI, 3.45-9.48; P < .001) per 1000 patients. The incidence rate was 3.09 (95% CI, 2.35-3.82; P < .001) cases per 1000 patient-years. Compared with healthy controls, cumulative T2DM risk (odds ratio [OR], 2.58; 95% CI, 1.56-4.24; P < .0001) and incidence rate ratio (IRR) (IRR, 3.02; 95% CI, 1.71-5.35; P < .0001) were significantly greater in antipsychotic-exposed youth. Similarly, compared with psychiatric controls, antipsychotic-exposed youth had significantly higher cumulative T2DM risk (OR, 2.09; 95% CI, 1.50-52.90; P < .0001) and IRR (IRR, 1.79; 95% CI, 1.31-2.44; P < .0001). In multivariable meta-regression analyses of 10 studies, greater cumulative T2DM risk was associated with longer follow-up (P < .001), olanzapine prescription (P < .001), and male sex (P = .002) (r 2 = 1.00, P < .001). Greater T2DM incidence was associated with second-generation antipsychotic prescription (P Յ .050) and less autism spectrum disorder diagnosis (P = .048) (r 2 = 0.21, P = .044). CONCLUSIONS AND RELEVANCE Although T2DM seems rare in antipsychotic-exposed youth, cumulative risk and exposure-adjusted incidences and IRRs were significantly higher than in healthy controls and psychiatric controls. Olanzapine treatment and antipsychotic exposure time were the main modifiable risk factors for T2DM development in antipsychotic-exposed youth. Antipsychotics should be used judiciously an...

Background: Drug-induced diabetes onset has not been adequately quantified in patients with bipolar disorder, although atypical antipsychotics have been widely used as new mood stabilizers. Objectives: To quantify the association between atypical antipsychotics and diabetes mellitus. Method: A retrospective, population-based, case-control study was conducted using the medical claims database from U.S. managed care organizations from January 1, 1998, to December 31, 2002. Nine hundred twenty incident cases of diabetes were matched with 5258 controls by age, sex, and bipolar index month and year. Diabetes cases were identified by either diagnosis of ICD-9 codes or diabetic medications. Patients with diabetes had a minimum 3-month exposure to any medications or at least 3 prescriptions for their bipolar or comorbidity treatment. Cox proportional hazard regression was conducted to assess the risk of diabetes associated with antipsychotic use. Results: Of 920 cases, 41% received atypical antipsychotics (e.g., olanzapine, risperidone, quetiapine, ziprasidone, clozapine) and 34% received conventional antipsychotics. Compared to patients receiving conventional antipsychotics, the risk of diabetes was greatest among patients taking clozapine (hazard ratio [HR] = 7.0, 95% confidence interval [CI] = 1.7 to 28.9), risperidone (HR = 3.4, 95% CI = 2.8 to 4.2), olanzapine (HR = 3.2, 95% CI = 2.7 to 3.8), and quetiapine (HR = 1.8, 95% CI =1.4 to 2.4), with controlling covariates of age; sex; duration of follow-up; use of lithium, anticonvulsants, antidepressants, or concomitant drugs; and psychiatric and medical comorbidities. Conclusion: Development or exacerbation of diabetes mellitus is associated with antipsychotic use in bipolar patients. Metabolic complications are a major issue in patients receiving antipsychotic therapy. Thus, the propensity of an antipsychotic to induce diabetes should be a consideration when selecting an agent for patients with bipolar disorder.

No abstract

This meta-analysis suggests that the risk of childhood overweight/obesity is significantly associated with excessive gestational weight gain.

SYNOPSISObjective. We examined patterns of enrollment, use, and frequency of use in school-based health centers (SBHCs), as well as the referral, diagnosis, and disposition of SBHC visits among newly implemented SBHCs.Methods. Four rural and four urban school districts implementing SBHCs were examined from 2000 to 2003. Total school enrollment for students was 13,046. SBHC enrollment and medical encounter data were tracked using a Web-based medical database. Descriptive analyses were conducted to evaluate primary care access and utilization patterns.Results. A total of 7,460 (57.2%) students were enrolled in their SBHCs, of which 4,426 used the SBHC at least once for a total of 14,050 visits. SBHC enrollment was greater in urban districts but rate of utilization was higher in rural districts. Black students, students with public or no health insurance, and students with asthma or attention deficit disorder had higher enrollment and utilization. Rural parents referred more children to SBHCs than urban parents. Teachers referred more students who were black, had asthma, had no public or health insurance, or had acute-type health issues. Total visits increased during the three years, with the largest increase in mental health services. Students who were younger, white, attended rural schools, had public or health insurance, or had infections were more likely to be sent home. Those with chronic conditions and visits for mental health were more likely to be returned to class.Conclusion. Utilization patterns suggest improved access to needed health care for disadvantaged children. SBHCs are an important part of the safety net for the populations they are intended to serve.