SummaryBackgroundA key component of achieving universal health coverage is ensuring that all populations have access to quality health care. Examining where gains have occurred or progress has faltered across and within countries is crucial to guiding decisions and strategies for future improvement. We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) to assess personal health-care access and quality with the Healthcare Access and Quality (HAQ) Index for 195 countries and territories, as well as subnational locations in seven countries, from 1990 to 2016.MethodsDrawing from established methods and updated estimates from GBD 2016, we used 32 causes from which death should not occur in the presence of effective care to approximate personal health-care access and quality by location and over time. To better isolate potential effects of personal health-care access and quality from underlying risk factor patterns, we risk-standardised cause-specific deaths due to non-cancers by location-year, replacing the local joint exposure of environmental and behavioural risks with the global level of exposure. Supported by the expansion of cancer registry data in GBD 2016, we used mortality-to-incidence ratios for cancers instead of risk-standardised death rates to provide a stronger signal of the effects of personal health care and access on cancer survival. We transformed each cause to a scale of 0–100, with 0 as the first percentile (worst) observed between 1990 and 2016, and 100 as the 99th percentile (best); we set these thresholds at the country level, and then applied them to subnational locations. We applied a principal components analysis to construct the HAQ Index using all scaled cause values, providing an overall score of 0–100 of personal health-care access and quality by location over time. We then compared HAQ Index levels and trends by quintiles on the Socio-demographic Index (SDI), a summary measure of overall development. As derived from the broader GBD study and other data sources, we examined relationships between national HAQ Index scores and potential correlates of performance, such as total health spending per capita.FindingsIn 2016, HAQ Index performance spanned from a high of 97·1 (95% UI 95·8–98·1) in Iceland, followed by 96·6 (94·9–97·9) in Norway and 96·1 (94·5–97·3) in the Netherlands, to values as low as 18·6 (13·1–24·4) in the Central African Republic, 19·0 (14·3–23·7) in Somalia, and 23·4 (20·2–26·8) in Guinea-Bissau. The pace of progress achieved between 1990 and 2016 varied, with markedly faster improvements occurring between 2000 and 2016 for many countries in sub-Saharan Africa and southeast Asia, whereas several countries in Latin America and elsewhere saw progress stagnate after experiencing considerable advances in the HAQ Index between 1990 and 2000. Striking subnational disparities emerged in personal health-care access and quality, with China and India having particularly large gaps between locations with the highest and lowest scores in 2016. In China,...

Our meta-analysis suggests that patients with NSTEMI who demonstrate a totally occluded culprit vessel on coronary angiography are at higher risk of mortality and major adverse cardiac events. Better risk stratification tools are needed to identify such high-risk acute coronary syndrome patients to facilitate earlier revascularization and potentially to improve outcomes.

Neuropsychiatric (ie, ADHD, substance abuse, CNS disorders/epilepsy) and medical (ie, obesity, asthma, cardiovascular disease) disorders temporally precede the diagnosis of early-onset BD in pediatric patients and are associated with discrete facets of illness presentation, but they do not substantially alter the clinical course of the BD over time.

Current literature on retention in HIV care fails to account for patients who continually/simultaneously access different providers. This statewide study examined retention in early HIV medical care and its impact on viro-immunological improvement and survival outcomes. It was a retrospective study of South Carolina residents ≥13 years old who were diagnosed with HIV infection in 2004-2007 and initially entered in care. CD4 count/percent and viral load (VL) tests that must be reported to the South Carolina HIV surveillance database were used as a proxy for a clinical visit. Retention was defined as at least one visit in each of four 6-month periods over 2 years postlinkage. Retention rates were categorized as "optimal" (visits in four intervals), "suboptimal" (visits in three intervals), sporadic (visits in two or one intervals), and "dropout" (no visits). Logistic regression and Cox proportional analyses were used to examine retention. Of the 2197 persons, about 50% failed to maintain optimal retention in care postlinkage. Male gender, nonwhite race/ethnicity, younger age, delayed linkage, and HIV-only status were significant predictors of lower rate of retention. Mean decrease in baseline log(10) VL was greater among those with optimal compared to suboptimal (-1.81 vs. -1.42; p < 0.001) and sporadic retention (-1.81 vs. -0.70; p < 0.001). Mean increase in baseline CD4 count was greater in optimal retention compared to suboptimal (169.70 vs. 107.5; p < 0.001) and sporadic retention (169.70 vs. 2.43; p < 0.001). Increased risk of mortality was associated with sporadic retention (aHR 2.91; 95% CI 1.54-5.50) and "dropout" (aHR 4.00; 95% CI 1.50-10.65). Rate of poor retention in early HIV medical care was relatively higher than reported in clinic-based data. Increasing the rate of retention in early HIV care could substantially improve viro-immunological parameters and survival outcomes.

Comorbid depression in sickle cell disease is associated with adverse course and outcomes. These findings underscore the need for earlier and more aggressive treatment of comorbid depression by primary care or psychiatric providers in order to reduce the chronic, severe pain-depression burden on these patients.

OBJECTIVETo investigate the association of preconception counseling with markers of care and maternal characteristics in women with pregestational diabetes.RESEARCH DESIGN AND METHODSThe study includes data from a regional multi-center survey on 588 women with pregestational diabetes who delivered a singleton pregnancy between 2001 and 2004. Logistic regression was used to obtain crude and adjusted estimates of association.RESULTSPreconception counseling was associated with better glycemic control 3 months preconception (odds ratio 1.91, 95% CI 1.10–3.04) and in the first trimester (2.05, 1.39–3.03), higher preconception folic acid intake (4.88, 3.26–7.30), and reduced risk of adverse pregnancy outcome (P = 0.027). Uptake of preconception counseling was positively associated with type 1 diabetes (1.87, 1.14–3.07) and White British ethnicity (2.56, 1.17–5.6) and negatively with deprivation score (0.78, 0.70–0.87).CONCLUSIONSEfforts are needed to improve preconception counseling rates. Uptake is associated with maternal sociodemographic characteristics.

We sought proof of concept of a Big Data Solution incorporating longitudinal structured and unstructured patient-level data from electronic health records (EHR) to predict graft loss (GL) and mortality. For a quality improvement initiative, GL and mortality prediction models were constructed using baseline and follow-up data (0-90 days posttransplant; structured and unstructured for 1-year models; data up to 1 year for 3-year models) on adult solitary kidney transplant recipients transplanted during 2007-2015 as follows: Model 1: United Network for Organ Sharing (UNOS) data; Model 2: UNOS & Transplant Database (Tx Database) data; Model 3: UNOS, Tx Database & EHR comorbidity data; and Model 4: UNOS, Tx Database, EHR data, Posttransplant trajectory data, and unstructured data. A 10% 3-year GL rate was observed among 891 patients (2007-2015). Layering of data sources improved model performance; Model 1: area under the curve (AUC), 0.66; (95% confidence interval [CI]: 0.60, 0.72); Model 2: AUC, 0.68; (95% CI: 0.61-0.74); Model 3: AUC, 0.72; (95% CI: 0.66-077); Model 4: AUC, 0.84, (95 % CI: 0.79-0.89). One-year GL (AUC, 0.87; Model 4) and 3-year mortality (AUC, 0.84; Model 4) models performed similarly. A Big Data approach significantly adds efficacy to GL and mortality prediction models and is EHR deployable to optimize outcomes.

HIV infection may not be independently associated with increased risk of diabetes. Among HIV-infected persons, exposure to protease inhibitor-based regimens may increase the risk of diabetes. Healthcare providers should make every effort to use combination antiretroviral therapy regimens with a better cardiometabolic profile.