Hypovascular nodules often occur together with hypervascular hepatocellular carcinoma (HCC). However, it remains controversial whether hypovascular nodules associated with hypervascular HCC have any prognostic value. This study evaluated the prognostic impact of hypovascular nodules co-existing with hypervascular HCC as diagnosed by computed tomography during arterial portography (CTAP) and computed tomography during hepatic arteriography (CTHA), which can sensitively capture the dynamic changes in blood flow through the portal vein and hepatic artery in patients with early stage HCC.A total of 152 patients with hypervascular HCC , who underwent initial local ablation, were analyzed retrospectively. All patients received CTAP and CTHA prior to treatment. Overall survival (OS) was compared among group A  and group B .P < 0.001). A Cox proportional-hazards model identified the presence of hypovascular nodules concurrent with hypervascular HCC as an independent poor prognostic factor.Among all hypovascular nodules, 81% (46 of 57) developed hypervascularization within the follow-up period. The 1- and 2-year hypervascularization rates were 17% and 51%, respectively. OS was significantly longer for group A than for group B (The prognosis of hypervascular HCC patients with hypovascular nodules detected during CTAP and CTHA is poor. Clinical HCC categories seem to be dissimilar between patients with and without hypovascular nodules. Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third leading cause of cancer-related deaths worldwide . HCC devHypovascular nodules do not only appear exclusively but also concur with hypervascular HCC. These two appearance patterns are considered to be separate clinical conditions. Although recent publications have indicated the prognosis of hypovascular nodules that appear alone , 10, fewThis study was approved by the Research Ethics Committees of Graduate School of Medicine, Chiba University . According to the policy of our institution, all of patients including in this study obtained informed consents of examinations and treatments. Patient records/information were anonymized and de-identified prior to analysis.Medical records were retrieved for consecutive patients with HCC treated at our institution. Patients were selected using the following inclusion criteria assessed at their initial local ablation: (1) pathological HCC diagnosis of hypervascular nodules (at least one nodule); (2) ≤ 3 hypervascular HCC lesions that were ≤ 30 mm (without macrovascular invasion or extrahepatic metastasis); (3) Child–Pugh A or B; and (4) CTAP/CTHA performed before initiation of local therapy. Patients meeting these requirements were divided into two groups: hypervascular HCC only (no hypovascular lesions) (group A) and hypovascular nodules co-existing with hypervascular HCC (group B). All patients provided written informed consent for their examination that included CTAP/CTHA and treatment according to the standard practice at our institution.CTAP/CTHA was performed as described previously . BrieflyWe then placed a catheter tip in the common or proper hepatic artery using the coaxial 2.7-Fr microcatheter system and then injected 18 ml of iohexol at a rate of 2.0 ml/s for CTHA. Two- and three-phase imaging was conducted for CTAP and CTHA, respectively. The scan delay times after starting the injection were 20 and 35 s for CTAP and 3, 18, and 60 s for CTHA.Pretreatment CTAP/CTHA results were reviewed by two hepatologists who specialized in HCC (TM and YO). The nodules were retrospectively classified into hypervascular and hypovascular nodules . We defiRadiofrequency ablation (RFA) was the first-choice local ablation therapy in our institution after it was approved in April 2004 by the Ministry of Health, Labour and Welfare of Japan. Percutaneous ethanol injection (PEI) was performed in cases before RFA was approved, and in patients with no indication for RFA. During follow-up, tumor markers, including alfa fetoprotein (AFP) and des-γ-carboxy prothrombin, were measured every 1–2 months, ultrasonography was performed every 2–4 months, and dynamic computed tomography/magnetic resonance imaging (CT/MRI) was performed every 4–6 months. In cases with suspected new HCC development or hypervascularization of previously hypovascular nodules, at least two radiological imaging examinations, including dynamic CT, dynamic MRI, and/or CTAP/CTHA, were performed to confirm diagnoses. Hypervascularization was defined as a nodule that showed hypervascularity in arterial-phase images and hypovascularity in portal venous or equilibrium-phase images of dynamic CT or dynamic MRI. In addition, we classified nodules as hypervascular when they showed the CTAP/CTHA hypervascular criteria defined above.Time-to-event data were estimated by Kaplan–Meier plots and calculation of the median . Demographic and clinical characteristics were compared using the chi-squared test or Fisher’s exact test, as appropriate. Time to hypervascularization was defined as the period from initial treatment until diagnosis of hypervascularization. The censoring date was defined as the last radiological assessment date.Recurrence-free survival (RFS) was measured from the date of initial treatment until the date of initial recurrence, or death. The censoring date was defined as the date of last follow-up. Local recurrence was excluded from the analysis. Overall survival (OS) was measured from the date of initial ablation until the date of death. The censoring date was defined as the date of last follow-up. Univariate and multivariate Cox proportional-hazard regression models were used to estimate the hazard ratios for risk factors in relation to OS. A probability value of < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS statistical software .Between April 2000 and March 2010, 323 consecutive patients with HCC who received initial local therapy were identified for potential inclusion in this study . Among tP = 0.791, single: 19.7 months (95% CI: 17.4–22.0), multiple: 24.9 months ]. Similarly, there were no significant differences of OS according to size and number of hypovascular nodules .The median size of the 57 hypovascular nodules was 10 mm  and 81% (46 nodules) became hypervascularized within the follow-up period. The 1- and 2-year hypervascularization rates were 17% and 51%, respectively. Median time for progression to hypervascularization was 24.9 months . By the end of the study, HCC recurrence occurred in 72 patients (67%) in group A and 36 patients (80%) in group B . In the multivariate analysis, hypovascular nodules that occurred with hypervascular HCC were an independent poor prognosis factor  , 92 pati group B . There w group B . The OS  group B . The meds factor .We have shown that most of the hypovascular nodules diagnosed using CTAP/CTHA developed into overt HCC and patients with both hypovascular nodules and hypervascular HCC had a poorer prognosis than patients with only hypervascular HCC. These results indicated that clinical HCC categories are dissimilar between patients with and without hypovascular nodules.The majority of the hypovascular nodules in the present study could be classified as DNs or well-differentiated HCCs, although they may not have been diagnosed correctly. A histological diagnosis should be obtained with a biopsy under ultrasound guidance. Several guidelines recommend that typical hypervascular nodules can be diagnosed as HCC without biopsy and that detection of atypical hepatic nodules, such as hypovascular nodules requires a biopsy to prove HCC –16. If pRecently, gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI has been spread in filed practice . In the Since CTAP/CTHA is an invasive examination for detecting HCC, it has been replaced with Gd-EOB-DTPA-enhanced MRI , 16. To Hypovascular nodules are often seen in practice. However, it is still controversial whether treating hypovascular nodules that occur with hypervascular HCC has any survival benefit, especially in early stage HCC patients who have an indication of curative therapy. Further large-scale and longer observation-period studies of hypovascular nodules detected by Gd-EOB-DTPA-enhanced MRI are warranted.In conclusion, hypovascular nodules detected by CTAP/CTHA progress to hypervascular HCC at a high rate, and the patients who had these hypovascular nodules concurrent with hypervascular HCC had a much poorer prognosis. The presence of hypovascular nodules seems to be taken into consideration of the management of early stage HCC.S1 FigKaplan-Meier curve of time to hypervascularization for size [A: ≤10 mm (dashed line) vs. > 10 mm (solid line)] and [B: single (dashed line) vs. multiple (solid line)] of hypovascular nodules in patients who had hypovascular nodules.(TIF)Click here for additional data file.S2 FigKaplan-Meier survival curve for size [A: ≤10 mm (dashed line) vs. > 10 mm (solid line)] and [B: single (dashed line) vs. multiple (solid line)] of hypovascular nodules in patients who had hypovascular nodules.(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.


Despite substantial research on school-based obesity prevention programs, it is unclear how widely they are disseminated. It is also unknown whether schools use obesity programs that inadvertently promote weight stigma or disordered weight-control behaviors.In spring 2016, we distributed an online survey about school wellness programming to a simple random sample of US public school administrators . We analyzed survey responses and conducted immersion/crystallization analysis of written open-ended responses.Slightly less than half  of schools offered any obesity prevention program. Only 17 (6.9%) reported using a predeveloped program, and 7 (2.8%) reported using a program with evidence for effectiveness. Thirty-seven schools (15.0%) reported developing intervention programs that focused primarily on individual students’ or staff members’ weight rather than nutrition or physical activity; 28 schools  used staff weight-loss competitions. School administrators who reported implementing a program were more likely to describe having a program champion and adequate buy-in from staff, families, and students. Lack of funding, training, and time were widely reported as barriers to implementation. Few administrators used educational  or scientific  literature for wellness program decision making.Evidence-based obesity prevention programs appear to be rarely implemented in US schools. Schools may be implementing programs lacking evidence and programs that may unintentionally exacerbate student weight stigma by focusing on student weight rather than healthy habits. Public health practitioners and researchers should focus on improving support for schools to implement evidence-based programs. Implementing school-based obesity prevention programs is a key public health strategy for countering the childhood obesity epidemic ,2. In adAs the American Academy of Pediatrics recently highlighted, a concern with obesity prevention programs is the potential for triggering unhealthy weight-control behaviors or exacerbating weight stigma . AdolescIt is unclear how broadly evidence-based programs have been disseminated and whether in the absence of effective dissemination schools instead use ineffective or even harmful programs. The Diffusion of Innovations Model , which sThis cross-sectional pilot study was conducted from February to June 2016. To reach the target population of US public school principals, we downloaded a list of all public kindergarten through twelfth-grade schools in the United States from the National Center for Education Statistics  and randWe developed a brief survey  to gatheBrain BreaksbSAFE-bFITaCoordinated Approach to Children’s Health (CATCH)COPEFOOD PLAYHealth Ahead/Heart SmartJust for Kids!KaBoomKID-FITaNew MovesaPlanet HealthPresident’s Challenge Physical Activity and FitnessProject Fit AmericaaSPARKStart For LifeaTAKE10Other (write in name)Alliance for a Healthier GenerationEat Smart/Move MoreBuild Our Kids Success (BOKS)Counselor-sponsored small groupsFitnessGramGo NoodleNutrition NetworkaSafe Routes to Schoola Programs categorized as having peer-reviewed evidence in support of the program’s efficacy.We estimated the frequency of responses to each survey item for the entire sample. To explore potential disparities in access to wellness programs, we constructed 2 multivariate logistic regression models that tested whether an association existed between schools’ percentage of students receiving free or reduced-price lunch, percentage of white students, grade level, or Census region  and the the likelihood of 1) having any wellness programs, and 2) having an evidence-based intervention program. Analyses were conducted using Stata, Release 13 (StataCorp LP).To analyze themes in the text written by study participants for the open-ended survey questions about facilitators or barriers for program adoption, the researchers used an immersion/crystallization approach . This apThe 247 survey respondents represented public school administrators from 48 states; approximately one-sixth of respondents were from the Northeast census region , a similar proportion were from the West , one-third were from the South , and more than one-third were from the Midwest  . Most reA minority of survey respondents ranked obesity, nutrition, or physical activity as among their top 3 health concerns for their school populations . Eighty-Fewer than half of respondents  indicated that their schools offered a school wellness or obesity prevention activity of any kind (including broad policies such as competitive foods standards and more structured programs) . AlthougAmong the 117 schools that reported implementing any type of wellness and obesity prevention activity, survey respondents, using a checklist of potential partners in decision making, ranked physical education teachers  most frequently as people involved in the program development or selection process. Participants whose schools offered obesity prevention activities indicated that recommendations from a district administrator , the fact that other schools in the district were using the same program , and recommendation from a teacher at their own school  were common factors in programming decisions. A handful of participants at schools with obesity prevention activities reported that their school programming decisions were influenced by reviews of educational  or scientific  literature. More than half of administrators at schools with wellness activities  perceived a positive impact of their activities on overall student health, and nearly two-thirds  perceived a positive impact on student physical health. School administrators generally perceived that their wellness programs did not increase eating disorders . Thirty-eight (32.5%) participants thought that their programming led to a decrease in unhealthy weight management by students.Results from the logistic regression models testing whether the school’s region or sociodemographic makeup predicted likelihood of having 1) any wellness program or 2) an evidence-based intervention suggested that none of these variables  were significantly associated with either outcome.Most survey respondents  offered commentary in response to open-ended questions about barriers and supports to implementing obesity prevention or nutrition and physical activity programs. Across schools, respondents frequently mentioned financial resources, time and curricular priorities, and staffing and training as key factors in determining uptake of a wellness program; some noted that financial resources and training opportunities were instrumental in facilitating the program, and others reported that a lack of these resources prohibited program adoption. One administrator noted, “Being grant-funded [by the state], our available resources are extremely limited. Once classrooms are staffed and materials for teaching purchased for core subject areas, very little is left,” echoing the comments of many administrators whose schools were lacking funding, staffing, and time to focus on wellness and obesity prevention. Others described district-level coordination and collaboration, and state and federal standards and requirements, as key structural facilitators and barriers to the success of their wellness programmingStratifying the qualitative data by whether the school used any kind of predeveloped program, we found that respondents from schools with such programs were more likely to raise the themes of having an important program champion, substantial buy-in for the program within the school community, or both.In this study of a national random sample of US public primary and secondary schools, we found that, despite the substantial amount of scientific research devoted to developing and testing school-based obesity prevention and nutrition or physical activity promotion programs , very feIn our sample of US kindergarten through twelfth-grade public schools, programs emphasizing weight loss were more commonly used than evidence-based, effective programs for promoting healthy eating and physical activity. Although programs that focus on improved nutrition, physical activity, and screen time behaviors generally prevent obesity and improve health, programs that focus instead on weight status and weight loss can exacerbate both weight stigma and unhealthy weight-control behaviors . ParticuThe themes that emerged from survey respondents’ comments about school wellness programs also suggest that schools need more support to adopt effective programs. Schools that had adopted any program, whether evidence-based or not, were more likely to describe as key facilitators having a program champion and adequate buy-in from stakeholders; a prior study of the postresearch sustainability of one evidence-based program, “New Moves,” also found buy-in, funding, and time to be critical implementation factors . These fStrengths of this study are its random sampling approach, which reduces sampling bias, and its use of mixed methods to gather a nuanced picture of school-based wellness programming. The study also has several limitations. Although a random sample allowed for a broad cross-section of US schools, we found that participating schools had slightly higher percentages of white students than did nonparticipating schools, suggesting that the results may not be generalizable to all US public schools. Schools that responded to our survey may have also differed from nonresponding schools in other ways (such as having more resources), raising the risk of response bias. Given study resource constraints, our sample was also small, raising the risk of error in our estimates. However, the margin of error for our findings on the percentage of schools using evidence-based programs was 3.2%, indicating that the study’s overall conclusions about low levels of dissemination of these programs are likely generalizable. Our small sample size may have precluded us from detecting regional or sociodemographic differences; future research, using larger samples, should further evaluate possible predictors of dissemination. The sample also did not include private or parochial schools. Additionally, the study relied on school administrators’ reports of school wellness programming, the validity of which are unknown; it is possible that respondents may have either overestimated the efficacy of wellness programs at their schools or overlooked them entirely or that school principals may have been unaware of some programs implemented in their school. Future research could compare school policies and curricular materials with school administrator reports to ascertain their accuracy.Few schools in this study implemented nutrition, physical activity, or obesity prevention programs that known to be effective. In the absence of evidence, many schools appear to be trying to address obesity prevention and wellness on their own, unintentionally implementing potentially ineffective or harmful programs. Public health agencies and funding bodies should focus on supporting schools in the dissemination and adoption of safe and effective programs for promoting healthy eating and physical activity and preventing obesity.


Journal of Experimental Botany, advance access: December 2017, doi: 10.1093/jxb/erx458The original published version of this article omitted details of the support provided to the authors’ project, and these details have now been corrected in the online version of the article. The exact change is listed below.The sentence: “This work was supported by the National Program on Key Basic Research Project (973 Program) of China (2013CB126905)” was changed to “This work was supported by the Ministry of Agriculture of China for Transgenic Research (2014ZX0800938B) and the National Program on Key Basic Research Project (973 Program) of China (2013CB126905)”


The U.S. Congress created the priority review voucher program in 2007 to encourage development of drugs for neglected diseases. Under the voucher program, the developer of a drug for a neglected or rare pediatric disease that is approved by the U.S. Food and Drug Administration receives a bonus priority review voucher for another drug. As of 2016, four vouchers have sold for an average price of $200 million. Recent experience with the voucher program indicates strengths and weaknesses of the program, as well as a need for legislative changes. The U.S. Congress created the priority review voucher program in 2007 to encourage development of drugs for neglected diseases. Congress expanded the program in 2012 to include rare pediatric diseases. Under the voucher program, the developer of a drug for a neglected or rare pediatric disease that is approved by the U.S. Food and Drug Administration (FDA) receives a bonus priority review voucher for another drug. Thus, two drugs are involved: the drug that wins a bonus priority review and the drug that uses the bonus priority review. The drugs need not be from the same company, because the voucher may be sold. Priority review typically reduces review time at the FDA by about 4 months.3Based on their first-hand experience with the voucher program, industry executives Berman and Radhakrishna evaluate the priority review voucher program in a recent paper in this journal.First, Berman and Radhakrishna argue that incentives for drug development created by the voucher program are limited by the narrowness and uncertainty of voucher eligibility. To be eligible to receive a bonus priority review, a developer's drug must itself receive priority review. In other words, there are two drugs with priority review for every voucher. To be awarded a voucher, the drug for a neglected or rare pediatric disease must earn priority review on its own merit by offering a major advance in treatment, or providing treatment where no adequate therapy exists. If a competing drug wins a race to market, then subsequent drugs for that disease are less likely to qualify for a voucher. The developer does not know prior to submission whether it will receive priority review. For a marginal drug, the uncertainty could discourage investment. Another critique of the eligibility criteria is that previously approved molecules are ineligible, even if they target new indications.Fortunately, developers can receive signals from FDA about the likelihood of receiving priority review. Drugs with accelerated approval or fast track status are highly likely to receive priority review. Also, for drugs treating rare pediatric diseases, the FDA provides a rare pediatric disease designation early in the development process. Furthermore, while the eligibility criteria could reduce investment in marginal drugs, it could also increase investment in innovative drugs. Because the voucher program is subject to supply and demand, reducing the quantity of vouchers pushes prices higher, which encourages development.Second, Berman and Radhakrishna argue that clinical trial costs are higher for drugs for neglected diseases than for rare, pediatric diseases, because developers of drugs for rare diseases are often permitted by FDA to conduct smaller trials. However, the cost of testing for neglected diseases such as dengue, malaria, and tuberculosis might not be so onerous relative to other drugs because of the ease of identifying patients for a clinical trial. Also, drugs with greater clinical benefits require smaller trials to prove that the benefits are real. Indeed, there is evidence that drugs for infectious and parasitic diseases tend to have lower development costs than other drugs.5Third, Berman and Radhakrishna argue that expected returns are reduced by legislative risk. Investors cannot be certain that a drug will win a voucher because they do not know how eligibility might evolve. Indeed, there is some risk that the rare pediatric program will not be renewed, although renewal seems likely given the affinity for the voucher program by members of Congress.In support of the voucher program, Berman and Radhakrishna argue that the voucher program imposes little cost to society or to other products under FDA review. This is a goal of the program and the reason that the voucher user must pay an additional FDA user fee of $2.7 million . However, even with the additional user fee, FDA cannot easily hire new staff each time a voucher is redeemed. FDA needs a steady stream of vouchers to be confident that it can commit to hiring staff. Furthermore, FDA struggles to attract new staff given Federal employment and pay limits.Finally, although the voucher program encourages drug development, the program does not ensure access to the drugs.9The voucher program would benefit from refinement. It is helpful to have policy makers, regulators, public health-care advocates, academics, and manufacturers engaged in a debate about the strengths and weaknesses of the voucher program to enhance the program for the future.


While evidence supports community health worker (CHW) capacity to improve maternal and newborn health in less-resourced countries, key implementation gaps remain. Tools for assessing CHW performance and evidence on what programmatic components affect performance are lacking. This study developed and tested a qualitative evaluative framework and tool to assess CHW team performance in a district program in rural Uganda.A new assessment framework was developed to collect and analyze qualitative evidence based on CHW perspectives on seven program components associated with effectiveness . Focus groups were conducted with four high/medium-performing CHW teams and four low-performing CHW teams selected through random, stratified sampling. Content analysis involved organizing focus group transcripts according to the seven program effectiveness components, and assigning scores to each component per focus group.Four components, ‘supportive supervision’, ‘good relationships with other healthcare workers’, ‘peer support’, and ‘retention and incentive structures’ received the lowest overall scores. Variances in scores between ‘high’/‘medium’- and ‘low’-performing CHW teams were largest for ‘supportive supervision’ and ‘good relationships with other healthcare workers.’ Our analysis suggests that in the Bushenyi intervention context, CHW team performance is highly correlated with the quality of supervision and relationships with other healthcare workers. CHWs identified key performance-related issues of absentee supervisors, referral system challenges, and lack of engagement/respect by health workers. Other less-correlated program components warrant further study and may have been impacted by relatively consistent program implementation within our limited study area.Applying process-oriented measurement tools are needed to better understand CHW performance-related factors and build a supportive environment for CHW program effectiveness and sustainability. Findings from a qualitative, multi-component tool developed and applied in this study suggest that factors related to (1) supportive supervision and (2) relationships with other healthcare workers may be strongly associated with variances in performance outcomes within a program. Careful consideration of supervisory structure and health worker orientation during program implementation are among strategies proposed to increase CHW performance. Globally, community health worker (CHW) programs have been positioned as an important strategy for achieving the Millennium Development Goals and now for progressing towards the universal health coverage targets set out in the 2030 Agenda and Sustainable Development Goals –3. CHWs whether an intervention works and not why and under what conditions it is effective .Two of the four low-performing teams reported significant gaps or no contact with supervisors. Lack of supervision was more common in cases when supervisors were based at the higher-level facility located in the main town, and with replacements of transferred supervisors. CHW associated positive relationships with supervisors with more frequent contact, opportunity for constructive feedback and advice, and demonstration of respect. Close relationships with supervisors were reported to be highly motivating for CHWs. Conversely, a lack of supervisory support was associated with lack of motivation. Contrasting experiences are described below:Sometimes we [CHWs] want to come up [do better] but people let us down. Like for example, we last met that health worker [supervisor] one year ago. We have only met her once ever since we started training. So we don’t see any teamwork with our supervisor [‘low’-performing CHW team member].When you have the phone number of the healthcare worker, you call her to let her know that you have referred a woman; when the woman reaches the clinic and delivers, the healthcare worker calls to tell you that the woman you sent has given birth. So that’s the relationship we have in the village and with the healthcare workers [‘high’/‘medium’-performing CHW team member].The healthcare workers don’t support our work at all…they don’t respect us at all. They don’t think the referrals are important to them; they just throw them away and don’t even want to hear about them. They even ask patients whether they think it is the referral we give them that will treat them [‘low’-performing CHW team member].Three of the four ‘high’- and ‘medium’-performing CHW teams provided evidence of involvement in health facility activities  while only one of the four low-performing CHW teams provided examples of facility engagement. Of the five CHW teams which voiced challenges with the referral system, four were ‘low’-performing. Working referral systems, in particular, were seen as an important sign of respect and validation of a CHW’s work, whereas CHWs felt that referrals that were rejected or ignored undermined their relationships with the community and the value of their work. Contrasting experiences are described below:This study builds on the CHW effectiveness literature and existing frameworks , 33 by pThis particular study used a practical evidence- and experienced-based conceptual framework to evaluate and explain implementation strengths and weaknesses and their contribution to (varied) performance outcomes. Testing of this program evaluation tool suggest that CHW effectiveness components with large variance in scores between ‘high’/‘medium’- versus ‘low’-performing teams may be associated with performance outcomes. Our study found that components related to ‘supportive supervision’ and ‘good relationships with other healthcare workers’ were most important in differentiating performance levels of CHW teams. This study reinforces evidence that supervision and referral-liaison provided by other healthcare workers support are among the most important program elements affecting CHW effectiveness , 34.. [We hypothesize that strong supervision helps foster a ‘virtuous’ cycle contributing to CHW confidence, cohesion, referral effectiveness, recognition in the community, and a sense of connectedness to the health system—factors linked to motivation. Conversely, weak or absent supervision seems to foster a ‘vicious’ cycle contributing to a feeling of neglect, lack of technical support and mentoring, weak referral systems, and poorer treatment by other healthcare workers—factors associated with lower motivation and performance. Such linkages may help explain why low-performing teams in our study received lower scores for components related to relationships with other healthcare worker and supportive supervision. Other authors similarly highlight the importance of regular supervisor interactions , the rol. , 39.To maximize CHW program effectiveness, policymakers and program implementers should consider strategies which orient healthcare workers, develop strong supervisory structures and budgetary support for supervisory activities, and provide channels for positive CHW healthcare worker interaction and communication. Exploration of innovative supervisory models, such as through group, community, and peer supervision could strengthen current healthcare worker-focused supervisory models as suggested by Lunsfeld  and CrigFurther development and testing of this innovative CHW program evaluation tool and similar tools could enhance more effective implementation as CHW programs are scaled up globally. Important limitations of our study include the use of relative scores, a lack of blinding to performance levels, and testing on a single program where a number of components had little variance in implementation. However, this tool accommodates heterogeneity in program design and generates contextually specific and locally relevant evidence based on universally applicable themes. The tool would be strengthened further by testing with a range of programs to determine generalizability of results, triangulating qualitative evidence-based scores with quantitative output data, and testing for inter-rater reliability. In the rural, southwestern Uganda study context, the small variance in components related to training, community embeddedness, and peer relationships was likely influenced by consistency in processes related to selection and training where clear guidelines and support for these processes were provided by the project. Gathering more nuanced perspectives on several of the CHW effectiveness components may help to further identify differentiating factors between higher- and lower-performing teams. For example, it would be useful to investigate relationships with local councilors as distinct from relationships with villagers, the role of peer leadership and group dynamics within CHW teams, and different facets of incentives and motivation. Such information may help to yield more precise information on performance levels.CHW programming has become a major strategy for task-shifting in resource-constrained contexts. Despite large funding investments, major implementation challenges remain. The lack of detailed, comparable, and systematic information on program design and implementation is hindering the global community’s capacity to effectively address these challenges and answer important questions about effective program design. For example, understanding what supervisory or incentive models work and under what conditions requires that the global health community develop innovative process-oriented, assessment tools to systematically evaluate and compare how various elements of program design affect overall program effectiveness. This study makes one of the first attempts to do so. Findings from the use of a qualitative, multi-component tool developed and applied in this study suggest that factors related to supportive supervision and relationships with other healthcare workers may be strongly associated with variances in performance outcomes within a program. Putting in place strategies and structures to support positive and mutually constructive engagements and relationships between CHWs, their supervisors, and the health system are proposed to support program effectiveness. With CHWs increasingly being turned to as the go-to community resource, understanding the enabling factors needed for a strong program will be essential for maintaining adequate CHW performance and retention.


Introduction: The extent of viral replication during acute HIV infection (AHI) influences HIV disease progression. However, information comparing viral load (VL) kinetics with and without antiretroviral therapy (ART) in AHI is limited. The knowledge gained could inform preventive strategies aimed at reducing VL during AHI and therapeutic strategies to alter the viral kinetics that may enhance the likelihood of achieving HIV remission.Methods: The analysis utilized VL data captured during the first year of HIV infection from two studies in Thailand: the RV217 study  and the RV254 study . Fiebig stages were I/II  and Fiebig III/IV . Data were modelled utilizing spline effects within a linear mixed model, with a random intercept and slope to allow for between-subject variability and adjustment for the differences in variability between studies. The number of knots in the quadratic spline basis functions was determined by comparing models with differing numbers of knots via the Akaike Information Criterion. Models were fit using PROC GLIMMIX in SAS v9.3.Results: At enrolment, there were 24 Fiebig I/II and 6 Fiebig III/IV individuals in the untreated group and 137 Fiebig I/II and 98 Fiebig III/IV individuals in the treated group. Overall, the median age was 27.5 years old, most were male (89%), and CRF01_AE was the most common HIV clade (76%). By day 12 (4 days after ART in RV254), the untreated group had a 2.7-fold higher predicted mean VL level compared to those treated . These differences increased to 135-fold by day 30 (predicted log VL 4.89 for RV217 and 2.76 for RV254) and 1148-fold by day 120 (predicted log VL 4.68 for RV217 and 1.63 for RV254) (p < 0.0001 for both) until both curves were similarly flat at about day 150 (p = 0.17 between days 150 and 160). The VL trajectories were significantly different between Fiebig I/II and Fiebig III/IV participants when comparing the two groups and within the treated group (p < 0.001 for both).Conclusions: Initiating ART in AHI dramatically changed the trajectory of VL very early in the course of infection that could have implications for reducing transmission potential and enhancing responses to future HIV remission strategies. There is an urgency of initiating ART when acute infection is identified. New and inexpensive strategies to engage and test individuals at high risk for HIV as well as immediate treatment access will be needed to improve the treatment of acute infection globally.Clinical Trial Number: NCT00796146 and NCT00796263 The extent of viral replication during acute HIV infection (AHI) influences HIV disease progression. Most notably, viral set point levels in early infection predict time to clinical AIDS ,2. RecenAntiretroviral therapy (ART) initiated in AHI rapidly lowers VL, reduces acute retroviral syndrome symptoms and mitigates CD4+ T cell depletion and immune dysfunction from HIV –9. We anWith access to data from two unique AHI cohorts in Thailand, we performed statistical modelling of plasma VL kinetics of untreated vs. treated AHI amongst 265 individuals, during the first year of infection with a focus on the first 120 days of infection. The knowledge gained could inform preventive strategies aimed at reducing VL during AHI as well as therapeutic strategies given in addition to ART to alter the viral kinetics that may enhance the likelihood of achieving HIV remission.The analysis utilized data captured during the first year of infection from two ongoing studies in Thailand: the RV217 study (data from July 2009 to January 2012) and the RV254 study (data from April 2009 to April 2015). RV217 is a prospective cohort of individuals at high risk for HIV who are screened for AHI with twice-weekly NAT. ART is initiated according to standard of care at local health centres, and none in this analysis had ART. Some RV217 participants were in Fiebig III/IV at inclusion because of delay in returning to clinic for confirmatory testing. The RV254 cohort (NCT00796146) enrols individuals diagnosed with AHI at an HIV testing centre that has routine AHI screening, and they are offered immediate ART (NCT00796263). The RV254 participants included in this analysis initiated ART at a median of 2 (Inter Quartile Range (IQR) 1 to 3) days from enrolment. Fiebig stages at AHI diagnosis were categorized according to Fiebig and Busch: Fiebig I/II: HIV RNA+, p24 antigen±, HIV IgM− and Fiebig III/IV: HIV IgM+, Western blot-/indeterminate . The folBecause the RV217 had longitudinal and frequent sampling for NAT to diagnose AHI, the advent of viremia (i.e. the first detectable viremia) was able to be determined for RV217 participants. The RV254 study, in contrast, diagnosed AHI based on cross-sectional testing of samples from walk-in clinic clients; therefore, the advent of viremia could not be directly assessed. However, the pretreatment VL trajectories should be similar, on average, between the two cohorts. Thus, to ensure comparability of data, the time scale for RV254 participants was shifted as follows: Fiebig stage I/II individuals had their screening visit when AHI was diagnosed set to day 5 (from advent of viremia in RV217) and Fiebig stage III/IV individuals had their screening visit set to day 11. These shifts were determined by examining the mean VLs at screening in RV254 for each Fiebig stage subgroup and finding the corresponding points in the RV217 mean trajectory.α = 0.05 level. These sensitivity analyses indicate that the decisions made to align the time scales of the two studies are not driving the results of the analyses.The choice of which day from the advent of viremia to assign to the screening visit in RV254 participants has a minimal impact on most of the estimates and hypothesis tests from the spline model shown in Forty-nine screening or baseline VL values for RV254 participants were above the assay’s upper limit of detection (LOD), and samples were unavailable for further dilution. For these observations, values were imputed as the mean of the observed RV254 VL values above the censoring threshold at the same visit. As a sensitivity analysis, these observations were dropped from the data set, and since this did not impact the inference or substantially alter the mean trajectory, the imputed values were included in the analyses. Additionally, 1216 values were censored at the highest lower LOD (50 copies/ml) and were imputed as LOD/2.VL data from the two studies were then combined and modelled utilizing quadratic spline effects within a linear mixed model, with a random intercept and slope to allow for between-subject variability and adjustment for the differences in variability between studies . Models with three to six knots, placed at the associated quantiles of the observed days from advent of viremia, were compared via the Akaike Information Criterion. The optimal model among these had four knots at the quintiles of days since the advent of viremia. Models were fit using PROC GLIMMIX in SAS v9.3.10 copies/ml in RV254) and greater CD4 counts (median of 707 vs. 371 cells/mm3 in RV254), likely due to differences in the baseline Fiebig stages. RV217 participants did not initiate ART after enrolment, whereas in RV254, 65% were on tenofovir/emtricitabine or lamivudine/efavirenz and 34% had maraviroc and raltegravir in addition to that regimen. The median (IQR) day from advent of viremia  at the start of treatment for RV254 (i.e. week 0) was day 8 . The loss-to-follow-up rate was low in the RV254 study: 1.7% (4/235) in this group and 3.7% (17/459) in the cohort in general. The loss of follow-up in the RV217 group reported here was 6.7% (2/30) and 28.5% (242/848) in the overall RV217 cohort in Thailand.A total of 265 Thai participants were included in the analyses: 30 from RV217 (24 Fiebig I/II and 6 Fiebig III/IV) and 235 from RV254 (137 Fiebig I/II and 98 Fiebig III/IV) . The anap < 0.001). The initial rise in VL was similar between the two studies (p = 0.34 for testing the difference in slopes between days 5 and 10), but after the RV254 peak at about 10 days, the trajectories differed  until both curves were similarly flat at about day 150 (p = 0.17 between days 150 and 160). At day 10, the predicted mean VL levels were similar between RV217 and RV254 with a fold change of 1.7 . By day 12, however, the RV217 cohort had a 2.7-fold higher predicted mean VL level compared to RV254 . These differences increased to 135-fold by day 30 (predicted log VLs 4.89 for RV217 and 2.76 for RV254) and 1148-fold by day 120 (predicted log VLs 4.68 for RV217 and 1.62 for RV254) (p < 0.0001 for both).The predicted VL values with 95% confidence bands are shown through day 120 in p value of 0.001 from a Wilcoxon rank-sum test. The true peak viremia was only observed for RV217 participants, since in the RV254 cohort treatment could have been initiated before the peak was reached in some participants. Peak VL levels did not differ significantly between cohorts . However, within Fiebig I/II, peak VL was significantly higher in RV217 compared to RV254 , but within Fiebig III/IV, peak VL was significantly lower in RV217 compared to RV254 . The latter could be attributed to the small sample size with only six RV217 participants in Fiebig III/IV. Additionally, peak VL likely occurred before entry into the study for some RV254 Fiebig III/IV participants. In a separate model, different trajectories were fit for each Fiebig stage subgroup and study, with predicted values shown in p < 0.001), but due to the small number of Fiebig III/IV among RV217 participants, interpretation is difficult. Among RV217 participants entering the study as Fiebig I/II, the predicted doubling time from the spline model was approximately 0.6 days. Additionally, within the RV254 cohort only, the VL trajectories were significantly different between Fiebig I/II and Fiebig III/IV participants (chi-squared test: p < 0.001). Also, mean VL differed between treated and untreated RV254 visits , after adjusting the time from the advent of viremia (p < 0.001). The VL trajectories between the stages were not statistically different in RV217.Observed peak viremia occurred at a median (IQR) of 13.5 (9–15) days in RV217 and 10 (7–11) days in RV254 , yielding a n = 80) resulted in faster VL decline with lower log VLs at day 25 (TDF/XTC/EFV predicted log VL 3.38) and day 100 (TDF/XTC/EFV predicted log VL 1.82) compared to regimens without raltegravir (n = 155) (TDF/XTC/EFV/MVC/RAL predicted log VLs 2.92 and 1.58 on day 25 and day 100, respectively, p < 0.001 for both comparisons, HIV subtype and gender or risk group did not significantly affect VL levels and trajectories across the studies. In RV254, treatment with raltegravir (Utilizing statistical modelling of VL kinetics based on one of the largest data sets from acutely infected participants, we observed that initiating ART in acute infection dramatically changed the trajectory of VL very early in the course of infection. Beginning 12 days from the advent of viremia or approximately 4 days after ART initiation, a 2.7-fold difference of VL means was predicted with the gap widening to 1148-fold by day 120. Peak viremia was also higher in untreated Fiebig I/II vs. those treated. These observations highlight the urgency of initiating ART as early as possible when acute infection is diagnosed.10 copies/ml and within two weeks rose to 6–7 log10 copies/ml. We observed a VL doubling time of 0.6 log 10 per day in the absence of treatment similar to those in the South African and US cohorts [The VL kinetics observed in our studies are similar to the earlier acute simian immunodeficiency virus and HIV studies, which showed that VL levels and patterns were influenced by the available target cells and host immune responses –16. The  cohorts ,4,14,17. cohorts ,4,14,17. cohorts .HIV replication is halted soon after ART initiation, resulting in an early divergence of VL trajectories between the treated and untreated individuals. Inhibiting viral replication prevents viral mutation development and reduces immune destruction, reservoir seeding and immune activation –25. ThisThe strength of our work is the inclusion of data from a relatively rare population of individuals in early HIV infection with both people who initiated ART early vs. those who did not. The population is homogenous: they are all Thais with fairly comparable age, sex and HIV subtypes. The main limitation is that time from advent of viremia was determined only for RV217 requiring a shift in the time scale for RV254 participants to align the untreated portions of both cohorts’ VL trajectories. This approach gives only a rough estimate of an RV254 participant’s entry into the study relative to the advent of viremia. Given the limitations of the available data, though it appears to do well in capturing the mean trajectory. Additionally, the number of RV217 participants in Fiebig III/IV was small and limits the comparisons within the same study and across the two studies.ART in acute infection significantly altered the trajectory of VL from day 4 after ART initiation, with the VL differences between the untreated and treated cohorts widening to 1148-fold by day 120. Altering the VL trajectory could have implications for reducing transmission potential and enhancing responses to future HIV remission strategies. These observations highlight the urgency of initiating ART when acute infection is diagnosed. Identifying acutely infected individuals is challenging. Innovative strategies will be needed to improve the detection and treatment of acute infection globally.


Adolescent obesity is a major public health concern. Open to all high school students regardless of weight status, HealthCorps is a nationwide program offering a comprehensive high school-based participatory educational program to indirectly address obesity. We tested a hypothesis that the HealthCorps program would decrease BMI z-scores among overweight or obese students, and reduce obesity rates, and evaluated its effects on health knowledge and behaviors.HealthCorps aimed to improve student knowledge and behaviors regarding nutrition quality, physical activity, sleep, breakfast intake, and mental resilience. Participating students received through HealthCorps coordinators weekly or bi-weekly classroom lessons either for a semester or a year in addition to various during- and after-school health-promoting activities and mentorship. Self-reported height and weight were collected along with questionnaires assessing knowledge and behaviors during 2013-2014 academic year among 14 HealthCorps-participating New York City high schools. This quasi experimental two-arm pre-post trial included 611 HealthCorps and 221 comparison arm students for the analytic sample. Sex-specific analyses stratified by weight status were adjusted for age and Hispanic ethnicity with clustering effects of schools and students taken into account.p = 0.004 for the former; and = −0.23 , p = 0.028, for the latter) whereas comparison female counterparts did not. The HealthCorps students, but not the comparison students, had a significant increase for all knowledge domains except for the breakfast realm, and reported a greater number of significant behavior changes including fruit and vegetable intake and physical activities.HealthCorps female overweight/obese and obese student had a significant decrease in BMI z-scores , The HealthCorps program was associated with reduced BMI z-score in overweight/obese and obese female adolescents, with enhanced health knowledge and behavior for both sexes. With its wide reach, this may be a promising program to help combat adolescent obesity in schools.NCT02277496 on September 10, 2014 (Retrospectively registered).This study is registered as a clinical trial at the ClinicalTrials.gov registry with trial number The online version of this article (10.1186/s12887-017-0975-9) contains supplementary material, which is available to authorized users. Adolescent obesity is a major public health concern that affects health status not only during adolescence but also during adulthood.  The effeEvidence-based obesity interventions in school settings would appear to be a logical strategy for helping to achieve this objective.  Several www.healthcorps.org), an United States nationwide extramural non-profit non-governmental organization, offers a unique comprehensive high school-based voluntary participatory educational program that is open to all students regardless of their weight status. It is estimated that more than 1.8 million adolescents to date have been exposed to the program since its foundation in 2003 when the HealthCorps program was the first of its kind that was implemented in New York City (NYC) public high schools with intense and challenging urban inner city environments. The program does not directly intervene on weight issues but offers substantive wellness education that could help students achieve a healthier form. Furthermore, the setting of HealthCorps high schools was also unique in terms of demography and inadequate resources to promote health behaviors. The program focuses on improving the health knowledge and behaviors on: nutrition, physical activities, breakfast intake, adequate sleep, and mental resilience.HealthCorps Inc.  z-score and obesity class. We hypothesized that the program would decrease BMI z-scores among overweight or obese students, and reduce obesity rates. Secondarily, we aimed to identify knowledge and health behavior domains that would be increased with the program.The present study is primarily aimed to test whether the HealthCorps program would improve weight status represented by body mass index  high schools before and after participating in HealthCorps programming during the 2013-2014 academic year, although during this year, a total of 62 schools over 13 states participated in HealthCorps program. The present study focused on implementation of the HealthCorps programming in selected NYC high schools. The selection criteria for the NYC HealthCorps schools were as follows:  (1) mustThe study design was a two parallel arm quasi-experimental pre-post comparison design as neither the schools nor the students were randomly assigned to accommodate real-world settings and constraints as much as possible, and students’ participation in the HealthCorps program was voluntary. This study was approved by the Institutional Review Boards of Albert Einstein College of Medicine and NYC Department of Education.www.healthcorps.org/resources/the-curriculum). All of these activities were coordinated and delivered by intensively trained well-qualified program coordinators each of whom is designated to a single school . All Coordinators are trained on all HC program components for 3 weeks in the summer as well as a week of professional development in the winter recession. Through weekly check-ins and reports, program supervisors ensure all program components are being implemented at their coordinators’ schools. Each supervisor visits a site at least once a year to observe delivery of the curriculum also to ensure standardization. Depending on school policies or school culture, however, coordinators are encouraged to tailor the program to meet the needs of their specific school and community. As such, the coordinators often work in conjunction with a school wellness council and its members.HealthCorps programming consists of a curriculum delivered in classrooms, through mentorship, and through a variety of during- and after-school health-promoting activities  and inventory to address gaps in the school’s health policies and programming in order to design action plans to improve nutrition, physical fitness and mental resilience of their students. Weekly afterschool clubs focus on nutrition, physical fitness and/or mental resilience whose contents were determined by the annual HealthCorps Community Needs Assessment instrument that systematically identifies areas of programming need at each school.Coordinators teach approximately 10 classroom HealthCorps lessons weekly or bi-weekly and for either a semester or a year. These lesson topics include developing tools that build mental resilience, healthy eating habits, and physical fitness. The coordinators also provide mentoring to students and staff with various health-related resources and action plans that address meeting personal health goals. Coordinators also co-facilitate wellness councils, comprised of staff members and students, who utilize the Alliance for a Healthier Generation platform (www.photovoice.org) and then develop specific projects that meet those needs.Activities outside the classroom included lunchroom “Café-o-Yeas” food samplings, Teen Battle Chef and other cooking programs, Youth Lead Action Research, and annual Highway to Health school and community-wide Festivals. The “Café-o-Yeas” is a monthly activity in that coordinator and students share samples of healthy foods with other students at lunchtime at a booth. The Teen Battle Chef program involves culinary coaching and cooking “battles” to help students learn how to cook healthy meals and build their public speaking, teamwork and leadership skills.  Finally,https://schools.healthiergeneration.org/dashboard/about_assessment/, www.cdc.gov/healthyschools/shi/index.htm), and HealthCorps Community Needs Assessment evaluation at the beginning of the school year. Total exposure hours for students voluntarily participating in the program could be as long as 45 h over a maximum 36 weeks for the school year. However, the individual student-level program exposure hours varied based on attendance. Nevertheless, individual-level HealthCorps class or activities attendance records were not possible for coordinators to collect, and thus not available for the present analysis.Depending on restrictions or schedules of schools, lengths of the program were a semester- or year-long. Regardless of the length, however, the school-level dosage of the program was identical and the aforementioned program activities were applied to all participating schools. Implementation process of the program activities was determined specific to each school depending on its needs and overall culture identified based on several evaluations: regular wellness council meetings, Alliance for a Healthier Generation’s School Health Index Assessment evaluations . The self-reported weight and height were well validated by measured weight and height from an internal validation random sample of N = 73 students with wide ranges of height and weight: correlations between self-reported and measured weight and height were 0.89 (p < 0.001) and 0.91 (p < 0.001), respectively.Self-reported weight and height were collected and converted to sex-age-specific BMI z-scores following the 2000 CDC growth curves,  which isA 77-item HealthCorps survey for the 2013-14 academic year included questions on demographic factors, weight-related knowledge and behaviors. Survey data were collected prior to and post teaching of the HealthCorps curriculum. The survey items were selected from well-established questionnaire items such as the biannual Youth Risk Behavior Survey (YRBS) conducted by the CDC, reliabilities of whose items are well supported.  ReliabilKnowledge items were multiple-choice questions and had a single correct answer  intake, High Energy Density (HED) food intake, Water and Juice (W&J) consumption, and Sugar-Sweetened Beverage (SSB) consumption. The subscales of physical activity domains were: Physical Activity (PA) Days, and Physical Activity (PA) Barriers. Finally, the subscales of mental resilience domains were: General Attitude, Confidence in Healthy Eating, Confidence in Exercising, and Future Exercise Plan.N = 611  for the HealthCorps and N = 221  for the comparison students. Detailed flowchart is depicted in Fig. A vast majority of the students who participated in the HealthCorps program received it as a part of their school classroom programming. Therefore, their participation was not necessarily driven only by motivations. The pre-program survey was administered during September 2013 whereas the post-program survey was administered during December 2013 for the semester-long program and during May 2014 for the year-long program. A total of 2279 students  responded to either or both the pre and post survey. Among them, 1708 of the students were enrolled in classes teaching the HealthCorps curriculum, and the remaining 571 students, who were not in classes that included teaching of the HealthCorps curriculum, served as comparison students. For the present analysis, we applied the following inclusion criteria. The study sample must have: 1) responses to both surveys; 2) biologically probable BMI values [Baseline characteristics were summarized with descriptive statistics such as mean, standard deviations, and percentages. Comparisons of the characteristics between HealthCorps and comparison students at baseline were made using t- or Chi-square tests. To test our first study aim, we applied mixed-effects linear models to examine the pre-post effect of HealthCorps programming on BMI z-score to takes into account potential outcome correlations between pre and post periods in addition to clustering effects of schools; specifically, school- and participant-specific intercepts were considered random.p < 0.05 were declared statistically significant.The main predictor for primary analysis testing significance of HealthCorps effect on changes in BMI z-sores was the time effect (pre vs. post) within each arm. To this end, we included arm indicator , time indicator, and arm-by-time interaction as fixed effects. The arm-by-time interaction term was included to construct and test contrasts pertinent to the arm-specific time effects using the sex-specific entire sample. Likewise, for the analysis of changes in obesity rates, we applied mixed-effects logistic models with the same fixed and random effects. The same modeling approach was applied to testing the secondary aim to examine the effect of HealthCorps on knowledge and health behaviors. All of primary and secondary analyses included time, age and Hispanic ethnicity as additional fixed-effects covariates, and were conducted separately for males and females and further stratified by baseline weight status. SAS v9.3 was used for all analyses and results with p > 0.05) between HealthCorps and comparison students regardless of sex. Sex distributions were neither significantly different between arms (p > 0.05).Distributions of age, Hispanic ethnicity, and various weight parameters were not significantly different (p = 0.004) and −0.23±0.10 (p = 0.028), respectively. In contrast, the comparison counterparts did have a significant change in BMI z-scores: post-pre delta = −0.03±0.09 (p = 0.707) for overweight/obese and −0.04±0.17 (p = 0.825) for obese comparison female students. However, both HealthCorps and comparison normal weight male students significantly increased BMI z-scores by 0.13±0.04 (p = 0.001) and 0.20±0.06 (p = 0.002), respectively. Nevertheless, their post BMI z-scores were much lower than those of their overweight/obese and obese counterparts. We note that there were no significant changes in percentages of overweight/obese or obese students in any arm regardless of sex. These non-significant changes may be a product of the outcome scale change from the continuous BMI z-score to dichotomous obesity status.There was a significant decrease in the BMI z-scores for overweight/obese (BMI ≥ 85th%-tile) and obese (BMI ≥ 95th%-tile) HealthCorps female students: post-pre delta = −0.16±0.05 . Furthermore, as noted earlier, the HealthCorps school environments are in general inadequate for promoting physical activities and health behaviors. Findings concerning the effect of HealthCorps on the knowledge and behaviors are consistent with our earlier report based on a different sample of students,  SpecificIn the present study, the level of physical activity and a few mental resilience items such as confidence in exercise and future exercise plan increased significantly as well. Therefore, the present study results support our earlier findings and the robustness of HealthCorps programming pertaining to those domains included as the Dietary Guidelines 2015-2020 recommendations,  which emAlthough overweigh/obese or obese male students tended to have reduced BMI z-score, the absence of a significant intervention effect may have been due to an insufficient dose and/or duration of the HealthCorps curriculum. Or, perhaps the attained knowledge and behavior changes for males might not have been strong enough to counteract environmental pressures outside of the school that undermine healthy weight control. Nevertheless, for males and females, future implementation strategies might need to better take into account contextual factors beyond the school that moderate treatment effects . It might include walkability on streets or neighbors, and utilizations of public sports parks or community gardens.  At the iwww.fns.usda.gov/school-meals/local-school-wellness-policy).Our study findings collectively imply that utilization of school-based comprehensive participatory educational program focusing on health knowledge and behaviors would be successful to improve students’ weight, even if such program is open to any student to ensure that it does not stigmatize students with obesity. In this context, HealthCorps program might be suitable for meeting a federal law that requires establishment of a school environment that promotes students’ health, well-being, and ability to learn by supporting healthy eating and physical activity. The Local School Wellness Policy requirement was established by the Child Nutrition and WIC Reauthorization Act of 2004, and further strengthened by the Healthy, Hunger-Free Kids Act of 2010. This legislation requires schools participating in the National School Lunch Program and/or School Breakfast Program to develop a local school wellness policy that promotes the health of students and addresses the growing problem of childhood obesity (The present study has several limitations that should be taken into consideration when interpreting the results and implications thereof. First, the experimental design was quasi-experimental but not a randomized design; however, that was more relevant to the real-world circumstances and settings, and the groups were comparable at baseline. Second, the analysis relied on self-reported data including weight and height. It was impossible to measure in a timely manner weights and heights of all participating students during school days not only due to logistical reasons but also due to limitation of access to students for measurements. Nevertheless, self-reported weight and height were validated by our internal sample and also supported as reliable proxy measures in other adolescent population.  The calcIn conclusion, despite the fact that the HealthCorps school settings were challenging especially in terms of adequate resources, it was effective for BMI z-score improvements among female students in addition to significant positive effects on knowledge and a few behaviors in both sexes. It remains an open question as to how one can develop implementation strategies concerning school-based extra or intramural program that takes into account home and community contextual factors at the societal level and SMART goals approach at the individual level to improve health behaviors, further weight improvement, and reduce adolescent obesity prevalence. To this end, future studies need to address how to design and evaluate strategies for achieving and sustaining adolescent weight improvement while meeting academic and other challenges in urban inner city high schools.


Neonatal screening for 21 hydroxylase deficiency is designed to detect classical form of congenital adrenal hyperplasia (CAH). It is still unclear whether newborns who result false positives at neonatal screening might later develop signs of androgen excess. The aim of this study is to verify whether a slightly elevated 17-OHP at newborn screening is a predictive factor for premature pubarche.We evaluated all infants born between 2001 and 2014 with premature pubarche. In case of increased bone age, they were submitted to functional tests to find out the cause of their symptoms. Their 17-OHP values at newborn screening for CAH were reconsidered.We identified 330 patients  with premature pubarche. All these children had a normal 17-OHP at newborn screening with the exception of a child, born preterm and not affected by CAH.An elevated 17-OHP at newborn screening is not a predictive factor for premature pubarche. A likely cause of increased 17-OHP level at screening is an immaturity of adrenal gland or a neonatal stress. Therefore a strict follow up of these neonates during childhood is not necessary. Since 2001 a neonatal screening program for congenital adrenal hyperplasia (CAH), based on 17-hydroxyprogesterone (17-OHP) assay on filter paper-dried blood spots, has been instituted in North-Eastern Italy. It aims to obtain an early identification of the newborns affected by classic form of 21 hydroxylase deficiency (21-OHD), and consequently to prevent both potential life-threatening shock due to salt waste, especially in male newborns, and incorrect sex assignment in affected girls with inappropriate virilization . NeverthHence, the aim of this study is to verify whether an elevated 17-OHP at newborn screening is a predictive factor for premature pubarche.We enrolled in this study all infants born between 2001 and 2014 that were later referred to the Pediatric Endocrinology Division of Verona Hospital, Italy, for premature pubarche defined as development of pubic hair before the age of 8 years in girls or 9 years in boys.All patients were evaluated in order to determine their weight, height, BMI, and pubertal development using Tanner and Whitehouse method . For boyACTH stimulation test was performed using soluble Synacthen 250 mg intravenously for all children with increased bone age . Serum levels of 17-OHP and cortisol were evaluated at baseline and after 60 min. Data were analyzed using Maria New nomogram .GnRH-analogous (GnRH-a) stimulation test was performed in patients with associated signs of precocious puberty (premature thelarche for girls or testicular volume > 4 mL for boys), administering 0.1 mg of GnRH-a (Triptorelin) subcutaneously after overnight fasting. Serum levels of FSH, LH, estradiol and testosterone were measured at baseline and 4 h after the injection.Serum 17-OHP was assayed by radioimmunoassay . Assay sensitivity was 0.01 ng/dL, whereas intra- and inter-assay coefficients of variation were both 9.0%.Serum total testosterone, ACTH, cortisol, Δ4-androstenedione, DHEAS, estradiol, FSH and LH were measured by a solid-phase, competitive chemiluminescent enzyme immunoassay . Analytical sensitivity was 0.05 mIU/mL (0.5 nmol/L) for total testosterone, 1.1 pmol/L for ACTH, 0.2 μg/dL for cortisol, 1 nmol/L for Δ4-androstenedione, 0.3 μg/dL for DHEAS, 0.1 mIU/mL for FSH, 0.05 mIU/mL for LH and 15 pg/mL for estradiol.Intra-assay and inter-assay coefficients of variation were 11.7 and 13.0% respectively for testosterone, 9.5 and 10.0% respectively for ACTH, 7.4 and 9.4% respectively for cortisol, 9.3 and 12% respectively for Δ4-androstenedione, 4.1 and 6.3% respectively for DHEAS, 2.9 and 4.1% respectively for FSH, 3.6% and 6.7 respectively for LH, 11.7 and 13% respectively for estradiol.Neonatal screening for CAH was performed using dried blood spots on filter paper. The blood sampling was collected between 48 and 72 h of life. 17-OHP was determined by immunofluorometric assay .The study was conducted in compliance with the terms of the Helsinki II Declaration. In Italy, this type of retrospective study do not require Institutional Review Board/Institutional Ethics Committee approval in order to publish the results. Written informed consent was obtained from the parents of each patient.t-test or the Wilcoxon test, when appropriate. Data are expressed as numbers with frequency, median plus range, or mean ± SD, as appropriate. Statistical significance was considered when p-values were less than 0.05.All statistical analyses were performed using software R. Comparisons between groups were performed using Student’s We identified 330 patients with premature pubarche, 269 females 81.5%) and 61 males (18.5%). Their mean age at diagnosis was 7.3 ± 1.8 years. The auxological data of our population are represented in Table .5% and 6Figure Figure To our knowledge, this is the first study analyzing the relation between premature pubarche and the 17-OHP level at newborn screening for CAH.Premature pubarche is not rare and usually appears as secondary condition of an early isolated maturation of adrenal gland –19. In sWithin a diagnostic frame, it remains controversial which patients should be submitted to ACTH stimulation test. Some Authors recommend to submit patients to this test as the ratio between bone age and statural age exceeds 1, or whenever androgen levels are elevated, or, finally, in case of bone age advancement, cystic acne or other signs of systemic virilisation detection . We deciThe remaining children  resulted affected by idiopathic premature pubarche. Among them, the 63.5% presented elevated DHEAS levels. Although DHEAS is a weak androgen, it is a substrate for the synthesis of more potent androgens, and levels of DHEAS superior than 40–50 μg/dL are associated with the appearance of a pubarche , 19, 20.The prevalence of CAH in our cohort of children with premature pubarche is comparable with previous data, which have estimated it to a range from 0 to 40% , 20–24. With the exception of one child born preterm, all patient with premature pubarche, including the 3 non classical CAH patients, presented 17-OHP levels in the reference range at newborn screening. The data presented in this manuscript show that an elevated 17-OHP at newborn screening is not a sign of increased risk of premature pubarche. As above mentioned, neonatal screening for CAH is not designed to detect non-classical form of the disease, since basal serum 17-OHP levels are often in the normal range in patients with this form of disease –11. TheiThe major drawback of neonatal screening for 21-OHD are the high false-positive rate and the low positive predictive value, mainly among preterm, low BW and ill neonates , 36–38. In conclusion, we exclude that an elevated 17-OHP evidenced in babies unaffected by classical 21-OHD at newborn screening might be a sign of future premature pubarche. The main identified causes of increased 17-OHP levels at birth remain an immaturity of adrenal gland and/or a neonatal stress. Consequently, a strict follow up of these infants during childhood is not necessary.


Diabetes is a major chronic disease that can lead to serious health problems and high healthcare costs without appropriate disease management and treatment. In the United States, the number of people diagnosed with diabetes and the cost for diabetes treatment has dramatically increased over time. To improve patients’ self-management skills and clinical outcomes, diabetes management education (DME) programs have been developed and operated in various regions.This community case study explores and calculates the economic and clinical impacts of expanding a model DME program into 26 counties located in South Texas.The study sample includes 355 patients with type 2 diabetes and a follow-up hemoglobin A1c level measurement among 1,275 individuals who participated in the DME program between September 2012 and August 2013. We used the Gilmer’s cost differentials model and the United Kingdom Prospective Diabetes Study (UKPDS) Risk Engine methodology to predict 3-year healthcare cost savings and 10-year clinical benefits of implementing a DME program in the selected 26 Texas counties.Changes in estimated 3-year cost and the estimated treatment effect were based on baseline hemoglobin A1c level. An average 3-year reduction in medical treatment costs per program participant was $2,033 (in 2016 dollars). The total healthcare cost savings for the 26 targeted counties increases as the program participation rate increases. The total projected cost saving ranges from $12 million with 5% participation rate to $185 million with 75% participation rate. A 10-year outlook on additional clinical benefits associated with the implementation and expansion of the DME program at 60% participation is estimated to result in approximately 4,838 avoided coronary heart disease cases and another 392 cases of avoided strokes.The implementation of this model DME program in the selected 26 counties would contribute to substantial healthcare cost savings and clinical benefits. Organizations that provide DME services may benefit from reduction in medical treatment costs and improvement in clinical outcomes for populations with diabetes. Diabetes is a major chronic health conditions in the United States (U.S.), and it is also one of the top 10 leading cause of death in the U.S. .While diabetes affects people of all ages, previous studies have observed differences in diabetes prevalence among specific race/ethnicity groups , 3. Non-Over one-third of the total U.S. adults population have pre-diabetes, which lead to high risk for developing type 2 diabetes, but only 10% of people recognizes it . People The cost of diabetes to the U.S. healthcare system is enormous. In 2012, the estimated total cost for diabetes treatment, including both direct and indirect costs, was $245 billion, which has increased rapidly from $98 billion in 1997 and $174 billion in 2007 . With anTo improve the early detection, monitoring, self-management, and quality of life of people with diabetes, a variety of interventions, such as patient education programs and comprehensive disease management programs, have been developed and tested in the U.S. and beyond. Effective education can lead to effective disease management and opportunities to avoid future medical treatments and development of comorbidities . DiabeteIn this study, the research team worked with a model DME center (“the Center”) located in a city that serves 20 surrounding counties in South Texas in 2014. The Center is one of the key community-based healthcare institutions in the area. It is affiliated with several health systems and an academic health center and university, and offers a DME program to support diabetic patients (participants in the DME program) living in surrounding counties. The Center’s DME programs help patients with diagnosed diabetes to self-manage their diabetes and teach those with pre-diabetes or those who are at risk of developing diabetes to prevent or delay the onset of the disease. The program offers diabetes self-management classes in both English and Spanish by certified diabetes educators, registered nurses, and nutritionists. The 8-h classes teach fundamental information about diabetes, the importance of healthy diet and physical activity, as well as how to test blood sugar levels. The program participants receive complimentary, follow-up labs, and one-on-one consultation. To improve the health and wellness of the people and increase the impact of the DME program in this geographic service area, the Center proposed to expand its service area from 20 surrounding counties to 26 counties in 2014. This community case study is to explore the economic and clinical impact of expanding the existing model of DME program to 26 selected counties around the Center.Considering the high cost of diabetes for patients, health systems, and the U.S. healthcare system, this paper sheds some light on how effective one model DME program can help increase people’s knowledge about diabetes and diabetes self-management, and ultimately in saving healthcare costs long term as participation in the program increases. Using the results of the Center’s DME program as a model, which is already in place and tracks program cost and participant biomarkers, the research team extrapolated the impact of expanding this model of DME program to a larger services area and achieving higher market penetration with this expanded geographic target area.To examine the impact of expanding the Center’s DME program model to 26 counties on saving healthcare-related cost and reducing future diabetes-related “bad events,” such as coronary heart disease (CHD) and stroke, we used the Center’s DME program de-identified participant-level data for the analysis. The data included a total of 1,275 individual who participated in the DME program between September 2012 and August 2013. We included only participants with a diagnosis of type 2 diabetes and those who had a follow-up hemoglobin A1c level measurement, and excluded those with gestational or type 1 diabetes diagnosis and those with only one initial hemoglobin A1c measurement. As a result, the final sample size included a total of 355 observations. This study was approved by the Texas A&M University’s Institutional Review Board (IRB).We used Gilmer and colleagues’  approachTo project the estimated change in 3-year treatment costs, we used the abovementioned Gilmer model’s results to generate estimates of program treatment effects in terms of estimated changes from baseline hemoglobin A1c level at 12 months, stratified by baseline hemoglobin A1c level. The Gilmer model’s results , we also estimated the cost savings for the Center’s DME program expansion into 26 counties, based on various levels of the program participation rates .We used the estimated 12-month program treatment effect for hemoglobin A1c level and systolic blood pressure to produce pre- and post-program values to use in the U.K. Prospective Diabetes Study (UKPDS) Risk Engine Excel file. The UKPDS Risk Engine is a type 2 diabetes-specific risk calculator that uses 20 years of UK Prospective Diabetes Study data to estimate future risks of developing CHD and stroke . The UKPEstimates of event risks for the pre- and post-program values were generated using the UKPDS Risk Engine for specific patient segments, delineated by age , gender , race (black or not-black), and smoking status (current smoker or not). The estimated changes in event risks for each of the patient segments were then applied to the size of the estimated population with diabetes for each patient segment and summed over patient segments to calculate the estimated impact of disease event rates.A total of 355 patients with type 2 diabetes were included in the analysis. Table About 54% of the Center’s DME program participants with type 2 diabetes were female and the majority of the participants were Hispanic, followed by non-Hispanic White, non-Hispanic Black, and other race. Most of the participants were between age 45 and older, with few people under the age of 30. About 80% of the sample participants had at least 12 years of education, and almost all the participants speak English, with only 4% of participants have difficulty understanding English. When choosing a type of DME class to attend, the majority of the participants choose English version of the class, and only 5% of participants attended the Spanish DME courses.When looking at the type of insurance coverage patients had, the majority of the patients had some kind of insurance coverage. Many participants were either enrolled in the county indigent program (38%) or had private insurance (42%). The remaining 13.5% of participants had Medicare, Medicaid, or other types of health insurance. About 6.5% of the participants did not have health insurance at all.Patients included in the analysis were previously diagnosed with type 2 diabetes. With the majority of patients with initial hemoglobin A1C level between 6.5 and 10%, about 25% of patients started the program with initial hemoglobin A1c level equal or greater than 10%.The results of Gilmer and colleagues’ study  in prediThe summary results reported in Gilmer et al.  reflect The proportion of the Center’s DME program participants in each baseline hemoglobin A1c level category is shown in column 2 of Table Table To project the cost impact of the 3.84 unit reduction in hemoglobin A1c level, we assumed that the cost savings associated with one unit hemoglobin A1c reductions shown in Appendix 2 in Supplementary Material in Data Sheet 1 were additive. Specifically, we estimated the cost impact of a total 3.84 unit reduction in A1c for participants with baseline A1c greater than 11% as the sum of the cost savings of a unit reduction from a hemoglobin A1c level greater than 11 to 11% , plus the cost impact of another unit reduction from a hemoglobin A1c level of 11 to 10% —a cumulative change 2 units of A1c, plus the cost impact of another unit reduction from a hemoglobin A1c level of 10 to 9% —a cumulative change of 3 units of hemoglobin A1c, plus the cost impact of a final 0.84 reduction in hemoglobin A1c level (a cumulative change of 3.84 units), estimated as $1,095 . A similar approach was used to project the cost impact of the 2.3 unit reduction in hemoglobin A1c level for program participants in the baseline hemoglobin A1c level of 10–11% cohort, the 1.1 unit reduction for the hemoglobin A1c level of 9–10% cohort, and the 1.0 unit reduction for the hemoglobin A1c level of 8–9% cohort.The overall 3-year treatment cost savings were estimated as the weighted average of the cost savings within each baseline hemoglobin A1c level strata, weighted by the sample share of program participants in each strata, as shown in Table Using the 2011 information from CDC about the type 2 diabetes prevalence by gender, race, and age groups, and the U.S. Census Bureau estimated population in 2012 for each of the 26 target counties, we extrapolated that a total of 187,303 people in the 26 target counties had type 2 diabetes in 2012. We then estimated the total healthcare cost savings after subtracting the total program cost calculated at $650 per participant from the calculated cost differential at the six different levels of program participation rates for the target counties. Table The UKPDS Risk Engine provides risk estimate for non-fatal and fatal CHD and non-fatal and fatal stroke. Using the UKPDS, we are able to estimate the number of events avoided over a 10-year period for CHD and stroke. An estimated 187,303 people diagnosed with type 2 diabetes reside in the 26 target counties. Table Based on the analysis of the participant-level data provided by the Center, we concluded that investment in community-based diabetes education self-management programs can provide effective education that helps increase patients competency in managing their diabetes and potentially reducing medical cost and avoid preventable adverse health outcomes. We estimate that the expansion of a DME Program, such as the program at the Center into the 26 target counties in South Texas will likely result in total healthcare cost savings of $50 million over 3 years, assuming a 20% participation rate among people with type 2 diabetes in the 26 counties. Therefore, it is important to engage in community-level recruitment of participants into these types of diabetes education and management programs. Furthermore, a 10-year outlook on additional clinical benefits associated with the implementation and expansion of the DME program (at 60% participation) could result in about 4,838 cases of avoided CHD and another 392 cases of avoided strokes in the 26 county areas.However, it is possible that at a low participation rate, the patients participating consist of those best suited for the program, whereas at high participation rates, patients less suited to the program also will be participating. Thus, the magnitude of the program treatment effect may diminish as the participation rate increases. The results in Table Conversely, as the participation rate of the DME program increases, it is likely that the Center will observe economies of scale resulting in reduced fixed costs associated with the DME program operations. This reduction in program cost can be due to managerial economies of scale, such as specialization of the workforce, better operational management, investment in human resource, and other factors affecting reductions in fixed cost. The assumed absence of economies of scale in program costs should at least partially offset the impact of the potential bias in estimated diabetes costs savings on net program cost estimates.Diabetes is currently one of the most common chronic diseases in the U.S. Without proper education and management, it will be able to increase patients’ risks for numerous serious health problems, which also increase the healthcare costs for the patient and the community. This case study demonstrated that organizations that provide DME programs, such as the program discussed in the case, have the potential to save medical treatment costs and also improve clinical outcomes.This study is not without limitation. First, the study included only data and results from a 1-year short-term DME program with only patients who were diagnosed with type 2 diabetes. Second, we also assumed that the last A1c level observation of each patient in the data is constant over the projected time interval. Third, we took a conservative approach when analyzed the Center’s data and extrapolated cost and clinical benefit impacts for the 26 target counties. Due to the conservative methodological approach used in this analysis to extrapolate the Gilmer model’s results to the higher baseline A1c levels observed in the data, coupled with an assumption of no economies of scale in DME program fixed costs, the projected cost savings associated with the DME program may be underestimated. Fourth, this DME program includes education and training to teach and help patients better manage and control their diabetes. We are unable to separate the parts of the DME program and study their impact on A1c levels, although it is possible that parts of the DME program could be more effective than the others. In this paper, we studied this DME program as a whole in helping patients manage their diabetes.The positive results of this pilot study helped the Center to expand their services area. Future studies are needed to include larger sample size and longitudinal data to examine the effectiveness of this DME program in helping patients avoid preventable adverse health outcome. There is also potential for comparative effectiveness studies as educational programs are implemented at various sites using different technologies and skill levels.BK, SHL, and RO contributed to the conception and design of the work, data acquisition, and analysis. BK, SHL, JB, and RO drafted, reviewed, and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.