Delay between pregnancy confirmation and sickle cell and [corrected] thalassaemia screening: a population-based cohort study.

BACKGROUND: Antenatal sickle cell and thalassaemia screening sometimes occurs too late to allow couples a choice regarding termination of affected fetuses. The target gestational age for offering the test in the UK is 10 weeks. AIM: To describe the proportion of women screened before 70 days' (10 weeks') gestation and the delay between pregnancy confirmation in primary care and antenatal sickle cell and thalassaemia screening. DESIGN OF STUDY: Cohort study of reported pregnancies. SETTING: Twenty-five general practices in two UK inner-city primary care trusts offering universal screening. METHOD: Anonymised data on all pregnancies reported to participating general practices was collected for a minimum of 6 months. RESULTS: There were 1441 eligible women intending to proceed with their pregnancies, whose carrier status was not known. The median (interquartile range [IQR]) gestational age at pregnancy confirmation was 7.6 weeks (6.0-10.7 weeks) and 74% presented before 10 weeks. The median gestational age at screening was 15.3 weeks (IQR = 12.6-18.0 weeks), with only 4.4% being screened before 10 weeks. The median delay between pregnancy confirmation and screening was 6.9 weeks (4.7-9.3 weeks) After allowing for practice level variation, there was no association between delay times and maternal age, parity, and ethnic group. CONCLUSION: About 74% of women consulted for pregnancy before 10 weeks' gestation but fewer than 5% of women were screened before the target time of 10 weeks. Reducing the considerable delay between pregnancy confirmation in primary care and antenatal sickle cell and thalassaemia screening requires methods of organising and delivering antenatal care that facilitate earlier screening to be developed and evaluated.     

Energy Balance,Eating Disorder Risk,and Pathogenic Behaviors Among Athletic Trainers

ContextResearch exists on energy balances (EBs) and eating disorder (ED) risks in physically active populations and occupations by settings, but the EB and ED risk in athletic trainers (ATs) have not been investigated.ObjectiveTo assess ATs'' energy needs, including the macronutrient profile, and examine ED risk and pathogenic behavioral differences between sexes (men, women) and job statuses (part time or full time) and among settings (college or university, high school, nontraditional).DesignCross-sectional study.SettingFree living in job settings.Patients or Other ParticipantsAthletic trainers (n = 46; male part-time graduate assistant ATs = 12, male full-time ATs = 11, female part-time graduate assistant ATs = 11, female full-time ATs = 12) in the southeastern United States.Main Outcome Measure(s)Anthropometric measures (sex, age, height, weight, body composition), demographic characteristics (job status [full- or part-time AT], job setting [college/university, high school, nontraditional], years of AT experience, exercise background, alcohol use), resting metabolic rate, energy intake (EI), total daily energy expenditure (TDEE), EB, exercise energy expenditure, macronutrients (carbohydrates, protein, fats), the Eating Disorder Inventory-3, and the Eating Disorder Inventory-3 Symptom Checklist.ResultsThe majority of participants (84.8%, n = 39) had an ED risk, with 26.1% (n = 12) engaging in at least 1 pathogenic behavior, 50% (n = 23) in 2 pathogenic behaviors, and 10.8% (n = 5) in >2 pathogenic behaviors. Also, 82.6% of ATs (n = 38) presented in negative EB (EI < TDEE). Differences were found in resting metabolic rate for sex and job status (F_1,45 = 16.48, P = .001), EI (F_1,45 = 12.01, P = .001), TDEE (F_1,45 = 40.36, P < .001), and exercise energy expenditure (F_1,38 = 5.353, P = .026). No differences were present in EB for sex and job status (F_1,45 = 1.751, P = .193); χ² analysis revealed no significant relationship between ATs'' sex and EB (= 0.0, P = 1.00) and job status and EB ( = 2.42, P = .120). No significant relationship existed between Daily Reference Intakes recommendations for all macronutrients and sex or job status.ConclusionsThese athletic trainers experienced negative EB, similar to other professionals in high-demand occupations. Regardless of sex or job status, ATs had a high ED risk and participated in unhealthy pathogenic behaviors. The physical and mental concerns associated with these findings indicate a need for interventions targeted at ATs'' health behaviors.     

Physical activity and motor skill outcomes of a 10-week intervention for children with intellectual and developmental disabilities ages 4–13: A pilot study

BackgroundChildren with intellectual and developmental disabilities (IDD) often experience increased barriers to engaging in physical activity (PA) which can stem from lack of gross motor function (GMF) development. Intervening on GMF at an early age can create better opportunities for children with IDD to engage in regular PA. In turn, increased PA can improve health outcomes and increase social skills.ObjectiveThe primary objective of this pilot study was to explore the effectiveness of a community-based GMF-focused PA intervention for improving overall motor skills and PA for children with IDD.MethodsAll study participants (n = 24) engaged in 10 weeks of programming for 1 h each week. A convenience sample was utilized.ResultsResults indicated no statistically significant changes pre to post for motor skill scores. However, a visual analysis of mean changes showed a consistent pattern of increased scores from pre to post on most skills. Additionally, we found that a change in participant locomotor skills significantly predicted change in Moderate to Vigorous Physical Activity (MVPA), F (1,11) = 5.16, Adj R² = .26, p = .04.ConclusionsThese results suggest individualized attention on GMF may help to increase motor skills for children with IDD. This study adds to the small but growing amount of research examining the efficacy of community based adapted PA interventions. Further, study results should support continued exploration of effective approaches to address the motor delays experienced by children with IDD.     

The expectations and realities of nutrigenomic testing in australia: A qualitative study

BackgroundConsumer genomic testing for nutrition and wellness, (nutritional genomics), is becoming increasingly popular. Concurrently, health‐care practitioners (HPs) working in private practice (including doctors interested in integrative medicine, private genetic counsellors, pharmacists, dieticians, naturopaths and nutritionists) are involved as test facilitators or interpreters.ObjectiveTo explore Australian consumers’ and HPs’ experiences with nutrigenomic testing.MethodSemi‐structured in‐depth interviews were conducted using predominantly purposive sampling. The two data sets were analysed individually, then combined, using a constant comparative, thematic approach.ResultsOverall, 45 interviews were conducted with consumers (n = 18) and HPs (n = 27). Many of the consumer interviewees experienced chronic ill‐health. Nutrigenomic testing was perceived as empowering and a source of hope for answers. While most made changes to their diet/supplements post‐test, self‐reported health improvements were small. A positive relationship with their HP appeared to minimize disappointment. HPs’ adoption and views of nutrigenomic testing varied. Those enthusiastic about testing saw the possibilities it could offer. However, many felt nutrigenomic testing was not the only ‘tool’ to utilize when offering health care.DiscussionThis research highlights the important role HPs play in consumers’ experiences of nutrigenomics. The varied practice suggests relevant HPs require upskilling in this area to at least support their patients/clients, even if nutrigenomic testing is not part of their practice.Patient or public contributionAdvisory group included patient/public group representatives who informed study design; focus group participants gave feedback on the survey from which consumer interviewees were sourced. This informed the HP data set design. Interviewees from HP data set assisted with snowball sampling.     

Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States

Provision of safe drinking water in the United States is a great public health achievement. However, new waterborne disease challenges have emerged (e.g., aging infrastructure, chlorine-tolerant and biofilm-related pathogens, increased recreational water use). Comprehensive estimates of the health burden for all water exposure routes (ingestion, contact, inhalation) and sources (drinking, recreational, environmental) are needed. We estimated total illnesses, emergency department (ED) visits, hospitalizations, deaths, and direct healthcare costs for 17 waterborne infectious diseases. About 7.15 million waterborne illnesses occur annually (95% credible interval [CrI] 3.88 million–12.0 million), results in 601,000 ED visits (95% CrI 364,000–866,000), 118,000 hospitalizations (95% CrI 86,800–150,000), and 6,630 deaths (95% CrI 4,520–8,870) and incurring US $3.33 billion (95% CrI 1.37 billion–8.77 billion) in direct healthcare costs. Otitis externa and norovirus infection were the most common illnesses. Most hospitalizations and deaths were caused by biofilm-associated pathogens (nontuberculous mycobacteria, Pseudomonas, Legionella), costing US $2.39 billion annually.     

Etiology of Severe Acute Respiratory Infections,Bangladesh, 2017

In April 2017, surveillance detected a surge in severe acute respiratory infections (SARI) in Bangladesh. We collected specimens from SARI patients and asymptomatic controls for analysis with multipathogen diagnostic tests. Influenza A(H1N1)pdm09 was associated with the SARI epidemic, suggesting that introducing vaccines and empiric antiviral drugs could be beneficial.     

SARS-CoV-2 Serial Interval Variation,Montana, USA,March 1–July 31, 2020

We report mean severe acute respiratory syndrome coronavirus 2 serial intervals for Montana, USA, from 583 transmission pairs; infectors’ symptom onset dates occurred during March 1–July 31, 2020. Our estimate was 5.68 (95% CI 5.27–6.08) days, SD 4.77 (95% CI 4.33–5.19) days. Subperiod estimates varied temporally by nonpharmaceutical intervention type and fluctuating incidence.