Orla Doyle

This study examines the long-term impact of childhood exposure to Glacier Lake Outburst Floods (GLOFs) on adult human capital in High Mountain Asia. Using historical GLOF data and geospatial mapping, we identify treatment and control groups based on geographic proximity. Exposed children attain 1.4–1.7 fewer years of schooling and have 1.9–2.2 kg/m2 lower adultBMI. Rural adolescents face greater educational losses, whereas urban residents experience more pronounced health impacts. Our findings highlight the long-term costs of climate-induced disasters and underscore the need for targeted risk reduction in vulnerable mountain communities.

This study examines the long-term impact of childhood exposure to Glacier Lake Outburst Floods (GLOFs) on adult human capital outcomes in the High Mountain Asia (HMA) region. Using historical GLOF data and geospatial mapping of downstream flood pathways, we identify treatment and control groups based on geographic proximity. Exposed children attain 1.4 to 1.7 fewer years of schooling and have a 1.9 to 2.2 kg/m² lower Body Mass Index (BMI) in adulthood compared to non-exposed children. Moreover, rural adolescents face greater educational losses, whereas urban residents experience more pronounced health impacts. Our findings provide valuable insights into the long-term costs of climate-induced disasters and underscore the need for targeted risk reduction strategies in vulnerable mountain communities.

Background/Objectives: Rare diseases are conditions that are individually rare but collectively common. These diseases can incur significant direct and indirect costs with a combination of high medical expenses, loss of income, and additional non-medical costs. Despite this, research into the economic cost for families of children with a rare disease is lacking. This scoping review aimed to document the evidence on the economic impacts of living with a rare disease for children and their families. Methods: Six electronic databases were searched to identify relevant peer-reviewed literature that discussed the family costs (direct medical, direct non-medical, and indirect) of having a child with a rare disease, published between January 1983 and April 2023. The geographical location, type of rare disease, and language were not limited. Data were extracted from the included studies following the screening process and are reported following the PAGER framework for reporting scoping review results. Results: The final analysis included 28 studies. The studies highlighted areas of high costs, including visits to healthcare professionals (n = 36), medication costs (n = 11), presenteeism (n = 17), and informal care (n = 11). However, gaps in the existing research, such as the focus on metabolic or musculoskeletal rare diseases and the lack of a distinction between rare and ultra-rare diseases, were apparent. Conclusions: Having a child with a rare disease can significantly impact a family’s financial health, and these costs extend beyond healthcare costs. Understanding the costs experienced by the rare disease population is important to better define and comprehend the economic impact of rare diseases.

Evidence on the impact of early intervention on childhood health is inconsistent. Using a randomized controlled trial, we examine the impact of an Irish early intervention program on the development of health capital in the first five years of life. The treatment provides intensive parenting supports including home visits and parenting classes from pregnancy until school entry. Modest effects by age four are found, mainly driven by reduced hospital attendances attributed to improved earlier health. In sum, the program has limited effects on children’s health capital in the short-run despite demonstrating large effects on other dimensions of human capital.

Pregnancy loss is often a traumatic event which may impact both parents and subsequent children. Using Norwegian registry data, we exploit the random nature of single, early miscarriages to examine the impact of pregnancy loss on parental investment and family outcomes. We find that pregnancy loss improves maternal health investments in the subsequent pregnancy regarding supplement use, smoking, preventative healthcare, and physician choice. While a miscarriage negatively affects labor market attachment, it has limited effects on children born after the loss. This suggests that investment in the next pregnancy may offset the negative consequences of stress associated with pregnancy loss.

Background: Cardiovascular disease (CVD) is the number one cause of death globally. Whilst initiatives such as Ireland’s ‘Chronic Disease Management Programme’ have helped in the fight against CVD, a more intensive approach may be required to meet high risk patients’ needs. The High-Risk Prevention Programme (HRPP), led by the Irish Heart Foundation, University College Dublin (UCD), and Ireland’s Health Service Executive (HSE) aims to design, deliver, and evaluate an intensive, six-week behaviour change programme among people at high risk of CVD living in Irish communities. In this paper, we aim to describe the HRPP intervention and the approach that will be used to evaluate it. Methods: The HRPP has been informed by an inclusive co-design process involving health provider and patient input, has been operating since February 2021 and will run until February 2023. Six general practices have agreed to take part in the project, and it is planned that 400 patients at high risk of CVD will participate. Participating patients attend either a practice nurse or health promotion coordinator-led six-week one-to-one consultation programme focusing on self-management of health issues. Feasibility, acceptability, and likely effectiveness will be assessed by means of a process evaluation involving quantitative survey (patient health and health behaviours assessments) at baseline and 12 months, and in-depth qualitative interviews with patients, practice nurses and general practitioners. Conclusions: Project findings will inform whether a general practice-based intervention to prevent CVD among at-risk patients is feasible, acceptable in practice, has a positive impact, and what outcome measures might form the focus of future research.

Many early intervention studies experience a dissolution of treatment effects in the aftermath of the intervention. Using a randomized trial, this work examines the impact of Preparing for Life, a pregnancy to age five home visiting and parenting program, on outcomes in middle childhood. I find significant treatment effects on cognitive skills (0.55 SD) and school achievement tests (0.30–0.54 SD) at age nine. There is no impact on socio‐emotional skills, and there is little evidence of treatment heterogeneity by gender, birth order, or distribution of ability. The effects are mainly driven by improvements in early parental beliefs.

Objectives To develop and evaluate machine learning models to detect suspected undiagnosed nonalcoholic steatohepatitis (NASH) patients for diagnostic screening and clinical management. Methods In this retrospective observational noninterventional study using administrative medical claims data from 1,463,089 patients, gradient-boosted decision trees were trained to detect likely NASH patients from an at-risk patient population with a history of obesity, type 2 diabetes mellitus (T2DM), metabolic disorder, or nonalcoholic fatty liver (NAFL). Models were trained to detect likely NASH in all at-risk patients or in the subset without a prior NAFL diagnosis (non-NAFL at-risk patients). Models were trained and validated using retrospective medical claims data and assessed using area under precision recall and receiver operating characteristic curves (AUPRCs, AUROCs). Results The 6-month incidence of NASH in claims data was 1 per 1,437 at-risk patients and 1 per 2,127 non-NAFL at-risk patients. The model trained to detect NASH in all at-risk patients had an AUPRC of 0.0107 (95% CI 0.0104 - 0.011) and an AUROC of 0.84. At 10% recall, model precision was 4.3%, which is 60x above NASH incidence. The model trained to detect NASH in non-NAFL patients had an AUPRC of 0.003 (95% CI 0.0029 - 0.0031) and an AUROC of 0.78. At 10% recall, model precision was 1%, which is 20x above NASH incidence. Conclusion The low incidence of NASH in medical claims data corroborates the pattern of NASH underdiagnosis in clinical practice. Claims-based machine learning could facilitate the detection of probable NASH patients for diagnostic testing and disease management.