Measuring the cost-effectiveness of treating rectovaginal and vesicovaginal fistulas: A multicenter global study by the Fistula Foundation.

OBJECTIVE: Surgery for obstetric fistula is a highly effective treatment to restore continence and improve quality of life. However, a lack of data on the cost-effectiveness of this procedure limits prioritization of this essential treatment. This study measures the effectiveness of fistula surgeries using disability-adjusted life years (DALYs) averted. METHODS: In 2021 and 2022, the Fistula Foundation funded 20 179 fistula surgeries and related procedures at 143 hospitals among 27 countries. We calculated DALYs averted specifically for vesicovaginal fistula and rectovaginal fistula procedure types (n = 13 235 surgeries) by using disability weights from the 2019 Global Burden of Disease study. We based cost calculations on direct treatment expenses, including medical supplies, health provider fees, and preoperative and postoperative care. We measured effectiveness using data on the risk of permanent disability, country-specific average life spans, and treatment outcomes. RESULTS: The total treatment cost was $7.6 million, and a total of 131 433 DALYs were averted. Thus, the cost per DALY averted-the cost to restore 1 year of healthy life-was $58. For this analysis, we took a cautious approach and weighted only surgeries that resulted in a closed fistula with restored continence. We calculated DALYs averted by country. Limitations of the study include data entry errors inherent in patient logs and lack of long-term outcomes. CONCLUSION: The current study demonstrates that obstetric fistula surgery, along with having a significant positive impact on maternal health outcomes, is highly cost-effective in comparison with other interventions. The study therefore highlights the benefits of prioritizing fistula treatment as part of the global agenda for maternal health care.

Wild lime psyllid Leuronota fagarae Burckhardt (Hemiptera: Psylloidea) picorna-like virus full genome annotation and classification.

Picorna-like viruses of the order Picornavirales are a poorly defined group of positive-sense, single-stranded RNA viruses that include numerous pathogens known to infect plants, animals, and insects. A new picorna-like viral species was isolated from the wild lime psyllid (WLP), Leuronota fagarae, in the state of Florida, USA, and labelled: Leuronota fagarae picorna-like virus isolate FL (LfPLV-FL). The virus was found to have homology to a picorna-like virus identified in the Asian Citrus Psyllid (ACP), Diaphorina citri, collected in the state of Florida. Computational analysis of RNA extracts from WLP adult heads identified a 10,006-nucleotide sequence encoding a 2,942 amino acid polyprotein with similar functional domain structure to polyproteins of both Dicistroviridae and Iflaviridae. Sequence comparisons of nucleic acid and amino acid translations of the conserved RNA-dependent RNA polymerase, along with the entire N-terminal nonstructural coding region, provided insight into an evolutionary relationship of LfPLV-FL to insect-infecting iflaviruses. Viruses belonging to the family Iflaviridae encode a polyprotein of around 3000 amino acids in length that is processed post-translationally to produce components necessary for replication. The classification of a novel picorna-like virus in L. fagarae, with evolutionary characteristics similar to picorna-like viruses infecting Bactericera cockerelli and D. citri, provides an opportunity to examine virus host specificity, as well as identify critical components of the virus' genome required for successful transmission, infection, and replication. This bioinformatic classification allows for further insight into a novel virus species, and aids in the research of a closely related virus of the invasive psyllid, D. citri, a major pest of Floridian citriculture. The potential use of viral pathogens as expression vectors to manage the spread D. citri is an area that requires additional research; however, it may bring forth an effective control strategy to reduce the transmission of Candidatus Liberibacter asiaticus (CLas), the causative agent of Huanglongbing (HLB).

Deep Learning for Predictive Analysis of Pediatric Otolaryngology Personal Statements: A Pilot Study.

OBJECTIVE: The personal statement is often an underutilized aspect of pediatric otolaryngology fellowship applications. In this pilot study, we use deep learning language models to cluster personal statements and elucidate their relationship to applicant rank position and postfellowship research output. STUDY DESIGN: Retrospective cohort. SETTING: Single pediatric tertiary care center. METHODS: Data and personal statements from 115 applicants to our fellowship program were retrieved from San Francisco Match. BERT (Bidirectional Encoder Representations From Transformers) was used to generate document embeddings for clustering. Regression and machine learning models were used to assess the relationship of personal statements to number of postfellowship publications per year when controlling for publications, board scores, Alpha Omega Alpha status, gender, and residency. RESULTS: Document embeddings of personal statements were found to cluster into 4 distinct groups by K-means clustering: 2 focused on "training/research" and 2 on "personal/patient anecdotes." Training clusters 1 and 2 were associated with an applicant-organization fit by a single pediatric otolaryngology fellowship program on univariate but not multivariate analysis. Models utilizing document embeddings alone were able to equally predict applicant-organization fit (receiver operating characteristic areas under the curve, 0.763 and 0.750 vs 0.419; P values >.05) as compared with models utilizing applicant characteristics and personal statement clusters alone. All predictive models were poor predictors of postfellowship publications per year. CONCLUSION: We demonstrate ability for document embeddings to capture meaningful information in personal statements from pediatric otolaryngology fellowship applicants. A larger study can further differentiate personal statement clusters and assess the predictive potential of document embeddings.