Objective: To assess the efficacy of strategies informed by behavioural economics for increasing participation in a vector control campaign, compared with current practice.
Design: Pragmatic cluster randomised controlled trial.
Setting: Arequipa, Peru.
Participants: 4922 households.
Interventions: Households were randomised to one of four arms: advanced planning, leader recruitment, contingent group lotteries, or control.
Main Outcome Measures: Participation (allowing the house to be sprayed with insecticide) during the vector control campaign.
Results: In intent-to-treat analyses, none of the interventions increased participation compared with control (advanced planning adjusted OR (aOR) 1.07 (95% CI 0.87 to 1.32); leader recruitment aOR 0.95 (95% CI 0.78 to 1.15); group lotteries aOR 1.12 (95% CI 0.89 to 1.39)). The interventions did not improve the efficiency of the campaign (additional minutes needed to spray house from generalised estimating equation regressions: advanced planning 1.08 (95% CI -1.02 to 3.17); leader recruitment 3.91 (95% CI 1.85 to 5.97); group lotteries 3.51 (95% CI 1.38 to 5.64)) nor did it increase the odds that houses would be sprayed in an earlier versus a later stage of the campaign cycle (advanced planning aOR 0.94 (95% CI 0.76 to 1.25); leader recruitment aOR 0.68 (95% CI 0.55 to 0.83); group lotteries aOR 1.19 (95% CI 0.96 to 1.47)). A post hoc analysis suggested that advanced planning increased odds of participation compared with control among households who had declined to participate previously (aOR 2.50 (95% CI 1.41 to 4.43)).
Conclusions: Achieving high levels of household participation is crucial for many disease prevention efforts. Our trial was not successful in improving participation compared with the existing campaign. The trial highlights persistent challenges to field experiments as well as lessons about the intervention design process, particularly understanding barriers to participation through a behavioural lens.
Trial Registration Number: American Economic Association AEARCTR-0000620.
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http://dx.doi.org/10.1136/bmjgh-2018-000757 | DOI Listing |
Curr Oncol Rep
January 2025
Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, USA.
Purpose: To review recent advances with radiation therapy (RT) for soft tissue sarcomas (STS).
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Mikrochim Acta
January 2025
Guizhou Province, Qianzhi Mingguang Soaphorn Rice Processing Base, Zhijin County, Maochang Town, Bijie CityBijie City, 552103, China.
A smartphone-based non-invasive method was developed for salivary uric acid detection using Gleditsia Sinensis carbon dots (GS-CDs). The GS-CDs synthesized by the one-pot hydrothermal method emitted blue fluorescence at a maximum excitation wavelength of 350 nm and had good fluorescence stability in the presence of different ions, while showing selectivity to uric acid solution. The ability of uric acid (UA) to quench the fluorescent substances present in the GS-CDs, was confirmed through HPLC-FLD and LC-MS, FTIR and XPS.
View Article and Find Full Text PDFJ Eval Clin Pract
February 2025
Department of Nursing, Trakya University Faculty of Health Sciences, Edirne, Turkey.
Objective: This study aims to assess the performance of machine learning (ML) techniques in optimising nurse staffing and evaluating the appropriateness of nursing care delivery models in hospital wards. The primary outcome measures include the adequacy of nurse staffing and the appropriateness of the nursing care delivery system.
Background: Historical and current healthcare challenges, such as nurse shortages and increasing patient acuity, necessitate innovative approaches to nursing care delivery.
Hum Brain Mapp
February 2025
Laboratory for Imaging Science and Technology, Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
Magnetic susceptibility source separation (χ-separation), an advanced quantitative susceptibility mapping (QSM) method, enables the separate estimation of paramagnetic and diamagnetic susceptibility source distributions in the brain. Similar to QSM, it requires solving the ill-conditioned problem of dipole inversion, suffering from so-called streaking artifacts. Additionally, the method utilizes reversible transverse relaxation ( ) to complement frequency shift information for estimating susceptibility source concentrations, requiring time-consuming data acquisition for (e.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Department of Stomatology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the potential of Artificial Intelligence (AI) to transform sepsis management, from early detection to personalized treatment and real-time monitoring. AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!