Publications

Take a deeper look into all the publications produced by researchers at The Dartmouth Institute.

Sadacharan R, Satcher MF

2025 Jun 2;8(6):e2513548doi: 10.1001/jamanetworkopen.2025.13548

JAMA Netw Open|2025 Jun 2

McDaniel CE, Ralston Daniel M, Freyleue SD, Seryozhenkov E, Peled A, Amaravadi H, Malla N, Leyenaar JK

2025 Jun 2;8(6):e2513527doi: 10.1001/jamanetworkopen.2025.13527

National statistics about regionalization and access to hospitals' pediatric services have been derived from different datasets with differing sampling frames, sizes, and designs, generating conflicting estimates about pediatric service accessibility.

JAMA Netw Open|2025 Jun 2

Chen G, O'Malley AJ

2025;10(1):18doi: 10.1007/s41109-025-00709-8

The recent published literature on linear network autocorrelation models of actor behaviors or other mutable attributes has revealed a curious finding. Irrespective of the size of the network and the status of other network features, likelihood-based estimators (e.g., maximum likelihood and Bayesian) of the autocorrelation parameter ([Formula: see text]) are negatively biased and become increasingly so as the density of the network increases. In this paper we investigate the pattern of bias of estimators of [Formula: see text] when analyzing multiple mutually exclusive sub-networks and directed networks with various levels of reciprocity. In addition to considering the case of a linear network autocorrelation model applied to a binary-valued network, the edges may be weighted and the attribute whose actor-interdependence (or peer-effect) we are interested in may be an event (i.e., a binary outcome), a count, or a rate outcome motivating the use of generalized linear network autocorrelation models. We perform a simulation study that reveals that bias reduces substantially as either the number of sub-networks increases or with increased variation across the network in the edge weights but this pattern is not observed with reciprocity. The findings for generalized linear network autocorrelation models are in general similar to those for linear network autocorrelation models. Finally, we perform a statistical power analysis based on these findings for use in designing future studies whose goal is to estimate or to detect peer-effects.

Appl Netw Sci|2025

Jacobse S, Rijkels-Otters H, Eikens-Jansen M, van der Weijden T, Elwyn G, van den Broek W, Bindels P, Zwaan L

2025 Dec;31(1):2501302doi: 10.1080/13814788.2025.2501302

Shared decision-making (SDM) is considered the preferred communication model, yet its applicability in the diagnostic process is understudied.

Eur J Gen Pract|2025 Dec

Loehrer AP, Wang Q, O'Malley AJ, Wong SL, Tosteson ANA

2025 May 31;doi: 10.1245/s10434-025-17469-5

Ann Surg Oncol|2025 May 31

Di Biase L, Zeitler EP, Zou F

2025 Jun;17(2):xiiidoi: 10.1016/j.ccep.2025.03.002

Card Electrophysiol Clin|2025 Jun

Milan G, Lee V, Gadaleta M, Ariniello L, Faksh A, Quer G, Ajayi T

2025 May 22;12:e70151doi: 10.2196/70151

Mental health disorders such as anxiety and depression are common among individuals of childbearing age. Such disorders can affect pregnancy and postpartum well-being. This study aims to study the impact of prenatal mental health on the pregnancy journey and highlights the use of mobile health technologies such as PowerMom for symptom tracking and screening.

JMIR Ment Health|2025 May 22

Duan D, Sun W, Hao J, Bi S, Zhang S, Zou L, Yu Z, Dong S, Li J

2025 May 19;:107658doi: 10.1016/j.amepre.2025.107658

Existing evidence from high-income countries suggests that higher income is associated with better health outcomes through health-promoting behaviors. However, limited evidence exists regarding income-related health inequalities mediated by health behaviors in low- and middle-income countries. This study focuses on socioeconomically deprived rural areas of China and examines how health behaviors contribute to the association between income and health.

Am J Prev Med|2025 May 19

Plaitano EG, Stanger C

2025 May 17;39(8):109083doi: 10.1016/j.jdiacomp.2025.109083

Nicotine inhibits glucose metabolism. In this national cross-sectional analysis of 388 young adults with type 1 diabetes and above target glycemic control, vaping was the most common route of nicotine use, and heavy nicotine use plus higher type 1 diabetes distress was related to worse objective measures of glycemic control. Trial registration: ClinicalTrials.govNCT04646473; https://clinicaltrials.gov/ct2/show/NCT04646473.

J Diabetes Complications|2025 May 17

Pohl H, Rex DK, Barber J, Moyer MT, Elmunzer BJ, Rastogi A, Gordon SR, Zolotarevsky E, Levenick JM, Aslanian HR, Elatrache M, von Renteln D, Wallace MB, Brahmbhatt B, Keswani RN, Kumta NA, Pleskow DK, Smith ZL, Abu Ghanimeh MK, Simmer S, Sanaei O, Mackenzie TA, Piraka C

2025 May 19;pii: gutjnl-2025-335075. doi: 10.1136/gutjnl-2025-335075

Complications of endoscopic mucosal resection (EMR) of large colorectal polyps remain a concern.

Gut|2025 May 19

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