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A Bayesian Multiscale Integrated Abundance Model for Estimating Latent Opioid Misuse Prevalence from Spatially Misaligned Data — arXiv:2610.07265

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.07265
License (as archived): CC0-1.0
Archived: 2026-10-07T11:46:18.007Z
SHA-256 of the archived text: 19c9555c7e4ce8658d639f8150f2a498c974ef265c54191a6a5cf827e8200fe0
Stable URL of this page: https://metraipolis.com/documento/dc_d02428cd2939

How to cite

A Bayesian Multiscale Integrated Abundance Model for Estimating Latent Opioid Misuse Prevalence from Spatially Misaligned Data — arXiv:2610.07265 — arXiv (en), https://arxiv.org/abs/2610.07265 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d02428cd2939 (Metraipolis archive dc_d02428cd2939, sha256 19c9555c7e4ce8658d639f8150f2a498c974ef265c54191a6a5cf827e8200fe0)

BibTeX

@misc{dcd02428cd2939,
  title = {A Bayesian Multiscale Integrated Abundance Model for Estimating Latent Opioid Misuse Prevalence from Spatially Misaligned Data — arXiv:2610.07265},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.07265},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d02428cd2939; sha256 19c9555c7e4ce8658d639f8150f2a498c974ef265c54191a6a5cf827e8200fe0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.07265. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Estimating small area prevalence of opioid misuse is critical for targeting public health interventions, yet direct measures are unavailable and related surveillance data are often reported on misaligned areal units. We propose a Bayesian Multiscale Integrated Abundance (MIA) model for estimating latent opioid misuse prevalence by jointly analyzing multiple indirect surveillance indicators observed on different geographic supports. The model extends existing integrated abundance models by representing all source geographies through a common set of atomic spatial units formed by the intersections of observed areal supports. Latent prevalence at the atomic level is modeled using a Fisher noncentral hypergeometric distribution, which preserves county-level prevalence totals while respecting local population constraints. To enable scalable inference, we develop a two-stage compositional Markov chain Monte Carlo algorithm that combines customized sampling strategies and parallel computing. Simulation studies show the proposed approach reduces bias and root mean squared error relative to common downscaling methods. We apply the model to Ohio data from 2010-2023, integrating state-level survey estimates, county-level counts of opioid overdose deaths and treatment admissions, and ZIP code tabulation area-level counts of emergency medical services naloxone administrations. Results reveal substantial within-county heterogeneity and identify localized areas of elevated opioid misuse prevalence that would be missed by county-level analyses.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME) ; Applications (stat.AP).
Pagina del lavoro: https://arxiv.org/abs/2610.07265 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T11:20:24.656Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).