ArticleHealth services research2023
Comparison of a national commercial pharmacy naloxone data source to state and city pharmacy naloxone data sources-Rhode Island, Massachusetts, and New York City, 2013-2019.
Article in Health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Pharmacy Naloxone Standing Order and Community Opioid Fatality Rates Over Time.JAMA network open · 2024Article
- Comparison of a national commercial pharmacy naloxone data source to state and city pharmacy naloxone data sources-Rhode Island, Massachusetts, and New York City, 2013-2019.Health services research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
objectiveAccurate naloxone distribution data are critical for planning and prevention purposes, yet sources of naloxone dispensing data vary by location, and completeness of local datasets is unknown. We sought to compare available datasets in Massachusetts, Rhode Island, and New York City (NYC) to a commercially available pharmacy national claims dataset (Symphony Health Solutions). DATA SOURCES AND STUDY
settingWe utilized retail pharmacy naloxone dispensing data from NYC (2018-2019), Rhode Island (2013-2019), and Massachusetts (2014-2018), and pharmaceutical claims data from Symphony Health Solutions (2013-2019). STUDY
designWe conducted a descriptive, retrospective, and secondary analysis comparing naloxone dispensing events (NDEs) captured via Symphony to NDEs captured by local datasets from the three jurisdictions between 2013 and 2019, when data were available from both sources, using descriptive statistics, regressions, and heat maps. DATA COLLECTION/EXTRACTION
methodsWe defined an NDE as a dispensing event documented by the pharmacy and assumed that each dispensing event represented one naloxone kit (i.e., two doses). We extracted NDEs from local datasets and the Symphony claims dataset. The unit of analysis was the ZIP Code annual quarter. PRINCIPAL
findingsNDEs captured by Symphony exceeded those in local datasets for each time period and location, except in RI following legislation requiring NDEs to be reported to the PDMP. In regression analysis, absolute differences in NDEs between datasets increased substantially over time, except in RI before the PDMP. Heat maps of NDEs by ZIP code quarter showed important variations reflecting where pharmacies may not be reporting NDEs to Symphony or local datasets.
conclusionsPolicymakers must be able to monitor the quantity and location of NDEs in order to combat the opioid crisis. In regions where NDEs are not required to be reported to PDMPs, proprietary pharmaceutical claims datasets may be useful alternatives, with a need for local expertise to assess dataset-specific variability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.