No abstract

Purpose Early detection of risky behaviors involving prescription opioids can assist prescribers in implementing safer prescribing. Patient‐to‐prescriber travel patterns may indicate potential opioid misuse. We introduce doctor hopping, patients bypassing nearby prescribers in favor of more distant ones, as a new spatial estimation of potentially risky behavior, and compare with traditional doctor shopping metrics. Methods We examined all filled opioid prescriptions between 2015 and 2016 from the Arkansas Prescription Drug Monitoring Program. We calculated patient‐to‐prescriber travel times and number of prescribers bypassed for each prescription, adjusted for payment method. Opioid recipients traveling further than the nearest urban area and bypassing more prescribers than 99% of other recipients from the same zip code were identified as doctor hoppers. We calculated odds ratios to evaluate how doctor hopping and doctor shopping correspond to high‐risk opioid uses. Results Approximately 0.72% of all opioid recipients in Arkansas engaged in doctor hopping two or more times during the study period. Rates of doctor hopping varied spatially but were more common in rural areas. Doctor shopping was more common in urban areas. Both hopping and shopping were significantly associated with higher odds of engaging in high‐risk opioid use. The combination of doctor hopping and doctor shopping metrics can predict high‐risk use better than either metric alone and may allow for earlier detection than doctor shopping alone. Conclusions Doctor hopping is positively associated with high‐risk opioid use and is distinct from and complementary to doctor shopping. We recommend Prescription Drug Monitoring Program (PDMP) vendors incorporate similar spatial analyses into their systems.

BACKGROUND: The National Center for Health Statistics (NCHS) links data from surveys to administrative data sources, but privacy concerns make accessing new data sources difficult. Privacy-preserving record linkage (PPRL) is an alternative to traditional linkage approaches that may overcome this barrier. However, prior to implementing PPRL techniques it is important to understand their effect on data quality. METHODS: Results from PPRL were compared to results from an established linkage method, which uses unencrypted (plain text) identifiers and both deterministic and probabilistic techniques. The established method was used as the gold standard. Links performed with PPRL were evaluated for precision and recall. An initial assessment and a refined approach were implemented. The impact of PPRL on secondary data analysis, including match and mortality rates, was assessed. RESULTS: The match rates for all approaches were similar, 5.1% for the gold standard, 5.4% for the initial PPRL and 5.0% for the refined PPRL approach. Precision ranged from 93.8% to 98.9% and recall ranged from 98.7% to 97.8%, depending on the selection of tokens from PPRL. The impact of PPRL on secondary data analysis was minimal. DISCUSSION: The findings suggest PPRL works well to link patient records to the National Death Index (NDI) since both sources have a high level of non-missing personally identifiable information, especially among adults 65 and older who may also have a higher likelihood of linking to the NDI. CONCLUSION: The results from this study are encouraging for first steps for a statistical agency in the implementation of PPRL approaches, however, future research is still needed.