Extracted main text — title through conclusion, appendix excluded. This is what our citation measures are computed over, published so the extraction can be checked by eye.
30,282 characters · 16 sections · 27 citation commands
Professional networks and the diffusion of clinical guidelines in opioid prescribing
\noindentKeywords: opioid prescribing; physician networks; social learning; CDC guideline; network centrality
The opioid epidemic has claimed more than 500,000 lives in the United States over the past two decades, and physician prescribing behavior has played a central role in its trajectory. Despite extensive research, large and persistent differences in prescribing intensity across physicians and regions remain difficult to explain, even after accounting for patient characteristics and the clinical context. Between 2007 and 2012, the share of Medicare Part D recipients receiving prolonged opioid therapy nearly doubled, and substantial geographic variation persisted after adjusting for patient factors kuo2016trends. Standard explanations emphasize the patient case mix, regulatory environments, and physician training; however, these factors do not fully account for the observed heterogeneity. A central question remains: why do physicians serving similar patients in similar settings prescribe so differently?
A growing body of literature suggests that professional networks may explain this variation. Physicians operate in environments where peer behavior, shared norms, and local information flow shape clinical decisions tasselli2014social,cunningham2012health. Evidence from pharmaceutical adoption indicates that physicians are more likely to prescribe new drugs when their peers have already done so, particularly when they share patients or practice in close proximity donohue2018influence,nair2010asymmetric,yang2014there. More centrally positioned physicians often act as opinion leaders and influence the diffusion of clinical practice soumerai1998effect,locock2001understanding,hao2018empirical. Similar patterns have been documented in antibiotic prescribing gong2025peer,wang2023can. Whether these network mechanisms operate in opioid prescribing and shape responses to major policy interventions remains unclear. In particular, two questions remain unresolved. Do peer effects persist after accounting for physician fixed effects and local shocks? Does network position influence how physicians respond to national clinical guidelines?
This study addresses these questions by developing a unified behavioral framework that links persistence in individual prescribing, exposure to peer behavior, and heterogeneous responses to a common policy signal. This framework builds on the DeGroot tradition of network-based social learning golub2010naive,chandrasekhar2020testing,choi2023learning. A key feature is that the responsiveness to external policy signals varies with network centrality, reflecting differences in exposure to peers and information flows kandhway2016using. The network structure plays two roles: determining the extent of peer exposure and shaping how policy signals are translated into behavioral changes.
The framework yields three testable hypotheses. First, physicians exposed to higher peer prescribing intensity exhibit higher subsequent prescribing, reflecting social learning within local professional networks gong2025peer,wang2023can,yang2014there. Second, physicians who are more centrally positioned in the network respond more strongly to common policy shocks because their greater exposure to peer behavior amplifies the transmission of external signals. Third, changes in peer prescribing are associated with changes in individual prescribing following policy intervention, consistent with network-mediated propagation of behavioral adjustment kreindler2014diffusion,onnela2010influence.
These hypotheses were evaluated using nationwide Medicare Part D prescribing data from 2013 to 2020 donohue2012sources,kuo2016trends,yin2008effect,ang2025expanded. The 2016 CDC opioid prescribing guideline provides a well-defined national policy shock bohnert2018opioid. Physician networks are constructed from geographic co-location within ZIP codes, a tractable proxy for local professional interaction tasselli2015social,cunningham2012health.
Our empirical analysis proceeded in three steps. First, we documented a positive association between peer and individual prescribing robust across specifications with physician and county-by-year fixed effects. Second, using an event study design around the 2016 CDC guideline, we showed that physicians with higher network centrality exhibited larger reductions in prescribing following the policy change, with no evidence of differential pre-trends. Third, we found that changes in peer prescribing were positively associated with changes in individual prescribing in the immediate post-guideline period.
These results are consistent with a process in which peer exposure, network position, and policy responses together shape prescribing behavior. Policy signals do not act on physicians in isolation, but move through professional connections, generating heterogeneous and evolving responses across the network. Consequently, policies that account for professional network structures may produce broader and more sustained reductions in prescribing than interventions that target physicians individually valente2012network,galeotti2020targeting,cabana1999don,kilaru2014physicians.
Beyond opioid prescribing, this study speaks to a broader question in the social science of medicine. It asks how professional norms and peer relationships shape the way clinicians respond to external policy. The physician setting lets us observe this social process at large scale and connect it to a major public health intervention.
Our analysis drew on three publicly available data sources. Our primary data source was the Medicare Part D Prescriber Public Use Files provided by the Centers for Medicare and Medicaid Services, covering 2013 to 2020. The unit of observation was physician-year.
We restricted the sample to seven commonly prescribed opioid analgesics, which together accounted for approximately 71% of all opioid claims (Table (ref)). Drugs were selected based on four criteria: active opioid ingredients classified under ICD-10 code T40.2, mappable morphine milligram equivalent (MME) conversion factors, consistent appearance across all sample years in the Medicare Part D data, and common use as analgesics for routine outpatient pain management. All prescriptions were converted to MME using conversion factors from the 2022 CDC Clinical Practice Guideline for Prescribing Opioids for Pain. Physician geographic information was obtained from prescriber ZIP codes in the CMS data, mapped to counties using HUD--USPS ZIP Code Crosswalk files.
The primary outcome was prescribing intensity, defined as the total MME prescribed divided by the number of opioid claims in a given physician-year. Patient volume was measured as the total number of unique Medicare beneficiaries associated with each physician in a given year.
Annual physician networks were constructed based on geographic proximity. Two physicians were connected in a given year if they practiced within the same ZIP code. The weight between physicians $i$ and $j$ was:
where $\text{Tot\_Benes}_i$ denotes the number of unique Medicare beneficiaries treated by physician $i$. Network position was measured using weighted degree centrality, defined as the sum of a physician's edge weights.
We modeled physician prescribing as a dynamic updating process following the DeGroot tradition golub2010naive,degroot1974consensus,ding2019consensus:
Here $p_{i,t}$ denotes prescribing intensity, $\bar{p}_{N(i),t}$ is the weighted average prescribing intensity of physician $i$'s network peers, $\alpha$ captures persistence, $\lambda$ captures social learning, and $G_t$ denotes the external signal corresponding to the 2016 CDC guideline. We assumed $\alpha + \lambda < 1$. We allowed centrality-dependent responsiveness:
where $C_i$ denotes physician $i$'s network centrality. We held $\lambda$ constant across physicians and focused inference on $\mu_1$.
Hypothesis 1. Physicians exposed to higher peer prescribing subsequently prescribe more:
A positive $\beta_1 > 0$ supports this hypothesis.
Hypothesis 2. Physicians with higher network centrality exhibit greater reductions in prescribing following the policy intervention:
$C_i$ is measured using the 2015 network and standardized as a z-score. Log-transformed centrality violates the parallel trends assumption ($p = 0.0002$) and is not adopted. More negative post-2016 $\delta_\tau$ coefficients are consistent with this hypothesis.
Hypothesis 3. Physicians whose peers reduce prescribing more exhibit larger own reductions:
where $\Delta p_i$ and $\Delta \bar{p}_{N(i)}$ denote changes in individual and peer prescribing between 2016 and 2017. The estimation sample consists of 246,481 physicians observed in both years with non-missing peer prescribing changes. Across all specifications, standard errors are clustered at the county level.
Table (ref) reports summary statistics for the same-ZIP physician network and opioid prescribing behavior from 2013 to 2020. The network was large and densely connected throughout the sample period, with more than 300,000 physicians per year and an average degree exceeding 80 even in later years. The network remained highly stable over time, with node Jaccard similarity around 0.70--0.75 across adjacent years. Total MME declined steadily after 2016, consistent with the CDC guideline timing.
Figure (ref) characterizes the network's structural properties, temporal stability, and geographic heterogeneity. Panel (a) shows a right-skewed degree distribution that was remarkably stable over time. Panel (b) shows substantial persistence in network structure. Panel (c) maps pronounced geographic heterogeneity in prescribing intensity across states.
Figure (ref) shows estimates from a sequence of specifications that progressively address potential confounding. The pooled OLS estimate indicated a strong positive association. After including physician fixed effects and county-by-year fixed effects, the estimated association remained positive, economically meaningful, and precisely estimated.
Table (ref) reports the corresponding estimates. In the preferred specification (A4), the estimated coefficient of peer prescribing is 0.0631 ($SE = 0.0050$), interpretable as an elasticity: a 1% increase in peer prescribing is associated with a 0.063% increase in individual prescribing in the subsequent period. Moving from the 10th to the 90th percentile of the peer prescribing distribution yields an increase of approximately 0.9% in a physician's own prescribing.
Figure (ref) presents event study estimates of prescribing responses by physician centrality. Pre-treatment coefficients for 2013 and 2014 were small and statistically indistinguishable from zero (F-test, $p = 0.85$), supporting the parallel trends assumption. Following the introduction of the guideline, physicians with higher centrality exhibited larger and increasingly pronounced reductions in prescribing intensity.
Table (ref) reports the corresponding estimates. Coefficients became negative and grew in magnitude from $-0.0004$ in 2016 to $-0.0014$ in 2020.
Figure (ref) plots event time coefficients separately for physicians at the 10th, 50th, and 90th percentiles of the baseline centrality distribution. By 2018, the difference in prescribing between the 90th and 10th percentile physicians reached 0.2109% and continued to increase to 0.2742% in 2019 and 0.3002% in 2020 (expressed as predicted percentage changes, $\exp(\Delta\log)-1$). The gap between the 50th and 10th percentiles remained substantially smaller at 0.0520%, 0.0677%, and 0.0741% over the same period.
Figure (ref) reports first-difference estimates. Table (ref) reports the corresponding estimates. In specification M1, a one-unit change in network-weighted peer prescribing is associated with a 0.0225 change in individual prescribing ($p = 0.030$). The estimate remained stable in specification M2 after controlling for baseline patient volume ($\beta = 0.0221$, $p = 0.032$), indicating the relationship is not driven by differences in physician scale.
This study showed that opioid prescribing is shaped by professional networks. Policy interventions appear to spread through these networks, producing responses that vary across physicians in ways that individual characteristics alone do not fully explain. All three results point toward a common mechanism: network position shapes both routine prescribing behavior and the transmission of policy-relevant behavioral change.
The stronger guideline responses observed among more central physicians are consistent with a well-established finding that centrally positioned professionals facilitate information transfer and serve as natural conduits for behavioral change tasselli2015social,cunningham2012health,soumerai1998effect. Our findings extend aggregate evidence that the 2016 CDC guideline accelerated the decline in high-dose opioid prescribing bohnert2018opioid. We show that these changes were concentrated among more central physicians and moved together with the prescribing of their local peers. By 2020, physicians at the 90th percentile of the centrality distribution exhibited prescribing reductions approximately 0.30 percentage points larger than physicians at the 10th percentile, a gap that widened steadily over the four years following the guideline's release.
The first-difference evidence is consistent with social learning models in which agents update by incorporating the behavior of their neighbors golub2010naive,chandrasekhar2020testing. This pattern is also connected to the clinical literature showing that peer comparison feedback and local norm setting are among the most effective tools for changing prescribing practices zhang2020systematic,stepan2019development,wetzel2018interventions. The absorption of peer variation by county fixed effects in the first-difference design underscores the general methodological principle that identification strategies must align with the geographic level at which peer interactions are defined marcus2021role,chiu2026causal.
These findings have direct implications for opioid stewardship programs. Network-based targeting strategies have been shown to generate substantial cascading effects in public health contexts alexander2022algorithms,kim2015social,valente2012network,valente2020diffusion. The theory of optimal network targeting predicts that interventions placed on high-centrality nodes generate disproportionately large spillovers when behaviors are strategically complementary galeotti2020targeting. Volume and centrality are related but distinct dimensions of physician network position, and our results suggest that centrality may be a more policy-relevant criterion for identifying physicians whose behavior changes will propagate most broadly through the network gouin2022identifying,iyengar2014social,hao2018empirical. At the same time, lower prescribing is not an unambiguous good. Efforts to reduce prescribing that are too broad or poorly placed can leave patients with legitimate pain undertreated. A network-based strategy would therefore need to be paired with attention to appropriate care, and should not be judged by reductions in prescribing volume alone.
This study had several limitations. The physician network was built from geographic co-location within ZIP codes, which serves as a proxy for local professional interaction. This construction treats all physicians in the same ZIP code as connected. It does not capture actual communication, referral, or shared-patient relationships. Patient-sharing network data donohue2018influence would allow a more precise characterization of interaction channels. The analysis relies on Medicare Part D data, which primarily reflect prescribing for older and disabled populations; opioid prescribing dynamics may differ in other settings ang2025expanded. Two issues limit causal interpretation. The reflection problem makes it hard to separate a physician's influence on peers from peers' influence on the physician manski1993identification. Shared local conditions and the sorting of physicians into practice areas can also produce correlated behavior that is not peer influence goldsmith2013social. Our physician and county-by-year fixed effects narrow these concerns for the peer-exposure results, and the event study leans on an external policy shock with no detectable pre-trends for the centrality results. Even so, we treat the peer estimates as associations rather than as fully identified causal effects. Our analysis also does not address the distributional consequences of network-based targeting. Concentrating attention on highly central physicians would shift where monitoring, education, and oversight fall. Where those physicians practice, and which patients they serve, carries equity implications that we do not measure here. Future work should ask whether such targeting narrows or widens existing disparities in access to pain care.
More broadly, this study contributes to a growing body of literature showing that professional behavior is shaped by the structure of the networks in which individuals are embedded lee2025granular,weng2026rewiring,lee2026adaptive. Future research could extend this approach using richer network data, additional policy shocks, or counterfactual simulations of alternative intervention-targeting strategies.
Physicians do not adjust their prescribing in isolation. They respond to the behavior of peers and to their position within local professional networks. Policies that recognize this social structure, by engaging well connected physicians or using peer feedback, may reach further than those that treat physicians as independent actors. Whether they do so safely and fairly is a question for the next stage of this work.
All data are publicly available from the Centers for Medicare and Medicaid Services (CMS) Medicare Part D Prescriber Public Use Files (\url{https://data.cms.gov}). ZIP-to-county crosswalk files are available from the HUD--USPS ZIP Code Crosswalk. Code for replication will be made available upon publication.
The authors declare no competing interests.