“Cultural” Variation in Antibiotic Prescribing: Have Regional Differences Had Their Day?
Year: 2023
Type: editorial
Abstract: Investigations going back to the mid-1990s have described regional variability in antibiotic prescribing in the United States (US) that cannot be explained by clinical differences, with the highest prescribing rates in the South [1–6]. Much attention has been paid to this finding, and for good reason. This variation suggests that some people are at risk of harm from unnecessary exposure to antibiotics. In addition, we all should be concerned about regions with higher rates of unnecessary antibiotic use. Antibiotic use causes antibiotic resistance and human-defined geographic regions are of no concern to microbes. Variation in antibiotic use is found at every level in which investigators have looked: across patients, prescribers in the same practice, practices in the same small area, small areas in the same state, states in the same region, regions in the same country, and countries across the globe [7–9]. Documenting the existence of variation in antibiotic prescribing that cannot be explained by clinical need is a bedrock justification for stewardship interventions and policy. As explanations for these large regional differences in the US, authors often point to distinct influences: patient-level factors (expectations for antibiotics, health literacy, educational attainment, underlying comorbidities such as obesity, income, and race), clinician prescribing habits, and culture. However, we question the utility of the continued focus on variation in antibiotic prescribing by US Census Region and the suggestion to explore “cultural” differences among them to address antibiotic overuse. In this issue of Open Forum Infectious Diseases, Bizune and colleagues contribute another study demonstrating regional variation in the volume and quality of antibiotic prescribing associated with outpatient visits in the US [10]. The authors used IBM MarketScan data to examine acute respiratory infection (ARI) visits to physician offices, retail clinics, urgent care clinics, and emergency departments, stratified by diagnoses for which antibiotics are always, sometimes, or rarely indicated. They found that clinicians prescribed antibiotics in 40% of ARI visits, more often in the South (43%) than the Midwest (41%), Northeast (37%), or the West (34%). Across regions, the antibiotic prescribing rate for always-indicated and sometimes-indicated diagnoses were roughly the same (about 58% to 69%) and lower for rarely indicated diagnoses (13% in the West, 16% in the Northeast and Midwest, and 18% in the South). In multivariable modeling with the West as the referent, for always-indicated diagnoses, there were no differences; for sometimes-indicated diagnoses, the South and Northeast had significantly greater antibiotic prescribing; and for rarely indicated diagnoses, there was incrementally greater antibiotic prescribing in the Midwest, Northeast, and South. Examining antibiotic prescribing across both regions and appropriateness tiers with rarely indicated ARI visits in the West as the referent, there was significantly greater antibiotic prescribing in all tiers and all regions, particularly in the South. Bizune and colleagues call for work addressing “the cultural factors that may be affecting regional differences,” state that “qualitative research could also provide more insight into the cultural context and regional differences,” and recommend that evidence-based interventions “be tailored to fit unique cultural needs.” However, invoking “culture” without clarification about its meaning obstructs actionability [11]. In popular use, “culture” typically refers to a bounded, consistent, and stable group of beliefs, symbols, and practices shared by one group in contrast to another. While “culture” is often used in reference to large groups (eg, nations, generations, religions), smaller groups have attributes of culture (eg, organizations, neighborhoods). Social scientists have challenged the notion that culture forms neatly coherent and distinct wholes, arguing instead that culture is contradictory, loosely integrated, weakly bounded, and subject to constant change [12]. Culture is not entirely consistent or shared for a given group, or even within an individual [13]. Instead, people draw fluidly on different cultural elements that are part of their identity or life history depending on the context and social structure around them [14]. The influence of culture on individual behavior is complex. “Cultural tailoring” of stewardship interventions to the 4 US Census Regions is probably of limited utility. Doing so assumes coherent and bounded Southern, Midwestern, Northeastern, or Western culture operate to shape antibiotic prescribing. Social science tells us this is unlikely. Even if one does exist, what is the mechanism by which regional culture influences antibiotic prescribing? Is accounting for regional culture in research and targeting it in interventions likely to make a difference in antibiotic use? Might other levels of culture, individual life experience, or more immediate contextual-structural factors play a bigger role in influencing antibiotic prescribing than a broad regional culture? We urge adopting a nuanced view of culture in antibiotic stewardship research to explain variation in prescribing, to ensure focus on the places where intervening can make the most difference. Consider a hypothetical scenario between a clinician and patient in which a decision about antibiotics will occur (Table 1). This scenario paints a detailed picture of the multiple interacting social, cultural, contextual, and structural factors that could shape a clinician's decision to prescribe an antibiotic, a patient's care-seeking behavior, and expectations for an unnecessary antibiotic. Assume the scenario occurs in a Southern state with a high rate of antibiotic prescriptions compared to other states in the US [15]. While both physician and patient spent their formative years in the South, presumably influenced by a broad regional culture, their individual life history and more immediate, modifiable factors—organizational context, micro-level social dynamics, economic incentives—around the clinical encounter are likely exerting more influence (Table 2). Hypothetical Clinical Encounter Abbreviation: US, United States. Hypothetical Clinical Encounter Abbreviation: US, United States. Potential Influences on Decision Making Family of origin culture Norms and beliefs about medication, illness, and health Memorable personal experience First-hand experiences with the harm of antibiotics Training environment Location in low antibiotic-utilizing state Clinic culture and structure Quality improvement infrastructure and resources Leadership support for antibiotic stewardship Social interactions and peer support Availability and design of electronic clinical decision support Professional culture and identity Patient satisfaction scores incentivized because of clinic concerns about local competition Workload and staffing Context of encounter Time of day Decision fatigue No prior relationship with the patient Gender culture Norms and beliefs about the body, medication, illness, and health Norms and beliefs about economic responsibility Economic pressures Lack of covered sick leave Supplemental income via gig economy, lack of worker protections, and unpredictable degree of income Inflation and rising cost of living Access to care Usability of appointment booking technology Change in relationship with primary care clinician Memorable personal experience History of receiving antibiotics for similar symptoms Social networks Prompts for antibiotics from others with similar symptoms Family of origin culture Norms and beliefs about medication, illness, and health Memorable personal experience First-hand experiences with the harm of antibiotics Training environment Location in low antibiotic-utilizing state Clinic culture and structure Quality improvement infrastructure and resources Leadership support for antibiotic stewardship Social interactions and peer support Availability and design of electronic clinical decision support Professional culture and identity Patient satisfaction scores incentivized because of clinic concerns about local competition Workload and staffing Context of encounter Time of day Decision fatigue No prior relationship with the patient Gender culture Norms and beliefs about the body, medication, illness, and health Norms and beliefs about economic responsibility Economic pressures Lack of covered sick leave Supplemental income via gig economy, lack of worker protections, and unpredictable degree of income Inflation and rising cost of living Access to care Usability of appointment booking technology Change in relationship with primary care clinician Memorable personal experience History of receiving antibiotics for similar symptoms Social networks Prompts for antibiotics from others with similar symptoms Potential Influences on Decision Making Family of origin culture Norms and beliefs about medication, illness, and health Memorable personal experience First-hand experiences with the harm of antibiotics Training environment Location in low antibiotic-utilizing state Clinic culture and structure Quality improvement infrastructure and resources Leadership support for antibiotic stewardship Social interactions and peer support Availability and design of electronic clinical decision support Professional culture and identity Patient satisfaction scores incentivized because of clinic concerns about local competition Workload and staffing Context of encounter Time of day Decision fatigue No prior relationship with the patient Gender culture Norms and beliefs about the body, medication, illness, and health Norms and beliefs about economic responsibility Economic pressures Lack of covered sick leave Supplemental income via gig economy, lack of worker protections, and unpredictable degree of income Inflation and rising cost of living Access to care Usability of appointment booking technology Change in relationship with primary care clinician Memorable personal experience History of receiving antibiotics for similar symptoms Social networks Prompts for antibiotics from others with similar symptoms Family of origin culture Norms and beliefs about medication, illness, and health Memorable personal experience First-hand experiences with the harm of antibiotics Training environment Location in low antibiotic-utilizing state Clinic culture and structure Quality improvement infrastructure and resources Leadership support for antibiotic stewardship Social interactions and peer support Availability and design of electronic clinical decision support Professional culture and identity Patient satisfaction scores incentivized because of clinic concerns about local competition Workload and staffing Context of encounter Time of day Decision fatigue No prior relationship with the patient Gender culture Norms and beliefs about the body, medication, illness, and health Norms and beliefs about economic responsibility Economic pressures Lack of covered sick leave Supplemental income via gig economy, lack of worker protections, and unpredictable degree of income Inflation and rising cost of living Access to care Usability of appointment booking technology Change in relationship with primary care clinician Memorable personal experience History of receiving antibiotics for similar symptoms Social networks Prompts for antibiotics from others with similar symptoms While we are enthusiastically supportive of considering context when implementing antibiotic stewardship interventions [16], doing so requires a working logic model of how proposed intervention elements will work to change behavior. To this end, we agree with Bizune and colleagues when they suggest future work should focus on smaller geographic areas to generate a more nuanced understanding of the factors shaping variation in antibiotic use and to target stewardship interventions. For example, they highlight the Kentucky Antibiotic Awareness (KAA) campaign as an example of a tailored stewardship effort. While Kentucky is in the Southern US Census region, the KAA campaign was tailored to the structure of the state, not Southern “culture.” State-level tailoring makes sense, especially when stewardship interventions involve techniques such as the provision of social norm feedback from a high-profile authority whose influence on prescribers operates via the state level (eg, licensing boards, departments of public health, state-wide payers such as Medicaid) [17]. Moving forward, we have three recommendations. First, investigators should be specific and clear about what they mean by “culture” when suggesting it as an explanation for observed geographic variation in antibiotic use. Second, research that seeks to use observed variation to tailor stewardship interventions should start with a working logic model to propose how implementation will address determinants of antibiotic overuse at multiple levels, including characteristics of the individuals involved, immediate contextual considerations, and larger sociostructural factors [18]. Third, investigators should retire their focus on the four US Census Regions when addressing variation in antibiotic prescribing. We should strive for more granularity in research on this topic by looking at smaller area variation to facilitate efficient policy- or intervention-targeting. Novel empirical strategies such as small area estimation or analyses that examine the migration of patients or clinicians from one region to another could be used to generate knowledge about the relative contribution of different factors to observed geographic variation in prescribing [19, 20]. While large area regional differences may be useful to point out what is possible, for addressing variability and designing interventions, regional differences have probably had their day. Acknowledgments. J. A. L. is supported by the National Institute on Aging (grant numbers R01AG070054, R33AG057383, R33AG057395, P30AG059988, and R01AG069762) and the Agency for Healthcare Research and Quality (grant numbers R01HS026506 and R01HS028127). Financial support. No funding source directly supported this work.
Source: Open Forum Infectious Diseases
DOI: 10.1093/ofid/ofad025
Authors: Julia E Szymczak, Jeffrey A Linder
Institutions: University of Pennsylvania, Northwestern University
Language: English
Subfield: Applied Microbiology and Biotechnology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,676
OpenAlex ID: W4319602804