{"doi":"10.1093/aje/kwae391","title":"Public health surveillance of outpatient antibiotic prescription trends, United States, 2011-2019","abstract":"Optimizing antibiotic use is critical for reducing adverse drug events and slowing the development of antimicrobial resistance. The US Centers for Disease Control and Prevention (CDC) has led efforts to track and report national antibiotic prescribing practices.1,2 A previously published analysis using IQVIA Xponent data showed a decrease in overall prescribing rates during 2011-2016, largely driven by decreases among pediatric patient populations.2 Using a patented projection methodology, IQVIA data capture about 93% of outpatient prescriptions dispensed by US retail pharmacies and project 100% of retail prescriptions sold.3 These data are used for national surveillance of antibiotic use, as well as other drugs, and are publicly shared on CDC’s Antimicrobial Resistance & Patient Safety Portal (https://arpsp.cdc.gov/).4,5 In 2017, IQVIA revised its methodology to only include filled and dispensed prescriptions, decreasing the number of prescriptions captured in the database.3 The objective of this analysis was to assess the antibiotic prescribing trends from 2011-2019 for public health surveillance, accounting for methodological changes by using the Joinpoint-Jump regression program. We used IQVIA Xponent data and US Census Bureau population estimates to determine annual rates of antibiotic prescribing (per 1000 population) stratified by patient age group, sex, prescriber’s US Census region, and antibiotic class during 2011-2019, as previously described.2 From 2011 to 2019, the database captured 74%-92% of all US retail outpatient prescriptions and projected to 100% of prescriptions nationally. Data were abstracted and descriptive analyses completed using SAS, version 9.4 (SAS Institute Inc). We used the Joinpoint-Jump regression program (version 4.9.1.0; National Cancer Institute) to assess statistically significant increases or decreases in antibiotic rate prescribing trends during 2011-2019. Antibiotic rates assessed include overall rate, and prescribing rate stratified by age, sex, region and antibiotic class.6 Joinpoint regression models are used to assess trends over time, similar to interrupted time series and segmented regression techniques. Joinpoint regressions have been used to estimate changes in rates of cancer incidence,6 mortality,7 and prescribing of opioids.8 The model identifies the best-fitting points (ie, years) with a statistically significant increase or decrease in antibiotic prescribing trends. To account for changes in the data methodology and avoid bias associated with a standard joinpoint model, mid-2016 was a parameter added to the model as a jump trend, because 2016 was the last year of data using the previous methodology.6,9,10 Joinpoint-Jump models have been applied to address discontinuous increases and decreases, or “jumps,” in a data series, even though they may not affect the underlying trend, such as code changes between the International Classification of Diseases, Ninth Revision, and the Tenth Revision.9 The jump model requires knowing the time when the discontinuous data change occurred, with the benefit of accounting for the size of the jump, if known. Data must be sorted in aggregate by the time-point variable as the last level of sorting. We fitted a log-linear model to the prescription rates, using year as the independent variable. The absence of a joinpoint segment was used to identify a linear trend during 2011-2019, and where nonlinear trends were identified, we assessed a single joinpoint with trend segments from 2011-2016 and 2016-2019. The methodology change is placed at the halfway point of 2016 (ie, 2016.5) to represent that 2016 is using the prior methodology and 2017 is using the revised methodology. We report the annual percent change (APC) with 95% CIs for each trend segment, and the average annual percent change (AAPC) with 95% CIs for the entire period. We estimated prevalence ratios and 95% CIs using Poisson regression models with a log link comparing prescribing rates ","journal":"American Journal of Epidemiology","year":2024,"id":474219,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9424,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":365798,"name":"Monina Bartoces","orcid":null,"position":1,"is_corresponding":false},{"id":975895,"name":"Katryna A. Gouin","orcid":"0000-0002-1202-681X","position":2,"is_corresponding":false},{"id":267654,"name":"Emily G. McDonald","orcid":"0000-0003-0783-0624","position":3,"is_corresponding":false},{"id":264786,"name":"Lauri A. Hicks","orcid":"0000-0002-2383-9357","position":4,"is_corresponding":false},{"id":651868,"name":"Sarah Kabbani","orcid":"0000-0002-1738-6443","position":5,"is_corresponding":false},{"id":1259294,"name":"Christine Kim","orcid":"0000-0001-6211-911X","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-19T02:06:09.108857Z","pmid":"39367708","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}