The Impact of Policy Guidelines on Hospital Antibiotic Use over a Decade: A Segmented Time Series Analysis

Abstract

IntroductionAntibiotic pressure contributes to rising antibiotic resistance. Policy guidelines encourage rational prescribing behavior, but effectiveness in containing antibiotic use needs further assessment. This study therefore assessed the patterns of antibiotic use over a decade and analyzed the impact of different modes of guideline development and dissemination on inpatient antibiotic use.MethodsAntibiotic use was calculated monthly as defined daily doses (DDD) per 100 bed days for nine antibiotic groups and overall. This time series compared trends in antibiotic use in five adjacent time periods identified as ‘Segments,’ divided based on differing modes of guideline development and implementation: Segment 1– Baseline prior to antibiotic guidelines development; Segment 2– During preparation of guidelines and booklet dissemination; Segment 3– Dormant period with no guidelines dissemination; Segment 4– Booklet dissemination of revised guidelines; Segment 5– Booklet dissemination of revised guidelines with intranet access. Regression analysis adapted for segmented time series and adjusted for seasonality assessed changes in antibiotic use trend.ResultsOverall antibiotic use increased at a monthly rate of 0.95 (SE = 0.18), 0.21 (SE = 0.08) and 0.31 (SE = 0.06) for Segments 1, 2 and 3, stabilized in Segment 4 (0.05; SE = 0.10) and declined in Segment 5 (−0.37; SE = 0.11). Segments 1, 2 and 4 exhibited seasonal fluctuations. Pairwise segmented regression adjusted for seasonality revealed a significant drop in monthly antibiotic use of 0.401 (SE = 0.089; p<0.001) for Segment 5 compared to Segment 4. Most antibiotic groups showed similar trends to overall use.ConclusionUse of overall and specific antibiotic groups showed varied patterns and seasonal fluctuations. Containment of rising overall antibiotic use was possible during periods of active guideline dissemination. Wider access through intranet facilitated significant decline in use. Stakeholders and policy makers are urged to develop guidelines, ensure active dissemination and enable accessibility through computer networks to contain antibiotic use and decrease antibiotic pressure.

Dynamic online antimicrobial guideline with stewardship program: Impact on antimicrobial prescribing

“…Similar rate of adherence to guidelines was observed in a number of previous studies (Abbo et al, 2011;Chandy et al, 2014;Alweis et al, 2014). Adherence to guideline could not be measured in the department of Surgery as very few diagnoses mentioned in the treatment sheet were present in the antimicrobial guideline.…”

A Review of Quality Measures for Assessing the Impact of Antimicrobial Stewardship Programs in Hospitals

“…The majority of the studies that reported significant cost savings did not provide the cost of implementing the program. Table 2 summarizes studies [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ] that assessed the impact of ASP on antimicrobial use and cost of antimicrobials.…”

The shift in seasonality of legionellosis in the USA

“…This apparent difference in trend marks two time segments, period 1 and period 2, best captured by a segmented regression model with the break point at 2003, in combination with harmonic terms describing the cyclic seasonal pattern of counts represented by the Poisson distribution. Thus, we defined the segmented Poisson harmonic regression model [35] in equation 4: $$\ln (E[Y(t)])={\beta }_{inter}+{\beta }_{tren{d}_{p1}}{t}_{p1}+{\beta }_{co{s}_{p1}}\cos \left(2\pi \omega t_{p1}\right)+{\beta }_{si{n}_{p1}}\sin \left(2\pi \omega t_{p1}\right)+{\beta }_{tren{d}_{p2}}{t}_{p2}+{\beta }_{co{s}_{p2}}\cos \left(2\pi \omega t_{p2}\right)+{\beta }_{{\sin }_{p2}}\sin \left(2\pi \omega t_{p2}\right)+\epsilon \text{ ,}$$ where Y(t) is disease incidence at month t ; β inter is the intercept reflecting counts at the break point (T break ); β trend are the coefficients for trend with t p1 and t p2 representing time in month before and after T break , respectively; similarly β cos and β sin are the coefficients of the harmonic terms for each of two segments or time periods, p1 and p2 , respectively; ω = 1/M, where M=12 is the length of the annual cycle in month; and ε is an error term.…”