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

<div><p>Introduction</p><p>Antibiotic 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.</p><p>Methods</p><p>Antibiotic 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.</p><p>Results</p><p>Overall 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; <i>p</i><0.001) for Segment 5 compared to Segment 4. Most antibiotic groups showed similar trends to overall use.</p><p>Conclusion</p><p>Use 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.</p></div

Time series of monthly antibiotic use over ten years with five segments demarcated by vertical lines.

<p>Time series of monthly antibiotic use over ten years with five segments demarcated by vertical lines.</p

Seasonal patterns and trends in antibiotic use for nine groups.

<p>Seasonal patterns and trends in antibiotic use for nine groups.</p

Pair-wise segmented analysis as the estimated rate of change in monthly DDD/100 bed days for adjacent segments and predicted values for the start of the <i>i</i>-segment, start and end of the <i>i</i>+1-segment.

<p>*S – Segment.</p><p>**<i>p</i> values reflect the results of standard z-test for a linear trend for <i>i</i>-segment and <i>i</i>+1 segments, respectively; <i>p</i>-values below 0.05 are italicized.</p

Exploratory analysis of trends in individual segments without (Model 1) and with (Model 2) adjustment for seasonality for overall antibiotic use (DDD/100 bed days).

<p>*<i>p</i>-values below 0.05 are italicized</p><p>**R²- values that increase in the seasonally adjusted Model 2 are shown in bold</p

Calendar plot showing trend and seasonal patterns of overall antibiotic use.

<p>Calendar plot showing trend and seasonal patterns of overall antibiotic use.</p

Recruitment.

<p>Recruitment.</p

Description of Cases.

<p>*More than 20% data unavailable.</p

Forward stepwise regression showing predictors of disease severity in 129 patients with scrub typhus.

<p>Data are given as correlation coefficient with corresponding P values in parenthesis.</p

Estimation of the Burden of Pandemic(H1N1)2009 in Developing Countries: Experience from a Tertiary Care Center in South India

<div><h3>Background</h3><p>The burden of the pandemic (H1N1) 2009 influenza might be underestimated if detection of the virus is mandated to diagnose infection. Using an alternate approach, we propose that a much higher pandemic burden was experienced in our institution.</p> <h3>Methodology/Principal Findings</h3><p>Consecutive patients (n = 2588) presenting to our hospital with influenza like illness (ILI) or severe acute respiratory infection (SARI) during a 1-year period (May 2009–April 2010) were prospectively recruited and tested for influenza A by real-time RT-PCR. Analysis of weekly trends showed an 11-fold increase in patients presenting with ILI/SARI during the peak pandemic period when compared with the pre-pandemic period and a significant (P<0.001) increase in SARI admissions during the pandemic period (30±15.9 admissions/week) when compared with pre-pandemic (7±2.5) and post-pandemic periods (5±3.8). However, Influenza A was detected in less than one-third of patients with ILI/SARI [699 (27.0%)]; a majority of these (557/699, 79.7%) were Pandemic (H1N1)2009 virus [A/H1N1/09]. An A/H1N1/09 positive test was correlated with shorter symptom duration prior to presentation (p = 0.03). More ILI cases tested positive for A/H1N1/09 when compared with SARI (27.4% vs. 14.6%, P = 0.037). When the entire study population was considered, A/H1N1/09 positivity was associated with lower risk of hospitalization (p<0.0001) and ICU admission (p = 0.013) suggesting mild self-limiting illness in a majority.</p> <h3>Conclusion/Significance</h3><p>Analysis of weekly trends of ILI/SARI suggest a higher burden of the pandemic attributable to A/H1N1/09 than estimates assessed by a positive PCR test alone. The study highlights methodological consideration in the estimation of burden of pandemic influenza in developing countries using hospital-based data that may help assess the impact of future outbreaks of respiratory illnesses.</p> </div