<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03776383</url>
  </required_header>
  <id_info>
    <org_study_id>2018-046.01</org_study_id>
    <nct_id>NCT03776383</nct_id>
  </id_info>
  <brief_title>Providing Antibiotic Prescribing Feedback to Primary Care Physicians: The Ontario Program To Improve AntiMicrobial USE</brief_title>
  <acronym>OPTIMISE</acronym>
  <official_title>Ontario Healthcare Implementation Laboratory: A Learning Health System Approach That Leverages Data to Improve Quality in Primary Care: Stage 1</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>Ontario Agency for Health Protection and Promotion</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>Canadian Institutes of Health Research (CIHR)</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Health Quality Ontario</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>Ontario Agency for Health Protection and Promotion</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      Antibiotic overuse occurs in multiple jurisdictions and is associated with rising rates of&#xD;
      antimicrobial resistance. Mailing letters to the highest antibiotic prescribing physicians is&#xD;
      a potentially effective method to optimize antibiotic use. The objectives of this study are&#xD;
      to improve enrollment to Health Quality Ontario's Primary Care Practice report and reduce&#xD;
      unnecessary antibiotic use. The investigators are conducting a randomized controlled trial&#xD;
      recruiting the 3500 highest antibiotic prescribing primary care physicians in Ontario. The&#xD;
      investigators have incorporated behavioural science theory into designing letters to modify&#xD;
      prescribing behaviour. Letter 1 is testing change ideas related to antibiotic initiation and&#xD;
      letter 2 is testing change ideas related to antibiotic duration. There will be 1500&#xD;
      physicians receiving letter 1, 1500 receiving letter 2, and 500 will serve as controls.&#xD;
      Twelve months later all 3500 physicians will receive a letter.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Background: Antibiotic overuse is the major driver of rising antimicrobial resistance rates.&#xD;
      Unnecessary antibiotic use is associated with adverse medication side effects and rising&#xD;
      rates of resistance. Mailing letters to high antibiotic prescribers in other countries have&#xD;
      shown variables effects on reducing antibiotic prescribing.1-3&#xD;
&#xD;
      Objective: To evaluate the impact of a letter to the 25% of primary care physicians who&#xD;
      prescribe the most antibiotics in Ontario on increasing enrollment for HQO primary care&#xD;
      practice reports.&#xD;
&#xD;
      Secondary objectives: To test the impact of different formats of a notification letter on&#xD;
      antibiotic prescribing. Our primary hypothesis is that notifying physicians that antibiotic&#xD;
      use is being monitored will decrease use of antibiotics. The investigators further&#xD;
      hypothesize that targeted change ideas on antibiotic initiation or duration will have varying&#xD;
      impacts on these different prescribing behaviours highlighting the importance of targeted&#xD;
      messaging.&#xD;
&#xD;
      Methods: The effect of letter mail outs to the top 25% prescribing primary care physicians&#xD;
      will be evaluated using a randomized controlled trial (RCT) design. The investigators will&#xD;
      allocate physicians to intervention versus control arms in a 3:3:1 ratio to maximize the&#xD;
      number of physicians exposed to the intervention. The investigators are using the IQVIA&#xD;
      Xponent database from March 2017 to February 2018 to identify the top 25% of primary care&#xD;
      physician antibiotic prescribers in Ontario (family physicians or general practitioners)&#xD;
      defined by total numbers of antibiotic prescriptions. They will be mailed a letter to&#xD;
      indicate that they prescribe more antibiotics than 75% of Ontario primary care physicians,&#xD;
      based on total numbers of antibiotic prescriptions per year. The letter will encourage them&#xD;
      to sign up for HQO My Practice primary care reports which in the future may include&#xD;
      antibiotic indicators.&#xD;
&#xD;
      The letter was designed using proven theoretical frameworks of behaviour change to improve&#xD;
      antibiotic prescribing by physicians. Most physicians are aware of the global public health&#xD;
      threat of antimicrobial resistance, however many underestimate its importance locally.&#xD;
      Furthermore, unnecessary antibiotic prescribing is frequently attributed to external factors&#xD;
      out of their control, such as patient expectations and patient adherence to medication.4 For&#xD;
      this intervention the investigators focused on two behaviour change theories previously&#xD;
      demonstrated to be associated with prescribing behaviour; Theory of Planned Behaviour (TBP)&#xD;
      and Operant Learning Theory (OLT).5 The letter will provide some social normative comparison&#xD;
      by indicating to these physicians that they prescribe more antibiotics than their peers&#xD;
      (TPB). By providing change ideas and information on the consequences of unnecessary&#xD;
      antibiotic use, the investigators are attempting to change the physicians' risk perception&#xD;
      (TPB) and anticipated consequences (OLT) of prescribing antibiotics for acute respiratory&#xD;
      tract infections. This letter represents a form of persuasive communication behaviour change&#xD;
      technique (BCT). This form of BCT has shown to be effective in modifying OLT driven&#xD;
      behaviours.6, 7&#xD;
&#xD;
      To test different change ideas, the investigators will simultaneously evaluate two types of&#xD;
      letters to be randomly distributed to the physicians in the top 25% of prescribers allocated&#xD;
      in 3:3:1 ratios; a) Providing change ideas from Choosing Wisely Canada on appropriate&#xD;
      initiation of antibiotics for acute respiratory conditions; b) Providing change ideas on&#xD;
      appropriate duration of antibiotics.&#xD;
&#xD;
      Designs of the letters went through user testing and were iteratively refined by a sample of&#xD;
      Ontario primary care physicians. The letters will be co-branded with the Ontario Medical&#xD;
      Association (OMA) Section of General and Family Practice and Choosing Wisely Ontario. The&#xD;
      letters were also reviewed by the Ontario College of Family Physicians, HQO and members of&#xD;
      the study team to provide input and edits to the language and content. Based on the trial&#xD;
      results the investigators will send a letter to all 3500 physicians the following year (12&#xD;
      months later).&#xD;
&#xD;
      Sample Size: Sample size calculations are based on Antibiotic outcome 1. A total of 3500&#xD;
      primary care physicians will be included in the randomized controlled trial: 3000 will&#xD;
      receive a letter and 500 will not receive a letter. In an analysis of covariance (ANCOVA),&#xD;
      sample sizes of 500, 1500, and 1500 physicians achieve &gt;80% power to detect a 1% absolute&#xD;
      difference in the primary antibiotic outcome between the average of the three intervention&#xD;
      arms versus control using an F-Test at the 5% level of significance. The calculations assumed&#xD;
      a standard deviation of 12 calculated using routinely collected data available within March 1&#xD;
      2017 and February 28 2018, and accounted for the baseline measure of the primary outcome&#xD;
      assuming a correlation of 0.8.&#xD;
&#xD;
      The primary outcome will be an intention to treat analysis of the RCT at 12 months for both&#xD;
      outcomes 1 and 2. The unit of analysis will be the physician. The primary analysis will use&#xD;
      the modified robust Poisson regression approach of Zou and Donner.(Zou and Donner 2013) The&#xD;
      dependent variable will be the numerator for each outcome, with the log of the denominator&#xD;
      specified as an offset term. The distribution will be Poisson and the link function will be&#xD;
      the log-link. The model will include a fixed term for time (pre/post), and the interaction&#xD;
      between time and group (intervention). Note that, in order to constrain the differences&#xD;
      between the arms at baseline to 0, the model will omit the main effect for the intervention.&#xD;
      Pairwise least square mean differences will be obtained from the model (each intervention arm&#xD;
      versus control) and expressed as Relative Risk (RR) and 97.5% confidence intervals.&#xD;
      Statistical significance will be assessed at the Bonferroni-corrected level of 0.05/3. Robust&#xD;
      standard errors will be used, with the correlation structure specified as Exchangeable.&#xD;
      Secondary pairwise comparisons will also be obtained from the model (e.g., the average of the&#xD;
      two letter versus control, and direct comparison between the two letters).&#xD;
&#xD;
      The investigators will use the same approach for the quarterly data, except that the least&#xD;
      square mean pairwise differences will be obtained at the first, second, third and final&#xD;
      quarters after randomization, to examine short and long-term effects of the intervention. The&#xD;
      investigators will evaluate subgroups including baseline physician prescribing (&gt;90th %ile vs&#xD;
      75-90th %ile), years in medical practice (&lt;10y, 11-24y, vs 25y), physician gender (M vs F),&#xD;
      rural vs urban practice, and patient age/sex groupings (&lt;18yM, &lt;18yF, 18-64yM, 18-64yF, 65+M,&#xD;
      and 65+F) using IQVIA data.&#xD;
&#xD;
      A secondary outcome will be new physician enrollment into HQO primary care practice reports.&#xD;
      Working with HQO, the investigators will track physicians in our study that sign up in&#xD;
      response to receiving the letter from Public Health Ontario (PHO) by February 28, 2019.&#xD;
      Physicians will be invited to sign up via a special weblink created for this study. The&#xD;
      primary analysis will calculate the absolute difference between the proportions of new sign&#xD;
      ups in the two intervention arms versus the control arm together with 95% confidence&#xD;
      interval. Multiplicity-adjusted pairwise comparisons will also be made between each of the&#xD;
      intervention arms versus the control and between the different versions of the letters. The&#xD;
      investigators will evaluate predictor variables of new enrollment including baseline&#xD;
      physician prescribing (&gt;90th %ile vs 75-90th %ile), years in medical practice (&lt;10y, 11-24y,&#xD;
      vs 25y), physician gender (M vs F), rural vs urban practice, and patient age/sex groupings&#xD;
      (&lt;18yM, &lt;18yF, 18-64yM, 18-64yF, 65+M, and 65+F) using logistic regression analysis.&#xD;
&#xD;
      To evaluate the overall effect of receiving any letter, compared to no letter, on prescribing&#xD;
      outcomes in Ontario, The investigators will use a regression discontinuity design (RDD) by&#xD;
      including antibiotic prescribing data from the rest of Ontario's primary care physicians. In&#xD;
      the traditional RDD, subjects are assigned to treatment or control based on a cut-off value&#xD;
      of a continuous assignment variable. The investigators will use the 3000 primary care&#xD;
      physicians who received letters (excluding the 500 physicians in the control arm) as the&#xD;
      treatment group and compare them to the remainder of Ontario physicians as controls. The&#xD;
      assignment variable in our design is the antibiotic prescription rate (defined as antibiotics&#xD;
      per 100 total medications over the year before the intervention).The investigators will&#xD;
      present effects graphically by plotting the continuous assignment variable on the x-axis and&#xD;
      the continuous outcome variable (antibiotic prescription rate over the year after the&#xD;
      intervention) on the y-axis. Different regression lines are then fit to the data on each site&#xD;
      of the cut-off. If the intervention is effective, there will be a discontinuity in the&#xD;
      regression lines at the value of the cut-off. The intervention effect is expressed as the&#xD;
      vertical displacement at the point of discontinuity.8 The investigators will also conduct&#xD;
      this analysis for antibiotic duration.&#xD;
&#xD;
      Limitations: The investigators can only monitor antibiotics prescribed per 100 total&#xD;
      medications with this dataset, instead of per patient volume. This measure appears to be a&#xD;
      reasonable antibiotic use measure within a physician as it is stable over time in high&#xD;
      prescribing physicians in our datasets.&#xD;
&#xD;
      Ethical considerations: Antimicrobial stewardship and combating antimicrobial resistance is a&#xD;
      provincial and national priority. The Xponent database with prescribing data at an individual&#xD;
      prescriber level is already held by PHO and is kept in a secure location. The agreement with&#xD;
      IQVIA allows PHO to provide direct feedback to physicians. The letters will be addressed to&#xD;
      the physician and marked confidential. The only data within them will be that they have been&#xD;
      identified as a high antibiotic prescriber. No specific prescription counts or any&#xD;
      patient-level data are being provided.&#xD;
&#xD;
      The investigators have designed this study to maximize HQO sign up and potential impact&#xD;
      through a 6:1 allocation. Furthermore, the investigators will send a debrief and follow-up&#xD;
      letter to all physicians the following year so that all high prescribing physicians will have&#xD;
      the opportunity to receive this information and sign up for HQO reports. Physicians will be&#xD;
      given an email address to contact PHO with any questions or concerns regarding the letter.&#xD;
      All results will be aggregated and reports will not divulge any prescriber details.&#xD;
&#xD;
      A waiver of consent for this study was provided. This intervention involves minimal risk and&#xD;
      burden to participants (letters are sent to physicians from government agencies routinely;&#xD;
      these letters do not require an immediate response). There is no anticipated impact on the&#xD;
      welfare of physicians by receiving this letter. The intervention would be impractical if&#xD;
      consent were required. A consent process even if feasible, would likely create selection bias&#xD;
      and make it impossible to answer the research question posed. In addition, the consent&#xD;
      process would include a greater burden to physicians than this single letter intervention&#xD;
      itself. This quality improvement initiate has the potential to benefit physicians by&#xD;
      providing helpful recommendations on optimal antibiotic prescribing practices. A PHO contact&#xD;
      email will be provided on the letter to provide physicians the opportunity to contact PHO and&#xD;
      opt-out of future letters.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">December 14, 2018</start_date>
  <completion_date type="Actual">November 30, 2020</completion_date>
  <primary_completion_date type="Actual">November 30, 2019</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <intervention_model_description>3:3:1 for intervention 1: intervention 2: control</intervention_model_description>
    <primary_purpose>Health Services Research</primary_purpose>
    <masking>Single (Outcomes Assessor)</masking>
    <masking_description>The data for the outcome are being provided from a third party with no involvement in the study</masking_description>
  </study_design_info>
  <primary_outcome>
    <measure>Antibiotic prescribing rate</measure>
    <time_frame>12 months</time_frame>
    <description>Number of antibiotics prescribed per 100 total prescriptions</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Prolonged duration prescribing rate</measure>
    <time_frame>12 months</time_frame>
    <description>Proportion of antibiotic prescriptions that are &gt;7 days per 100 total prescriptions</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>New enrollment for Health Quality Ontario primary care practice reports</measure>
    <time_frame>24 months</time_frame>
    <description>Proportion of new sign ups for Health Quality Ontario primary care practice reports</description>
  </secondary_outcome>
  <other_outcome>
    <measure>New enrollment for Health Quality Ontario primary care practice reports</measure>
    <time_frame>6 months</time_frame>
    <description>Proportion of new sign ups for Health Quality Ontario primary care practice reports</description>
  </other_outcome>
  <other_outcome>
    <measure>New enrollment for Health Quality Ontario primary care practice reports</measure>
    <time_frame>12 months</time_frame>
    <description>Proportion of new sign ups for Health Quality Ontario primary care practice reports</description>
  </other_outcome>
  <other_outcome>
    <measure>Antibiotic prescribing rate</measure>
    <time_frame>3 months</time_frame>
    <description>Number of antibiotics prescribed per 100 total prescriptions</description>
  </other_outcome>
  <other_outcome>
    <measure>Antibiotic prescribing rate</measure>
    <time_frame>6 months</time_frame>
    <description>Number of antibiotics prescribed per 100 total prescriptions</description>
  </other_outcome>
  <other_outcome>
    <measure>Antibiotic prescribing rate</measure>
    <time_frame>9 months</time_frame>
    <description>Number of antibiotics prescribed per 100 total prescriptions</description>
  </other_outcome>
  <other_outcome>
    <measure>Antibiotic prescribing rate</measure>
    <time_frame>24 months</time_frame>
    <description>Number of antibiotics prescribed per 100 total prescriptions</description>
  </other_outcome>
  <other_outcome>
    <measure>Prolonged duration prescribing</measure>
    <time_frame>3 months</time_frame>
    <description>Proportion of antibiotic prescriptions that are &gt;7 days per 100 total prescriptions</description>
  </other_outcome>
  <other_outcome>
    <measure>Prolonged duration prescribing</measure>
    <time_frame>6 months</time_frame>
    <description>Proportion of antibiotic prescriptions that are &gt;7 days per 100 total prescriptions</description>
  </other_outcome>
  <other_outcome>
    <measure>Prolonged duration prescribing</measure>
    <time_frame>9 months</time_frame>
    <description>Proportion of antibiotic prescriptions that are &gt;7 days per 100 total prescriptions</description>
  </other_outcome>
  <other_outcome>
    <measure>Prolonged duration prescribing</measure>
    <time_frame>24 months</time_frame>
    <description>Proportion of antibiotic prescriptions that are &gt;7 days per 100 total prescriptions</description>
  </other_outcome>
  <number_of_arms>3</number_of_arms>
  <enrollment type="Actual">3500</enrollment>
  <condition>Antibiotic Prescribing Audit and Feedback</condition>
  <arm_group>
    <arm_group_label>Antibiotic use feedback letter 1</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>Antibiotic use feedback letter 1 provides physicians with information on their antibiotic use plus change ideas on appropriate antibiotic prescribing for acute respiratory conditions</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Antibiotic use feedback letter 2</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>Antibiotic use feedback letter 2 provides physicians with information on their antibiotic use plus change ideas on appropriate antibiotic durations for common infections</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Control</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
    <description>Controls will not receive a letter</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Antibiotic use feedback letter</intervention_name>
    <description>Mailed letters indicating that the physician prescribes more antibiotics than 75% of their peers</description>
    <arm_group_label>Antibiotic use feedback letter 1</arm_group_label>
    <arm_group_label>Antibiotic use feedback letter 2</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Ontario primary care physicians&#xD;
&#xD;
          -  Prescribe more antibiotics by volume than 75% of Ontario primary care physicians&#xD;
&#xD;
          -  A College of Physicians and Surgeons of Ontario number in 2017-2018&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  None&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>N/A</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Kevin Schwartz, MD MSc</last_name>
    <role>Principal Investigator</role>
    <affiliation>Ontario Agency for Health Protection and Promotion</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>Public Health Ontario</name>
      <address>
        <city>Toronto</city>
        <state>Ontario</state>
        <zip>M5G 1V2</zip>
        <country>Canada</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Canada</country>
  </location_countries>
  <reference>
    <citation>Hallsworth M, Chadborn T, Sallis A, Sanders M, Berry D, Greaves F, Clements L, Davies SC. Provision of social norm feedback to high prescribers of antibiotics in general practice: a pragmatic national randomised controlled trial. Lancet. 2016 Apr 23;387(10029):1743-52. doi: 10.1016/S0140-6736(16)00215-4. Epub 2016 Feb 18.</citation>
    <PMID>26898856</PMID>
  </reference>
  <reference>
    <citation>Hemkens LG, Saccilotto R, Reyes SL, Glinz D, Zumbrunn T, Grolimund O, Gloy V, Raatz H, Widmer A, Zeller A, Bucher HC. Personalized prescription feedback to reduce antibiotic overuse in primary care: rationale and design of a nationwide pragmatic randomized trial. BMC Infect Dis. 2016 Aug 17;16:421. doi: 10.1186/s12879-016-1739-0.</citation>
    <PMID>27530528</PMID>
  </reference>
  <reference>
    <citation>Nudge vs Superbugs: A behavioural economics trial to reduce the overprescribing of antibiotics, Available online at: http://behaviouraleconomics.pmc.gov.au/sites/default/files/projects/report-nudge-vs-superbugs.pdf (accessed June 27, 2018).</citation>
  </reference>
  <reference>
    <citation>McCullough AR, Rathbone J, Parekh S, Hoffmann TC, Del Mar CB. Not in my backyard: a systematic review of clinicians' knowledge and beliefs about antibiotic resistance. J Antimicrob Chemother. 2015 Sep;70(9):2465-73. doi: 10.1093/jac/dkv164. Epub 2015 Jun 20.</citation>
    <PMID>26093375</PMID>
  </reference>
  <reference>
    <citation>Eccles MP, Grimshaw JM, Johnston M, Steen N, Pitts NB, Thomas R, Glidewell E, Maclennan G, Bonetti D, Walker A. Applying psychological theories to evidence-based clinical practice: identifying factors predictive of managing upper respiratory tract infections without antibiotics. Implement Sci. 2007 Aug 3;2:26. doi: 10.1186/1748-5908-2-26.</citation>
    <PMID>17683558</PMID>
  </reference>
  <reference>
    <citation>Hrisos S, Eccles M, Johnston M, Francis J, Kaner EF, Steen N, Grimshaw J. Developing the content of two behavioural interventions: using theory-based interventions to promote GP management of upper respiratory tract infection without prescribing antibiotics #1. BMC Health Serv Res. 2008 Jan 14;8:11. doi: 10.1186/1472-6963-8-11.</citation>
    <PMID>18194527</PMID>
  </reference>
  <reference>
    <citation>Pennell ML, Hade EM, Murray DM, Rhoda DA. Cutoff designs for community-based intervention studies. Stat Med. 2011 Jul 10;30(15):1865-82. doi: 10.1002/sim.4237. Epub 2011 Apr 17. Erratum In: Stat Med. 2011 Sep 20;30(21):2669.</citation>
    <PMID>21500240</PMID>
  </reference>
  <verification_date>March 2021</verification_date>
  <study_first_submitted>December 12, 2018</study_first_submitted>
  <study_first_submitted_qc>December 13, 2018</study_first_submitted_qc>
  <study_first_posted type="Actual">December 14, 2018</study_first_posted>
  <last_update_submitted>March 2, 2021</last_update_submitted>
  <last_update_submitted_qc>March 2, 2021</last_update_submitted_qc>
  <last_update_posted type="Actual">March 3, 2021</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>Ontario Agency for Health Protection and Promotion</investigator_affiliation>
    <investigator_full_name>Kevin Schwartz</investigator_full_name>
    <investigator_title>PI</investigator_title>
  </responsible_party>
  <keyword>Antibiotic use</keyword>
  <keyword>Audit and feedback</keyword>
  <keyword>implementation science</keyword>
  <keyword>Antimicrobial stewardship</keyword>
  <intervention_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Anti-Bacterial Agents</mesh_term>
  </intervention_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
    <ipd_description>The data are proprietary and private and cannot be shared with external parties. Study results will be aggregated for publication</ipd_description>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03776383
org study id: 2018-046.01
nct id: NCT03776383
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
Antibiotic overuse occurs in multiple jurisdictions and is associated with rising rates of
      antimicrobial resistance. Mailing letters to the highest antibiotic prescribing physicians is
      a potentially effective method to optimize antibiotic use. The objectives of this study are
      to improve enrollment to Health Quality Ontario's Primary Care Practice report and reduce
      unnecessary antibiotic use. The investigators are conducting a randomized controlled trial
      recruiting the 3500 highest antibiotic prescribing primary care physicians in Ontario. The
      investigators have incorporated behavioural science theory into designing letters to modify
      prescribing behaviour. Letter 1 is testing change ideas related to antibiotic initiation and
      letter 2 is testing change ideas related to antibiotic duration. There will be 1500
      physicians receiving letter 1, 1500 receiving letter 2, and 500 will serve as controls.
      Twelve months later all 3500 physicians will receive a letter.
Background: Antibiotic overuse is the major driver of rising antimicrobial resistance rates.
      Unnecessary antibiotic use is associated with adverse medication side effects and rising
      rates of resistance. Mailing letters to high antibiotic prescribers in other countries have
      shown variables effects on reducing antibiotic prescribing.1-3

      Objective: To evaluate the impact of a letter to the 25% of primary care physicians who
      prescribe the most antibiotics in Ontario on increasing enrollment for HQO primary care
      practice reports.

      Secondary objectives: To test the impact of different formats of a notification letter on
      antibiotic prescribing. Our primary hypothesis is that notifying physicians that antibiotic
      use is being monitored will decrease use of antibiotics. The investigators further
      hypothesize that targeted change ideas on antibiotic initiation or duration will have varying
      impacts on these different prescribing behaviours highlighting the importance of targeted
      messaging.

      Methods: The effect of letter mail outs to the top 25% prescribing primary care physicians
      will be evaluated using a randomized controlled trial (RCT) design. The investigators will
      allocate physicians to intervention versus control arms in a 3:3:1 ratio to maximize the
      number of physicians exposed to the intervention. The investigators are using the IQVIA
      Xponent database from March 2017 to February 2018 to identify the top 25% of primary care
      physician antibiotic prescribers in Ontario (family physicians or general practitioners)
      defined by total numbers of antibiotic prescriptions. They will be mailed a letter to
      indicate that they prescribe more antibiotics than 75% of Ontario primary care physicians,
      based on total numbers of antibiotic prescriptions per year. The letter will encourage them
      to sign up for HQO My Practice primary care reports which in the future may include
      antibiotic indicators.

      The letter was designed using proven theoretical frameworks of behaviour change to improve
      antibiotic prescribing by physicians. Most physicians are aware of the global public health
      threat of antimicrobial resistance, however many underestimate its importance locally.
      Furthermore, unnecessary antibiotic prescribing is frequently attributed to external factors
      out of their control, such as patient expectations and patient adherence to medication.4 For
      this intervention the investigators focused on two behaviour change theories previously
      demonstrated to be associated with prescribing behaviour; Theory of Planned Behaviour (TBP)
      and Operant Learning Theory (OLT).5 The letter will provide some social normative comparison
      by indicating to these physicians that they prescribe more antibiotics than their peers
      (TPB). By providing change ideas and information on the consequences of unnecessary
      antibiotic use, the investigators are attempting to change the physicians' risk perception
      (TPB) and anticipated consequences (OLT) of prescribing antibiotics for acute respiratory
      tract infections. This letter represents a form of persuasive communication behaviour change
      technique (BCT). This form of BCT has shown to be effective in modifying OLT driven
      behaviours.6, 7

      To test different change ideas, the investigators will simultaneously evaluate two types of
      letters to be randomly distributed to the physicians in the top 25% of prescribers allocated
      in 3:3:1 ratios; a) Providing change ideas from Choosing Wisely Canada on appropriate
      initiation of antibiotics for acute respiratory conditions; b) Providing change ideas on
      appropriate duration of antibiotics.

      Designs of the letters went through user testing and were iteratively refined by a sample of
      Ontario primary care physicians. The letters will be co-branded with the Ontario Medical
      Association (OMA) Section of General and Family Practice and Choosing Wisely Ontario. The
      letters were also reviewed by the Ontario College of Family Physicians, HQO and members of
      the study team to provide input and edits to the language and content. Based on the trial
      results the investigators will send a letter to all 3500 physicians the following year (12
      months later).

      Sample Size: Sample size calculations are based on Antibiotic outcome 1. A total of 3500
      primary care physicians will be included in the randomized controlled trial: 3000 will
      receive a letter and 500 will not receive a letter. In an analysis of covariance (ANCOVA),
      sample sizes of 500, 1500, and 1500 physicians achieve >80% power to detect a 1% absolute
      difference in the primary antibiotic outcome between the average of the three intervention
      arms versus control using an F-Test at the 5% level of significance. The calculations assumed
      a standard deviation of 12 calculated using routinely collected data available within March 1
      2017 and February 28 2018, and accounted for the baseline measure of the primary outcome
      assuming a correlation of 0.8.

      The primary outcome will be an intention to treat analysis of the RCT at 12 months for both
      outcomes 1 and 2. The unit of analysis will be the physician. The primary analysis will use
      the modified robust Poisson regression approach of Zou and Donner.(Zou and Donner 2013) The
      dependent variable will be the numerator for each outcome, with the log of the denominator
      specified as an offset term. The distribution will be Poisson and the link function will be
      the log-link. The model will include a fixed term for time (pre/post), and the interaction
      between time and group (intervention). Note that, in order to constrain the differences
      between the arms at baseline to 0, the model will omit the main effect for the intervention.
      Pairwise least square mean differences will be obtained from the model (each intervention arm
      versus control) and expressed as Relative Risk (RR) and 97.5% confidence intervals.
      Statistical significance will be assessed at the Bonferroni-corrected level of 0.05/3. Robust
      standard errors will be used, with the correlation structure specified as Exchangeable.
      Secondary pairwise comparisons will also be obtained from the model (e.g., the average of the
      two letter versus control, and direct comparison between the two letters).

      The investigators will use the same approach for the quarterly data, except that the least
      square mean pairwise differences will be obtained at the first, second, third and final
      quarters after randomization, to examine short and long-term effects of the intervention. The
      investigators will evaluate subgroups including baseline physician prescribing (>90th %ile vs
      75-90th %ile), years in medical practice (<10y, 11-24y, vs 25y), physician gender (M vs F),
      rural vs urban practice, and patient age/sex groupings (<18yM, <18yF, 18-64yM, 18-64yF, 65+M,
      and 65+F) using IQVIA data.

      A secondary outcome will be new physician enrollment into HQO primary care practice reports.
      Working with HQO, the investigators will track physicians in our study that sign up in
      response to receiving the letter from Public Health Ontario (PHO) by February 28, 2019.
      Physicians will be invited to sign up via a special weblink created for this study. The
      primary analysis will calculate the absolute difference between the proportions of new sign
      ups in the two intervention arms versus the control arm together with 95% confidence
      interval. Multiplicity-adjusted pairwise comparisons will also be made between each of the
      intervention arms versus the control and between the different versions of the letters. The
      investigators will evaluate predictor variables of new enrollment including baseline
      physician prescribing (>90th %ile vs 75-90th %ile), years in medical practice (<10y, 11-24y,
      vs 25y), physician gender (M vs F), rural vs urban practice, and patient age/sex groupings
      (<18yM, <18yF, 18-64yM, 18-64yF, 65+M, and 65+F) using logistic regression analysis.

      To evaluate the overall effect of receiving any letter, compared to no letter, on prescribing
      outcomes in Ontario, The investigators will use a regression discontinuity design (RDD) by
      including antibiotic prescribing data from the rest of Ontario's primary care physicians. In
      the traditional RDD, subjects are assigned to treatment or control based on a cut-off value
      of a continuous assignment variable. The investigators will use the 3000 primary care
      physicians who received letters (excluding the 500 physicians in the control arm) as the
      treatment group and compare them to the remainder of Ontario physicians as controls. The
      assignment variable in our design is the antibiotic prescription rate (defined as antibiotics
      per 100 total medications over the year before the intervention).The investigators will
      present effects graphically by plotting the continuous assignment variable on the x-axis and
      the continuous outcome variable (antibiotic prescription rate over the year after the
      intervention) on the y-axis. Different regression lines are then fit to the data on each site
      of the cut-off. If the intervention is effective, there will be a discontinuity in the
      regression lines at the value of the cut-off. The intervention effect is expressed as the
      vertical displacement at the point of discontinuity.8 The investigators will also conduct
      this analysis for antibiotic duration.

      Limitations: The investigators can only monitor antibiotics prescribed per 100 total
      medications with this dataset, instead of per patient volume. This measure appears to be a
      reasonable antibiotic use measure within a physician as it is stable over time in high
      prescribing physicians in our datasets.

      Ethical considerations: Antimicrobial stewardship and combating antimicrobial resistance is a
      provincial and national priority. The Xponent database with prescribing data at an individual
      prescriber level is already held by PHO and is kept in a secure location. The agreement with
      IQVIA allows PHO to provide direct feedback to physicians. The letters will be addressed to
      the physician and marked confidential. The only data within them will be that they have been
      identified as a high antibiotic prescriber. No specific prescription counts or any
      patient-level data are being provided.

      The investigators have designed this study to maximize HQO sign up and potential impact
      through a 6:1 allocation. Furthermore, the investigators will send a debrief and follow-up
      letter to all physicians the following year so that all high prescribing physicians will have
      the opportunity to receive this information and sign up for HQO reports. Physicians will be
      given an email address to contact PHO with any questions or concerns regarding the letter.
      All results will be aggregated and reports will not divulge any prescriber details.

      A waiver of consent for this study was provided. This intervention involves minimal risk and
      burden to participants (letters are sent to physicians from government agencies routinely;
      these letters do not require an immediate response). There is no anticipated impact on the
      welfare of physicians by receiving this letter. The intervention would be impractical if
      consent were required. A consent process even if feasible, would likely create selection bias
      and make it impossible to answer the research question posed. In addition, the consent
      process would include a greater burden to physicians than this single letter intervention
      itself. This quality improvement initiate has the potential to benefit physicians by
      providing helpful recommendations on optimal antibiotic prescribing practices. A PHO contact
      email will be provided on the letter to provide physicians the opportunity to contact PHO and
      opt-out of future letters.
allocation: Randomized
intervention model: Parallel Assignment
intervention model description: 3:3:1 for intervention 1: intervention 2: control
primary purpose: Health Services Research
masking: Single (Outcomes Assessor)
masking description: The data for the outcome are being provided from a third party with no involvement in the study
measure: Antibiotic prescribing rate
time frame: 12 months
description: Number of antibiotics prescribed per 100 total prescriptions
measure: Prolonged duration prescribing rate
time frame: 12 months
description: Proportion of antibiotic prescriptions that are >7 days per 100 total prescriptions
measure: New enrollment for Health Quality Ontario primary care practice reports
time frame: 24 months
description: Proportion of new sign ups for Health Quality Ontario primary care practice reports
measure: New enrollment for Health Quality Ontario primary care practice reports
time frame: 6 months
description: Proportion of new sign ups for Health Quality Ontario primary care practice reports
measure: New enrollment for Health Quality Ontario primary care practice reports
time frame: 12 months
description: Proportion of new sign ups for Health Quality Ontario primary care practice reports
measure: Antibiotic prescribing rate
time frame: 3 months
description: Number of antibiotics prescribed per 100 total prescriptions
measure: Antibiotic prescribing rate
time frame: 6 months
description: Number of antibiotics prescribed per 100 total prescriptions
measure: Antibiotic prescribing rate
time frame: 9 months
description: Number of antibiotics prescribed per 100 total prescriptions
measure: Antibiotic prescribing rate
time frame: 24 months
description: Number of antibiotics prescribed per 100 total prescriptions
measure: Prolonged duration prescribing
time frame: 3 months
description: Proportion of antibiotic prescriptions that are >7 days per 100 total prescriptions
measure: Prolonged duration prescribing
time frame: 6 months
description: Proportion of antibiotic prescriptions that are >7 days per 100 total prescriptions
measure: Prolonged duration prescribing
time frame: 9 months
description: Proportion of antibiotic prescriptions that are >7 days per 100 total prescriptions
measure: Prolonged duration prescribing
time frame: 24 months
description: Proportion of antibiotic prescriptions that are >7 days per 100 total prescriptions
arm group label: Antibiotic use feedback letter 1
arm group type: Active Comparator
description: Antibiotic use feedback letter 1 provides physicians with information on their antibiotic use plus change ideas on appropriate antibiotic prescribing for acute respiratory conditions
arm group label: Antibiotic use feedback letter 2
arm group type: Active Comparator
description: Antibiotic use feedback letter 2 provides physicians with information on their antibiotic use plus change ideas on appropriate antibiotic durations for common infections
arm group label: Control
arm group type: No Intervention
description: Controls will not receive a letter
intervention type: Other
intervention name: Antibiotic use feedback letter
description: Mailed letters indicating that the physician prescribes more antibiotics than 75% of their peers
arm group label: Antibiotic use feedback letter 1
arm group label: Antibiotic use feedback letter 2
criteria: 
gender: All
minimum age: N/A
maximum age: N/A
healthy volunteers: No
last name: Kevin Schwartz, MD MSc
role: Principal Investigator
affiliation: Ontario Agency for Health Protection and Promotion
facility: 
country: Canada
citation: Hallsworth M, Chadborn T, Sallis A, Sanders M, Berry D, Greaves F, Clements L, Davies SC. Provision of social norm feedback to high prescribers of antibiotics in general practice: a pragmatic national randomised controlled trial. Lancet. 2016 Apr 23;387(10029):1743-52. doi: 10.1016/S0140-6736(16)00215-4. Epub 2016 Feb 18.
PMID: 26898856
citation: Hemkens LG, Saccilotto R, Reyes SL, Glinz D, Zumbrunn T, Grolimund O, Gloy V, Raatz H, Widmer A, Zeller A, Bucher HC. Personalized prescription feedback to reduce antibiotic overuse in primary care: rationale and design of a nationwide pragmatic randomized trial. BMC Infect Dis. 2016 Aug 17;16:421. doi: 10.1186/s12879-016-1739-0.
PMID: 27530528
citation: Nudge vs Superbugs: A behavioural economics trial to reduce the overprescribing of antibiotics, Available online at: http://behaviouraleconomics.pmc.gov.au/sites/default/files/projects/report-nudge-vs-superbugs.pdf (accessed June 27, 2018).
citation: McCullough AR, Rathbone J, Parekh S, Hoffmann TC, Del Mar CB. Not in my backyard: a systematic review of clinicians' knowledge and beliefs about antibiotic resistance. J Antimicrob Chemother. 2015 Sep;70(9):2465-73. doi: 10.1093/jac/dkv164. Epub 2015 Jun 20.
PMID: 26093375
citation: Eccles MP, Grimshaw JM, Johnston M, Steen N, Pitts NB, Thomas R, Glidewell E, Maclennan G, Bonetti D, Walker A. Applying psychological theories to evidence-based clinical practice: identifying factors predictive of managing upper respiratory tract infections without antibiotics. Implement Sci. 2007 Aug 3;2:26. doi: 10.1186/1748-5908-2-26.
PMID: 17683558
citation: Hrisos S, Eccles M, Johnston M, Francis J, Kaner EF, Steen N, Grimshaw J. Developing the content of two behavioural interventions: using theory-based interventions to promote GP management of upper respiratory tract infection without prescribing antibiotics #1. BMC Health Serv Res. 2008 Jan 14;8:11. doi: 10.1186/1472-6963-8-11.
PMID: 18194527
citation: Pennell ML, Hade EM, Murray DM, Rhoda DA. Cutoff designs for community-based intervention studies. Stat Med. 2011 Jul 10;30(15):1865-82. doi: 10.1002/sim.4237. Epub 2011 Apr 17. Erratum In: Stat Med. 2011 Sep 20;30(21):2669.
PMID: 21500240
responsible party type: Principal Investigator
investigator affiliation: Ontario Agency for Health Protection and Promotion
investigator full name: Kevin Schwartz
investigator title: PI
mesh term: Anti-Bacterial Agents
sharing ipd: No
ipd description: The data are proprietary and private and cannot be shared with external parties. Study results will be aggregated for publication