Associations between pharmaceutical industry interactions and prescribing practices in chronic non-malignant pain management: A protocol for a systematic review

Review question

In healthcare professionals who prescribe analgesics, how do interactions with the pharmaceutical industry influence the prescribing patterns of analgesics for chronic non-malignant pain management, compared to healthcare professionals with no or minimal pharmaceutical industry interactions?

Eligibility criteria

The eligibility criteria are outlined in Table 1. This review will include primary studies that evaluate the association between healthcare professionals’ interactions with the pharmaceutical industry and their prescribing of analgesics for chronic non-malignant pain management. Observational studies, including cross-sectional and cohort designs, will be included as they provide insights into prescribing practices. Excluded publications will include conference abstracts and letters (not reporting results of primary research), commentaries, news releases, and study protocols, as they typically do not present empirical findings.

Search strategy

An information specialist from the host institution’s library with expertise in systematic reviews is involved in developing the search strategies. A systematic search will be conducted in the following databases: MEDLINE (Ovid), EMBASE (Ovid), CINAHL (EBSCOhost), PsycINFO (EBSCOhost), and Web of Science, covering studies from inception to the present with no restrictions placed on language or year of publication.

Search terms include controlled vocabulary (e.g., MeSH, Emtree) and free-text keywords covering three primary concepts: i) Pharmaceutical industry interactions, using terms such as “pharmaceutical industry,” “pharmaceutical payment*,” and “corporate sponsorship*”; ii) Analgesic prescribing, including terms such as “pain management,” “opioid*,” and “pain medication*”; iii) Healthcare professionals and healthcare settings, using terms such as “physician*,” “nurse*,” “clinic*,” and “hospital*.”

The full electronic search strategies, including all terms and database-specific details, are documented in supplementary materials to ensure transparency and reproducibility.

Study selection

Identified studies will be exported to EndNote (Version 21) for reference management, which will be used to remove duplicate records. After duplicates are removed, the remaining references will be transferred to Covidence systematic review software. Necessary licenses for both EndNote and Covidence have been secured. Initially, a pilot phase will be conducted where two reviewers independently screen the same 50 studies to ensure consistent application of the eligibility criteria. Following the pilot phase, titles and abstracts of the identified records will be screened by the reviewers to eliminate studies that do not meet the inclusion criteria. Full-text articles of the remaining studies will then be reviewed independently by the reviewers to assess their suitability for inclusion. The rationale for excluding studies during the full-text review phase will be documented and reported using a PRISMA flow diagram. Reviewers will be blinded to each other’s decisions during the screening process to minimize potential bias and ensure impartial evaluation. If there are disagreements at any stage, they will be settled through discussion. If disagreements cannot be resolved through discussion, then a third reviewer will be consulted.

Data extraction and management

Data extraction will be conducted using a standardized Excel form, initially tested on five studies independently by the reviewers. Following this, the reviewers will convene to discuss and resolve any discrepancies, ensuring consistent data extraction procedures. The form will collect study details (e.g., setting, design, publication year), population characteristics, intervention details (e.g., pharmaceutical interactions), comparison groups, outcome measures, and results. Fields will also capture funding sources, exposure levels, types of analgesics prescribed, and relevant statistical data. The full data extraction table is provided in the supplementary materials.

Two reviewers will independently extract data to reduce bias and enhance accuracy. Discrepancies will be addressed through discussion, and if necessary, a third reviewer will be consulted. Efforts will be made to contact lead authors of primary studies to retrieve unavailable or incomplete data.

Methodological quality assessment

While the risk of bias assessment will not be used to exclude studies, it will shape our understanding by highlighting potential biases that could affect the outcomes. The Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tool for evaluating observational studies¹⁶ will be employed. This instrument evaluates seven bias domains: confounding, participant selection, intervention classification, deviations from planned interventions, missing data, outcome measurement, and choice of reported results. This assessment will guide our interpretation and the weight given to each study's results in our overall analysis. Two independent reviewers will assess each study and assign ratings of “low,” “moderate,” “serious,” or “critical” for each domain. Disagreements will be resolved by discussion or, if needed, by consulting a third reviewer.

Two reviewers will assess the reliability of the cumulative evidence using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach¹⁷. This will involve evaluating study limitations, results consistency, effect estimate precision, evidence directness, and publication bias. Results will be summarized in GRADE tables to present the strength of evidence for each finding.

Analysis

We will conduct a narrative synthesis of the included studies, structured according to the Synthesis Without Meta-Analysis (SWiM) reporting guideline¹⁸. Studies will be grouped based on key characteristics, including the type of pharmaceutical industry interaction, healthcare setting, professional group, and prescribing outcomes. The rationale for these groupings is to ensure conceptual clarity and comparability across diverse data sources.

We will describe and justify the synthesis methods used to summarize the associations between pharmaceutical industry interactions and prescribing practices. This will include calculating summary statistics (e.g., ranges, medians, interquartile ranges) for key prescribing-related outcomes where appropriate and using vote counting based on the direction of effect to interpret patterns in cases where quantitative effect estimates cannot be pooled.

Findings will be presented in tables and visual summaries, such as effect direction plots, to provide a clear and systematic representation of the results. The narrative will explore the broader implications of these findings on healthcare professionals’ knowledge, attitudes, and patient outcomes, identifying patterns and contrasts across study groups. Limitations of the synthesis, including heterogeneity in study design and outcome reporting, will be transparently documented.

A quantitative synthesis (meta-analysis) will be conducted if at least two homogeneous studies examining the same outcomes in comparable settings are found¹⁹. If meta-analysis is undertaken, a random-effects model will be used to account for expected heterogeneity among study findings. The generic inverse variance method will be used to aggregate relative risks for binary outcomes and mean differences or standardized mean differences for continuous outcomes. Initially, meta-analyses will aggregate data across all types of interactions. Subsequently, to refine our understanding of their distinct impacts, we will perform stratified meta-analyses by interaction type, such as financial incentives or gifts.

We will evaluate data heterogeneity both visually using forest plots and through a logical assessment of differences in study characteristics, such as design, to understand their impact on the results. Statistical heterogeneity will be quantified using the I² statistic, with values exceeding 50% indicating substantial heterogeneity²⁰. When the meta-analysis includes ten or more studies, funnel plots will be employed to assess potential publication bias. This threshold is chosen because it typically provides a stable evaluation of funnel plot asymmetry and sufficient power to detect bias²¹.

Unavailable outcome data will be recorded on the data extraction form and included in the risk of bias table. If primary outcomes cannot be assessed due to unavailable data and unsuccessful attempts to contact study authors, these studies will be reported separately. The reasons for excluding studies from meta-analysis will be described in the results and presented in a supplementary table.

Subgroup analyses will explore the relationship between pharmaceutical industry interactions and prescribing patterns for specific groups. Subgroups of interest include healthcare professional types (e.g., physicians, nurse practitioners, pharmacists), medication class (e.g., opioids, gabapentinoids), and healthcare settings (e.g., hospital-based, general practice). These analyses aim to identify patterns and differences that may inform more tailored and context-specific conclusions.

Strengths and limitations

This systematic review will adhere to Cochrane guidance¹⁴, ensuring rigor and methodological integrity throughout the review process. To enhance transparency and mitigate the risk of selective reporting, the review has been prospectively registered on PROSPERO. Comprehensive searches will be conducted to reduce the potential impact of publication bias.

Screening of titles and abstracts, full-text reviews, and methodological quality assessments will be conducted by two independent reviewers blinded to each other’s evaluations. This method reduces the potential for bias and decreases the chance of errors in the selection and evaluation of studies.

This review acknowledges certain limitations inherent to the nature of observational studies it will include. Such studies may be subject to various biases, including selection and reporting biases, and often exhibit methodological heterogeneity. This variability can impact the consistency and generalizability of findings across different studies. While the review will seek to mitigate these issues through advanced meta-analytical techniques that adjust for study variability and potential biases, the heterogeneity of observational studies may still limit the ability to perform a quantitative synthesis of the findings.

Potential implications for future research, policy and clinical practice

This systematic review has the potential to identify significant gaps in the current understanding of how pharmaceutical industry interactions influence prescribing behaviors. These findings can guide future research efforts, particularly in developing interventions to mitigate undue industry influence on healthcare professionals.

The review’s results may inform policy initiatives aimed at enhancing transparency and accountability in pharmaceutical industry engagements with healthcare professionals. Such policies could include stricter regulations on financial interactions or improved reporting mechanisms, fostering more ethical prescribing practices.

For clinical practice, this review will provide a synthesized understanding of the dynamics between industry interactions and analgesic prescribing for chronic pain management. By highlighting both the benefits and risks of these interactions, healthcare professionals can make more informed decisions regarding the acceptance of industry support, ultimately improving prescribing quality, patient safety, and outcomes.