Dinesafe Knowledge Base
  • Welcome
  • Return to DineSafe.com
  • Real-Time Norovirus Surveillance
    • Introduction
  • Methodology
  • Data Collection & Processing
  • Metrics & Indicators
  • Implementation Guidelines
  • FAQs
  • Benchmarking
    • Introduction
      • The Importance of Food Safety
      • Introduction to Our Benchmarking System and Its Purpose
      • Why Food Safety Benchmarking Matters: A Stakeholder Overview
    • Methodology & Systems
      • The Metrics We Use
        • Reports per 100 Stores
        • Reports per $1M Revenue
        • Persons Reported Sick per 100k Customers Served
      • Data Sources & Methodology
        • How We Gather Data
        • Estimating Data When Exact Figures Aren't Available
        • Ensuring Accuracy and Transparency
      • Our Benchmark Indexing System
        • Why We Use Indexing
        • Index Composition
        • Index Calculation Process
        • Understanding Index Comparisons
    • Case Study
      • Case Study - Chipotle Mexican Grill
    • Applications
      • Global Applicability of Our Benchmarking System
      • Product-Specific Benchmarking for Food Producers
    • Service Information
      • Why Subscribe to Our Benchmarking Service
      • Limitations & Considerations
      • Conclusion
      • FAQ
        • Q1: How often are the benchmarks updated?
        • Q2: Are the food poisoning reports reviewed before being included in the benchmarks?
        • Q3: How do you account for differences in restaurant size when comparing benchmarks?
        • Q4: Can restaurants submit their own data to improve the accuracy of the benchmarks?
        • Q5: How do your benchmarks relate to official health inspections and ratings?
        • Q6: How can businesses improve their benchmark scores?
  • Data Dictionary
    • Introduction
    • Primary Fields
    • Specialized Fields
    • Delivery Methods
    • Glossary
    • Further Questions
  • IWP Reporting Widget
    • Introduction
    • Why Our Widget? Key Features & Benefits
    • Embedding the Widget
      • How to Embed the Code
      • When to Use Simple Embedding Code vs Advanced Embedding Code
        • Benefits of the Advanced Embedding Code
        • Considerations When Using the Advanced Embedding Code
      • Test the Code
      • Standalone URL Option
      • Customization Options
      • Language Support
      • Mobile-Friendly Link
    • Data Management and Access
    • Compliance with FDA’s Voluntary National Retail Food Regulatory Program Standards
    • Examples of Embedded Widgets
    • FAQ
      • Q1: Who can use the Iwaspoisoned.com reporting widget and what is it?
      • Q2: How do I embed the widget on my website?
      • Q3: Is the widget customizable?
      • Q4: How do I access the reports?
      • Q5: How does the widget support FDA compliance?
  • Email Notifications
    • Introduction
    • Widget Email Alerts
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  1. Benchmarking
  2. Service Information

Limitations & Considerations

PreviousWhy Subscribe to Our Benchmarking ServiceNextConclusion

Last updated 9 months ago

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While our food safety benchmarking system provides valuable insights into restaurant performance, it's important to understand its limitations and the factors that should be considered when interpreting the results. This section outlines key points to keep in mind when using our benchmarks.

Potential Limitations in Our Methodology

  1. Crowdsourcing (Self-Reported Data):

    • While crowdsourcing offers unique real-time insights directly from consumers, we recognize the inherent challenges, such as variability in report quality and potential biases. We mitigate these limitations through rigorous data moderation and quality controls, and continuous refinement of our methodologies. With these controls in place, we have a proven track record of detecting outbreaks in real time (examples ) and providing critical benchmarking insights (case study ), earning industry and public health adoption, with over 500 health agencies globally subscribed to our services.

  2. Attribution Challenges:

    • Attribution issues, such as last meal bias and the absence of medical diagnoses in many reports, can complicate the process of linking foodborne illnesses to specific brands. We operate under two key assumptions:

      • These biases affect all brands proportionally, with no single brand being more susceptible than others.

      • These biases remain relatively consistent over time, barring significant changes in reporting methods or public awareness.

    • While these factors may influence absolute numbers, they do not undermine the validity of relative comparisons between brands or trend analyses over time. This consistency allows for meaningful benchmarking and trend identification despite the inherent data limitations.

  3. Estimations for Private Companies:

    • For private companies, we often rely on estimates for metrics such as revenue and store counts. Although we strive for accuracy, these estimates introduce a degree of uncertainty that must be considered when interpreting the results.

  4. Timing of Financial Data:

    • Our reliance on 10-K reports for financial & location data may understate or overstate metrics for businesses that had significant growth or contraction during a fiscal year. Additionally, not all financial years end at the same time. We adjust our analysis to the most appropriate timeframe, and believe that as long as this adjustment is applied consistently, it does not impact the validity of the benchmarks.

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