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Related Experiment Videos

Consultation frequencies in general practice

S J Kilpatrick

    Health Services Research
    |January 1, 1977
    PubMed
    Summary
    This summary is machine-generated.

    The negative binomial distribution accurately models patient medical consultation frequencies. This finding applies broadly, including to new illnesses and follow-up visits, suggesting a unified approach to healthcare seeking behavior.

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    Area of Science:

    • Biostatistics
    • Health Services Research
    • Epidemiology

    Background:

    • Understanding patient healthcare-seeking behavior is crucial for resource allocation and service planning.
    • Previous research suggested the negative binomial distribution for illness episodes but not comprehensively for consultation frequencies.

    Purpose of the Study:

    • To test the applicability of the negative binomial distribution to individual patient consultation frequencies.
    • To determine if this distribution holds for new illnesses and subsequent follow-up visits.

    Main Methods:

    • Utilized data from the 1970-71 National Morbidity Survey of General Practice.
    • Analyzed consultation frequencies in aggregate and stratified by patient demographics (age, sex) and registration duration.
    • Applied statistical modeling to assess the fit of the negative binomial distribution.

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    Main Results:

    • The negative binomial distribution demonstrated a successful fit for overall consultation frequencies.
    • The distribution also accurately represented consultation frequencies when analyzed by age, sex, and registration duration.
    • Evidence supports the negative binomial distribution for consultations arising from new illnesses and their follow-up visits.

    Conclusions:

    • The negative binomial distribution is a robust model for patient consultation frequencies in primary care.
    • This finding supports a unifying statistical model for understanding healthcare-seeking behavior, encompassing both initial consultations and follow-up visits.
    • The study provides a strong statistical foundation for modeling patient pathways in general practice.