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An Econometric Analysis of The Impact of Telecare on The Length of Stay in Hospital


  • Momanyi, Kevin


This paper presents some preliminary results of a study investigating the effect of telecare on the length of stay in hospital using linked administrative health and social care data in Scotland. We make various assumptions about the probability distribution of the outcome measure and formulate three Negative Binomial Models to that effect i.e. a basic Negative Binomial Model, a zero-inflated Negative Binomial Model and a zero-truncated Negative Binomial Model. We then bring the models to data and estimate them using a strategy that controls for the effects of confounding variables and unobservable factors. These models provide an alternative to the Propensity Score Matching technique used by the previous studies. The empirical results show that telecare users are expected to spend a shorter time in hospital than non-users, holding other factors constant. The results also show that older individuals, males, rural residents and individuals with comorbidities have a longer length of stay in hospital, on average, than their counterparts, all things equal. Future research will involve conducting a sub-group analysis, investigating the effectiveness of various telecare devices and determining the impact of telecare on admission to hospital.

Suggested Citation

  • Momanyi, Kevin, 2017. "An Econometric Analysis of The Impact of Telecare on The Length of Stay in Hospital," EconStor Preprints 168559, ZBW - Leibniz Information Centre for Economics, revised 2017.
  • Handle: RePEc:zbw:esprep:168559

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    Blog mentions

    As found by, the blog aggregator for Economics research:
    1. Thesis Thursday: Kevin Momanyi
      by Chris Sampson in The Academic Health Economists' Blog on 2019-05-16 06:00:19

    More about this item


    Telecare; Negative Binomial Models; length of stay in hospital;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D13 - Microeconomics - - Household Behavior - - - Household Production and Intrahouse Allocation
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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