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Licensed Unlicensed Requires Authentication Published by De Gruyter June 13, 2019

Health Care Home: Early Evidence from Linked Administrative Data in New Zealand

Kabir Dasgupta EMAIL logo and Gail Pacheco


Our analysis presents a case study on the impacts of Health Care Home (HCH) – a large-scale technology-based healthcare innovation in New Zealand’s primary healthcare system. For our analysis, we link the registered population of health practices within the Wellington region to administrative hospital admission data for quarterly periods between 2014 and 2017. By employing variation in the timing of HCH implementation across practices (selected via propensity score matching), we estimate differences-in-differences models to investigate the effects of the intervention on multiple patient outcomes. Additionally, we incorporate a number of empirical specifications to test the robustness of estimates. HCH results in a statistically significant reduction in the likelihood of emergency department (ED) presentations by 6–8 %, with no significant impacts on other health outcomes. The impact on ED presentations aligns with the expectation that the HCH intervention would produce downstream effects of a reduced economic burden on public hospital services.

JEL Classification: I11; I18; C12


Table 6:

Number of registered individuals per practice-quarter.

2014 Quarters2015 Quarters2016 Quarters2017 Quarters
HCH implementation quarterPractice1st2nd3rd4th1st2nd3rd4th1st2nd3rd4th1st2nd3rd4th
  1. Notes: The practice identifiers marked in bold implemented the health care homes model.

Table 7:

Difference-in-differences model with binary health events using all practices.

Dependent variables (binary indicator of health events)
Excess stayAcute admissionED admissionASH eventReadmission
Sample mean0.0110.0170.0150.0060.002
Model 1: Time and practice-specific fixed effects
HCH implementation−0.00035−0.00013−0.00120**−0.00024−0.00010
Model 2: Model 1 + demographic controls (age, sex, ethnicity, quintile)
HCH implementation−0.00033−0.00010−0.00118**−0.00023−0.00009
Model 3: Model 2 + practice-specific linear time trends
HCH implementation−0.000180.00011−0.00111***−0.000020.00011
Model 4: Model 2 + event study
HCH implementation−0.000280.00002−0.00135***−0.00011−0.00003
Pre-treatment (δpt)0.000000.000060.000030.000040.00001
δptX HCH implementation−0.00004−0.00027*0.00001−0.00021**−0.00008
  1. Notes: The marginal effects from probit regressions using all the matched practices along with the respective standard errors (in parentheses) are reported in the above table. The standard errors are corrected for clustering at the practice-level. ***, **, * denote statistical significance at the 1 %, 5 %, and 10 % levels, respectively. We estimate probit regressions for Models 1–4.

  1. Conflict of Interest: The authors hereby acknowledge that the analysis does not involve any conflict of interest, as per the journal requirements.


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Published Online: 2019-06-13

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