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Heart Failure's First Shock and Nurse-Led Chronic Care

Moslem Rashidi, Luke B. Connelly, Gianluca Fiorentini

Abstract

We study how a first heart-failure hospitalization, an adverse health shock, changes patients' care, and whether a nurse-led chronic-care program sustains those post-shock investments. Using linked population-wide administrative records from Italy's Romagna Local Health Authority (2017-2023), we anchor event time at each patient's first CHF admission and exploit staggered timing to estimate dynamic effects. The shock triggers a sharp post-discharge surge: beta-blocker adherence, cardiology follow-up, and echocardiography rise immediately, while emergency-room use spikes just before admission and then stabilizes. We then estimate the incremental impact of enrollment in the Nurse-led Program for Chronic Patients (NPCP) using the interaction-weighted event-study estimator for staggered adoption. Under conventional difference-in-differences inference, NPCP strengthens long-run preventive engagement, with little detectable change in emergency-room use. HonestDiD sensitivity analysis indicates these gains are economically meaningful but not statistically definitive under modest departures from parallel trends.

Heart Failure's First Shock and Nurse-Led Chronic Care

Abstract

We study how a first heart-failure hospitalization, an adverse health shock, changes patients' care, and whether a nurse-led chronic-care program sustains those post-shock investments. Using linked population-wide administrative records from Italy's Romagna Local Health Authority (2017-2023), we anchor event time at each patient's first CHF admission and exploit staggered timing to estimate dynamic effects. The shock triggers a sharp post-discharge surge: beta-blocker adherence, cardiology follow-up, and echocardiography rise immediately, while emergency-room use spikes just before admission and then stabilizes. We then estimate the incremental impact of enrollment in the Nurse-led Program for Chronic Patients (NPCP) using the interaction-weighted event-study estimator for staggered adoption. Under conventional difference-in-differences inference, NPCP strengthens long-run preventive engagement, with little detectable change in emergency-room use. HonestDiD sensitivity analysis indicates these gains are economically meaningful but not statistically definitive under modest departures from parallel trends.
Paper Structure (14 sections, 1 theorem, 11 equations, 30 figures, 7 tables)

This paper contains 14 sections, 1 theorem, 11 equations, 30 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Background and Institutional Setting
  3. Economic Framework
  4. Data and Identification
  5. Empirical Design
  6. Results
  7. Conclusion
  8. A1. Health transition with NPCP channels.
  9. A2. Linearized health deviations.
  10. A3. Investment outcomes and ER use.
  11. A4. Mapping to event-study coefficients under an absorbing treatment.
  12. Censoring rule.
  13. Key idea.
  14. Takeaway.

Key Result

Lemma 1

If $R_{it}$ is a deterministic function of $(T_i^H,T_i^E)$ and conditional parallel trends hold on the kept support, then the Sun and Abraham (2021) interaction-weighted event-study estimand remains identified on $\{R_{it}=1\}$.

Figures (30)

  • Figure 1: Moving-average event-time trajectories around the first CHF hospitalization ($t=0$) for panels (a)--(d): $\beta$-blocker adherence, cardiologist visits, echocardiograms, and ER visits. Shaded bands show 95% CIs over $\pm 12$ quarters.
  • Figure 2: NPCP effects from the Sun--Abraham (2021) event study. Event time is in quarters around the first CHF hospitalization ($\tau=0$; reference = $-2$). Panels report $\beta$-blocker adherence, cardiology visits, echocardiograms, and ER visits. Points are estimates; bars are 95% CIs; the long-run bin pools $\tau \ge 6$.
  • Figure 3: HonestDiD sensitivity analysis (Rambachan and Roth, 2023) for the Sun--Abraham estimates. The figure reports 95% confidence intervals across $M \in \{0,0.5,1,1.5,2\}$, where larger $M$ permits larger departures from parallel trends.
  • Figure B1: New enrollments into the nurse-led program by district and quarter, 2017–2023. Each panel plots intake_dt, the number of patients who newly enroll in the current quarter (enroll_q = quarter_period). The variable is constructed from the CHF patient–quarter panel and aggregated to the district–quarter level.
  • Figure C1: Moving-average event-time trajectories around the first CHF hospitalization ($t=0$) for panels (a)--(d): $\beta$-blocker adherence, cardiologist visits, echocardiograms, and ER visits. Lines plot forward and backward moving averages anchored at $t=0$ and $t=-1$; shaded bands report 95% confidence intervals for treated and control groups over $\pm 12$ quarters. The sample includes treated and untreated patients and excludes the 121 patients who enrolled in NPCP before the index hospitalization.
  • ...and 25 more figures

Theorems & Definitions (2)

  • Lemma 1
  • proof