Impact Evaluation of a System-Wide Chronic Disease Management Program on Health Service Utilisation: A Propensity-Matched Cohort Study

<div><p>Background</p><p>The New South Wales Health (NSW Health) Chronic Disease Management Program (CDMP) delivers interventions to adults at risk of hospitalisation for five target chronic conditions that respond well to ambulatory care: diabetes, hypertension, chronic obstructive pulmonary disease, congestive heart failure, and coronary artery disease. The intervention consists of two main components: (1) care coordination across sectors (acute, ambulatory, and community care from both public and private sectors) and clinical specialties, facilitated by program care coordinators, and (2) health coaching including management of lifestyle risk factors and medications and self-management. These components were broadly prescribed by the head office of NSW Health, which funded the program, and were implemented by regional health services (local health districts) in ways that best suited their own history, environment, workforce, and patient need. We used a propensity-matched cohort study to evaluate health service utilisation after enrolment in the CDMP.</p><p>Methods and Findings</p><p>The evaluation cohort included 41,303 CDMP participants enrolled between 1 January 2011 and 31 December 2013 who experienced at least one hospital admission or emergency department (ED) presentation for a target condition in the 12 mo preceding enrolment. Potential controls were selected from patients not enrolled in the CDMP but experiencing at least one hospital admission or ED presentation over the same period. Each CDMP patient in the evaluation cohort was matched to one control using 1:1 propensity score matching. The primary outcome was avoidable hospitalisations. Secondary outcomes included avoidable readmissions, avoidable bed days, unplanned hospitalisations, unplanned readmissions, unplanned bed days, ED presentations, and all-cause death.</p><p>The primary analysis consisted of 30,057 CDMP participants and 30,057 matched controls with a median follow-up of 15 mo. Of those, 25,638 (85.3%) and 25,597 (85.2%) were alive by the end of follow-up in the CDMP and control groups, respectively. Baseline characteristics (including history of health service utilisation) were well balanced between the matched groups. In both groups, utilisation peaked just before the time of enrolment/matching, declined sharply immediately following enrolment, and then continued to decrease more gradually; however, after enrolment, avoidable and unplanned health service utilisation remained higher for CDMP participants compared to controls. The adjusted yearly rate of avoidable hospital admissions was 0.57 (95% CI 0.52 to 0.62) in the CDMP group versus 0.33 (95% CI 0.31 to 0.37) in the control group (adjusted rate ratio 1.70, 95% CI 1.62 to 1.79, <i>p <</i> 0.001). Significant increases in service utilisation were also observed for unplanned hospitalisations (1.42, 95% CI 1.37 to 1.47, <i>p <</i> 0.001) and ED presentations (1.37, 95% CI 1.32 to 1.42, <i>p <</i> 0.001) as well as avoidable (2.00, 95% CI 1.80 to 2.22, <i>p <</i> 0.001) and unplanned (1.51, 95% CI 1.40 to 1.62, <i>p <</i> 0.001) readmissions and avoidable (1.70, 95% CI 1.59 to 1.82, <i>p <</i> 0.001) and unplanned (1.43, 95% CI 1.36 to 1.49, <i>p <</i> 0.001) bed days. No evidence of a difference was seen for all-cause death (adjusted risk ratio 0.96, 95% CI 0.96 to 1.01, <i>p</i> = 0.10) or non-avoidable hospitalisations (all hospitalisations minus avoidable hospitalisations; adjusted rate ratio 1.03, 95% CI 0.97 to 1.10, <i>p</i> = 0.26).</p><p>Despite the robustness of these results to sensitivity analyses, in the absence of a randomised control group, one cannot exclude the possibility of residual or unmeasured confounding that was not controlled for by the matching process and multivariable analyses.</p><p>Conclusions</p><p>Participation in the CDMP was associated with an increase in avoidable hospital admissions compared to matched controls but no difference in the rate of other types of hospitalisation or death. A possible explanation is that the program identified conditions that required participants to be hospitalised. Service utilisation decreased sharply following its peak for both groups. This finding reflects the natural tendency for high-risk patients to show reductions in use following intense phases of service utilisation and highlights that, despite the additional complexity, a carefully selected control group is essential when assessing the effectiveness of interventions on hospital use.</p></div

Monthly rate of health service utilisation after matching.

<p>Curves were smoothed using a cubic spline with a 15% degree of smoothness. Note varying vertical axis scales. <i>y</i>-Axes represent the average monthly event rate per patient.</p

Study flowchart.

<p>Study flowchart.</p

Breakdown of avoidable hospitalisations by ICD category before and after matching.

<p>Numbers indicate the percentage of participants experiencing at least one avoidable hospitalisation with a primary diagnosis in the corresponding ICD category.</p

Average rate of hospitalisations per month in CDMP participants before and after matching.

<p>Monthly rate of hospitalisation per 100 patients. Dashed lines correspond to the entire CDMP evaluation cohort (<i>n</i> = 41,303); solid lines correspond to the matched CDMP group (<i>n</i> = 30,057). Denominators are participants who are alive. Curves were smoothed using a cubic spline with a 15% degree of smoothness.</p

Result of matched analyses using Poisson regression.

<p>Result of matched analyses using Poisson regression.</p

Correction: Optimization of prophylaxis for hemophilia A

<p>Correction: Optimization of prophylaxis for hemophilia A</p

Optimization of prophylaxis for hemophilia A

<div><p>Background & aims</p><p>Prophylactic injections of factor VIII reduce the incidence of bleeds and slow the development of joint damage in people with hemophilia. The aim of this study was to identify optimal person-specific prophylaxis regimens for children with hemophilia A.</p><p>Methods</p><p>Analytic and numerical methods were used to identify prophylaxis regimens which maximize the time for which plasma factor VIII concentrations exceed a threshold, maximize the lowest plasma factor VIII concentrations, and minimize risk of bleeds.</p><p>Results</p><p>It was demonstrated analytically that, for any injection schedule, the regimen that maximizes the lowest factor VIII concentration involves sharing doses between injections so that all of the trough concentrations in a prophylaxis cycle are equal. Numerical methods were used to identify optimal prophylaxis schedules and explore the trade-offs between efficacy and acceptability of different prophylaxis regimens. The prophylaxis regimen which minimizes risk of bleeds depends on the person’s pattern of physical activity and may differ greatly from prophylaxis regimens that optimize pharmacokinetic parameters. Prophylaxis regimens which minimize risk of bleeds also differ from prophylaxis regimens that are typically prescribed. Predictions about which regimen is optimal are sensitive to estimates of the effects on risk of bleeds of factor VIII concentration and physical activity.</p><p>Conclusion</p><p>The methods described here can be used to identify optimal, person-specific prophylaxis regimens for children with hemophilia A.</p></div

Optimal prophylaxis regimens identified using Broderick’s model.

<p>A, a ‘very active’ child, B, an ‘inactive’ child, and C, a ‘weekend active’ child. In each panel, the 100 prophylaxis regimens that best minimize bleeds incidence are shown. Each prophylaxis regimen is shown by a horizontal line joining three circles. The horizontal location of the symbols indicates the timing of the injections for each prophylaxis regimen and the vertical location indicates the incidence rate ratio associated with the prophylaxis regimen (lower values indicate lower incidence of bleeds). Time is expressed as hour of the week, starting at midnight on Sunday night. Patterns of physical activity are shown as bars in the lower part of each panel. Blue bars are periods of category 2 activity. Red bars are periods of category 3 activity. Unfilled bars are periods of sleep. The size and colour of the circles indicates the dose (small green circles 15 IU/kg, intermediate black circles 30 IU/kg, large pink circles 45 IU/kg).</p

Effect of timing and dose of injections on the lowest factor VIII concentration.

<p>Panels A and B show the effect of timing of injections when the dose of injections is fixed. Panels C and D show the effect of dose of injections when the timing of injections is fixed. In panels A and C, the dose of all three injections is equal (<i>D</i>_<i>1</i> = <i>D</i>_<i>2</i> = <i>D</i>_<i>3</i> = 30 IU/kg). In panels B and D, the dose of injections is unequal (<i>D</i>_<i>1</i> = <i>D</i>_<i>2</i> = 15 IU/kg; <i>D</i>_<i>3</i> = 30 IU/kg).</p