Currently Viewing:
The American Journal of Managed Care September 2014
Impact of Atypical Antipsychotic Use Among Adolescents With Attention-Deficit/Hyperactivity Disorder
Vanja Sikirica, PharmD, MPH; Steven R. Pliszka, MD; Keith A. Betts, PhD; Paul Hodgkins, PhD, MSc; Thomas M. Samuelson, BA; Jipan Xie, MD, PhD; M. Haim Erder, PhD; Ryan S. Dammerman, MD, PhD; Brigitte Robertson, MD; and Eric Q. Wu, PhD
Effective Implementation of Collaborative Care for Depression: What Is Needed?
Robin R. Whitebird, PhD, MSW; Leif I. Solberg, MD; Nancy A. Jaeckels, BS; Pamela B. Pietruszewski, MA; Senka Hadzic, MPH; Jürgen Unützer, MD, MPH, MA; Kris A. Ohnsorg, MPH, RN; Rebecca C. Rossom, MD, MSCR; Arne Beck, PhD; Kenneth E. Joslyn, MD, MPH; and Lisa V. Rubenstein, MD, MSPH
Is All "Skin in the Game" Fair Game? The Problem With "Non-Preferred" Generics
Gerry Oster, PhD, and A. Mark Fendrick, MD
Targeting High-Risk Employees May Reduce Cardiovascular Racial Disparities
James F. Burke, MD, MS; Sandeep Vijan, MD; Lynette A. Chekan, MBA; Ted M. Makowiec, MBA; Laurita Thomas, MEd; and Lewis B. Morgenstern, MD
HITECH Spurs EHR Vendor Competition and Innovation, Resulting in Increased Adoption
Seth Joseph, MBA; Max Sow, MBA; Michael F. Furukawa, PhD; Steven Posnack, MS, MHS; and Mary Ann Chaffee, MS, MA
Out-of-Plan Medication in Medicare Part D
Pamela N. Roberto, MPP, and Bruce Stuart, PhD
New Thinking on Clinical Utility: Hard Lessons for Molecular Diagnostics
John W. Peabody, MD, PhD, DTM&H, FACP; Riti Shimkhada, PhD; Kuo B. Tong, MS; and Matthew B. Zubiller, MBA
Should We Pay Doctors Less for Colonoscopy?
Shivan J. Mehta, MD, MBA; and Scott Manaker, MD, PhD
Long-term Glycemic Control After 6 Months of Basal Insulin Therapy
Harn-Shen Chen, MD, PhD; Tzu-En Wu, MD; and Chin-Sung Kuo, MD
Characteristics Driving Higher Diabetes-Related Hospitalization Charges in Pennsylvania
Zhen-qiang Ma, MD, MPH, MS, and Monica A. Fisher, PhD, DDS, MS, MPH
Quantifying Opportunities for Hospital Cost Control: Medical Device Purchasing and Patient Discharge Planning
James C. Robinson, PhD, and Timothy T. Brown, PhD
Currently Reading
Effects of a Population-Based Diabetes Management Program in Singapore
Woan Shin Tan, BSocSc, MSocSc; Yew Yoong Ding, MBBS, FRCP, MPH; Wu Christine Xia, BS(IT); and Bee Hoon Heng, MBBS, MSc
Cost-effectiveness Evaluation of a Home Blood Pressure Monitoring Program
Sarah J. Billups, PharmD; Lindsy R. Moore, PharmD; Kari L. Olson, BSc (Pharm), PharmD; and David J. Magid, MD, MPH

Effects of a Population-Based Diabetes Management Program in Singapore

Woan Shin Tan, BSocSc, MSocSc; Yew Yoong Ding, MBBS, FRCP, MPH; Wu Christine Xia, BS(IT); and Bee Hoon Heng, MBBS, MSc
Patients utilizing Medisave for a diabetes management program in Singapore were more compliant with care processes, but reductions in hospitalization and costs were not sustained.
Baseline characteristics are described with mean and standard deviation for continuous variables and number and percentage for categorical variables. Differences between CDMP participants and nonparticipants were compared using absolute standardized differences.26 A standardized difference of 0.1 or less has been suggested to denote negligible imbalance between the participant and nonparticipant groups.27 After propensity-score matching, differences in process outcomes were compared For all-cause and diabetes-related hospitalization risk and healthcare cost, we used a combination of nonparametric propensity score matching and difference-in-differences (DiD) analysis, which some argue improves the quality of nonexperimental evaluation study results.28

Since participation in Medisave for CDMP was not random, we first estimated the probability of each subject selecting treatment conditional upon their baseline covariates.29 The propensity score was determined using multivariable logistic regression using the covariates: age, gender, ethnicity, obesity, hypertension, hyperlipidemia, treatment regime, DCSI score, and glycemic control. Subsequently, pairs of treated and untreated subjects were formed using nearest neighbor matching within a caliper of 0.2 of the standard deviation of the propensity score.29 We also imposed a common support condition in the matching algorithm to ensure that the distribution of the propensity scores of participants and nonparticipants were located in the same domain. The matching was performed using the PSMATCH2 software in Stata.30

We used a DiD approach to assess the effect of Medisave for CDMP on the outcomes. This method accounts for secular trends in outcomes by subtracting the change in outcomes in the nonparticipant group from the concurrent change in the participant group to derive the policy impact.31,32 The following equation was employed:

yst = β0 + β1 CDMP + β2 Post1 + β3 Post2 + β4 Post3 + β5 (CDMP × Post1) + β6 (CDMP × Post2) + β7 (CDMP × Post3) + β8 Adjustors + βst

where yst is the dependent variable. CDMP is a dummy variable representing participation in the program (CDMP = 1). Three time dummies (Post1, Post2, Post3) were included to denote the years (2007, 2008, 2009) after policy implementation. The coefficient of CDMP represents the difference in the outcome of interest between participants and nonparticipants before the plan was implemented. The coefficients of the time dummies represent changes of nonparticipants in the different periods. The coefficients of the 3 interaction terms, CDMP × Post1, CDMP × Post2 and CDMP × Post3, reflect the impact of Medisave for CDMP in 2007, 2008, and 2009 respectively.

To address the correlation between repeated annual observations in outcomes across time for the same patient, we used a generalized estimating equation approach. 33 This method accounts for the correlation between observations. For the dichotomous response variables of process indicators, and hospitalization, we specified a binomial distribution with logit link. For the continuous variable of total healthcare cost, we specified a gamma distribution with log-link. In these regression models, the correlation matrix was assumed to be unstructured.

For the outcomes of all-cause hospitalization risk and total healthcare cost, we separately estimated the DiD effects for participants who at baseline, had: 1) diabetes with no complications and acceptable glycemic control (A1C <8%), 2) diabetes with no complications and poor glycemic control (A1C ≥8%), 3) diabetes with complications and acceptable glycemic control, and 4) diabetes with complications and poor glycemic control.

All analyses were conducted using Stata 11.0 (StataCorp; College Station, Texas). The National Healthcare Group Institutional Review Board approved the study protocol.


We identified 10,559 participants and 22,089 controls before propensity-score matching. The matched sample comprised 8881 participants and 8881 unique nonparticipants. Baseline characteristics of the unmatched and propensity-score matched samples are shown in Table 1. The propensity-score matched patients were well matched in 6 covariates.

Process Indicators

Post policy, sustained improvements in compliance with blood pressure measurement and weight measurement were seen in both groups of patients but participants were consistently more compliant. The policy was also associated with an improvement in compliance with A1C, lipid, and nephropathy screening tests in the participant cohort, but continued decline was observed among nonparticipants. The compliance rates for blood pressure and weight measurement improved for both groups between 2006 and 2009, but foot and retinal screening fell across the years for both cohorts during the 3-year follow-up (Table 2).

Utilization Outcomes

Table 3 presents the unadjusted all-cause and diabetes-related hospitalization rates and mean total healthcare cost per year. The data revealed that the unadjusted utilization and cost increased annually for the nonparticipants. For CDMP participants, the all-cause and diabetes-related hospitalization rates declined in the first year post policy compared with pre-policy. On average, all outcomes were better for the participants in the post policy years compared to nonparticipants.

The regression-adjusted DiD estimates are presented in Table 4. With all-cause hospitalization (Yes/No) as the outcome, the estimates of the policy effect in 2007 (OR: 0.76; 95% CI, 0.65-0.88) and 2008 (OR: 0.79; 95% CI, 0.68-0.92) were statistically significant, suggesting that we cannot reject the hypothesis that the Medisave for CDMP reduced the risk of hospitalization. However, the positive impact was not sustained in 2009 (OR: 0.91; 95% CI, 0.79-1.05). The results were similar for diabetesrelated hospitalizations but the policy effect sizes were relatively larger.

For all-cause total annual healthcare costs, compared with those of nonparticipants, participants’ costs were reduced significantly, by 15% (95% CI, −6% to −24%) and 14% (95% CI, −4% to −24%) in 2007 and 2008, respectively. In 2009, however, differences in cost between the 2 groups narrowed significantly (Table 4). Diabetes-related inpatient cost more than halved in 2007 and 2008 for the participant cohort. The decrease was significant and reflected the odds ratio observed for diabetes-related hospitalization for participants relative to nonparticipants. However, the policy effect was similarly not sustained in the third year.

Sub Group Analysis

Figures 1A and 1B reflect the impact of the policy on all-cause hospitalization rates and total healthcare cost for patient subgroups differentiated by the presence of diabetes-related complications and level of glycemic control at baseline. The policy effects on participants with poor glycemic control at baseline were significantly positive although diminishing over the 3-year follow-up. However, the policy did not appear to benefit participants who did not have diabetes-related complications at baseline and acceptable levels of blood glucose.


Evidence-based management was thought to reduce morbidity, and therefore healthcare costs, for chronic diseases. Globally, governments are pursuing payment reforms to align economic and health incentives to shift the focus towards preventive and outpatient-oriented care. Singapore is no exception. The extension of Medisave to population-based disease management programs represents an important shift from an episodic model of care toward overall patient management.

Our study included a large cohort of primary care diabetes patients using a unique diabetes registry database in Singapore. Compared to matched control patients, program participants were more compliant with processes of diabetes care and had lower odds of hospitalization in the first 2 years of follow-up. Total healthcare costs were similarly reduced but the positive effects were not maintained in the third year.

Higher cost sharing creates financial barriers that discourage patients from using recommended services.34 The Medisave for CDMP policy effectively lowers the relative prices of outpatient treatment to encourage regular monitoring of clinical outcomes within the context of a disease management program. Studies that have evaluated the influence of copayment reductions on medications have noted positive impact on medication adherence.35,36 Medisave for CDMP, too, reduces the out-of-pocket costs to patients. That, combined with the monitoring of outcomes by the Ministry of Health, appear to have motivated both patients and providers to better meet the standards of care based on the set of process indicators monitored by MOH. Similar to other studies,5,10,37,38,39 our results showed a significant improvement in the participant group in meeting the required examination frequencies for A1C, blood pressure, and blood cholesterol measurements, and foot and nephropathy screening. Higher rates of compliance with A1C, blood cholesterol and nephropathy screening were achieved because the required laboratory tests are grouped as an annual T2DM monitoring panel in the NHG primary care clinics.

Comparatively, the rates for nonlaboratory assessments, such as weight and blood pressure measurement, and retinal and foot screening, were lower, although we observed significant improvements in blood pressure and weight measurements because these activities have become part of the routine care to be carried out before the patient consults the physician. One reason the rates of diabetic retinal photography screening and foot examination may have dropped at the primary care clinics is because more patients previously screened positive and were referred for specialist care. However, for retinal screening, which registered the lowest compliance rates, previous studies have suggested that receiving diabetes education is associated with an increased screening rate for diabetic retinopathy.40,41 Our results suggest that patient education regarding eye care might be inadequate in this population and can be strengthened.

Copyright AJMC 2006-2020 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
Welcome the the new and improved, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up