Currently Viewing:
The American Journal of Managed Care January 2015
Disease-Modifying Therapy and Hospitalization Risk in Heart Failure Patients
Fadia T. Shaya, PhD, MPH; Ian M. Breunig, PhD; and Mandeep R. Mehra, MD, FACC, FACP, FRCP
Frequency and Costs of Hospital Transfers for Ambulatory Care-Sensitive Conditions
R. Neal Axon, MD, MSCR; Mulugeta Gebregziabher, PhD; Janet Craig, PhD, RN; Jingwen Zhang, MS; Patrick Mauldin, PhD; and William P. Moran, MD, MS
Celebrating Our 20th Anniversary
A. Mark Fendrick, MD, and Michael E. Chernew, PhD Co-Editors-in-Chief, The American Journal of Managed Care
Value-Based Insurance Design: Benefits Beyond Cost and Utilization
Teresa B. Gibson, PhD; J. Ross Maclean, MD; Michael E. Chernew, PhD; A. Mark Fendrick, MD; and Colin Baigel, MBChB
Changing Physician Behavior: What Works?
Fargol Mostofian, BHSc; Cynthiya Ruban, BSc; Nicole Simunovic, MSc; and Mohit Bhandari, MD, PhD, FRCSC
State of Emergency Preparedness for US Health Insurance Plans
Raina M. Merchant, MD, MSHP; Kristen Finne, BA; Barbara Lardy, MPH; German Veselovskiy, MPP; Casey Korba, MS; Gregg S. Margolis, NREMT-P, PhD; and Nicole Lurie, MD, MSPH
Relationship of Diabetes Complications Severity to Healthcare Utilization and Costs Among Medicare Advantage Beneficiaries
Leslie Hazel-Fernandez, PhD, MPH; Yong Li, PhD; Damion Nero, PhD; Chad Moretz, ScD; S. Lane Slabaugh, PharmD, MBA; Yunus Meah, PharmD; Jean Baltz, MMSc, MSW; Nick C. Patel, PharmD, PhD; and Jonathan R. Bouchard, MS, RPh
Revisiting Hospital Length of Stay: What Matters?
Mollie Shulan, MD; and Kelly Gao
Medical Homes: Cost Effects of Utilization by Chronically Ill Patients
Jason Neal, MA; Ravi Chawla, MBA; Christine M. Colombo, MBA; Richard L. Snyder, MD; and Somesh Nigam, PhD
Value-Based Insurance Design and Medication Adherence: Opportunities and Challenges
Kevin A. Look, PharmD, PhD
New Start Versus Continuing Users on Aripiprazole: Implications for Policy
Rashid Kazerooni, PharmD, BCPS; Joseph B. Nguyen, PharmD, BCPS; Mark Bounthavong, PharmD, MPH; Michael H. Tran, PharmD, BCPS; and Nermeen Madkour, PharmD, CSP
Multiple Chronic Conditions in Type 2 Diabetes Mellitus: Prevalence and Consequences
Pei-Jung Lin, PhD; David M. Kent, MD, MSc; Aaron Winn, MPP; Joshua T. Cohen, PhD; and Peter J. Neumann, ScD
Prognostic Factors of Mortality Among Patients With Severe Hyperglycemia
Ya-Wun Guo, MD; Tzu-En Wu, MD, MS; and Harn-Shen Chen, MD, PhD
Survey Nonresponders Incurred Higher Medical Utilization and Lower Medication Adherence
Seppo T. Rinne, MD, PhD; Edwin S. Wong, PhD; Jaclyn M. Lemon, BS; Mark Perkins, PharmD; Christopher L. Bryson, MD; and Chuan-Fen Liu, PhD
Currently Reading
Using Financial Incentives to Improve the Care of Tuberculosis Patients
Cheng-Yi Lee, MS; Mei-Ju Chi, PhD; Shiang-Lin Yang, MS; Hsiu-Yun Lo, PhD; and Shou-Hsia Cheng, PhD

Using Financial Incentives to Improve the Care of Tuberculosis Patients

Cheng-Yi Lee, MS; Mei-Ju Chi, PhD; Shiang-Lin Yang, MS; Hsiu-Yun Lo, PhD; and Shou-Hsia Cheng, PhD
Patients enrolled in the tuberculosis pay-for-performance program received more comprehensive ambulatory care with slightly lower costs and a higher treatment success rate.
The basic characteristics of the subjects and the healthcare providers by P4P and non-P4P groups are shown in Table 1. The logistic regression used for the propensity score prediction had a goodness-of-fit examined by the Hosmer-Lemeshow test (P = .308 > .05). Before the propensity score matching process, the subjects in the P4P group were less likely to be aboriginal or living in indigenous areas and more likely to have active sputum status than those in the non-P4P group; P4P enrollees tended to be treated in public and higher levels of healthcare institutions. After the propensity score matching process, 12,018 patients were included in the analysis, with 6009 patients in each group. The demographics and disease characteristics of the subjects and the characteristics of their healthcare providers became similar between the 2 groups.

Table 2 shows TB-related healthcare utilization, including the numbers of outpatient visits, the number of ED visits, and the number of hospitalizations. The P4P enrollees used 14% more TB-related outpatient services (incidence rate ratio, 1.14; P <.001) than the non-P4P subjects; yet, there was no significant difference in the number of ED visits or hospitalizations between the P4P and non-P4P groups. Patients with comorbidities (CCI score ≥1) tended to have higher numbers of outpatient and ED visits and hospital admissions (P <.001). Patients 65 years or older had fewer outpatient visits, but more ED visits and hospital admissions (P <.001). Patients who were male or aboriginals or had a positive sputum test had more ED visits and hospital admissions (P <.001). Table 3 shows the results from the generalized linear model regressions for TB-related healthcare expenses. After controlling for related variables, patients enrolled in the P4P group incurred more TB-related outpatient expenses than the non-P4P patients (β = 0.28; cost ratio [CR], 1.33; P <.001). The P4P enrollees also incurred higher TB-related ED expenses (β = 0.19; CR, 1.20; P <.001), but lower hospitalization expenses (β = –0.10; CR, 0.90; P <.001) than nonenrollees. Finally, the P4P enrollees had slightly lower total TB-related healthcare expenses (β = –0.05; CR, 0.95; P <.001) than non-enrollees, with a cost savings of US$215, or 4.6%, for P4P enrollees versus nonenrollees (US$4674 vs US$4889).

The results also indicated that patients in any of the following groups had significantly higher total TB-related healthcare expenses: demonstrating comorbidities, 65 years or older, positive sputum test, male, treated in a public institution or higher accredited provider (medical centers and regional hospitals). Table 4 shows the effects of the TB P4P program on treatment outcomes at the end of the study period. Of the 12,018 study subjects, 9265 patients were cured (treatment success), 1628 patients were deceased, and 1125 patients were either still under treatment, lost to follow-up, or had treatment failure (noted as “other” in the analysis). Since there was no established software to test the goodness of fit for the multinomial logistic regression, we conducted 2 separate tests for the P4P and non-P4P groups of the outcomes suggested by Begg and Gray.20 The Hosmer-Lemeshow tests showed marginal goodness of fit (P = .020 and .344, respectively), yet the areas under the receiver operating characteristic curves were .844 and .847, respectively, which indicated a fairly good fit.

After controlling for potential confounding factors and using patients who were deceased as the reference group, we found that patients enrolled in the P4P program were more likely to complete treatment successfully (odds ratio [OR], 1.56; P <.001). The patients who were aboriginals or treated at higher levels of healthcare providers were more likely to be treated successfully, while patients 65 years and older (with positive sputum tests, treated in public healthcare institutions, or with comorbidities) were more likely to die during the treatment course. Of deceased patients, 73.7% had CCI scores ≥1, 81.9% were aged 65 years or older, 73.3% were sputum-positive, 73.9% were male, 98.3% were nonaboriginal, 75.5% were treated in public institutions, and 82.2% were treated in district hospitals and clinics.


This study aimed to examine the effects of the TB P4P program jointly introduced by Taiwan’s CDC and Bureau of NHI. Findings from the analysis indicated that financial incentives to healthcare providers for better TB treatment resulted in more follow-up visits and higher outpatient expenses. However, the P4P enrollees incurred fewer hospitalization expenses and had slightly lower total TB-related healthcare expenses (4.6%, or US$215 savings) than non-P4P enrollees, implying that reduced TB-related hospitalization expenses might offset the higher expenses associated with outpatient visits. This is the first report on possible savings from the TB P4P program that is similar to the findings of a previous study on the P4P program for diabetes in Taiwan.21

Regarding the P4P program’s effect on treatment outcomes, we found that the P4P enrollees with TB were more likely to be cured (OR, 1.56, or 236 lives saved). These findings are similar to those of previous reports using only NHI claim data in Taiwan.16,17 The results reported by Li et al revealed that both the treatment success rate and the average length of treatment for treatment-success cases improved significantly after the introduction of the P4P program for TB.16 Tsai et al suggested that the P4P program on TB improved the treatment default rate (ie, lost to follow-up) for TB patients.17 The present study used more comprehensive data by combining the Taiwan CDC mandatory reporting data and the NHI claims data, and the statistical models may have produced more precise estimates. Furthermore, this study employed a propensity score matching technique to increase the comparability of the P4P and non-P4P groups, which may enhance the robustness of the findings.

The TB P4P program included not only the incentives for processes and outcomes measures, but also the “information integrated platform,” which improved the cooperation of the public health agencies and healthcare institutions in Taiwan. Via this electronic information–sharing platform, both healthcare providers and public health officials can access the most updated information  regarding the treatment progress of individual TB patients, such as their visit records, side effects experienced, prescription records, and follow-up status. Overall, the platform facilitates the monitoring of patient progress by physicians and case managers. Bardach and colleagues have reported that electronic health records might play an important role among small practices in response to financial incentive programs in the United States.22 Therefore, the P4P scheme accompanying a well-designed electronic information system could enhance the patient care quality and effectiveness of case management.

While the P4P program for TB care in Taiwan was implemented nationwide under a universal coverage plan, P4P programs in the United States have tended to be on a smaller scale and implemented by individual health plans. We suggest that the scope of application might be expanded, or that a uniform financial design might be implemented across various payers,8 to strengthen the potential effects in the United States. In addition, the public health authorities might consider incorporating financial incentive components into communicable disease control programs to improve better healthcare outcomes.


Several limitations of the study should be mentioned. The enrollment of hospitals and clinics into the TB P4P program was voluntary, and there may be specific characteristics of these health institutions that introduce selection bias into this study. Furthermore, the enrollment of patients into the P4P program was voluntary, so selection bias might exist, although we used propensity score matching to increase comparability between the enrolled and nonenrolled patients. Finally, other factors that might influence the treatment outcome, such as the patients’ socioeconomic status, health literacy, and severity of TB, were not controlled for in the analysis. The interpretation of the findings should be conservative.


The study showed that the financial incentive program for TB treatment and management was associated with an increased number of follow-up visits. Patients enrolled in the program incurred higher expenses for outpatient services but lower hospitalization expenses and total TB-related healthcare expenses. We conclude that the program enrollees were less likely to die, more likely to finish treatment, and incurred lower costs compared with nonenrolled counterparts. Providing financial incentives to healthcare institutions could be a feasible model for better TB control. More detailed cost-effectiveness studies are needed in the future.

Author Affiliations: Centers for Disease Control, Ministry of Health and Welfare (C-YL, S-LY, H-YL), Taipei, Taiwan; Institute of Health Policy and Management, College of Public Health, National Taiwan University (C-YL, S-HC), Taipei, Taiwan; School of Gerontology Health Management; Master's program in Long-Term Care, College of Nursing, Taipei Medical University (M-JC), Taipei, Taiwan.

Source of Funding: None.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (C-YL, S-LY, S-HC); acquisition of data (C-YL); analysis and interpretation of data (C-YL, M-JC, S-LY, H-YL, S-HC); drafting of the manuscript (C-YL); critical revision of the manuscript for important intellectual content (C-YL, S-LY, S-HC); statistical analysis (C-YL, M-JC); provision of study materials or patients (C-YL); administrative, technical, or logistic support (C-YL, M-JC, S-LY, H-YL); supervision (C-YL, S-LY, H-YL).

Address correspondence to: Shou-Hsia Cheng, PhD, Institute of Health Policy and Management, College of Public Health, National Taiwan University, 17 Xu-Zhou Rd, Taipei, Taiwan. E-mail:
1. WHO Report 2011: Global Tuberculosis Control. Geneva, Switzerland: World Health Organization; 2011.

2. United Nations. Millennium Development Goals (MDGs). WHO website. Accessed January 21, 2015.

3. Zumla A, Cobelens F. Operational research and MDG tuberculosis control targets. Lancet Infect Dis. 2012;12(4):262-263.

4. Zachariah R, Harries AD, Ishikawa N, et al. Operational research in low-income countries: what, why, and how? Lancet Infect Dis. 2009;9(11):711-717.

5. Meddings JA, McMahon LF. Measuring quality in pay-for-performance programs: from ‘one-size-fits-all’ measures to individual patient risk-reduction scores. Dis Manag Health Outcomes. 2008;16(4):205-216.

6. Petersen LA, Woodard LD, Urech T, Daw C, Sookanan S. Does pay-for-performance improve the quality of health care? Ann Intern Med. 2006;145(4):265-272.

7. Christianson JB, Leatherman S, Sutherland K. Lessons from evaluations of purchaser pay-for-performance programs: a review of the evidence. Med Care Res Rev. 2008;65(6 suppl):5S-35S.

8. Van Herck P, De Smedt D, Annemans L, Remmen R, Rosenthal MB, Sermeus W. Systematic review: effects, design choices, and context of pay-for-performance in health care. BMC Health Serv Res. 2010;10:247.

9. Emmert M, Eijkenaar F, Kemter H, Esslinger AS, Schöffski O. Economic evaluation of pay-for-performance in health care: a systematic review. Eur J Health Econ. 2012;13(6):755-767.

10. Dolor RJ, Schulman KA. Financial incentives in primary care practice: the struggle to achieve population health goals. JAMA. 2013;310(10):1031-1032.

11. Lo HY, Chou P, Yang SL, Lee CY, Kuo HS. Trends in tuberculosis in Taiwan, 2002-2008. J Formos Med Assoc. 2011;110(8):501-510.

12. National Development Council (former Research Development and Evaluation Commission), Executive Yuan. Effectiveness analysis of Taiwan National Tuberculosis Program (Chinese version). 2009:87. National Development Council website. Accessed January 21. 2015.

13. Storla DG, Yimer S, Bjune GA. A systematic review of delay in the diagnosis and treatment of tuberculosis. BMC Public Health. 2008;8:15.

14. Bureau of National Health Insurance. National Health Insurance in Taiwan. Taipei: Department of Health; 2010.

15. Bureau of National Health Insurance. Five pay for performance programs: preliminary outcomes (Chinese version). National Health Insurance Bimonthly Journal. 2003;44. Accessed January 21, 2015.

16. Li YH, Tsai WC, Khan M, et al. The effects of pay-for-performance on tuberculosis treatment in Taiwan. Health Policy Plan. 2010;25(4):334-341.

17. Tsai WC, Kung PT, Khan M, et al. Effects of pay-for-performance system on tuberculosis default cases control and treatment in Taiwan. J Infect. 2010;61(3):235-243.

18. Nambiar D. Performance incentives for global health: potential and pitfalls. Global Public Health. 2011;6(1):106-109.

19. Rosenthal MB, Frank RG. What is the empirical basis for paying for quality in health care? Med Care Res Rev. 2006;63(2):135-157.

20. Begg CB, Gray R. Calculation of polychotomous logistic regression parameters using individualized regressions. Biometrika.1984;71(1):11-18.

21. Cheng SH, Lee TT, Chen CC. A longitudinal examination of a pay-for-performance program for diabetes care: evidence from a natural experiment. Med Care. 2012;50(2):109-116.

22. Bardach NS, Wang JJ, De Leon SF, et al. Effect of pay-for-performance incentives on quality of care in small practices with electronic health records: a randomized trial. JAMA. 2013;310(10):1051-1059.
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