Yip W, Powell-Jackson T, Chen W, Hu M, Fe E, Hu M, et al. Capitation combined with payfor-performance improves antibiotic prescribing practices in rural China. Health Aff (Millwood). 2014;33(3). Published online February 26, 2014. Appendix Experimental design and randomization We used matched-pair cluster-randomization to assign the twenty-eight towns to intervention and control. Each cluster, a town, consists of a THC, all the VPs under the THC s supervision and the population in its catchment area. On average, each town had 11 VPs and each VP served on average 1,500 people. The majority of VPs were staffed by one village doctor. We first paired towns before randomization, ensuring that matches were as similar as possible on a range of baseline characteristics (whether the THC provided hospitalisation services; whether the THC was classified as a centre THC to indicate its higher level of capacity; distance to the county seat; average expenditure per outpatient visit; number of outpatient visits per year; number of village posts under its management and percentage of agricultural population in the town to indicate socioeconomic development). 1,2 We then randomly assigned one cluster from each pair with the flip of a coin to receive the provider payment intervention described above, starting July 2010, and the other in the pair as control. All towns agreed to their assignment. However, a small-sized control THC lost its manager after the intervention began. The
county health bureau requested an intervention THC (not in the same matched pair) to manage this THC, which it did as if it were subject to capitation plus pay-for-performance incentives. We therefore dropped the paired cluster to which this control THC belonged. These events could not have been anticipated and serve to illustrate the politically robust usefulness of the pair-cluster randomised deign in this setting. 2 At the time of writing, the payment intervention in the intervention clusters remains ongoing. Identical trainings on appropriate drug prescription were provided to both the intervention and control THCs and VPs. THCs and their VPs were not masked to the intervention. Cluster residents, however, were masked in the sense that there was no public announcement of the change in payment system. All residents faced the same NCMS insurance policies and other relevant policies introduced in Ningxia province. We anticipated heterogeneity in intervention THCs compliance with the policy intervention, and tried to minimise this risk by having teachers from Ningxia Medical University conduct monitoring visits to the NCMS offices, THCs and selected VPs on a monthly but random basis. To examine whether there are compositional changes in patients due to the intervention, for example, changes in demand when patients respond to the capitation plus p4p payment
intervention by selecting into the control health centres/village posts, which might therefore bias the results, we use the household data to show that the characteristics of patients seeking care are very similar between intervention and control. These results are shown below in Exhibit A10. This is not surprising because from qualitative interviews with households, they were not aware of what provider payment methods their health centres or village posts faced. This is also consistent with the results shows in Exhibit 9A which showed that there was no effect of the intervention on utilization of healthcare. We further checked and found that there was very little movement between intervention and control clusters in the health seeking behaviour of residents. Only 4.8% of patient visits involved individuals living in an intervention (or control) cluster and seeking care from a control (or intervention) cluster. Validating randomization Using data from a survey of households and a survey of health providers conducted in 2009, we validate randomization by comparing the treatment and control groups. Characteristics of individuals living within study clusters assessed at baseline in the household survey were found to be similar between intervention groups, with the exception of ethnicity (Exhibit A3). Educational attainment and per capita consumption are low by Chinese standards. THC and VP characteristics also appeared
similar between groups (Exhibit A4). There were no notable differences on a wide range of variables measured at baseline in the health provider surveys. Sample size We conducted the sample size estimation separately for THCs and VPs on the basis of 28 clusters. The sample size at the level of THC assumed 49% of patients are prescribed antibiotics at baseline, with 5000 patient visits per cluster and a coefficient of variation of 0 2 providing 80% power at a 5% level of significance to detect a 15% (7 5 point) fall due to the intervention. 3 For VCs, we assumed 38% of patients are prescribed antibiotics at baseline, with 4000 patient visits cluster and a coefficient of variation of 0 2 providing 80% power to detect a 15% (5 7 point) treatment effect. Empirical analysis The effect of the intervention was estimated by fitting regressions of each outcome on a binary indicator of treatment status. For continuous outcomes, we used least squares regressions of the form: where is the outcome observed for individual in
village/township, is a binary indicator taking value 1 if village/township belongs to the treatment group (and 0 otherwise), is a vector of individual characteristics including gender and patient age, captures unobserved paired township characteristics through pair fixed-effects and is an independent error term such that. In the case of binary outcomes, we used a logistic regression of the form: and report marginal effects. The expenditure data showed a relatively small number of implausible values. We therefore trimmed the sample at the 99 95 th percentile, while noting that estimates on the full sample are almost identical. We report unadjusted estimates (excluding and ) as well as estimates adjusted for the inclusion of patient gender, patient age and a dummy variable for each pair of matched clusters which accounted for heterogeneity across pairs of clusters. Robust standard errors, clustered at town level, were computed to allow for arbitrary correlations of observations within clusters. 4 For all outcomes, except patient satisfaction, we conducted a subgroup analysis by patient gender. For antibiotic use, we reported results limiting the
sample to patients diagnosed with a cold to provide more clear-cut evidence on the extent of unnecessary prescribing practices. All statistical analyses were done with Stata (version 12). Endnotes 1. King G, Gakidou E, Imai K, Lakin J, Moore RT, Nall C, et al. Public policy for the poor? A randomised assessment of the Mexican universal health insurance programme. Lancet. 2009; 373(9673): 1447-54. 2. King G, Gakidou E, Ravishankar N, Moore RT, Lakin J, Vargas M, et al. A "Politically Robust" Experimental Design for Public Policy Evaluation, with Application to the Mexican Universal Health Insurance Program. J Policy Anal Manag. 2007;26:479-506. 3. Hayes RJ, Bennett S. Simple sample size calculation for cluster-randomized trials. Int J Epidemiol. 1999; 28(2): 319-26. 4. Moulton BR. An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Units. Rev Econ Statistics. 1990; 72(2): 334-8.
Exhibits Exhibit A1. Indicators of performance and scoring system under capitation with pay-for-performance Antibiotic prescription IV-antibiotic prescription Item Item Description Weight Score Data used for calculating performance score Falsifying visits Completeness of visit record Patient satisfaction Percentage of visits prescribed with antibiotics 150 Weight x (1 - % visits with antibiotics prescribed) Among visits with antibiotics prescribed, percentage given via IV injection Percentage of visits with identical patient name and health problems repeated within one day Percentage of visit record that has at least one of the following incomplete: symptoms; diagnosis; drugs prescribed (types, dosage); exam/test prescribed (for township health centres); expenditure Are you satisfied with the cleanliness of the clinic (1) Very satisfied (2) so- so (3) not satisfied Are the providers patient and careful in explaining to you your health problems? (1) Always (2) sometimes (3) never Do the providers explain to you how to take your medications? (1) Always (2) sometimes (3) never Are you satisfied with the providers technical quality? (1) Definitely (2) so- so (3) no Are you able to see the provider without long waiting? (1) Always (2) sometimes (3) never TOTAL 500 150 Weight x (1 - % with IV injection among those prescribed antibiotics) 50 Weight x (1 - % of visits with identical patient name and health problems repeated within one day) 50 Weight x (1 - % visit record deemed incomplete) Management information system Management information system Management information system Random sample of 200 records 20 Weight x (% response 1 ) Interview with 30 randomly selected households in each 20 Weight x (% response 1 ) village 20 Weight x (% response 1 ) 20 Weight x (% response 1 ) 20 Weight x (% response 1 )
Exhibit A2. Description of the data sources used in the study Source of data Outcomes Description of survey Management information system Antibiotic use per visit Expenditure per visit Patient visits per day Study information system providing data on the universe of patient visits across a census of township health centres in the study area. Household survey Village clinic survey Patient satisfaction across the following dimensions: waiting time; cleanliness; doctor politeness; doctor explanation of illness and treatment plan; drug availability; equipment; and confidence in doctor. Data also used to assess differences in baseline characteristics between treatment and control Data used to assess differences in baseline characteristics between treatment and control. Two-stage sample survey representative of each town within a county. In each study town (ie. cluster), 40 percent of villages were randomly selected, and within each village 33 households were randomly interviewed. In 2009, 3,828 households were interviewed. In 2012, 3,887 households were interviewed. Random sample of village clinics, covering 40 percent of all village clinics in the study area. In 2009, 114 village clinics were interviewed. Township health centre survey Data used to assess differences in baseline characteristics between treatment and control. Census of all township health centres in the study area. In 2009, 26 township health centres were interviewed.
Exhibit A3. Household characteristics at baseline Total number of individuals Intervention (capitation 7989 with P4P) Control (fee-for-service) 8877 Age (years) 31.4 (19.4) 30.3 (19.4) Male 4170/7989 (52%) 4601/8877 (52%) Han 4861/7989 (61%) 4176/8877 (47%) Hui 3092/7989 (39%) 4675/8877 (53%) Other ethnicity 36/7989 (0.4%) 26/8877 (0.3%) No education 2340/7989 (29%) 2622/8877 (30%) Elementary school 2876/7989 (36%) 3540/8877 (40%) Middle school 2016/7989 (25%) 2072/8877 (23%) High school or above 757/7989 (9%) 643/8877 (7%) Migrant worker 1457/7989 (18%) 1574/8877 (18%) Consumption per capita 5090 (5271) 5409 (9469) Distance village clinic (km) 3.9 (13.6) 5.5 (27.9) Distance town health centre (km) 19.9 (16.4) 20.4 (16.9) Distance county hospital (km) 107.4 (66.5) 82.4 (56.9) Head of the household 1848/7989 (23%) 1980/8877 (22%) Female headed household 307/7989 (4%) 327/8877 (4%) Household size (members) 5.0 (1.4) 4.8 (1.4) Source: Authors analysis of study data Notes: Data are n/n (%) or mean (standard deviation) unless otherwise stated. Data from the household survey conducted in 2009, before the intervention started, in the study clusters.
Exhibit A4. Health provider characteristics at baseline Township health centre Intervention (capitation with P4P) Control (fee-for-service) Number of physicians 18.1 (16.7) 20 (15.3) Number of nurses 10.2 (6.1) 10.7 (6.8) Number of other medical staff 3.6 (5.1) 4.5 (6.6) Number of beds 13.3 (19.4) 11.9 (9.6) Inpatient admissions per year 394.6 (497.4) 431.7 (527) Outpatient visits per year 14081 (12955) 14305 (6317) Income per year 1098725 (1219762) 1092835 (666706) Cost per year 995145 (1117475) 933271 (612418) Service revenue per year 343342 (253446) 370315 (244136) Village clinic doctor Doctor's years of experience 23.2 (14.2) 23.9 (15) Barefoot indicator 34/54 (62.96%) 40/60 (66.67%) Doctor has multiple jobs 40/54 (74.07%) 42/60 (70%) Male 46/54 (85.19%) 11/60 (81.67%) Age 45.8 (12.3) 45.9 (13) Doctor's household size 5 (1.9) 4.8 (1.5) Source: Authors analysis of study data Notes: Data are n/n (%) or mean (standard deviation) unless otherwise stated. Data from the health provider surveys conducted in 2009, before the intervention started, in the study clusters.
Exhibit A5. Effect of payment intervention on antibiotic prescribing practices (full version of Exhibit 3) Control mean Antibiotic use in township health centres Unadjusted Adjusted Treatment effect Treatment effect p value (95% CI) (95% CI) All 0.442-0.107 (-0.211, -0.003) 0.044-0.066 (-0.123, -0.008) 0.026 Oral antibiotics 0.279-0.042 (-0.110, 0.025) 0.222-0.014 (-0.057, 0.028) 0.507 Injectable antibiotics 0.208-0.078 (-0.168, 0.013) 0.094-0.051 (-0.104, 0.002) 0.058 Patient diagnosed with a cold 0.506-0.049 (-0.239, 0.140) 0.609-0.093 (-0.173, -0.014) 0.020 Male 0.436-0.097 (-0.200, 0.007) 0.072-0.063 (-0.118, -0.009) 0.022 Female 0.447-0.118 (-0.223, -0.125) 0.028-0.068 (-0.130, -0.005) 0.034 Antibiotic use in village posts All 0.342-0.052 (-0.132, 0.027) 0.195-0.060 (-0.115, -0.005) 0.032 Oral antibiotics 0.275-0.025 (-0.091, 0.041) 0.465-0.027 (-0.068, 0.014) 0.192 Injectable antibiotics 0.123-0.039 ( 0.000, 0.049) 0.049-0.041 (-0.072, -0.010) 0.010 Patient diagnosed with a cold 0.384-0.129 (-0.263, 0.004) 0.057-0.160 (-0.245, -0.075) 0.000 Male 0.330-0.060 (-0.138, 0.018) 0.133-0.068 (-0.136, -0.010) 0.021 Female 0.354-0.044 (-0.125, 0.038) 0.295-0.052 (-0.105, 0.001) 0.055 Adjusted estimates include cluster pair fixed effects as well as controls for patient gender and patient age. There are 440,473 patient visits (208,482 treatment group; 231,991 control group) in the township health centre analysis and 714,661 patient visits (338,185 treatment group; 376,476 control group) in the village post analysis. p value
Exhibit A6. Effect of payment intervention on expenditure per visit (full version of Exhibit 4) Control mean Unadjusted Total expenditure per visit in township health centres Adjusted Treatment effect Treatment effect p value (95% CI) (95% CI) p value All 20.91-0.45 (-5.56, 4.66) 0.857 0.02 (-5.48, 5.52) 0.994 Male 20.25-0.46 (-5.27, 4.34) 0.844-0.04 (-5.01, 4.92) 0.986 Female 21.56-0.40 (-5.87, 5.08) 0.883 0.12 (-5.99, 6.22) 0.969 Total expenditure per visit in village posts All 16.57-0.47 (-1.27, 0.34) 0.246-1.04 (-1.65, -0.42) 0.002 Male 15.95-0.41 (-1.24, 0.42) 0.315-1.01 (-1.62, -0.39) 0.002 Female 16.81-0.51 (-1.33, 0.31) 0.209-1.07 (-1.70, -0.44) 0.002 Drug expenditure per visit in township health centres All 18.55-1.07 (-4.67, 2.54) 0.547-0.88 (-4.28, 2.52) 0.600 Male 18.31-1.05 (-4.74, 2.63) 0.561-0.79 (-4.19, 2.61) 0.637 Female 18.78-1.07 (-4.63, 2.50) 0.543-0.97 (-4.39, 2.45) 0.564 Drug expenditure per visit in village posts All 11.41 0.10 (-0.57, 0.77) 0.758-0.24 (-0.64, 0.16) 0.227 Male 11.07 0.15 (-0.55, 0.84) 0.667-0.22 (-0.60, 0.17) 0.258 Female 11.78 0.06 (-0.61, 0.73) 0.863-0.27 (-0.698, 0.17) 0.215 Adjusted estimates include cluster pair fixed effects as well as controls for patient gender and patient age. Total expenditure and drug expenditure are trimmed at the 99.95 percentile. There are 440,144 observations (208,300 treatment group; 231,844 control group) in the township health centre trimmed sample and 714,304 observations (338,031treatment group; 376,273 control group) in the village post trimmed sample. Total expenditure includes spending on drugs, visit fee, tests and diagnostics.
Exhibit A7. Distribution of total expenditure per visit in township health centres Density 0.01.02.03.04 0 100 200 300 400 Total expenditure per visit Intervention Control Exhibit A8. Distribution of total expenditure per visit in village posts Density 0.02.04.06.08.1 0 20 40 60 80 Total expenditure per visit Intervention Control
Exhibit A9. Effect of payment intervention on healthcare utilisation and patient satisfaction Control mean Unadjusted Number of patient visits per day in township health centres Adjusted Treatment effect Treatment effect p value (95% CI) (95% CI) p value All 37.3-4.28 (-27.6, 19.0) 0.708-5.32 (-19.59, 8.95) 0.450 Male 19.1-1.66 (-12.89, 9.58) 0.764-2.37 (-9.03, 4.30) 0.471 Female 19.4-2.74 (-15.45, 9.96) 0.660-3.37 (-11.12, 4.39) 0.380 Number of patient visits per day in village posts All 9.7-1.56 (-5.64, 2.49) 0.432-0.90 (-3.60, 1.80) 0.498 Male 5.7-0.90 (-3.14, 1.34) 0.416-0.50 (-1.94, 0.94) 0.480 Female 5.2-0.96 (-2.91, 0.99) 0.320-0.59 (-1.85, 0.67) 0.342 Patient satisfaction score Township health centres 26.4 0.15 (-0.88, 1.19) 0.759-0.03 (-0.69, 0.62) 0.913 Village posts 26.0-0.12 (-0.84, 0.60) 0.739-0.10 (-0.63, 0.43) 0.693 Adjusted estimates include cluster pair fixed effects as well as controls for patient gender and patient age. For healthcare utilisation, the unit of observation is the number of visits per day in each health provider. For patient satisfaction, the unit of observation is a household, irrespective of whether they have used health services. The patient satisfaction score ranges between 7 and 35.
Exhibit A10: Characteristics of patients seeking care during the intervention period intervention control #observation mean #observation mean t-test (cluster at town level) sought care in village posts (2012) age 163 41.5092 286 40.3881-0.3591 male 163 0.4969 286 0.4615-0.7220 circulatory system diseases 163 0.0368 286 0.0315-0.2695 respiratory system diseases 163 0.8221 286 0.7937-0.4235 digestive system diseases 163 0.0798 286 0.0734-0.2403 genitourinary system diseases 163 0.0245 286 0.0175-0.3660 musculoskeletal system diseases 163 0.0429 286 0.0559 0.3480 other diseases (not in top 5) 163 0.0307 286 0.0559 1.0421 chronic disease 163 sought care in township health centres (2012) 0.2515 286 0.2727 0.4393 age 108 42.5370 122 48.1885 1.3918 male 108 0.4630 122 0.4262-0.5587 circulatory system diseases 108 0.0741 122 0.1475 0.9196 respiratory system diseases 108 0.6204 122 0.5082-1.0749 digestive system diseases 108 0.1296 122 0.1721 0.8943 genitourinary system diseases 108 0.0278 122 0.0902 1.6513 musculoskeletal system diseases 108 0.1019 122 0.0902-0.2089 other diseases (not in top 5) 108 0.1019 122 0.0984-0.0732 chronic disease 108 0.4444 122 0.4918 0.4691
sought care in village posts or township health centres (2012) age 265 41.6528 400 43.0550 0.5483 male 265 0.4792 400 0.4475-0.8038 circulatory system diseases 265 0.0528 400 0.0675 0.6646 respiratory system diseases 265 0.7396 400 0.7050-0.6438 digestive system diseases 265 0.0981 400 0.1025 0.1838 genitourinary system diseases 265 0.0264 400 0.0400 0.7180 musculoskeletal system diseases 265 0.0679 400 0.0675-0.0126 other diseases (not in top 5) 265 0.0604 400 0.0700 0.4488 chronic disease 265 0.3283 400 0.3375 0.1935 Data is from household survey data 2012. Patients are those who sought outpatient care in the past two weeks dating back from the day of interview.
Exhibit A11: Non-incentivized maternal health indicators (household data) intervention control #observation mean #observation mean t-test (cluster at town level) gynaecological exam 1815 0.1466 2283 0.1231-1.0837 deliver baby 1815 0.0331 2283 0.0350 0.3026 #prenatal exam 60 4.3833 80 4.2375-0.3267 measure weight in prenatal exam 60 0.8000 80 0.7625-0.4473 blood test in prenatal exam 60 0.6333 80 0.5750-0.5642 measure blood pressure in prenatal exam 60 0.8333 80 0.7625-0.9512 urine exam in prenatal exam 60 0.7333 80 0.5750-1.8036 have ultrasound in prenatal exam 60 0.9500 80 0.9500 0.0000 Data is from household survey 2012. Sample is female aging 15-45 and on site during the interview. Except for the first two indicators, the other indicators were only asked for women who had a birth in the year preceding the survey.
Exhibit A12: Non-incentivized child health indicators (household data) #observation intervention mean #observation control mean t-test (cluster at town level) #check-up 631 0.7274 870 0.7172-0.0809 #DPT 603 3.1592 825 3.1697 0.1008 #polio 605 3.1355 831 2.8869-1.6043 #hepatitisb 611 2.8756 839 2.8665-0.2229 BCG 619 0.9774 845 0.9716-0.5293 measles 623 0.8026 841 0.7622-1.3346 meningitis 616 0.8052 833 0.7587-1.3382 encephalitis 618 0.7783 829 0.7503-0.7488 hepatitisa 619 0.5929 835 0.5581-0.7605 MMR 619 0.6090 832 0.5493-0.9858 Data is from household survey 2012. Sample is children 7 years old or younger.