Abstract
Background Although CKD screening programs have been provided in many settings, little is known as to how we can effectively translate those screening programs into improved health.
Methods We conducted a randomized clinical trial on national health screening for CKD in Japan between April 2018 and March 2019. A total of 4011 participants in CKD screening programs aged 40–63 years were randomly assigned to two interventions or the control, with a ratio of 2:2:1, respectively: (1) the nudge-based letter that contained a message on the basis of behavioral economics, (2) the clinical letter including general information about CKD risks, and (3) the control (informed only of the screening results). The main outcome was adherence to a recommended physician visit within 6 months of the intervention. The secondary outcomes were eGFR, proteinuria, and BP 1 year after the intervention.
Results Compared with the control group, the probability of undergoing a recommended physician visit was higher among participants who received the nudge-based letter (19.7% for the intervention group versus 15.8% for the control; difference, +3.9 percentage points [pp]; 95% CI, +0.8 to +7.0; P=0.02) and the clinical letter (19.7% versus 15.8%; difference, +3.9 pp; 95% CI, +0.8 to +7.0; P=0.02). We found no evidence that interventions were associated with improved early health outcomes.
Conclusions The behavioral economics intervention tested in this large RCT had limited effect on changing behavior or improving health outcomes. Although the approach has promise, this study demonstrates the challenge of developing behavioral interventions that improve the effectiveness of CKD screening programs.
Clinical Trial registry name and registration number: University Hospital Medical Information Network Clinical Trial Registry, UMIN000035230
- chronic kidney disease
- behavioral economic intervention
- randomized clinical trial
- behavioral economics
- outcome assessment
- health care
CKD is a major global burden of disease that affects 9.1% of individuals worldwide.1 It is associated with higher risks of cardiovascular diseases, such as myocardial infarction and stroke,2 and potentially progresses to ESKD, requiring dialysis or kidney transplantation. The prevalence of CKD is rising with the aging of the population and increases in risk factors, such as obesity, hypertension, and diabetes.3⇓⇓–6 Research has shown that a substantial number of patients with CKD may be undiagnosed, and consequently, they are not receiving appropriate treatments.7⇓–9 Although screening programs for early detection of CKD have been implemented in many settings (The Netherlands,10 the United States,11,12 Canada,13⇓–15 and Japan16), little is known as to how we can effectively translate those screening programs into improved health. The behavioral economics interventions have been shown to be effective in changing human behavior; however, to our knowledge, whether the behavioral economics intervention improves the adherence to the recommended physician visit after the CKD screening program has not been investigated.
Despite the possibility that clinical care provided by physicians has the potential to slow the progression of CKD, allowing the patient to avoid dialysis or kidney transplant due to ESKD, many people do not see a physician and receive appropriate medical treatments when the risk of disease progression is recognized to be high.17 An estimated 90% of CKD detected by screening is untreated, and only 2% of these individuals visited physicians for CKD care after the screening in Japan.9 A study conducted in the United States found that <25% of participants of the screening program with CKD were aware of their CKD.17 Dual-system models in behavioral economics indicate that people’s decision making and behavior are determined by two cognitive processes (system 1 is the fast, automatic, and nonconscious system, whereas system 2 is the slow, controlled, and conscious system) that operate in parallel.18 Nudge strategies have been recognized as a promising approach to influence habitual or automatic behavior by targeting the subconscious routines and biases that are present in human decision-making processes and behavior. Although behavioral economics interventions, such as nudging strategies, are reported to be effective in changing participants’ healthy behaviors,19,20 little is known as to whether nudge-based strategies are effective in improving the rates of screening program participants’ adherence to recommended physician visits.
Therefore, we conducted a large-scale clinical randomized, controlled trial (RCT) to investigate whether behavioral economics interventions could improve the likelihood of participants in the screening programs with identified CKD adhering to the recommended physician visit within 6 months after the intervention. We also examined whether such interventions affected early health outcomes (eGFR, proteinuria, and BP) 1 year after the intervention.
Methods
Study Design
We conducted an RCT to assess the effect of behavioral economics interventions on physician visits and early health outcomes among participants with CKD using the health system database. This RCT had three arms (the nudge-based letter, the clinical letter, and the control) to examine an add-on effect of the nudge-based letter compared with the clinical letter. The study protocol was published21 and registered in the University Hospital Medical Information Network Clinical Trial Registry (UMIN000035230).
Setting
The study participants were selected from beneficiaries of the nationwide employment-based health insurance plan in Japan (i.e., the national sample of employees of civil engineering and construction companies). In Japan, all adults over age 40 are required by law to undergo the national health screening program every year. The health system database includes annual health screening results and monthly measured medical claims data. The health screening results include demographics, body mass index, eGFR, urine protein, BP, hemoglobin A1c, medication use (antihypertensive, antidiabetic, and antihyperlipidemic drugs), and history of diseases (stroke and cardiovascular diseases). The claims data include diagnosis codes and procedure codes, which include information on all medical care provided and covered by the Japanese universal health insurance system.22
Participant Selection
We selected study participants from a group of individuals who underwent health screening between April 2018 and March 2019 at ages 40–63 and were identified as having CKD (defined as an eGFR <60 ml/min per 1.74 m2 or urine proteinuria [≥1+ by dipstick test]) during the screening. We excluded participants with a history of ESKD (eGFR<15 ml/min per 1.74 m2, a history of receiving dialysis therapy, or a history of kidney transplants) (Figure 1). We estimated eGFR using the Japanese coefficient–modified Chronic Kidney Disease Epidemiology Collaboration equation, which has been validated using data on the Japanese population.23 Urine dipstick is used to examine proteinuria in the Japanese health screening program. The institutional review board of Kyoto University approved all study procedures and waived informed consent for participants (approval no. C1420).
Selection process for the study participants. The figure shows contents of the two types of letters, recommending physician visits.
Outcome Measures
The primary outcome was a visit to a physician within 6 months following the intervention. The visit was identified using medical claims data using diagnosis codes related to CKD (International Classification of Diseases, Tenth Revision codes [Supplemental Table 1]). The physician visit for this population is recommended by the guidelines developed by the Ministry of Health, Labor, and Welfare of Japan.24 The secondary outcomes were eGFR, proteinuria, and systolic and diastolic BP 1 year after the intervention, measured in the following year’s health screening.
Intervention
We tested two types of interventions: nudge-based and clinical letters. Both letters were sent by mail to eligible participants with identified CKD. The letters recommended a visit to a physician to receive appropriate treatment for CKD and included a link to a website where participants could search for nearby health care providers. Both letters included information about the recipient’s results on eGFR, urine protein, and CKD-related risk factors (hypertension, diabetes, and smoking) identified during the screening. Figure 2 and Table 1 have details of the contents of the letters.
Interventions with two types of letters. (A) Nudge-based letter. (B) Clinical letter. These letters were translated from Japanese into English. HbA1c, hemoglobin A1c.
Contents of the letters
Both the nudge-based and clinical letters provided the basic information (education component) about CKD (i.e., risk of cardiovascular disease and stroke due to CKD and risk of ESKD due to CKD). The difference between the two letters was that although the nudge-based letter used three nudge strategies developed in behavioral economics (loss aversion, default, and commitment), the clinical letter did not include them. Loss aversion emphasizes losses (progression to ESKD and incidence of cardiovascular diseases) if no action was taken by the recipient. The letters also described the inconveniences that participants would face in their daily lives if their CKD progressed to ESKD and required dialysis treatments. Given that people have the tendency to prefer avoiding losses to acquiring equivalent gains,25 such loss-framed messaging has the potential to more effectively change participants’ behavior compared with focusing on the benefits of seeing a physician. We also provided the information on the clear action steps as the default option in order to avoid participants procrastinating because they are cognitively overwhelmed by too many options (e.g., which physician to see) to choose from (“choice overload”). Finally, a commitment device encourages participants to write down the name of the health care provider and the time of the physician visit in advance in the letter, which attempts to enforce their voluntarily imposed restrictions regarding their plan to visit a physician.
All three groups (two groups assigned to interventions and the control group) were informed of their results from the screening program (they received notification of their screening results, which did not include recommendations to visit physicians). The notification included information about test results, highlighting abnormal results.
Randomization
According to the timing of the screening, we delivered the interventions in two waves in order to ensure that intervention was received soon after undergoing the screening program: January 2019 (the first wave) and July 2019 (the second wave). After stratifyed by eGFR (≥60 or <60 ml/min), and urine protein (≥1+ or <1+), we randomized participants in a 2:2:1 ratio into the nudge-based letter group, the clinical letter group, and the control group, respectively, using a permuted block method with a block size of five for each stratum (Supplemental Figure 1).
Statistical Analyses
The sample sizes of 1700, 1700, and 850 patients for the nudge-based letter group, the clinical letter group, and the control group, respectively, were determined to achieve ≥80% power overall for all pairwise comparisons with the chi-squared test. The necessary sample sizes were calculated on the basis of a simulation under the assumption of the proportions of patients visiting physicians in each group at 0.25, 0.20, and 0.15, respectively.
The primary statistical analysis was performed according to the methods predetermined in the study protocol and trial registration shown in Supplemental Material. First, we described participant characteristics by groups. Also, to test whether participants’ characteristics were balanced after the randomization, we calculated pairwise standardized differences between assignment groups (clinical letter versus control and nudge-based letter versus control). Second, we examined the effect of the intervention on visits to physicians using an intention-to-treat analysis. For the analysis of the primary end point, we used logistic regression models adjusted for the allocation stratification variables (timing of the screening, eGFR, and urine protein). We calculated P values using an omnibus test (Wald test) to see whether there were any differences in outcomes (proportion of visits to physicians) between the three groups. A two-sided P value using the omnibus test of 0.05 was interpreted as a statistically significant difference in outcomes between the groups, and we proceeded to perform pairwise comparisons. We calculated adjusted proportions of physician visits for each group and their differences between groups (percentage point [pp]) using the model-based standardization technique, adjusting for the stratification variables (timing of the screening, eGFR, and urine protein).26,27 Adjustment for the stratification variables in the primary analysis of the RCT is recommended to appropriately account for the study design.28
As secondary outcomes, we also examined the effect of the interventions on early health outcomes (eGFR, proteinuria, and systolic and diastolic BP) 1 year after the intervention in the total population and in hypertensive participants (systolic BP ≥140 mm Hg, diastolic BP ≥90 mm Hg, or antihypertensive drug use). For continuous outcomes (eGFR and BP), we fitted ordinary least square regression models and estimated differences between groups, adjusting for the stratification variables (timing of the screening, eGFR, and urine protein). For binary outcomes (proteinuria), we used a logistic regression model and estimated adjusted proportion differences between groups (pp) using the marginal standardization technique, adjusting for the stratification variables. To account for multiple comparisons in health outcomes, we performed post hoc adjustments for P values by using the Benjamini–Hochberg procedure.29
Secondary Analyses
We conducted several secondary analyses. First, to examine differences in the effect of interventions according to participants’ characteristics, we conducted stratified analyses by participants’ age, sex, coexistence of hypertension and diabetes, receipt of previous treatment, and baseline eGFR categories (<45, 45–59, and ≥60 ml/min). We conducted stratified analyses by diabetes, hypertension, age, sex, and previous treatment as prespecified in the study protocol. The previous treatment was defined as the presence of CKD treatment in the 6 months prior to the intervention by medical claims data. We also conducted the post hoc stratified analyses by baseline eGFR and health guidance intervention (we added these analyses during the analysis stage without seeing the actual data). We formally tested the interaction using the likelihood ratio test. Second, we tested for the possibility that observed changes in physician visits and early health outcomes may be explained by an increased likelihood of receiving a health guidance intervention after the health screening (participants who meet the eligibility criteria are required by law to receive health guidance provided by a trained instructor). Supplemental Material has additional details. Third, we reanalyzed the data additionally adjusting for the receipt of a health guidance intervention. Fourth, to account for missing health outcomes, we analyzed the data using weighted regression models using the inverse probability of observing the outcome variables as weights (observed individual-level variables were used to estimate the weights).30 Among participants, 7.6% had missing values of health outcomes due to absence of second health screening results. Fifth, to address the possibility that some physician visits were due to health issues other than CKD, we reanalyzed the data using a narrower definition of physician visits: physician visits with the diagnosis of CKD (Supplemental Table 1) and claims related to the diagnostic tests for CKD (i.e., urine test for proteinuria and/or serum creatinine test). Sixth, to examine the effect of the interval between the initial screening and the intervention on the effects of intervention, we conducted stratified analysis by the intervals (<6, 6–9, and ≥10 months). Seventh, we conducted stratified analysis by waves (timing of the intervention). Eighth, we examined whether additionally adjusting for baseline characteristics (age, sex, body mass index, systolic BP, hemoglobin A1c, antihypertensive drugs, antidiabetic drugs, antihyperlipidemic drugs, and history of stroke and cardiovascular disease) affected our findings. Ninth, we reanalyzed the data, restricting to participants without previous CKD treatment. Finally, to investigate how much time it takes for a physician visit to affect health outcomes, we compared health outcomes of individuals on the basis of the time interval between a physician visit and the measurement of the follow-up health outcomes (<4 or ≥4 months).
All analyses were performed using Stata version 16.1 (StataCorp, College Station, TX) and SAS version 9.4 (SAS Institute).
Results
Participant Characteristics
Among 112,871 screened participants, 4011 individuals met our inclusion criteria (eGFR=15–59 ml/min or urine protein ≥1+) and were randomized (with the ratio of 2:2:1) into the nudge-based letter group (n=1605), the clinical letter group (n=1605), or the control group (n=801). The average (SD) age of participants was 53.5 (6.6) years old, and 12.0% (481) were women (Table 2). Overall, 758 (18.9%) participants visited physicians for CKD care within 6 months following the interventions. Among participants who were identified to have CKD, 15.8% had an existing diagnosis of CKD before undergoing the screening program, 37.8% were taking antihypertensive drugs, and 15.7% were taking antidiabetic drugs.
We examined the balance in participants’ characteristics between assigned groups and confirmed comparability between groups (P values of 0.05, |standardized differences|<0.1) (Supplemental Table 2). These results support comparability between groups after randomization.
Effect of Interventions on Physician Visits
The adjusted proportions of participants with CKD who attended a physician visit within 6 months of the screening were 19.7% (95% confidence interval [95% CI], +17.8 to +21.6; number of visits, 307), 19.7% (95% CI, +17.8 to +21.6; number of visits, 307), and 15.8% (95% CI, +13.3 to +18.3; number of visits, 117) in the nudge-based letter group, the clinical letter group, and the control group, respectively. The P value of the omnibus test for comparing the groups was 0.04; therefore, we proceeded to perform pairwise comparisons. Compared with the control group, the probability of undergoing a recommended physician visit was higher among participants who received the nudge-based letter (19.7% for the intervention group versus 15.8% for the control; difference, +3.9 pp; 95% CI, +0.8 to +7.0; P=0.02) or the clinical letter (19.7% versus 15.8%; difference, +3.9 pp; 95% CI, +0.8 to +7.0; P=0.02) (Table 3).
Participant characteristics
Effect of Interventions on Early Health Outcomes
We found no evidence that assignment to one of the interventions was associated with early health outcomes (eGFR, the proportion of proteinuria, and systolic and diastolic BP) (Table 4).
Effect of the interventions on physician visits
Effect of the interventions on early health outcomes of participants 1 year after the intervention
Secondary Analyses
Our stratified analyses showed that receipt of the nudge-based letter compared with the control group was associated with an increased probability of adhering to a recommended physician visit among participants aged 40–49 (difference, +5.7 pp; 95% CI, +0.8 to +10.6; P=0.03) but not among participants aged 50–59 and ≥60. Receipt of the nudge-based letter compared with the control group was associated with an increased probability of adhering to a recommended physician visit among men but not among women. Receipt of the nudge-based letter compared with the control group was associated with an increased probability of adhering to a recommended physician visit among hypertensive patients (difference, +4.6 pp; 95% CI, +0.1 to +9.0; P=0.04), nondiabetic patients (difference, +4.6 pp; 95% CI, +1.5 to +7.8; P=0.004), and patients without previous treatment (difference, +3.6 pp; 95% CI, +1.3 to +5.9; P=0.002). The tests for interaction were not statistically significant (Supplemental Figure 2).
The effect of interventions was qualitatively unaffected by additional adjustment for the receipt of a health guidance intervention, accounting for missing health data at the second screening using weighted regression models and using a narrower definition of physician visits (i.e., physician visits with both the diagnosis of CKD and claims for diagnostic tests of CKD) (Supplemental Tables 3–5). The effect size of the intervention on physician visits was greater among participants with a shorter interval between initial screening and the intervention (Supplemental Table 6). We found similar results between waves (Supplemental Table 7). An additional adjustment for the baseline characteristics of participants did not qualitatively affect our findings (Supplemental Table 8). Among patients without previous treatment, we did not find significant effects of the interventions on early health outcomes (Supplemental Table 9). We found no evidence that health outcomes differ on the basis of the time interval between a physician visit and the measurement of the follow-up health outcomes (Supplemental Table 10).
Discussion
The large-scale RCT among participants of the health screening program found that behavioral economics interventions and the provision of clinical information using letters were both effective in improving the adherence to a recommended physician visit among those with identified CKD. On the other hand, we found no evidence that these interventions were associated with improved early health outcomes. Our findings should be informative for policy makers and insurers in many countries who are considering introducing behavioral economics interventions to improve patients’ adherence to recommended care. Our findings highlight the challenges of targeting populations not previously engaged in care and underscore the importance of future research that identifies interventions that can effectively improve human behavior and health outcomes.
The estimated effect of the nudge-based and clinical letters on physician visits (nearly 5 pp of improvement) was comparable with the findings from prior studies of similar behavioral economics interventions (many of which were conducted by the Nudge Unit in the United Kingdom).31 However, the behavioral economics intervention used in our study was arguably different from the interventions used in prior research in multiple aspects. First, the setting of our study focused on individuals who were identified to have CKD during the routine health screening program, whereas previous studies targeted health care professionals or patients in specific medical facilities.32 Our findings indicate that behavioral economics interventions are effective even among those in the relatively healthy general population who were identified to have relatively mild CKD (mostly asymptomatic) during the health screening program. Second, we used an efficient approach to identify our target population using the national health system database. This study took advantage of Japan's unique health system, in which all adults are required to undergo an annual health screenings by law. Therefore, we were able to identify patients with undiagnosed CKD (individuals who were newly diagnosed as having CKD during the screening), who were the target population of this study, and follow them to evaluate physician visits and early health outcomes using the data from medical claims and health screening results. This was useful for estimating the effects of interventions in a real-world setting, improving the generalizability (which is the major limitation of classic RCTs) of our findings. Third, the interventions designed in this study were simply sending letters, which were low cost (the estimated cost is approximately $9 per person). Such low-cost population approaches may be attractive for policy makers in countries struggling to curb health expenditures with limited financial resources to fund expensive interventions.
There are several potential reasons as to why we found that nudge-based and clinical letters were equally effective in improving adherence to a recommended physician visit. Given that the education component (information) was included both in the nudge-based letter and in the clinical letter, it is likely that it was the main driver of the increased likelihood of physician visits. On the contrary, we found no evidence that the behavioral economics components had an additional effect on changing people’s behavior and health outcomes. Although the education component included in the nudge-based letter informed the risk of the progression of CKD, it is possible that participants did not respond due to limited or underestimated recognition of the disease progression risk. Future behavioral economics interventions should ensure that study participants clearly understand the payoffs of altering their behavior. The effectiveness of the nudges depends on the actual design of the intervention, and improvements may be necessary regarding how we frame messages. In order to design more effective behavioral intervention materials, we may need to use more intuitive nudge-based designs. Intuitive nudge-based design is advocated as easy, attractive, simple, and timely.33 For example, there are some techniques, such as changing the color of text, and illustration in line with each message, in order to clearly convey the intent of the message to the recipient.
The interval between the initial screening and the intervention varied between individuals, and this variation may affect the effects of the intervention. The result of the stratified analysis indicates that the effect of the letters was attenuated as the interval between the initial screening and the intervention became longer. This finding suggests that timely intervention with a shorter interval may be more effective in improving the effectiveness of the interventions.
We found no evidence that the interventions were associated with improved early health outcomes. In this study, a short follow-up period of 12 months for health outcomes may make intervention effects harder to detect. Future research is warranted to investigate the health effect of these interventions with a longer follow-up period.
Our study has limitations. First, given that the sample size was determined according to our main outcome of physician visits and that only 20% of intervention group actually visited physicians (expected to improve health outcomes), analyses on health outcomes might be underpowered. Second, we were not able to evaluate the proportion of those individuals who reviewed the test results (or letter) out of those who received it. However, in Japan’s health screening program, all participants receive test results in writing, highlighting findings such as the existence of CKD. Therefore, those who are engaged enough to undergo a screening, including a blood draw, are likely to review results. Further, given that we observed increased physician visits in the intervention groups compared with the control group, it is probable that the substantial number of individuals who receive the letter actually read it. Third, because we could not determine the previous CKD treatment at the time of random assignment, we did not exclude those who were undergoing CKD treatment at the time of the study (15.8% of study participants) in our main analysis, potentially biasing our estimates toward the null. However, our findings were qualitatively unchanged in our sensitivity analysis, which excluded those who were receiving CKD treatments. Fourth, only the urine dipstick test was available, and albuminuria could not be examined in the Japanese heath screening program. Fifth, individuals with CKD may have a lower socioeconomic status (SES) than those without CKD, and SES may explain why we found a limited effect of the interventions on their health outcomes. Individuals with low SES may face many challenges, including limited transportation access to a health care provider, inflexible work schedule, and limited social support, making it harder for them to visit a physician and receive appropriate care. However, the lack of data on individual SES precluded us from investigating how SES interacts with our interventions. Lastly, this study was conducted in Japan, where access to health care is guaranteed by the universal health insurance system and where there is no gatekeeper (people can make an appointment with a specialist without a referral from a primary care physician in Japan). The results of this study are from a population with few barriers to health care access, and therefore, we may not find the same pattern if a similar study is conducted in countries with higher barriers to health care.
In summary, among participants in the national screening program with identified CKD, both behavioral economics and clinical interventions equally improved adherence to the recommended visit to a physician, whereas we found no evidence that the interventions led to changes in early health outcomes. These findings indicate that behavioral economics interventions may have limited effect on changing human behavior and improving health outcomes. Future research is warranted to understand the detailed design of an intervention that can effectively alter human behavior and translate the CKD screening programs to improved health.
Disclosures
S. Fukuma reports consultancy agreements with Kyowa Hakko Kirin and Rege Nephro; research funding from CANCERSCAN Co., Ltd. and Kyowa Hakko Kirin; honoraria from Kyowa Hakko Kirin; and scientific advisor or membership with Kyowa Hakko Kirin. R. Goto reports ownership interest in Hagoromo Foods, Inc. S. Sasaki reports consultancy agreements with FUJITSU RESEARCH INSTITUTE and scientific advisor or membership with Japan's National Behavioral Sciences Team; the Ministry of Economy, Trade and Industry; and the Japan Institute of Life Insurance. All remaining authors have nothing to disclose.
Funding
This work is supported by the Keihanshin Consortium for Fostering the Next Generation of Global Leaders in Research (K-CONNEX) and the Japan Society for the Promotion of Science (KAKENHI: 19H03870).
Acknowledgments
We thank the participants in this trial; the staff at the Health Insurance Association for Architecture and Civil Engineering Companies and CANCERSCAN Co., Ltd.; Dr. Tatsuyoshi Ikenoue (Kyoto University Graduate School of Medicine) for data management and data analysis and for developing the intervention materials; and Pharm D. Yoshiyuki Saito (Kyoto University Graduate School of Medicine) for project management and data management.
S. Fukuma conceived and designed the study and performed the statistical analyses; S. Fukuma and Y. Tsugawa drafted the initial manuscript; all authors interpreted the data, critically revised the manuscript for important intellectual content, and approved the final manuscript; and S. Fukuma is the guarantor and attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.
Data Sharing Statement
Data underlying the findings described in this manuscript may be obtained in accordance with the Health Insurance Association’s (Health Insurance Association for Architecture and Civil Engineering Companies) data sharing policy.
Supplemental Material
This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2021050664/-/DCSupplemental.
Supplemental Figure 1. Study time line.
Supplemental Figure 2. Effect of the interventions on physician visit–subgroup analysis by age, sex, hypertension, diabetes, and previous treatment.
Supplemental Material. Health guidance intervention after screening in Japan.
Supplemental Table 1. ICD-10 codes to define physician visits related to CKD care.
Supplemental Table 2. Assessment of balance after randomization.
Supplemental Table 3. Effect of the interventions on physician visit, with additional adjustment for the receipt of the health guidance intervention.
Supplemental Table 4. Effect of the interventions on health outcomes, accounting for missing follow-up screening results.
Supplemental Table 5. Assessment of validity of the definition of CKD visit according to CKD-related diagnosis codes.
Supplemental Table 6. Effect of interventions on physician visits by the interval between initial screening and intervention.
Supplemental Table 7. Effect of the interventions on early health outcomes by intervention waves.
Supplemental Table 8. Effect of the interventions on physician visits with additional adjustment for baseline characteristics.
Supplemental Table 9. Effect of the interventions on early health outcomes of participants without previous treatment 1 year after the intervention.
Supplemental Table 10. Early health outcomes among the individuals who visited physicians in the intervention groups by time from the physician visits to the assessment of early health outcomes.
Footnotes
Published online ahead of print. Publication date available at www.jasn.org.
See related editorial, “Nudging Behavioral Economics into Nephrology Care Delivery Research,” on pages 9–11.
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