Reduced medical spending associated with increased use of a remote diabetes management program and lower mean blood glucose values

Figures & data

Table 1. Demographic and clinical characteristics of Livongo and non-Livongo populations matching.

	Members		Non-members		p-value
	n/mean	%	n/mean	%
Sample size	2,261		8,741
Female	1,236	55%	4,748	56%	0.77
Age, mean	52		51		<0.01*
Age category
0–18	12	0.5%	71	0.8%	0.17
18–34	133	5.9%	751	8.6%	0.01*
35–44	321	14.2%	1,352	15.5%	0.13
45–54	733	32.4%	2,730	31.2%	0.28
55–64	1,062	47.0%	3,837	43.9%	0.01*
Charlson Comorbidity Index
Pre-program	1.61		1.09		<0.01*
Post-program	1.68		1.22		<0.01*
Type 1 Diabetes	329	14.6%
Type 2 Diabetes	1,914	84.7%
Diabetes type unknown	18	0.7%
With self-reported HbA1c at registration	1,321	58%
Self-reported HbA1c, mean	7.83

* p-values less than or equal to 0.05.

Figure 1. Density plot of Livongo program usage by member-month.

Program usage is the number of days in a month with a BG value recorded, mobile app login, web portal login, or participation in a CDE coaching session.

Table 2. Continuous treatment results: association between DiD term (membership length*post time period) and healthcare spending and utilization.

Dependent Variable	Coefficient	Standard Error
Medical spending	−0.00107***	(0.00010)
Condition-specific spending
Diabetes-related	−0.00060***	(0.00007)
Hypoglycemia-related	−0.00002	(0.00001)
Hypercholesterolemia-related	−0.00023***	(0.00005)
Hypertension-related spending	−0.00033***	(0.00006)
Site of service spending
Emergency Department	0.00005	(0.00004)
Inpatient spending	0.00004	(0.00003)
Outpatient hospital spending	−0.00015**	(0.00007)
Office visit spending	−0.00115***	(0.00008)
Utilization
Number of ED visits	0.00001	(0.00001)
Number of inpatient visits	0.000003	(0.00001)
Number of outpatient visits	−0.00005	(0.00001)
Number of office visits	−0.00024***	(0.00002)

Table displays the association between continuous use of the Livongo program and medical spending and utilization. Continuous use of the program is measured by the number of days of a member’s Livongo program enrollment. Each dependent variable is log-transformed, and so the coefficients can be interpreted as percentage changes by applying $\exp \left(\mathit{coefficient}\right)-1.$ The percentage changes are stated in the text and shown in Supplementary Figure S1. Robust standard errors clustered at the patient-level are in parentheses.

*** p < 0.01, ** p < 0.05, * p < 0.1.

Table 3. Binary treatment results: association between DiD term (membership*post time period) and healthcare spending and utilization.

Dependent variable	Coefficient	Standard Error
Medical spending	−0.247***	(0.0257)
Condition-specific spending
Diabetes-related	−0.113***	(0.0184)
Hypoglycemia-related	−0.00492	(0.00337)
Hypercholesterolemia-related	−0.0280**	(0.0129)
Hypertension-related spending	−0.0545***	(0.0148)
Site of service spending
Emergency Department	0.0187*	(0.0106)
Inpatient	0.0227***	(0.00874)
Outpatient hospital	−0.0376**	(0.0176)
Office visit	−0.282***	(0.0215)
Utilization
Number of ED visits	0.00215	(0.00137)
Number of inpatient visits	0.00215	(0.00189)
Number of outpatient visits	−0.0129***	(0.00379)
Number of office visits	−0.0572***	(0.00479)

This table measure the association between use of the Livongo program and medical spending and utilization. Each dependent variable is log-transformed, and so the coefficients can be interpreted as percentage changes by applying $\exp \left(\mathit{coefficient}\right)-1.$ The percentage changes are stated in the text and shown in Supplementary Figure S2. Robust standard errors clustered at the patient-level are in parentheses.

*** p < 0.01; ** p < 0.05; * p < 0.1.

Table 4. Correlation between monthly percentage of BG checks in range and monthly medical spending.

Blood glucose (BG) checks (mg/dL)	Medical spending area
	Total	Inpatient	Outpatient	ER	Diabetes-related	Hypoglycemia-related
% BG < 70	0.061	−0.031	0.046	0.062	0.071	−0.025
% BG 70–180	−0.045	0.029	−0.010	0.033	−0.062	−0.124
% BG 180–250	0.037	−0.065	0.020	0.004	0.035	−0.460
% BG > 250	0.055	−0.013	−0.004	−0.039	0.067	0.117

This table presents the correlation between mean monthly percentage of BG checks in each range and monthly medical spending. BG values are obtained through the Livongo device, while medical spending data is obtained from the medical claims.

Table A1. Continuous treatment results: association between DiD term (membership length*post time period) and healthcare spending and utilization.

Dependent variable	90-day membership		180-day membership
	Coefficient	Standard Error	Coefficient	Standard Error
Total medical spending	−0.00106***	(0.00010)	−0.00106***	(0.00010)
Condition-specific spending
Diabetes-related medical spending	−0.000595***	(0.00007)	−0.000593***	(0.00007)
Hypoglycemia-related medical spending	−0.00002	(0.00001)	−0.00002	(0.00001)
Hypercholesterolemia-related spending	−0.000218***	(0.00005)	−0.000226***	(0.00005)
Hypertension-related spending	−0.000333***	(0.00006)	−0.000339***	(0.00006)
Site of service spending
Emergency Department spending	0.00006	(0.00004)	0.00006	(0.00004)
Inpatient spending	0.00004	(0.00003)	0.00003	(0.00003)
Outpatient hospital spending	−0.00012	(0.00007)	−0.00012	(0.00007)
Office visit spending	−0.00116***	(0.00008)	−0.00116***	(0.00009)
Utilization
Number of ED visits	0.00001	(0.00001)	0.00001	(0.00001)
Number of inpatient visits	0.000003	(0.00001)	0.000001	(0.00001)
Number of outpatient visits	−0.00004***	(0.00001)	−0.00004***	(0.00001)
Number of office visits	−0.000246***	(0.00002)	−0.000247***	(0.00002)

This table measures the association between continuous use of the Livongo program and medical spending and utilization. Continuous use of the program is measured by the number of days of a member’s Livongo membership. The number of members is 1,982 in a 90-day sub-population and 1,635 in a 180-day sub-population. Each dependent variable is log-transformed, and so the coefficients can be interpreted as percentage changes by applying $\exp \left(\mathit{coefficient}\right)-1.$ Robust standard errors clustered at the patient-level are in parentheses.

***p < 0.01.

Table A2. Binary treatment results: association between DiD term (membership*post time period) and healthcare spending and utilization.

Dependent variable	90-day membership		180-day membership
	Coefficient	Standard Error	Coefficient	Standard Error
Total medical spending	−0.277***	(0.0275)	−0.299***	(0.0304)
Condition-specific spending
Diabetes-related medical spending	−0.140***	(0.0197)	−0.154***	(0.0216)
Hypoglycemia-related medical spending	−0.00433	(0.00361)	−0.00539	(0.00403)
Hypercholesterolemia-related spending	−0.0427***	(0.0138)	−0.0544***	(0.0152)
Hypertension-related spending	−0.0717***	(0.0158)	−0.0873***	(0.0174)
Site of service spending
Emergency Department spending	0.0162	(0.0114)	0.0200	(0.0126)
Inpatient spending	0.0155*	(0.00934)	0.00983	(0.0103)
Outpatient hospital spending	−0.0361*	(0.0189)	−0.0299	(0.0209)
Office visit spending	−0.304***	(0.0231)	−0.325***	(0.0255)
Utilization
Number of ED visits	0.00234	(0.00146)	0.00231	(0.00161)
Number of inpatient visits	0.00139	(0.00203)	0.000641	(0.00223)
Number of outpatient visits	−0.0120***	(0.00405)	−0.0117***	(0.00448)
Number of office visits	−0.0639***	(0.00513)	−0.0688***	(0.00566)

This table measures the association between use of the Livongo program and medical spending and utilization. The number of members is 1,982 in the 90-day sub-population and 1,635 in the 180-day sub-population. Each dependent variable is log-transformed, and so the coefficients can be interpreted as percentage changes by applying $\exp \left(\mathit{coefficient}\right)-1.$ Robust standard errors clustered at the patient-level are in parentheses.

***p < 0.01; *p < 0.1.

Table B1. Reduced form results: association between program access and healthcare spending and utilization.

Dependent Variable	Coefficient	Standard Error
Total medical spending	−0.129***	(0.0378)
Condition-specific spending
Diabetes-related medical spending	−0.0446	(0.0288)
Hypoglycemia-related medical spending	−0.00455	(0.00506)
Hypercholesterolemia-related spending	−0.0245	(0.0191)
Hypertension-related spending	0.00213	(0.0225)
Site of service spending
Emergency Department spending	0.0204	(0.0133)
Inpatient spending	0.0164	(0.0127)
Outpatient hospital spending	−0.0635**	(0.0275)
Office visit spending	−0.166***	(0.0316)
Utilization
Number of ED visits	0.00244	(0.00190)
Number of inpatient visits	0.00235	(0.00290)
Number of outpatient visits	−0.0175***	(0.00642)
Number of office visits	−0.0405***	(0.00759)

The table measures the association between access to the Livongo program and medical spending and utilization. Each dependent variable is log-transformed, and so the coefficients can be interpreted as percentage changes by applying $\exp \left(\mathit{coefficient}\right)-1.$ Robust standard errors clustered at the patient-level are in parentheses.

***p < 0.01; **p < 0.05.

Table B2. Instrumental variables results: association between each 1-day increase in use of program and healthcare spending and utilization.

Dependent Variable	Coefficient	Standard Error
Total medical spending	−0.0519***	(0.0153)
Condition-specific spending
Diabetes-related medical spending	−0.0180	(0.0116)
Hypoglycemia-related medical spending	−0.00183	(0.00204)
Hypercholesterolemia-related spending	−0.00986	(0.00772)
Hypertension-related spending	0.00086	(0.00906)
Site of service spending
Emergency Department spending	0.00820	(0.00537)
Inpatient spending	0.00659	(0.00513)
Outpatient hospital spending	−0.0256**	(0.0111)
Office visit spending	−0.0668***	(0.0129)
Utilization
Number of ED visits	0.00098	(0.00077)
Number of inpatient visits	0.00095	(0.00117)
Number of outpatient visits	−0.0071***	(0.00260)
Number of office visits	−0.0163***	(0.00311)

The table presents the two-stage least squares results that measure the association between use of the Livongo program and medical spending and utilization. Use of the program is measured by the number of days per month that a patient uses the Livongo device to check blood glucose. Each dependent variable is log-transformed, and so the coefficients can be interpreted as percentage changes by applying $\exp \left(\mathit{coefficient}\right)-1.$ Robust standard errors clustered at the patient-level are in parentheses.

***p < 0.01; **p < 0.05.