Prevention: The Cost-effectiveness of the California Diabetes and Pregnancy Program
Richard M. Scheffler, PhD, Lisa B. Feuchtbaum, MPH, and Ciaran S. Phibbs, PhD
Introduction The cost savings potential of many prevention programs may not be realized for years, making these programs unattractive investments for health policy decision makers. 1,2 Fortunately, many studies of the cost savings potential of prenatal prevention programs need only look at the immediate future (i.e., 1-year care) to assess the cost effectiveness of the intervention. These studies have generally focused on the benefits of prenatal care or the prevention of low birth weight. Importantly, in the past 10 years there has been increasing recognition that the infant and maternal complications associated with diabetes in pregnancy can be prevented.-7 This paper provides the first cost-effectiveness analysis of a new and widely accepted prenatal prevention program, the California Diabetes and Pregnancy Program (CDAPP). The CDAPP, also known as the "Sweet Success" program, focuses on improving matemal and infant perinatal outcomes in pregnancies complicated by diabetes. The program's multidisciplinary team approach to care emphasizes early recruitment of pre-pregnant (prior to conception, or "pre-conception") and pregnant women with diabetes. Once enrolled, the women receive comprehensive prenatal care, including nutrition, education, and support services that have been specifically developed to meet the needs of diabetic women during pregnancy. This management approach has been shown to reduce diabetes-related complications during pregnancy to the rate seen in the nondiabetic population.' "' This approach to care, which has been shown to be effective in improving birth weight in the non-
diabetic population," is particularly well suited for managing high-risk pregnancies. Although maternal and infant mortality have been greatly reduced by advances in insulin therapy over the past 10 years, preexisting diabetes mellitus remains a significant risk to the unbom child. Congenital malformations still account for nearly 50% of the deaths among infants bom to diabetic mothers today.'2 Serious malformations are three to four times more likely to occur among infants of diabetic women than among infants of nondiabetics,'3 and affect 6% to 9% of all diabetic pregnancies.3 These malformations occur very early in pregnancy, at a time when the diabetic woman may not yet be aware that she is pregnant. '4 For these malformations to be prevented, it is critical that maximum diabetes control be maintained prior to pregnancy and in the early weeks of embryogenesis.'5 It is still not clear what specific biological mechanism accounts for these malformations, but it is widely accepted that the congenital malformation rate is correlated with the degree of glycemic control during pregnancy.7'6 Although the appropriate degree of control is still being deRichard M. Scheffler is with the Schools of Public Health and Public Policy and Lisa B. Feuchtbaum is a doctoral candidate with the School of Public Health, both at the University of California, Berkeley. Ciaran S. Phibbs was with the School of Public Health, Columbia University, New York, NY, at the time of this study, and is now with the Center for Health Care Evaluation, Veterans Administration Medical Center, Palo Alto, Calif. Requests for reprints should be sent to Richard M. Scheffler, PhD, School of Public Health, Warren Hall, University of California, Berkeley, Berkeley, CA 94702. This paper was submitted to the journal February 2, 1991, and accepted with revisions September 13, 1991.
February 1992, Vol. 82, No. 2
Cost-effiveness of the California Diabetes and Prenacy Program bated,17 the more tightly diabetes is controlled before and during pregnancy, the better the pregnancy prognosis. In studies in which women have achieved and maintained normal blood glucose levels before and throughout pregnancy, the congenital malformation rate has been reduced to the rate seen in the nondiabetic population.3,18,19 The aim of the present study was to determine the cost-effectiveness of the CDAPP. We compared the hospital charges and lengths of stay for mothers and their infants in the CDAPP with those for a sample of births to diabetic mothers not in the program. A regression framework was used to estimate the differences in hospital charges and lengths of stay between the CDAPP group and the control group; the regression models included measures of age, race, diabetes duration and severity, and the birth weight of the child.
Methods Stdy Design We used a case-control study design and incorporated the costs of the program into the cost-effectiveness calculations. For ethical and practical reasons, we did not randomly assign women to the study group or the control group. However, we minimized the differences between cases and controls by matching participants on maternal age, ethnicity, and duration and severity of diabetes. To minimiize the effect of selection bias, we chose the control hospitals from areas outside the catchment areas of the CDAPP hospitals.
HoWital Site Selection Data for the 102 CDAPP cases were collected for all program participants at three CDAPP hospitals (Children's Hospital of San Francisco, n = 18; Sutter Memorial Hospital, n = 36; and Loma Linda University Medical Center, n = 48). We included all births to program participants between July 1, 1986, and July 30, 1988, for whom complete data could be obtained. All obstetricians who regularly delivered infants at these hospitals referred their diabetic cases to the program. Thus, virtually all of the diabetic deliveries at these hospitals were included in our data. These hospitals were selected because they were the first to implement the CDAPP and their programs were well established at the time data collection started. All CDAPP hospitals have a level 3 neonatal intensive care unit.
February 1992, Vol. 82, No. 2
Data for the 218 control cases were collected from diabetic deliveries identified at five hospital sites throughout the state: Alta Bates Hospital in Berkeley (n = 89; 1984-1987), Huntington Memorial Medical Center in Pasadena (n = 71; 1985-1987), Sharp Memorial Medical Center in San Diego (n = 12; 1985-1987), St. Agnes Medical Center in Fresno (n = 15; 1985-1986), and Fresno Community Hospital (n = 31; 1985-1986). The control hospitals were selected from among hospitals that (1) were in regions that had yet to have a CDAPP implemented, (2) had at least 2000 births per year, and (3) had at least a level 2 neonatal intensive care unit. The demographic characteristics and health status indicators ofmothers and babies at these hospitals were similar. Information from the 1986 Maternal and Child Health Data Base Statistical Appendix20 was used to compare demographic characteristics of study and control hospitals. For study and control hospitals, respectively, the average number of births in 1986 was 3867 and 4355; Black births represented 10.4% and 14.5% of total births; the percentage of women enrolled in prenatal care in the first trimester of pregnancy was 85% and 81%; the observed mortalityratewas 1.8% and 1.2%; theperinatal complication rate was 17.5% and 13.8%; and the congenital malformation rate was .70% and .56%. Only women with overt diabetes were included in the study. The severity and duration of diabetes were assessed with White's classification system21: A Asymptomatic diabetes recognized prior to pregnancy; chemical diabetes. B Disease that began after age 20, or that has been present for 9 or fewer years with no vascular disease.
C Disease that began between the ages of 10 and 19, or that has been present for between 10 and 19 years with no vascular disease. D Disease that began before age 10, or that has been present for 20 or more years, or with vascular disease, such as background retinopathy or calcified iliac or femoral arteries. F Disease complicated by diabetic
nephrology. H Disease complicated by cardiovascular disease. R Disease complicated by proliferat-
ing retinopathy.
T Disease complicated by renal transplant. Both CDAPP cases and control cases were excluded from the study if their White's classification could not be identified from the patient's medical record. Because of the very small number of cases in classes F, H, R, and T, these classes were combined with class D in a group we labeled D+. CDAPP cases were identified by tracking enrollment records at each of the three CDAPP collection sites. Clinical and demographic data for each case were obtained from the CDAPP program database. Hospitalization charge data were collected for each mother and baby after delivery by means of the billing summary reports obtained from the hospital records. Complete billing histories were obtained for each mother for the duration of pregnancy and delivery, and for the babies from birth to discharge. Control cases were identified by searching the computerized medical record systems for the specific ICD-9 code specifying diabetic deliveries. Billing records were then requested for the mothers and their babies to reflect all inpatient hospital admissions of the mother during her pregnancy and delivery, in addition to infant hospital charge data for the period from birth through discharge. In the event that the baby was transferred to another hospital for care, billing summaries were requested for the care delivered at the second institution. Costs for the care provided by the CDAPP were estimated by means of "time-motion" methodology. The cost of staff time was calculated on the basis of expected staff time necessary to deliver patient care services as specified in the
state Guidelnes For Care.22 The costs of CDAPP services were estimated on the
basis of average salaries, fringe benefits, and overhead. For women who enrolled before conception, these costs averaged $1300. For late enrollees (enrolled after 12 weeks' gestation), the costs averaged
$800. T7e Match To control for individual risk factors that may have an ex ante effect on hospital charges, we matched the data on mother's age, race, and severity and duration of the mother's diabetes using White's classification. Race was classified into two groups (Black and non-Black) because Black infants have been shown to be significantly different from infants of other races with respect to incidence of low
American Journal of Public Health 169
Scheffier et al.
birth weight, birth-weight-specific mortality, and overall mortality. Mother's age is usually associated with risk status, especiaily for older mothers and the infants of younger mothers. We used three age groups (less than 20 years, 20 to 34 years, and 35 and over). Finally, White's classificationwas stratified into three groups: A, B and C, and D+, yielding a total of 18 possible cells. Each CDAPP participant was randomly matched with an individual from the control group who was of the same age, ethnicity, and White's class. This procedure yielded a data set of 180 matched cases, 90 cases from the CDAPP group and 90 cases from the control group. We excluded a control group infant who incurred a hospital bill of over $1.3 million (length of stay, over 400 days). Infants requiring hospitalization beyond 6 months of age are quite rare; one was included in our sample just by chance. Including this observation would greatly increase the savings due to the CDAPP.
Vatiable Constnsction The dependent variable is total hospital charges, which was constructed by aggregating the charges for the mothers and the babies. Charges were adjusted using the hospital component of the Consumer Price Index, so that all charges are in June 1988 dollars. Because the study hospitals are in several different labor markets across the state, a second adjustment was required. Data on wages for hospital personnel from the 1986 American Hospital Association Survey of Hospitals were used to construct a hospitalspecific wage index. The hospital wage index controls for the difference in labor costs, which are the largest component of hospital costs. The average wage, weighted by the number of cases from each hospital, was used as the base for the index. Hospital-specific charges were multiplied by 1, plus or minus the percentage by which the hospital's wage differed from the weighted average wage across hospitals. Normalizing charges with this wage index makes the charges across hospitals more comparable. The use of hospital charges to conduct a cost-effectiveness analysis has some limitations; differences in charges can be due to accounting practices or hospital inefficiencies. Length of stay is less likely to be subject to these differences. We therefore conducted a parallel analysis, using length of stay as the dependent variable. Length of stay, like charges, was 170 American Journal of Public Health
aggregated for each maternal and infant hospitalization. Because both charges and length of stay have a very skewed distribution, the natural logarithms of charges and length of stay were used as the dependent variables in the regression analysis. Independentvariables were included to control for risk factors that independently affect the costs of care and the length of hospital stay. These factors included maternal age, race, diabetes classification, birth weight, and source of payment. Variables were also included to test for the impact of the CDAPP. Binary variables were used to control for the presence of these risk factors. Almost all of the births were paid for by either private insurance or MediCal (the Medicaid program for California). Therefore, a binary variable for a MediCal admission was included. Duration and severity of diabetes was controlled for with White's classifications. These classifications were aggregated into four groups: A, B, C, and D+ (includes classes F, H, R, and T). Ethnicity was controlled for with a binary variable for race (Black or nonBlack). Maternal age was controlled for by including the mother's age, in years, at the time the birth occurred. Birth weight was controlled forwith a set ofbinaryvariables: less than 1500 g, between 1500 g and 2500 g, and greater than 4000 g. Birth weights between 2500 g and 4000 g were the excluded category. The inclusion of birth weight in the model is appropriate because maternal diabetes tends to affect birth weight.23,24 Moreover, previous research shows that birth weight is the single best predictor of newborn mortality and costs.25 To test for simultaneity bias, we reestimated the models reported below, excluding birth weight as a regressor. We also estimated models with birth weight as the dependent variable. These estimates showed no program effect on birth weight, and showed that excluding birth weight had a minimal effect on the estimated program coefficients. Thus, we are confident that any potential simultaneity bias is small. We used several different specifications to measure the impact of the CDAPP in our regression model. The basic specification included a binary variable for our CDAPP participants. However, to test the effect of time of enrollment in the CDAPP, we created binary variables for mothers enrolled before 8 weeks' gestation and one for mothers enrolled after 8 weeks' gestation (the study did not contain enough pre-
conception cases to allow a completely separate analysis of these cases).
Structure of the Regression Models All of the regression models control for mother's age, MediCal status, White's class, race (Black), and birth weight, and include a CDAPP effect variable. Model 1 presents the results of the basic model. Model 2 splits the program effect variable by time of enrollment in CDAPP, the expectation being that those women who enrolled earlier would incur lower costs and length of stay. Model 3 attempts to differentiate the degree of the program effect across severity of diabetes according to White's classification system. To test for severity of diabetes, the program effect variable, a binary variable for the CDAPP cases, is interacted with the White's class variables. This procedure splits the control variable into three mutually exclusive groups by White's class (classes A and B, class C, and class D+). The controls for White's class remain in the model. The excluded category becomes control cases whose diabetes is defined by White's class A or B. Thus, the coefficient for each of these new variables gives an estimate of the differential effect of the program for each White's class group. Given the smaller sample sizes, we expected limited significance with these interaction variables.
Cost-effectiveness Calculations Since the dependent variables for all of the models are in logarithms, direct interpretations of the charge or length-ofstay differences in the program are nontrivial. The log transformation moves the distribution toward normality, but it is still not normal. When the distribution of errors is not log-normal, the conventional formula for converting logarithms back to levels is inconsistent. Therefore, we used the smearing method developed by Duan26 to provide consistent estimates. The savings due to CDAPP do not occur instantly (i.e., costs of the program precede the hospital cost savings at delivery), so they must be discounted to present-value terms. To do this we allocated all hospitalization costs to the time of delivery, using an 8% discount rate. Given that these costs include maternal and infant costs, the time of delivery is probably near the midpoint of the time these costs were incurred. We used two different program patterns for this part of the evaluation. The first used the average time from entry into the program to delivery. This information was obtained from program
February 1992, Vol. 82, No. 2
Cost-effectiveness of the California Diabetes and Pregnancy Program
records for all mothers who entered the program between January 1, 1986, and December31, 1987. Average timewas calculated to be 26.5 weeks. This time reflects a mixture of pre- and post-conception enrollments. This method evaluates the program as it was actually provided; for notation purposes we refer to this as the Pre/Post analysis. CDAPP was designed as a pre-conception program, so in our second analysis we looked at the effects of an all-preconception program under conservative assumptions. We assumed that the preconception program would yield no more benefits than would the existing mix of programs (see method 1 above), and that women in this program would take an average of 6 months to conceive once the go-ahead was given. This 6-month period, plus the estimated 3-month program time, results in an 18-month lag from time of enrollment to time of delivery. We refer to this as the All Pre analysis. To be conservative in our estimate of the program savings, we used an 8% discount rate for the evaluation. This procedure produced discount factors of 0.962 for the Pre/Post analysis and 0.891 for the All Pre analysis. Note that we are using a very restrictive definition of program savings-only the savings in maternal and neonatal costs. Longer-term medical costs due to adverse birth outcomes, which are potential savings of the CDAPP, and any potential health benefits to the mother are not included. The discounted savings were compared with the program costs. Even though the program treatment costs were spread over time, they were all allocated to the time of initial treatment. Treatment costs of $1300 for pre-conception enrollment and $800 for post-conception enrollment were obtained from a previous analysis of the education component of the program. For the Pre/Post analysis, we used the Pre/Post mix of our sample to calculate a weighted average of $820. These costs were inflated further to reflect the program dropout rate and the failure of some participants to give birth to a live infant. Program data to date show that over 70%o of the participants have given birth to a live infant, yielding effective program costs per live infant of $1857 for the All Pre analysis and $1171 for the Pre/Post analysis. These numbers are almost certainly overestimates of the per-delivery program costs, inasmuch as some of the mothers counted in these figures who ultimatelywill give birth to a live infant have not yet had time to do so.
February 1992, Vol. 82, No. 2
Results Selected demographic and clinical characteristics of the cases and controls are presented in Table 1. These characteristics are average charges and length of stay, percentage byWhite's class, charges and length of stay by White's class, average age of mother, percentage by specific birth-weight group, percentage by race, and percentage by payor. The relative differences between the CDAPP cases and the control group are similar for both the entire sample and the matched sample, except for those factors over which the match was stratified. For the entire sample, the CDAPP group contained women with more severe diabetes; the majority of the women in the control group were of White's classes A and B (26% and 55%, respectively), whereas the majority of the CDAPP group were of classes B, C, and D (41%, 27%, and 29%, respectively). Average length of stay was slightly higher in
the control group than in the CDAPP group, for mothers as well as babies. The control group had slightly more babies in the < 1500 g birth-weight group, whereas the CDAPP group had more babies in the 1501-2500 g birth-weight group. Control group mothers were slightly older, and more were Black, than mothers in the CDAPP group. The proportions of MediCal and non-MediCal payors were equal in both groups. When all cases are included, the control group charges (for mother and baby combined) were 23% higher than those of the CDAPP group ($18 449 vs $14 958). For the matched cases, the combined charges of the control group were 41% higher than those of the CDAPP group ($21 699 vs $15 344). Most of this difference can be attributed to charge differences for the babies, particularly in the matched comparison ($10 674 for controls vs $5918 for CDAPP). Charges and length American Journal of Public Health 171
Schller et al.
of stay increased with the severity of diabetes. Patient charges were higher when Medicaid was the primary payor, and within this group, the charges for the control cases were always higher than those for the CDAPP cases. Blacks incurred higher average charges than did nonBlacks, across the board. Among non172 American Journal of Public Health
Blacks, charges were much lower for the CDAPP cases than for the control cases, whereas there was essentially no difference in charges for Blacks. Table 2 contains charges for CDAPP participants by length of gestation at program enrollment: before 8 weeks (this group includes the pre-conception enroll-
ees) and after 8 weeks. The 8-week cutoff was used because this is the time in organogenesis when major congenital malformations can occur. Lower maternal and infant charges were incurred by the women enrolled earlier in the program (the charges of the after-8-week group were 44% higher than those of the before-8week group). The length-of-stay comparisons follow a similar pattern. We now turn to the results of the regression analysis. The coefficient estimates and t statistics for model 1 are shown in Table 3. For a one-tailed test, t statistics above 1.65 are statistically significant at P
Richard M. Scheffler, PhD, Lisa B. Feuchtbaum, MPH, and Ciaran S. Phibbs, PhD
Introduction The cost savings potential of many prevention programs may not be realized for years, making these programs unattractive investments for health policy decision makers. 1,2 Fortunately, many studies of the cost savings potential of prenatal prevention programs need only look at the immediate future (i.e., 1-year care) to assess the cost effectiveness of the intervention. These studies have generally focused on the benefits of prenatal care or the prevention of low birth weight. Importantly, in the past 10 years there has been increasing recognition that the infant and maternal complications associated with diabetes in pregnancy can be prevented.-7 This paper provides the first cost-effectiveness analysis of a new and widely accepted prenatal prevention program, the California Diabetes and Pregnancy Program (CDAPP). The CDAPP, also known as the "Sweet Success" program, focuses on improving matemal and infant perinatal outcomes in pregnancies complicated by diabetes. The program's multidisciplinary team approach to care emphasizes early recruitment of pre-pregnant (prior to conception, or "pre-conception") and pregnant women with diabetes. Once enrolled, the women receive comprehensive prenatal care, including nutrition, education, and support services that have been specifically developed to meet the needs of diabetic women during pregnancy. This management approach has been shown to reduce diabetes-related complications during pregnancy to the rate seen in the nondiabetic population.' "' This approach to care, which has been shown to be effective in improving birth weight in the non-
diabetic population," is particularly well suited for managing high-risk pregnancies. Although maternal and infant mortality have been greatly reduced by advances in insulin therapy over the past 10 years, preexisting diabetes mellitus remains a significant risk to the unbom child. Congenital malformations still account for nearly 50% of the deaths among infants bom to diabetic mothers today.'2 Serious malformations are three to four times more likely to occur among infants of diabetic women than among infants of nondiabetics,'3 and affect 6% to 9% of all diabetic pregnancies.3 These malformations occur very early in pregnancy, at a time when the diabetic woman may not yet be aware that she is pregnant. '4 For these malformations to be prevented, it is critical that maximum diabetes control be maintained prior to pregnancy and in the early weeks of embryogenesis.'5 It is still not clear what specific biological mechanism accounts for these malformations, but it is widely accepted that the congenital malformation rate is correlated with the degree of glycemic control during pregnancy.7'6 Although the appropriate degree of control is still being deRichard M. Scheffler is with the Schools of Public Health and Public Policy and Lisa B. Feuchtbaum is a doctoral candidate with the School of Public Health, both at the University of California, Berkeley. Ciaran S. Phibbs was with the School of Public Health, Columbia University, New York, NY, at the time of this study, and is now with the Center for Health Care Evaluation, Veterans Administration Medical Center, Palo Alto, Calif. Requests for reprints should be sent to Richard M. Scheffler, PhD, School of Public Health, Warren Hall, University of California, Berkeley, Berkeley, CA 94702. This paper was submitted to the journal February 2, 1991, and accepted with revisions September 13, 1991.
February 1992, Vol. 82, No. 2
Cost-effiveness of the California Diabetes and Prenacy Program bated,17 the more tightly diabetes is controlled before and during pregnancy, the better the pregnancy prognosis. In studies in which women have achieved and maintained normal blood glucose levels before and throughout pregnancy, the congenital malformation rate has been reduced to the rate seen in the nondiabetic population.3,18,19 The aim of the present study was to determine the cost-effectiveness of the CDAPP. We compared the hospital charges and lengths of stay for mothers and their infants in the CDAPP with those for a sample of births to diabetic mothers not in the program. A regression framework was used to estimate the differences in hospital charges and lengths of stay between the CDAPP group and the control group; the regression models included measures of age, race, diabetes duration and severity, and the birth weight of the child.
Methods Stdy Design We used a case-control study design and incorporated the costs of the program into the cost-effectiveness calculations. For ethical and practical reasons, we did not randomly assign women to the study group or the control group. However, we minimized the differences between cases and controls by matching participants on maternal age, ethnicity, and duration and severity of diabetes. To minimiize the effect of selection bias, we chose the control hospitals from areas outside the catchment areas of the CDAPP hospitals.
HoWital Site Selection Data for the 102 CDAPP cases were collected for all program participants at three CDAPP hospitals (Children's Hospital of San Francisco, n = 18; Sutter Memorial Hospital, n = 36; and Loma Linda University Medical Center, n = 48). We included all births to program participants between July 1, 1986, and July 30, 1988, for whom complete data could be obtained. All obstetricians who regularly delivered infants at these hospitals referred their diabetic cases to the program. Thus, virtually all of the diabetic deliveries at these hospitals were included in our data. These hospitals were selected because they were the first to implement the CDAPP and their programs were well established at the time data collection started. All CDAPP hospitals have a level 3 neonatal intensive care unit.
February 1992, Vol. 82, No. 2
Data for the 218 control cases were collected from diabetic deliveries identified at five hospital sites throughout the state: Alta Bates Hospital in Berkeley (n = 89; 1984-1987), Huntington Memorial Medical Center in Pasadena (n = 71; 1985-1987), Sharp Memorial Medical Center in San Diego (n = 12; 1985-1987), St. Agnes Medical Center in Fresno (n = 15; 1985-1986), and Fresno Community Hospital (n = 31; 1985-1986). The control hospitals were selected from among hospitals that (1) were in regions that had yet to have a CDAPP implemented, (2) had at least 2000 births per year, and (3) had at least a level 2 neonatal intensive care unit. The demographic characteristics and health status indicators ofmothers and babies at these hospitals were similar. Information from the 1986 Maternal and Child Health Data Base Statistical Appendix20 was used to compare demographic characteristics of study and control hospitals. For study and control hospitals, respectively, the average number of births in 1986 was 3867 and 4355; Black births represented 10.4% and 14.5% of total births; the percentage of women enrolled in prenatal care in the first trimester of pregnancy was 85% and 81%; the observed mortalityratewas 1.8% and 1.2%; theperinatal complication rate was 17.5% and 13.8%; and the congenital malformation rate was .70% and .56%. Only women with overt diabetes were included in the study. The severity and duration of diabetes were assessed with White's classification system21: A Asymptomatic diabetes recognized prior to pregnancy; chemical diabetes. B Disease that began after age 20, or that has been present for 9 or fewer years with no vascular disease.
C Disease that began between the ages of 10 and 19, or that has been present for between 10 and 19 years with no vascular disease. D Disease that began before age 10, or that has been present for 20 or more years, or with vascular disease, such as background retinopathy or calcified iliac or femoral arteries. F Disease complicated by diabetic
nephrology. H Disease complicated by cardiovascular disease. R Disease complicated by proliferat-
ing retinopathy.
T Disease complicated by renal transplant. Both CDAPP cases and control cases were excluded from the study if their White's classification could not be identified from the patient's medical record. Because of the very small number of cases in classes F, H, R, and T, these classes were combined with class D in a group we labeled D+. CDAPP cases were identified by tracking enrollment records at each of the three CDAPP collection sites. Clinical and demographic data for each case were obtained from the CDAPP program database. Hospitalization charge data were collected for each mother and baby after delivery by means of the billing summary reports obtained from the hospital records. Complete billing histories were obtained for each mother for the duration of pregnancy and delivery, and for the babies from birth to discharge. Control cases were identified by searching the computerized medical record systems for the specific ICD-9 code specifying diabetic deliveries. Billing records were then requested for the mothers and their babies to reflect all inpatient hospital admissions of the mother during her pregnancy and delivery, in addition to infant hospital charge data for the period from birth through discharge. In the event that the baby was transferred to another hospital for care, billing summaries were requested for the care delivered at the second institution. Costs for the care provided by the CDAPP were estimated by means of "time-motion" methodology. The cost of staff time was calculated on the basis of expected staff time necessary to deliver patient care services as specified in the
state Guidelnes For Care.22 The costs of CDAPP services were estimated on the
basis of average salaries, fringe benefits, and overhead. For women who enrolled before conception, these costs averaged $1300. For late enrollees (enrolled after 12 weeks' gestation), the costs averaged
$800. T7e Match To control for individual risk factors that may have an ex ante effect on hospital charges, we matched the data on mother's age, race, and severity and duration of the mother's diabetes using White's classification. Race was classified into two groups (Black and non-Black) because Black infants have been shown to be significantly different from infants of other races with respect to incidence of low
American Journal of Public Health 169
Scheffier et al.
birth weight, birth-weight-specific mortality, and overall mortality. Mother's age is usually associated with risk status, especiaily for older mothers and the infants of younger mothers. We used three age groups (less than 20 years, 20 to 34 years, and 35 and over). Finally, White's classificationwas stratified into three groups: A, B and C, and D+, yielding a total of 18 possible cells. Each CDAPP participant was randomly matched with an individual from the control group who was of the same age, ethnicity, and White's class. This procedure yielded a data set of 180 matched cases, 90 cases from the CDAPP group and 90 cases from the control group. We excluded a control group infant who incurred a hospital bill of over $1.3 million (length of stay, over 400 days). Infants requiring hospitalization beyond 6 months of age are quite rare; one was included in our sample just by chance. Including this observation would greatly increase the savings due to the CDAPP.
Vatiable Constnsction The dependent variable is total hospital charges, which was constructed by aggregating the charges for the mothers and the babies. Charges were adjusted using the hospital component of the Consumer Price Index, so that all charges are in June 1988 dollars. Because the study hospitals are in several different labor markets across the state, a second adjustment was required. Data on wages for hospital personnel from the 1986 American Hospital Association Survey of Hospitals were used to construct a hospitalspecific wage index. The hospital wage index controls for the difference in labor costs, which are the largest component of hospital costs. The average wage, weighted by the number of cases from each hospital, was used as the base for the index. Hospital-specific charges were multiplied by 1, plus or minus the percentage by which the hospital's wage differed from the weighted average wage across hospitals. Normalizing charges with this wage index makes the charges across hospitals more comparable. The use of hospital charges to conduct a cost-effectiveness analysis has some limitations; differences in charges can be due to accounting practices or hospital inefficiencies. Length of stay is less likely to be subject to these differences. We therefore conducted a parallel analysis, using length of stay as the dependent variable. Length of stay, like charges, was 170 American Journal of Public Health
aggregated for each maternal and infant hospitalization. Because both charges and length of stay have a very skewed distribution, the natural logarithms of charges and length of stay were used as the dependent variables in the regression analysis. Independentvariables were included to control for risk factors that independently affect the costs of care and the length of hospital stay. These factors included maternal age, race, diabetes classification, birth weight, and source of payment. Variables were also included to test for the impact of the CDAPP. Binary variables were used to control for the presence of these risk factors. Almost all of the births were paid for by either private insurance or MediCal (the Medicaid program for California). Therefore, a binary variable for a MediCal admission was included. Duration and severity of diabetes was controlled for with White's classifications. These classifications were aggregated into four groups: A, B, C, and D+ (includes classes F, H, R, and T). Ethnicity was controlled for with a binary variable for race (Black or nonBlack). Maternal age was controlled for by including the mother's age, in years, at the time the birth occurred. Birth weight was controlled forwith a set ofbinaryvariables: less than 1500 g, between 1500 g and 2500 g, and greater than 4000 g. Birth weights between 2500 g and 4000 g were the excluded category. The inclusion of birth weight in the model is appropriate because maternal diabetes tends to affect birth weight.23,24 Moreover, previous research shows that birth weight is the single best predictor of newborn mortality and costs.25 To test for simultaneity bias, we reestimated the models reported below, excluding birth weight as a regressor. We also estimated models with birth weight as the dependent variable. These estimates showed no program effect on birth weight, and showed that excluding birth weight had a minimal effect on the estimated program coefficients. Thus, we are confident that any potential simultaneity bias is small. We used several different specifications to measure the impact of the CDAPP in our regression model. The basic specification included a binary variable for our CDAPP participants. However, to test the effect of time of enrollment in the CDAPP, we created binary variables for mothers enrolled before 8 weeks' gestation and one for mothers enrolled after 8 weeks' gestation (the study did not contain enough pre-
conception cases to allow a completely separate analysis of these cases).
Structure of the Regression Models All of the regression models control for mother's age, MediCal status, White's class, race (Black), and birth weight, and include a CDAPP effect variable. Model 1 presents the results of the basic model. Model 2 splits the program effect variable by time of enrollment in CDAPP, the expectation being that those women who enrolled earlier would incur lower costs and length of stay. Model 3 attempts to differentiate the degree of the program effect across severity of diabetes according to White's classification system. To test for severity of diabetes, the program effect variable, a binary variable for the CDAPP cases, is interacted with the White's class variables. This procedure splits the control variable into three mutually exclusive groups by White's class (classes A and B, class C, and class D+). The controls for White's class remain in the model. The excluded category becomes control cases whose diabetes is defined by White's class A or B. Thus, the coefficient for each of these new variables gives an estimate of the differential effect of the program for each White's class group. Given the smaller sample sizes, we expected limited significance with these interaction variables.
Cost-effectiveness Calculations Since the dependent variables for all of the models are in logarithms, direct interpretations of the charge or length-ofstay differences in the program are nontrivial. The log transformation moves the distribution toward normality, but it is still not normal. When the distribution of errors is not log-normal, the conventional formula for converting logarithms back to levels is inconsistent. Therefore, we used the smearing method developed by Duan26 to provide consistent estimates. The savings due to CDAPP do not occur instantly (i.e., costs of the program precede the hospital cost savings at delivery), so they must be discounted to present-value terms. To do this we allocated all hospitalization costs to the time of delivery, using an 8% discount rate. Given that these costs include maternal and infant costs, the time of delivery is probably near the midpoint of the time these costs were incurred. We used two different program patterns for this part of the evaluation. The first used the average time from entry into the program to delivery. This information was obtained from program
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Cost-effectiveness of the California Diabetes and Pregnancy Program
records for all mothers who entered the program between January 1, 1986, and December31, 1987. Average timewas calculated to be 26.5 weeks. This time reflects a mixture of pre- and post-conception enrollments. This method evaluates the program as it was actually provided; for notation purposes we refer to this as the Pre/Post analysis. CDAPP was designed as a pre-conception program, so in our second analysis we looked at the effects of an all-preconception program under conservative assumptions. We assumed that the preconception program would yield no more benefits than would the existing mix of programs (see method 1 above), and that women in this program would take an average of 6 months to conceive once the go-ahead was given. This 6-month period, plus the estimated 3-month program time, results in an 18-month lag from time of enrollment to time of delivery. We refer to this as the All Pre analysis. To be conservative in our estimate of the program savings, we used an 8% discount rate for the evaluation. This procedure produced discount factors of 0.962 for the Pre/Post analysis and 0.891 for the All Pre analysis. Note that we are using a very restrictive definition of program savings-only the savings in maternal and neonatal costs. Longer-term medical costs due to adverse birth outcomes, which are potential savings of the CDAPP, and any potential health benefits to the mother are not included. The discounted savings were compared with the program costs. Even though the program treatment costs were spread over time, they were all allocated to the time of initial treatment. Treatment costs of $1300 for pre-conception enrollment and $800 for post-conception enrollment were obtained from a previous analysis of the education component of the program. For the Pre/Post analysis, we used the Pre/Post mix of our sample to calculate a weighted average of $820. These costs were inflated further to reflect the program dropout rate and the failure of some participants to give birth to a live infant. Program data to date show that over 70%o of the participants have given birth to a live infant, yielding effective program costs per live infant of $1857 for the All Pre analysis and $1171 for the Pre/Post analysis. These numbers are almost certainly overestimates of the per-delivery program costs, inasmuch as some of the mothers counted in these figures who ultimatelywill give birth to a live infant have not yet had time to do so.
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Results Selected demographic and clinical characteristics of the cases and controls are presented in Table 1. These characteristics are average charges and length of stay, percentage byWhite's class, charges and length of stay by White's class, average age of mother, percentage by specific birth-weight group, percentage by race, and percentage by payor. The relative differences between the CDAPP cases and the control group are similar for both the entire sample and the matched sample, except for those factors over which the match was stratified. For the entire sample, the CDAPP group contained women with more severe diabetes; the majority of the women in the control group were of White's classes A and B (26% and 55%, respectively), whereas the majority of the CDAPP group were of classes B, C, and D (41%, 27%, and 29%, respectively). Average length of stay was slightly higher in
the control group than in the CDAPP group, for mothers as well as babies. The control group had slightly more babies in the < 1500 g birth-weight group, whereas the CDAPP group had more babies in the 1501-2500 g birth-weight group. Control group mothers were slightly older, and more were Black, than mothers in the CDAPP group. The proportions of MediCal and non-MediCal payors were equal in both groups. When all cases are included, the control group charges (for mother and baby combined) were 23% higher than those of the CDAPP group ($18 449 vs $14 958). For the matched cases, the combined charges of the control group were 41% higher than those of the CDAPP group ($21 699 vs $15 344). Most of this difference can be attributed to charge differences for the babies, particularly in the matched comparison ($10 674 for controls vs $5918 for CDAPP). Charges and length American Journal of Public Health 171
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of stay increased with the severity of diabetes. Patient charges were higher when Medicaid was the primary payor, and within this group, the charges for the control cases were always higher than those for the CDAPP cases. Blacks incurred higher average charges than did nonBlacks, across the board. Among non172 American Journal of Public Health
Blacks, charges were much lower for the CDAPP cases than for the control cases, whereas there was essentially no difference in charges for Blacks. Table 2 contains charges for CDAPP participants by length of gestation at program enrollment: before 8 weeks (this group includes the pre-conception enroll-
ees) and after 8 weeks. The 8-week cutoff was used because this is the time in organogenesis when major congenital malformations can occur. Lower maternal and infant charges were incurred by the women enrolled earlier in the program (the charges of the after-8-week group were 44% higher than those of the before-8week group). The length-of-stay comparisons follow a similar pattern. We now turn to the results of the regression analysis. The coefficient estimates and t statistics for model 1 are shown in Table 3. For a one-tailed test, t statistics above 1.65 are statistically significant at P
