Does Treating Maternal Depression Improve Child Health Management?
The Case of Pediatric Asthma
Cynthia D. Perry*
Robert Wood Johnson Scholar in Health Policy Research
Harvard University
Abstract: Past studies have demonstrated an association between maternal depression and poor
management of pediatric asthma. Using an instrumental variables strategy to address the endogeneity of
depression diagnosis, I build on this literature to answer the question of whether treating maternal
depression leads to an improvement in pediatric asthma management. I show that treatment of mother’s
depression improves management of child’s asthma, manifested in reduced emergency room visits for
asthma care. I estimate a reduction in asthma costs in the six month period following diagnosis of $347 per
asthmatic child whose mother is diagnosed with, and offered treatment for, depression.
*
Email: cdperry@rwj.harvard.edu. I am grateful to Christina Fu for assistance with the Florida Medicaid
claims data, and to David Autor, Jon Gruber, Tom McGuire, Robin McKnight, Michael Steinberger, and
participants in the RWJ Seminar and Annual Meeting and MIT Labor Lunch for helpful comments and
suggestions.
I. Introduction
Asthma is the most common chronic disorder in children in the U.S., affecting 9
million children under the age of 18 (Dey and Bloom 2005). The estimated cost for
treating asthma in children is $3.2 billion annually (Centers for Disease Control 2005).
In addition, some of the most costly asthma-related health care expenditures – emergency
department use and hospitalizations – can be avoided if the condition is properly
controlled, suggesting potentially large cost savings can be realized if management is
improved.1 Recent reports from the National Institutes of Health (2002) and RAND
(2001) summarize the recommendations of expert panels for improving asthma
outcomes; both stress the role of the child’s family in asthma management, and therefore
in asthma outcomes. Because a child’s caretaker is called upon to carry out the day-today elements of an asthma treatment plan, avoiding unnecessary hospitalizations and
emergency department visits depends critically on the asthma management of the
caretaker.
Past studies have shown an association between maternal depression and poor
asthma management.
In this paper, rather than compare the asthma outcomes for
children of depressed women with those of children of women who are not depressed as
past studies have done, I use a pre-post empirical design to look at the asthma health care
utilization for the same children before and after their mothers are diagnosed with, and
offered treatment for, depression. These children are compared to a matched sample of
asthmatic children whose mothers are not diagnosed with depression. Knowing the effect
of offering depression treatment is important both because it is a variable that can be
1
Using the National Hospital Discharge Survey (1990), Pappas et al find that 39% of potentially avoidable
hospitalizations of children under age 15 were due to asthma.
1
affected by policy tools and because there could be other variables (e.g., distance to
health care provider, number of children in the household, family structure) causing both
depression and poor asthma management that would not change with depression
treatment. If these other components are the cause of the correlation that past literature
has found between depression and asthma outcomes, a policy that offers depression
treatment would not be expected to improve asthma outcomes.
In the context of this empirical design, it is important to address the potential
endogeneity of who is diagnosed with depression and offered treatment. In particular, the
likelihood that depressive symptoms will be recognized increases with the amount of
contact that a person has with health care professionals (Bertakis et al 2001). Because
children with higher asthma morbidity can cause their mothers to have more contact with
health professionals, a direct estimate of the effect of diagnosis on asthma outcomes
could show a spurious positive correlation between depression treatment and higher
asthma health services utilization when in fact the causation runs in the opposite
direction. To address this problem, I use an instrumental variables strategy that exploits
exogenous variation in the propensity of primary care physicians (PCPs) to diagnose and
treat depression.
An extensive literature has found that certain characteristics of a
physician, both observable and not observable to the researcher, can predict the
physician’s propensity to diagnose and treat medical conditions, even controlling for the
prevalence of those conditions in their patient populations. I argue that physicians have
what can be thought of as individual thresholds for the diagnosis of depression, and use
this to predict whether a woman’s depression is diagnosed, which in turn allows her to
access treatment.
2
Applying this empirical design to Florida Medicaid claims data, I find evidence
that asthma management improves in the six month period following diagnosis of
mother’s depression. This improvement is manifested in reduced emergency room visits
for asthma care. I estimate an overall savings in asthma costs in the six months following
diagnosis of $347 dollars per asthmatic child whose mother is diagnosed with, and
offered treatment for, depression.
II. Background
A. What is asthma?
The National Institutes of Health defines asthma as a chronic inflammation of the
airways; symptoms include wheezing, chest tightness, cough, shortness of breath, and
fast or noisy breathing. Asthma can begin at any age, but most frequently begins in
childhood (NIH 1997). Using the 2002 National Health Interview Survey, the Centers
for Disease Control reports that 9 million children (12%) under the age of 19 have ever
been diagnosed with asthma, and 4 million children (6%) had an asthma attack in the 12
months preceding the survey.
Children in poor families, children in single-mother
households, non-Hispanic Black children, and boys were all more likely to have ever
been diagnosed and to have had a recent asthma attack (Dey et al, 2004).
There is no cure for asthma, but in almost all cases it can be controlled.
Treatment programs include avoidance of asthma “triggers” (e.g. exercise, allergens,
irritants such as cigarette smoke), use of long- and short-term medications, and frequent
monitoring of symptoms (informally, or using an inexpensive device called a “peak flow
monitor”) (American Lung Association, 2002).
3
B. What is depression?
Major depression is a common mood disorder affecting approximately 6.6%
percent of the U.S. population annually, with lifetime prevalence rates of 16.2% (Kessler
et al 2003). Depression is characterized by two or more weeks of either depressed mood
or loss of interest or pleasure, accompanied by 4 of the following symptoms: significant
weight loss or gain; insomnia or hypersomnia; restlessness; fatigue or loss of energy;
feelings of worthlessness or inappropriate guilt; poor concentration or indecisiveness;
recurrent thoughts of death or suicide attempt (American Psychiatric Association, 1994).
Women are approximately two times more likely than men to suffer from major
depression (Kessler 2001), and depressive symptoms are particularly common in low
income populations.
Adelmann (2003) uses the National Comorbidity Survey to
calculate that depression rates are twice as high in the Medicaid population as in the
overall population.
A recent literature review of studies of depression in welfare
populations concluded that “on average 1 in 5 welfare recipients met criteria for major
depression and that almost half had levels of depressive symptoms that warrant concern”
(Lennon et al 2002).
Recent prevalence estimates have also found that symptoms
qualifying an individual for major depressive disorder are more common among whites
than among non-Hispanic Blacks, especially in low income populations (Riolo et al 2005,
Kessler et al 2003).
Common treatments for depression include prescription anti-depressants,
psychotherapy, or a combination of the two. Anti-depressant therapy, when completed,
has been found to be effective for approximately two thirds of patients (Bollini et al,
4
1999); psychotherapy has been shown to be at least as effective as anti-depressant
therapy in treating mild and moderate cases of depression (Casacalenda et al 2002), but
may not be as effective in chronic cases (Goff 2002, Olfson et al 2002). Depression is
widely understood to be under-recognized and under-treated, especially in low income
populations (e.g., Coyne et al 1995, Olfson et al 2000).
C. Previous Research
There is no universally accepted model delineating the causal pathways between
family characteristics and pediatric asthma outcomes in the existing literature. A recent
review of this literature (Kaugars et al 2004) lists the family characteristics, mechanisms,
and asthma outcomes that have been studied (table 1).
While maternal depression could indirectly affect a number of the characteristics
on this list, this paper specifically addresses the question of how caregiver psychological
functioning affects asthma management behaviors (including the caretaker’s perception
of the severity of the child’s asthma symptoms) which in turn affect healthcare
utilization.2
A caretaker of a pediatric asthma patient is called upon to manage the condition in
a number of ways. She is asked to reduce asthma triggers (e.g., dust mites, animal dander
cockroach allergens, indoor mold, tobacco smoke, aerobic activity when pollution levels
are high) for her children. In addition, she may be asked to check the child’s breathing
daily using a peak flow meter, or monitor symptoms informally, and she is the person
who must decide if the child’s symptoms are severe enough to necessitate medical care
2
In this list, symptom perception is listed under physiological functioning. The symptom perception that I
will refer to in this paper is slightly different, referring not to how the child perceives his or her symptoms
but how the mother judges whether or not symptoms are severe enough to warrant medical care.
5
from a clinician. She must fill prescriptions for long-term management, as well as shortterm exacerbations. If the child has persistent asthma symptoms, she must administer
medication daily, or in some cases more frequently. Finally, she must bring the child to
see a physician more frequently than she would if her child was not asthmatic (Shapiro
2001).
Managing pediatric asthma in one’s child can be time consuming and stressful for
a healthy parent; depression can exacerbate the stress of these tasks. There are two main
reasons why we might expect a causal relationship between maternal depression and
management of pediatric asthma. First, the caretaker is responsible for, among other
things, helping the child adhere to prescribed medical regimens. Previous evidence
suggests that depressed individuals have lower rates of adherence to their own medical
treatment regimes. In a meta-analysis of depression and compliance, DiMatteo et al
(2000) conclude “depressed patients were three times more likely than nondepressed
patients to be noncompliant [in following a medical treatment regime].”
A
complementary strand of literature has found a negative association between maternal
depression and adherence to a pediatric asthma regime. Flynn et al (2004) find that
elevated depression is related to missed pediatric outpatient visits and greater emergency
department use. Bartlett et al (2004) find in a sample of inner-city children with asthma
that depressive symptoms in the child’s mother were associated with lower levels of
adherence to the child’s prescribed asthma regime. Coupled with the fact that lower rates
of compliance to an asthma treatment regime have been shown to be associated with
higher asthma morbidity (e.g., Bauman et al 2002, Lieu et al 1997), these findings
suggest that a caretaker’s depression exacerbates a child’s asthma morbidity through
6
noncompliance with a prescribed treatment regime. This also suggests that gains in
efficiency of health care delivery could be realized if depression is treated.
The second reason we might expect maternal depression to affect child asthma
management is that depressed caretakers may be more likely to overreact to a given set of
asthma symptoms, rushing to the emergency department when the episode could in fact
be managed at home. This finding is less developed in the literature, but three studies in
particular have suggested the existence of such a channel. Bartlett et al (2004), in a
survey of inner-city families with children with asthma, found that mothers with high
levels of depressive symptoms were more likely to report “feeling unable to address acute
asthma episodes at home.” In a study of overall health care utilization not limited to
asthmatic children, Janicke et al (2001) conclude that “the mother’s perception of need,
more than actual symptoms, is the key factor in whether the mother decides to seek
physician assistance.” Bartlett et al (2001) find that even after adjusting for asthma
morbidity and other factors, mothers with depressive symptoms were 30% more likely to
have taken their child to the emergency room than mothers not showing depressive
symptoms over a 6 month period; mothers with the highest levels of depressive
symptoms reported the highest levels of emergency department use for treatment of their
child’s asthma. Correcting the misperception of the severity of symptoms by treating
maternal depression could also lead to a reduction in unnecessary utilization of health
care.
The empirical strategy and data used in this paper do not allow for a sharp
distinction between the two channels of actual asthma morbidity and perceived asthma
morbidity. If improved adherence to an asthma management regime reduces asthma
7
morbidity, we would expect to see that treating depression reduces both asthma inpatient
stays and emergency room visits. If it is true that physicians do not allow a mother to
dictate whether her child is admitted to the hospital, misperception of symptoms should
only affect emergency room visits. Because the main findings of this paper are for
emergency room use, they do not help to determine which of the two potential causal
channels is primary.
D. A framework for maternal depression and pediatric asthma care
The relationships between maternal depression and child’s asthma health care
utilization can be thought of in the following four equation system:
(1) HCU,t
=
f (HCUt-1, AMt,t-1, DEPt, t-1, V),
(2) AMt
=
g (AMt-1, DEPt , W),
(3) DEPt
=
h (DEPt-1, PROPt, X),
(4) PROPt
=
j (Yt, Z),
where:
HCU = Asthma Health Care Utilization
AM = Asthma Morbidity
DEP =Mother’s Depression Status
PROP = Propensity of PCP to diagnose depression
V = other factors affecting HCU, including insurance coverage
W = other factors affecting asthma morbidity including age, sex, genetics, race,
maternal depression, seasonality/environmental conditions
X = other factors affecting maternal depression, including external stressors, race
and genetics
Y = factors associated with the PCP’s patient population
Z = personal factors related to PCP’s propensity to diagnosis depression,
including training, personal threshold and taste
Based on existing literature, the health care utilization of an asthmatic child is
determined by a number of factors, including past health care utilization, level of asthma
8
morbidity, mother’s depressive symptoms, and insurance status.3 This relationship is
depicted in equation 1. While the data used in this study do not allow me to control for
level of asthma symptoms, I do control for health care utilization in the period before
mother’s depression diagnosis.
Equation 2 states that asthma morbidity is a function of past asthma morbidity,
mother’s depressive symptoms, seasonal and environmental factors, age, sex, race, and
genetics. This analysis matches – based on age, sex, and race – asthmatic children whose
mothers are diagnosed with depression with asthmatic children whose mothers are not
diagnosed. In addition, I control for the calendar month of mother’s diagnosis to account
for potential seasonality effects.
Equation 3 states that mother’s current depressive symptoms are determined by
her history of depression, external stressors (which could include child’s asthma
morbidity), age, race, genetics, physician’s propensity to diagnose depression, and her
level of contact with the medical community (which could be affected by her child’s
asthma morbidity, as discussed in more detail below).
Finally, equation 4 says that a physician’s propensity to diagnose depression is a
function of her patient population and her own characteristics such as her training in
mental health recognition and attitude toward mental health disorders. The data used in
this analysis are not ideal in terms of controlling for patient populations of the PCPs. In
order to calculate the diagnosing propensity for the physicians among a relatively
homogenous patient population, I calculate this variable using the physician’s patients
who are female, aged 18-50, and who are Medicaid eligible because of their low-income
3
Insurance coverage is held constant in this analysis, as all individuals are continuously eligible for Florida
Medicaid coverage for the 13 month sample period.
9
status. Ideally, I would also control for depression symptoms among each PCP’s patient
population; this is unfortunately not available. To account for geographic differences in
potential patient populations, the analyses also include county fixed effects. The only
physician characteristic that I have access to in these data is physician specialty; this is
held constant in the analysis, as PCPs are all family practitioners, generalists, or
internists. Further supporting evidence for equation 4 is presented in section IV.
III. Data
The data used in this analysis are Florida Medicaid claims data and eligibility files
from July of 1996 through June of 1999. State Medicaid programs are charged with
providing health insurance to, among other groups, low-income families.
Children
insured by Medicaid are more likely to have asthma than those in the non-Medicaid
population, and rates of latent depression and untreated depression are higher for women
in this population than for similar aged women in the non-Medicaid population (Olfson et
al 2000, Adelmann 2003). High prevalence rates of these two conditions, coupled with
the fact that Medicaid is funded by taxpayer dollars, make this an ideal data set with
which to study this question.
To qualify for Medicaid family coverage (title IV of Section 1931 of the Social
Security Act), families must be eligible for State Welfare based on family income.
Monthly income requirements in Florida reflect a requirement that income must be less
than or equal to 23.2% of the Federal Poverty Line.4 There is also a $2000 asset limit.
4
For example, in 2005, the monthly income cutoff for a family of 4 is $364.
(http://www.dcf.state.fl.us/ess/fammedfactsheet.pdf)
10
Because of these restrictions, my sample will contain some of the poorest citizens of
Florida.
The Florida Medicaid claims data allow for the construction of health care
utilization measures as well as the determination of the day of diagnosis of maternal
depression and whether treatment followed the diagnosis. In these data, the mothers that
are diagnosed and treated are not randomly assigned into this group; I use an instrumental
variables strategy to address this problem. The data are also not rich enough to determine
the level of depressive symptoms; I can only determine if a woman has been diagnosed
with and/or treated for depression. False positive diagnoses are rare in this population
(Robbins et al 1994), and latent rates of depression are high, so we can be reasonably sure
that most women who are diagnosed are indeed depressed at the time of diagnosis.
To construct my estimation sample, I start with 100% of Florida Medicaid claims
merged with monthly eligibility data from July of 1996 through June of 1999. Families
(children and mothers) in my final sample satisfy all of the following conditions:
1. Mother had at least one visit with a primary care physician from 1996-1999,
and that physician has at least 25 Medicaid patients who are female and aged 1850.5
2. Mother has no claims for bipolar or schizophrenia from 1996-1999.6
3. Mother has 6 month period before the date of diagnosis with no depression
diagnoses or prescriptions for anti-depressants.7
5
This cutoff was chosen to maximize sample size while ensuring a reasonable number of observations
from which to estimate propensity.
6
In establishing diagnostic cohorts by mental illness, a diagnosis of bipolar or schizophrenia “trumps” a
diagnosis of depression. (conversation with Dr. Alisa Busch, MD, December 2004).
7
The literature varies in defining a “new” diagnosis, ranging from a requirement of no diagnoses for 4
months up to a requirement of 12 months. 6 months is a commonly used period for depression.
11
4. At least one child had an asthma diagnosis between July 1996 and June 1999;
this child also had at least one claim with an asthma diagnosis or for an asthmarelated pharmaceutical prior to the diagnosis of maternal depression.
5. Mother and child are both eligible for Medicaid for at least six months before
diagnosis and six months after month of diagnosis.
I divide the asthmatic children into two groups: children of mothers who are
diagnosed with depression (“diagnosed children”) and children whose mothers are not
(“not-diagnosed children”).8 Children in the former group have a mother who was
diagnosed with depression9 after a minimum period of 6 months without any depression
claims; children in the latter group have a mother who had no depression-related claims
from July 1996 through June 1999. Not-diagnosed children are matched exactly onto
diagnosed children by age group (2-5, 6-10, 11-17), race (Black, White, Hispanic) and
sex. Because some not-diagnosed children will match with more than one diagnosed
child on the basis of these criteria, each possible match is ranked by the absolute
difference in asthma spending in the 6 months before the diagnosis date of the respective
diagnosed child’s mother. Each not-diagnosed child is assigned to the diagnosed child
with whom it has the smallest absolute difference based on asthma spending in the 6
month pre-period.
The not-diagnosed child is then assigned the same month of
“diagnosis” as its match for the purpose of defining and pre- and post- periods. Finally,
up to 15 not-diagnosed children for each diagnosed child are selected based on asthma
8
Note that the shorthand used does not refer to the child’s diagnosis, but that of his or her mother. All
children must be diagnosed as asthmatics to be included in the sample.
9
I define a depression diagnosis as a diagnosis of International Classification of Diseases, Ninth Revision,
Clinical Modification [ICD9-CM] code 296.2-296.3 (major depression), 300.4 (dysthymic disorder; anxiety
depression; depression with anxiety; depressive reaction; neurotic depressive state; reactive depression), or
311 (depressive disorder/depression not otherwise specified) following a minimum period of six months
with no depression diagnoses and no anti-depressant prescription claims.
12
spending in the pre-period. The final sample contains 221 children with diagnosed
mothers and 1966 children whose mothers were not diagnosed; the median number of
matches per diagnosed child is 10.
For each child, I calculate measures of health care utilization and cost for the 6
month pre-period before the month of diagnosis and for six months after diagnosis,
excluding the month of diagnosis itself.
Table 2 shows summary statistics for the
diagnosed and not-diagnosed groups. The two groups are balanced on age group, race,
and sex of the child by virtue of the matching technique. The diagnosed group has higher
average total asthma costs (mean $101, standard deviation 288) in the six month preperiod than the not-diagnosed group (mean $45, standard deviation 177). This difference
is driven by the relatively high number of children that have very low asthma costs in the
pre-period; because of this, diagnosed children with very low pre-period asthma costs
have more matched not-diagnosed children than diagnosed children with high pre-period
asthma costs.
Similarly, there are relatively more not-diagnosed children for each
diagnosed child who is Black because, as noted in section II, part B, prevalence of major
depressive disorder is higher among Whites than among non-Hispanic Blacks, especially
in low income populations.
To construct a measure of a PCP’s propensity to diagnose depression, I use a
sample of women aged 18-50 who are eligible for Florida Medicaid because of their
eligibility for cash welfare. I assign each woman’s PCP to be the first physician visited
with the specialty of general practice, family practice, or internal medicine in the three
year period from July 1996 to June 1999.10 Upon assignment, all claims of that patient
10
When an individual signs up for the Florida Medicaid Medipass (fee-for-service) system, she has 30 days
to designate a PCP of her choice. If she does not submit a choice within 30 days, she is randomly assigned
13
are assigned to the PCP. This is to account for referrals to specialists for depression
treatment, as the data do not include information about referrals. If a woman has no
physician visits with a PCP, she is not included in this calculation.
The PCP’s propensity to diagnose depression is defined as:
depressed _ patients D − 1
patients D − 1
if mother m is depressed
depressed _ patients D
patients D − 1
if mother m is not depressed
propensityD(m)=
This measure is the number of total patients a physician D, the PCP of mother m, has in
my data with any depression diagnoses, less one if mother m is one of the PCP’s
depressed patients, divided by the total number of patients the physician has in my data
minus one.11 Only physicians with at least 25 patients in the data are used in the analysis.
There are 721 physicians represented in the estimation sample. The mean propensity to
diagnosis is .13, with a standard deviation of .09. The lowest propensity to diagnose is 0
(20 physicians), and the highest propensity to diagnose is .74. Figure 1 shows the
distribution of diagnosing propensity of primary care physicians in this sample.
For the purposes of defining health utilization for treatment of asthma, I define an
“asthma day” to be any day when at least one claim has a primary diagnosis of asthma
(ICD9-CM 493.00-493.99). In addition, if the primary diagnosis code is 786.09
a PCP based on geography. She can change her PCP at any time for any reason, but empirically it is rare
that Medicaid participants do so. For example, a recent study (Tai-Seale 2004) using nationally
representative data (including approximately 3000 Medicaid participants) found no voluntary PCP changes
in a 12 months period among Medicaid recipients. I use the first PCP each woman sees in the data in the
event that doctor switching is still a concern.
11
Because of the nature of the calculation, women with the same PCP may have slightly different values
for the PCP’s propensity to diagnose depression based on whether the individual woman was one of the
patients with a depression diagnosis.
14
(respiratory abnormality) and a secondary diagnosis code is asthma, I assign this day to
be an asthma day as well.
For the six month periods before and after the month of
diagnosis (or assigned diagnosis, in the case of not-diagnosed children), I divide asthma
visits into 3 categories: inpatient stays, emergency room visits, and outpatient visits.
Inpatient stays are defined as any asthma visits that contain at least one inpatient claim.
Emergency room visits are any asthma days with procedure codes 99281, 99282, 99283,
99284, or 99285; to avoid double counting, an emergency room visit that results in an
inpatient stay is counted only as an inpatient stay. Finally, all remaining days of service
that include claims for at least one service other than pharmacy claims are considered
outpatient visits.
In a similar fashion, I construct measures of costs incurred for asthma-related
treatment. I divide these costs into four categories: inpatient costs, emergency room
costs, outpatient costs, and pharmacy costs. Total asthma costs consist of all costs
incurred on a day when at least one primary diagnosis was asthma plus any pharmacy
claims in therapeutic class 12:12.00 (bronchodilators) or 68:04.00 (inhaled or oral
steroids), both commonly prescribed for asthma management (Gross and Ponte 1998,
Laumann and Bjornson 1998).
Inpatient stays are rare occurrences in this sample: In the six month pre-period,
the diagnosed group had only one inpatient stay, and only two individuals in the notdiagnosed group had inpatient stays. In the 6 month post period, there are 5 diagnosed
children and 19 not-diagnosed children with inpatient stays. When they do occur, these
stays are expensive, costing an average of $2715 per inpatient stay (median $2284).
Treatment in the emergency department for asthma is more common, with 129 total
15
emergency room visits (among 113 individuals) in this sample; the average cost for a day
in the emergency room that did not result in an inpatient admission in this sample was
$364 (median $331). Finally, only 37% of the sample had at least one outpatient visit
during their 13 months in the sample. Since the recommendation for children whose
asthma is under control is for at least two physician visits per year, the individuals in this
sample are not meeting these guidelines on average.12
IV. Empirical Strategy
My empirical strategy is drawn from the relationships described in section II, part
D of this paper. To determine whether diagnosing depression has an effect on asthma
management, I estimate a model of the following form:
(5) Yc = α + βdiagnosedm + χYc,t −1 + λX c + ρX m + φmonth+ γcounty+ ςACostc,t −1 + ε
Y is a measure of the child’s healthcare utilization or costs incurred in the period after a
child’s mother was diagnosed (or the matched diagnosed child’s mother was diagnosed)
for depression. β , the coefficient of interest, is the marginal effect of the child’s mother,
m, being diagnosed with depression. To account for past asthma morbidity, I control for
Yc ,t −1 , the utilization or costs incurred by the child, c, in the six months prior to diagnosis
or assigned diagnosis (corresponding to the left hand side variable); I also control for preperiod total asthma spending, ACost c ,t −1 , in all regressions regardless of the left hand side
variable. X c is a vector of indicator variables for whether the child is female, the child’s
12
Current guidelines recommend that if the child’s asthma is under control, he or she should have at least
two physician visits per year; a child using daily therapy requires 3-4 visits per year; and a child with
unstable asthma may require physician visits every two weeks until the asthma is under control (Shapiro
2001).
16
age group (2-5, 6-10, 11-17), and the child’s race (Black, Hispanic, White). Mother
control variables, X m , are age group (18-22, 23-27, 28-32, 33-37, 38-42, 43-50), whether
there are any children in the home under the age of 2, and number of children in home (1,
2, 3, 4 or more).13,14 To control for the seasonality of asthma (including increased
morbidity due to winter viral infections and seasonal allergies), I include calendar month
dummies for month of diagnosis. County fixed effects are included both to account for
possible regional differences in asthma morbidity and to account for regional differences
in PCP patient populations. The standard errors are clustered at the primary care
physician level.
The central challenge in estimating equation 5 is that mother’s depression affects
asthma morbidity and asthma health care utilization, but asthma health care utilization
could also affect the probability that a woman’s depression is diagnosed and treated. In
particular, a mother may have more contact with the medical profession because of her
child’s asthma morbidity, and therefore we may find spuriously that diagnosis of
depression is associated with higher asthma health services utilization when in fact the
causation runs in the opposite direction.
While there have been no studies to my
knowledge of the effect of child health utilization on maternal health utilization using
U.S. data (it is generally assumed that the mother’s health utilization causes the child’s to
explain the correlation between the two), there have been studies that show that
individuals who have more frequent contact with physicians are more likely to have their
depressive symptoms recognized. A 2001 study by Bertakis et al, for example, found
that frequency of clinic use was a predictor of a depression diagnosis, controlling for
13
Any children under age 2 and number of children under age 18 in the household are both factors that can
affect mother’s contact with health care professionals and factors that can act as external stressors.
14
Mother and child have the same race in 97% of cases.
17
level of depressive symptoms. My own sample lends support to this possibility; the mean
number of total asthma visits for children whose mothers were diagnosed is twice as large
(.61) as the mean for children whose mothers were not diagnosed (.28), although these
means are not statistically different from one another.
Because of the potential endogeneity of mother’s depression treatment,
controlling directly for the identification of mother’s depression will produce biased
results. I address this problem with an instrumental variables strategy that exploits the
relationship depicted in equation 4. The propensity of a woman’s physician to diagnose
depression affects mother’s depressive symptoms by affecting the probability that her
depressive symptoms are recognized and diagnosed. The identifying assumption is that
the PCP’s propensity to diagnose depression should be otherwise unrelated to child
asthma health care utilization.
To further conceptualize this variable, I am assuming that physicians have what
can be thought of as individual thresholds for the diagnosis of depression given a constant
set of symptoms in their patient populations. Patient populations should be comparable
across physicians as their propensity to diagnose depression is calculated from a
population of cash welfare eligible women aged 18-50, so that patient sex, income, and
insurance status are held constant. County controls and race dummies are included in the
regressions to account for geographic and racial differences in patient populations.
The assumption of physician-specific thresholds for diagnosis of depression is
supported by an extensive literature, only a small portion of which is discussed here, that
broadly documents variations in physician practice styles even when controlling for
patient symptoms. Mojtabai (1999) looks specifically at the propensity of primary care
18
physicians to diagnose depression and prescribe anti-depressants and finds that even after
controlling for the presence of psychiatric symptoms, physicians who gave more
depression diagnoses in a random half of their patients were also more likely to diagnose
their other half of patients with depression. Robbins et al (1994) rated characteristics of
55 PCPs and tested physicians for sensitivity to nonverbal communication using a video
test and scored 600 of the PCPs’ patients for depression symptoms using the CES-D
(Center for Epidemiologic Studies – Depression) scale. They report that physicians who
were more sensitive to non-verbal expressions of emotion made more psychiatric
assessments of their patients, and physicians who tended to blame patients for causing,
exaggerating, or prolonging their depression were less accurate in detecting psychiatric
distress. Other studies have noted that factors such as the physician’s age, race, specialty,
skill set, or educational level in the area of mental health are determinants of how a
physician defines a threshold for depression diagnosis (e.g., McKinlay et al 2002, Giron
et al 1998, Olson et al 2002).
V. Results
A. OLS Results: Diagnosis of Depression
The expected effect of treating maternal depression is clear only for some
outcome variables based on asthma research. Based on correlations documented in the
previous literature and discussed above, if depression treatment is effective, asthma
management will improve, which will in turn reduce both inpatient stays and emergency
room visits. There is no clear prediction for outpatient visits or pharmaceutical claims. If
asthmatic children are not visiting their physician for maintenance as often as
19
recommended, we might expect an increase in outpatient visits following diagnosis of
maternal depression. Conversely, if the reason for outpatient visits in the pre-period was
that asthma was not being properly managed and therefore more medical treatment was
necessary, it could also be the case that outpatient visits would go down following
treatment. In the case of pharmaceuticals, if long-term management pharmaceutical
prescriptions are not being filled by parents (either because they didn’t visit a physician
and therefore did not have the prescription or because they neglected to fill the script that
was given), an increase in pharmaceutical claims could represent an improvement in
asthma management. Similarly, because most asthmatic children are prescribed shortterm medications to keep on hand for emergencies, filling one prescription for such a
medication could represent an improvement in asthma management. If children are
filling more short-term medication prescriptions because they are using the medications
more, however, an increase in prescriptions could also represent a worsening of asthma
management.15
Table 3 and 4 show estimates from Ordinary Least Squares regressions. Table 3
shows the results for asthma visits, by type, in the six months following mother’s
depression diagnosis. Table 4 is measured in terms of costs. The precision in this sample
is not strong enough to detect changes in any of the outcomes of interest, whether in
terms of cost or in terms of visits.
As discussed previously, the challenge in obtaining an unbiased estimate of the
effect of depression treatment on asthma management is the endogeneity of who has
access to treatment – in particular, mothers who are diagnosed and offered treatment are
15
A recent study found that the number of short acting beta-agonists prescription fills can be used as a
marker of asthma morbidity in large-scale population studies (Naureckas et al 2005).
20
likely to be a mix of a) mothers whose diagnosis is exogenous to their child’s asthma
morbidity, and b) mothers who are more likely to be diagnosed because their child has
higher asthma morbidity and therefore have more interaction with health care providers.
If it is also the case that the children in group (b) are in a period of increasing asthma
morbidity (unrelated to their mother’s depression or treatment), this could swamp any
effect of improved management from those mothers who were diagnosed for reasons
unrelated to their child’s asthma morbidity. It is for this reason that I now move on to
discuss the central results of this paper, those using an instrumental variables approach to
break the potential endogeneity of depression diagnosis and child’s asthma morbidity.
B. IV Results: Intention to Treat
The propensity of a physician to diagnose depression, and therefore allow a
woman access to depression treatment, is defined in section III. I use this measure as an
instrumental variable for whether a child’s mother is diagnosed with depression. Table 5
shows the results of a first stage regression of whether a woman is diagnosed with
depression on her PCP’s propensity to diagnose depression. The coefficient of .6 implies
that a 10 percentage point increase in a PCP’s propensity to diagnose depression would
lead to a 6 percentage point increase in the probability that a woman in the care of that
PCP would be diagnosed with depression.
Tables 6 and 7 show the results of intention to treat depression (i.e. diagnosis) on
the number of each type of asthma visit and the costs by type, respectively. Table 6
shows that 6 months post-diagnosis, there is a large and statistically significant reduction
in asthma related ER visits of .19 caused by access to depression treatment for a child’s
21
mother. To better frame the magnitude of this estimate, it is useful to understand what
other variables have similar effects on the number of ER visits for asthma treatment.
Two such variables can be found among the county fixed effects. Figure 2 is a map of
counties in Florida, and figure 3 shows a map of toxic air pollution levels in the state.
Putnam County and Escambia County have the highest risk levels for air pollutants in the
state. The effect of moving from Escambia County to nearby Jackson County reduces the
probability of ER visits for asthma by .10 at the mean; moving from Putnam County to
nearby Flager County reduces ER visits by .15.
Given what we know about the
importance of asthma management for asthma outcomes, it is not surprising that
improving asthma management by offering treatment for maternal depression would have
an even larger effect on asthma outcomes than the effect of moving to an environment
with slightly cleaner air.
Another way to think about the size of this effect is to consider a policy
intervention that would increase the average propensity to diagnose of all PCPs by 5
percentage points, thereby increasing the percentage of diagnosed mothers by 6
percentage points, according to my first stage estimate. This would lead to a reduction
of .006 ER visits in a six month period at the mean, or 15% of the mean number of ER
visits in the six months following diagnosis in this sample (.038).
The point estimate for inpatient visits is negative, but not statistically different
from zero, so I cannot reject the null hypothesis that offering depression treatment has no
effect on asthma inpatient stays. Because inpatient stays are relatively rare in my sample,
the power to detect an effect is low; the effect would have to be nearly 15 times the mean
number of inpatient stays to be detected at conventional levels.
22
The estimate on
outpatient stays is also negative but not statistically different from zero; in this case,
however, there was no clear prediction as to the direction of the effect.
Table 7 presents results for the effect of offering treatment on maternal
depression, measured in terms of cost. Average savings for the same 5 percentage point
change in PCP propensity to diagnose discussed above would translate into a reduction of
ER costs of slightly more than $2 per asthmatic child in costs per six month period, or
about 16% of the mean ER costs for this sample in the six months post diagnosis ($14.4
per child).
I also estimate that overall asthma costs are reduced following the offer of
depression treatment, by $347 per asthmatic child whose mother is offered treatment.
Again using the experiment of increasing physician recognition of depression by 5
percentage points, we could expect asthma costs to fall at the mean by approximately $10
per asthmatic child, or about 3% of average per child asthma spending.
C. Treatment of Depression
While it is the treatment of maternal depression that is expected to cause an
improvement in the management of pediatric asthma, adherence to a treatment plan given
diagnosis is low with drop-out rates of up to one third for medication regimes and one
quarter for psychotherapy regimes (Pampallona et al 2002). Because the decision to start
and complete depression treatment may not be exogenous to child health management
practices, I have reported the main results with the variable of interest being diagnosis of
depression, or intention to treat.
Depression treatment cannot be accessed until a diagnosis is made. Once a
diagnosis has been made, a woman then makes the decision of whether to comply with
23
the physician’s suggested treatment plan. I define treatment in a very broad sense,
defining a woman as “treated” if she fills at least two prescriptions for anti-depressant
medication OR has at least two additional visits with a physician (including a
psychiatrist) with the diagnosis of depression following the initial diagnosis.16 Returning
to table 5, the first stage relationship between a PCP’s propensity to diagnose depression
and the probability that a woman under his care is diagnosed and treated for depression is
.228, implying that a 10 percentage point increase in the PCP’s propensity to diagnose
depression would lead to a 2.28 percentage point increase in the probability that a woman
in the care of that PCP would be diagnosed and treated for depression.
Although the group of women who choose to follow through with treatment given
diagnosis is a selected sample, it is still notable how much larger the magnitudes of
effects of asthma management are for this sample of women. Tables 8 and 9 show the
results with the variable of interest now changed to be equal to 1 if the child’s mother
was diagnosed and treated and equal to zero if the child’s mother was diagnosed but not
treated, or not diagnosed.
The main point to be taken from a comparison of Tables 6 and 7 with Tables 8
and 9 is that, while the direction and statistical significance of the effect is similar, the
magnitudes for ER visits (again, the only type of visit that is statistically different from
zero) are universally larger. This is to be expected since it is likely that the overall results
16
Length of treatment regime can range from 10 weeks to more than 6 months, depending on symptom
severity and responsiveness to treatment. Because I cannot observe the symptom severity or
responsiveness to treatment for the mothers in my sample, I use a very low bar to signal compliance with
treatment regime.
24
for intention to treat are being driven by those women who followed diagnosis with
treatment.17
D. Shared PCP for Mother and Child
One attractive feature of the empirical design used here is that mothers and
children are different people so that one can reasonably argue that a change in the child’s
utilization of asthma care that coincides with the period following depression treatment is
likely caused by his mother’s depression treatment. But what if the mother and child
have the same PCP, and that PCP is both good at diagnosing depression and good at
caring for asthma? It could be the case that both mother and child visited this very
skilled physician at the same time and both therefore saw improvements in health at the
same time. Tables 10 and 11 show estimates corresponding to the main results in tables 6
and 7, excluding children and mothers who visit with the same PCP during the study
period. The results for this sample are not statistically different from those in tables 6
and 7, suggesting that these mother-child pairs are not driving the results.
VI. Cost Offset Calculation
Particularly in a Medicaid population like the one that I have used in this study,
we know from numerous sources that depression is prevalent and under-diagnosed, and
that pediatric asthma is common. We know from this study that these two conditions can
interact to increase health care utilization beyond the sum of the individual costs of the
17
We might also expect that some of the women who are diagnosed but do not complete any treatment will
also see a reduction in symptoms even without treatment as depression can subside in the absence of
treatment in some cases (e.g., this could happen if the depression was precipitated by stressful conditions
that had since improved).
25
two conditions. If the Florida Medicaid system were to implement a low-cost policy
intervention such as mandatory depression screening of mothers of asthmatic children,
how much of a cost offset, if any, could be expected?
Among women with asthmatic children who are eligible for Florida Medicaid
because of their eligibility for cash welfare at some point between July 1998 and June
1999, approximately 12% of these women had at least one depression diagnosis or filled
a prescription for an anti-depressant during these 12 months.18 The median estimate
among the studies reviewed in Lennon et al (2001) implies a latent rate of major
depressive disorder in this population of 22 percent. Valenstein et al (2001) report that
the average sensitivity of the nine most commonly used depression screening instruments
is 84%; average specificity is 72%.
Using these numbers, it would be reasonable to estimate that implementing a
depression screen for mothers of asthmatic children could improve the average PCP’s
propensity to diagnose depression by 5 percentage points.
This would identify
approximately 74019 depressed but previously undiagnosed mothers (of approximately
1092 total asthmatic children). According to the estimates in this paper, in the six months
following the screening, the Florida Medicaid system could expect to save approximately
$380,000 from reductions in asthma spending. This is a conservative estimate of the total
18
Using this as a measure of “identified depression” is likely to underestimate the undiagnosed population
among women on Medicaid, because anti-depressants are commonly used to treat disorders other than
depression, such as anxiety.
19
There were approximately 13,000 asthmatic children (corresponding to approximately 10,000 mothers)
who were eligible for Florida Medicaid because of their cash welfare status for at least 12 continuous
months, at least one of which was between July 1998 and June 1999. The number 740 is 84% of the 10%
of women who are estimated to be depressed but not identified
26
savings of such an intervention, as improvements in asthma management could produce
cost savings well into the future.20
Valenstein et al (2001) estimate that implementing a depression screening test
would cost approximately $5 per patient, or $44,000 for the population of 8800 women
who are not currently diagnosed as depressed and would therefore be screened; this
includes the cost of producing the screening instrument, patient time required to complete
the instrument, and nurse and physician time required to score the instrument and assess
the patient. For the population of women who are truly depressed and are offered
treatment because of the screen, I assume that the costs of depression treatment for the
mother (other than the costs of screening) are offset by the benefits of treatment to the
mother.21
An unintended effect of implementing such a screen is that “false positives” –
women who are not depressed but are screened as such by the tool – could be diagnosed
and therefore offered services. Valenstein et al (1998) estimate that a positive screen
would increase the rate of diagnosis by 50% for patients with major depression. The rate
of increase of diagnosis for a false positive screen should be even lower. If 25% of
women with a false positive screen are diagnosed, and approximately half of these
women take-up treatment upon diagnosis,22 approximately 282 women who were not
actually depressed but were offered treatment because of a false positive screen would be
expected to use services that do not produce any benefit.23 The average direct costs of
20
This estimate assumes that the reductions in asthma costs estimated in this analysis represent
improvements in efficiency of asthma care delivery and do not leave asthmatic children worse off.
21
Lave et al (1998) estimate that the cost of depression treatment relative to quality-year-of-life gained is
comparable to estimates for other services provided in routine practice.
22
This is consistent with my own empirical sample.
23
Starting from a population of 10,000 women, roughly 12% already diagnosed as depressed, so that the
screening test would be given to 8800 undiagnosed women. Of these, approximately 740 would be
27
depression treatment per person are approximately $1000 (Lave et al 1998), yielding a
total cost of misdiagnosis of $282,000.
The estimates discussed here imply that implementing a one-time depression
screen for mothers of asthmatic children would cost Florida Medicaid approximately
$44,000, plus $282,000 in costs for misdiagnosis. The estimates calculated in this paper
suggest that such an intervention would produce a cost savings from reduced asthma
costs of $380,000, for a net savings of $54,000. This suggests that offering a depression
screen to mothers of asthmatic children on Medicaid would roughly pay for itself through
costs savings from improved asthma management.
VII. Conclusion
This paper has shown that there is a significant improvement in the management
of pediatric asthma, manifested in reduced emergency room visits, when maternal
depression is diagnosed and treatment is offered. The implications of this finding are
that, particularly in populations that are at high risk for maternal depression (such as
Medicaid populations), increased screening for depressive symptoms for parents of
asthmatics could improve the efficiency of health care delivery.
A recent study (Olson et al, 2002) found that only 57% of pediatricians felt
responsible for diagnosing maternal depression, and only 45% reported that they felt
confident in their ability to diagnose maternal depression.
Notably, 64% reported
insufficient training/knowledge to diagnose. These results, together with the findings of
diagnosed and truly depressed. Therefore, the denominator for the specificity calculation is 8060. Using
the average specificity for depression screening tools, we expect a misdiagnosis of 2257 healthy patients.
25% of these women (564) would be incorrectly diagnosed because of their false positive screen, and half
of those women (282) would take-up treatment at diagnosis.
28
this paper, suggest that a broader policy implication of these findings may be that an
investment in training pediatricians to diagnose maternal depression in their patients’
caregivers could pay off in the form of increased efficiency in the use of health care by
their pediatric patients, particularly in the case of a chronic disease like pediatric asthma.
Finally, it is an open question whether the results of this study could extend to
mothers of children without a chronic disease. Minkovitz et al (2005) find that mothers
with depressive symptoms in the three years after their child’s birth were less likely to
complete age-appropriate well-care visits and more likely to have acute care use,
including hospitalizations and emergency department use. Further research is necessary
to determine whether increased or improved depression screening of mothers could lead
to general improvements in the efficiency of health care delivery for all children.
29
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Table 1: Summary of Family Characteristics, Mechanisms, and Asthma Outcomes
Family Characteristics
Caregiver psychological functioning
Parent-child relationship
Child attachment
Parenting difficulties
Family Conflict
Emotion Regulation
Child adjustment and psychopathology
Racial and ethnic background
Family organization and responsibility
Mechanisms
Asthma Management Behaviors
Medication Adherence
Exposure to allergies and tobacco smoke
Daily Decision Making
Patient and family health beliefs
Asthma Outcomes
Healthcare utilization
Asthma and wheezing symptoms
Quality of life
Activity restrictions due to asthma
Risk of death
Physiological functioning
School attendance
Hypothalamic pituitary adrenal axis and immune system
Autonomic Nervous System
Symptom Perception
Source: Reproduction of Table 1 from Kaugers et al, 2004.
34
Table 2: Summary Statistics
Average Number of Visits
Six month pre-period
Six month post-period
Average Cost Per Child
Six month pre-period
Six month post-period
Diagnosed Sample
Outpatient
Inpatient
ER
0.00 (0.07)
0.03 (0.19)
0.02 (0.15)
0.05 (0.27)
0.58 (1.34)
0.68 (1.48)
15.68 (233.13)
85.98 (724.58)
10.69 (68.70)
15.84 (89.26)
44.82 (95.47)
61.09 (132.13)
Inpatient
ER
Not Diagnosed Sample
Outpatient
Average Number of Visits
Six month pre-period
Six month post-period
Average Cost Per Child
Six month pre-period
Six month post-period
0.00 (0.07)
0.01 (0.11)
0.02 (0.15)
0.04 (0.21)
0.25 (0.69)
0.50 (1.34)
3.95 (132.35)
38.34 (447.52)
6.13 (48.72)
14.23 (91.48)
23.08 (67.25)
44.45 (139.99)
Demographics
Age 2-5
Age 6-10
Age 11-17
Female
Age of mother
Black
White
Hispanic
Children under 18 in household
At least one child under age 2 in household
Mother Treated for Depression
Physician Propensity to Diagnose
Diagnosed
0.41 (0.49)
0.39 (0.49)
0.20 (0.40)
0.40 (0.49)
31.80 (7.31)
0.20 (0.40)
0.37 (0.48)
0.43 (0.49)
2.19 (0.98)
0.42 (0.50)
0.46 (0.50)
0.19 (0.13)
Not Diagnosed
0.47 (0.50)
0.35 (0.48)
0.18 (0.38)
0.41 (0.49)
28.97 (6.67)
0.28 (0.45)
0.33 (0.47)
0.39 (0.49)
3.19 (3.48)
0.55 (0.50)
0.13 (0.08)
35
Pharmacy
Total
0.61 (1.39)
0.76 (1.55)
30.05 (63.86)
30.04 (67.78)
101.23 (288.84)
192.95 (781.29)
Pharmacy
Total
0.28 (0.72)
0.54 (1.41)
12.03 (34.87)
23.29 (56.37)
45.18 (177.26)
120.30 (530.63)
Table 3: OLS Estimates, Asthma Visits by Type
Diagnosed
Pre-Period
Hispanic
Black
Age 2-5
Age 6-10
Female
Observations
Inpatient
0.016
(0.013)
-0.042
(0.049)
-0.005
(0.008)
-0.01
(0.008)
-0.001
(0.009)
0.001
(0.009)
0.012
(0.006)*
ER
0.011
(0.024)
0.063
(0.067)
0.018
(0.015)
0.047
(0.018)**
0.021
(0.017)
0.017
(0.016)
-0.016
(0.009)+
Outpatient
0.06
(0.104)
0.263
(0.067)**
0.169
(0.082)*
-0.059
(0.076)
0.136
(0.105)
0.054
(0.092)
-0.177
(0.066)**
Total
-0.018
(0.044)
0.129
(0.045)**
0.063
(0.050)
-0.014
(0.046)
0.032
(0.059)
0.029
(0.050)
-0.08
(0.035)*
2187
2187
2187
2187
Note: The dependent variable is listed at the top of each column. Additional controls
include pre-period asthma costs, dummies for age group of mother, calendar month
dummies corresponding to month of diagnosis (or assigned diagnosis), and county fixed
effects . + denotes significant at 10%; * denotes significant at 5%; ** denotes significant
at 1%
36
Table 4: OLS Estimates, Asthma Costs by Type
Diagnosed
Pre-Period
Hispanic
Black
Age 2-5
Age 6-10
Female
Observations
Inpatient
45.978
(52.795)
-0.045
(0.110)
-21.651
(32.838)
-51.706
(30.700)+
-3.288
(33.540)
14.429
(31.137)
49.773
(24.100)*
ER
-0.422
(6.897)
0.231
(0.169)
7.994
(6.186)
12.902
(5.001)*
8.127
(7.373)
4.341
(5.617)
-5.128
(4.056)
Outpatient
7.51
(9.905)
0.129
(0.063)*
8.334
(9.226)
-10.592
(8.205)
-3.586
(14.646)
-6.79
(12.321)
-9.779
(6.894)
Pharmacy
-1.951
(5.703)
0.42
(0.077)**
-1.287
(3.502)
-9.019
(3.686)*
-2.916
(4.584)
-2.597
(4.569)
-0.769
(2.711)
Total
58.901
(57.850)
0.063
(0.057)
-6.097
(38.986)
-48.968
(35.586)
0.657
(41.069)
10.198
(38.337)
32.954
(27.959)
2187
2187
2188
2187
2187
Note: The dependent variable is listed at the top of each column. Additional controls include preperiod asthma costs, dummies for age group of mother, calendar month dummies corresponding to
month of diagnosis (or assigned diagnosis), and county fixed effects . + denotes significant at 10%; *
denotes significant at 5%; ** denotes significant at 1%
37
Table 5: First Stage Relationship
Doc Propensity
Hispanic
Black
Ages 2-5
Ages 6-10
Female
R-squared
Observations
Diagnosed
0.603
(0.114)**
-0.041
(0.019)*
-0.033
(0.019)+
0.024
(0.023)
0.016
(0.021)
-0.013
(0.014)
Diagnosed &
Treated
0.228
(0.082)**
-0.021
(0.013)
-0.031
(0.013)*
0.023
(0.017)
0.003
(0.015)
0.005
(0.010)
0.11
2187
0.08
2187
Note: The dependent variable is listed at the top of each column.
Additional controls include pre-period asthma costs, dummies for
age group of mother, calendar month dummies corresponding to
month of diagnosis (or assigned diagnosis), and county fixed effects.
+ denotes significant at 10%; * denotes significant at 5%; ** denotes
significant at 1%
38
Table 6: IV Estimates, Asthma Visits by Type
Diagnosed
Pre-Period
Hispanic
Black
Age 2-5
Age 6-10
Female
Observations
Inpatient
-0.036
(0.033)
-0.078
(0.057)
-0.007
(0.008)
-0.014
(0.009)
0
(0.010)
0.002
(0.009)
0.011
(0.006)+
ER
-0.197
(0.101)+
0.054
(0.067)
0.011
(0.015)
0.037
(0.018)*
0.027
(0.017)
0.021
(0.018)
-0.019
(0.010)+
Outpatient
-0.52
(0.540)
0.285
(0.074)**
0.148
(0.082)+
-0.092
(0.079)
0.151
(0.109)
0.064
(0.095)
-0.185
(0.067)**
Total
-0.425
(0.294)
0.144
(0.050)**
0.049
(0.051)
-0.037
(0.049)
0.042
(0.060)
0.037
(0.052)
-0.086
(0.036)*
2187
2187
2187
2187
Note: The dependent variable is listed at the top of each column. Additional controls
include pre-period asthma costs, dummies for age group of mother, calendar month
dummies corresponding to month of diagnosis (or assigned diagnosis), and county fixed
effects . + denotes significant at 10%; * denotes significant at 5%; ** denotes significant
at 1%
39
Table 7: IV Estimates, Asthma Costs by Type
Diagnosed
Pre-Period
Hispanic
Black
Age 2-5
Age 6-10
Female
Observations
Inpatient
-208.265
(146.346)
-0.12
(0.114)
-30.431
(33.470)
-67.025
(33.593)*
3.052
(34.128)
18.745
(31.830)
46.12
(23.945)+
ER
-75.564
(40.884)+
0.237
(0.169)
5.423
(6.345)
8.999
(5.585)
10.218
(7.425)
5.694
(5.830)
-6.273
(4.069)
Outpatient
-61.469
(59.229)
0.143
(0.066)*
5.986
(9.349)
-14.249
(8.963)
-1.727
(14.693)
-5.578
(12.403)
-10.844
(7.184)
Pharmacy
16.811
(26.664)
0.403
(0.083)**
-0.672
(3.634)
-7.944
(3.960)*
-3.434
(4.784)
-2.916
(4.635)
-0.505
(2.749)
Total
-346.909
(188.441)+
0.116
(0.085)
-19.893
(39.975)
-69.591
(38.841)+
12.089
(42.777)
17.511
(39.968)
26.699
(28.329)
2187
2187
2187
2187
2187
Note: The dependent variable is listed at the top of each column. Additional controls include pre-period
asthma costs, dummies for age group of mother, calendar month dummies corresponding to month of
diagnosis (or assigned diagnosis), and county fixed effects . + denotes significant at 10%; * denotes
significant at 5%; ** denotes significant at 1%
40
Table 8: IV Estimates, Asthma Visits by Type
Treated
Pre-Period
Hispanic
Black
Age 2-5
Age 6-10
Female
Observations
Inpatient
-0.094
(0.093)
-0.082
(0.061)
-0.007
(0.009)
-0.015
(0.010)
0.001
(0.010)
0.001
(0.009)
0.012
(0.006)*
ER
-0.524
(0.297)+
0.031
(0.070)
0.008
(0.017)
0.028
(0.021)
0.035
(0.020)+
0.02
(0.019)
-0.014
(0.011)
Outpatient
-1.368
(1.554)
0.286
(0.075)**
0.141
(0.087)
-0.118
(0.095)
0.17
(0.119)
0.06
(0.095)
-0.172
(0.067)**
Total
-1.119
(0.932)
0.142
(0.054)**
0.042
(0.054)
-0.058
(0.061)
0.059
(0.067)
0.033
(0.054)
-0.076
(0.036)*
2187
2187
2187
2187
Note: The dependent variable is listed at the top of each column. Additional controls
include pre-period asthma costs, dummies for age group of mother, calendar month
dummies corresponding to month of diagnosis (or assigned diagnosis), and county fixed
effects . + denotes significant at 10%; * denotes significant at 5%; ** denotes significant
at 1%
41
Table 9: IV Estimates, Asthma Costs by Type
Treated
Pre-Period
Hispanic
Black
Age 2-5
Age 6-10
Female
Observations
% Inpatient
ER
Outpatient
-551.108
-199.659
-162.695
(424.127) (119.933)+ (157.399)
-0.1
0.227
0.13
(0.122)
(0.172)
(0.069)+
-33.597
4.294
5.043
(34.817)
(6.926)
(9.695)
-76.876
5.342
-17.206
(38.645)*
(7.158)
(10.647)
11.237
13.143
0.703
(36.214)
(8.132)
(14.960)
17.179
5.101
-6.032
(32.524)
(6.405)
(12.514)
51.211
-4.424
-9.319
(24.757)*
(4.699)
(6.914)
2187
2187
2187
Pharmacy
44.26
(70.718)
0.397
(0.086)**
-0.425
(3.775)
-7.067
(4.714)
-4.068
(5.338)
-2.777
(4.578)
-0.929
(2.691)
Total
-912.748
(552.125)+
0.138
(0.106)
-25.129
(42.292)
-86.865
(46.062)+
25.226
(46.820)
14.764
(41.480)
35.279
(29.424)
2187
2187
Note: The dependent variable is listed at the top of each column. Additional controls include pre-period
asthma costs, dummies for age group of mother, calendar month dummies corresponding to month of
diagnosis (or assigned diagnosis), and county fixed effects . + denotes significant at 10%; * denotes
significant at 5%; ** denotes significant at 1%
42
Table 10: Asthma Visits, excluding kids who share mom's doctor
Diagnosed
Pre-Period
Hispanic
Black
Age 2-5
Age 6-10
Female
Observations
Inpatient
-0.059
(0.041)
-0.045
(0.051)
-0.011
(0.009)
-0.015
(0.008)+
0
(0.009)
0.003
(0.008)
0.017
(0.006)**
ER
-0.244
(0.124)*
0.052
(0.070)
0.011
(0.017)
0.038
(0.020)+
0.014
(0.021)
0.017
(0.022)
-0.018
(0.011)
Outpatient
-0.638
(0.594)
0.311
(0.084)**
0.182
(0.094)+
-0.062
(0.086)
0.14
(0.122)
0.034
(0.102)
-0.223
(0.068)**
Total
-0.351
(0.327)
0.151
(0.054)**
0.066
(0.057)
-0.022
(0.051)
0.003
(0.071)
-0.012
(0.061)
-0.101
(0.036)**
1854
1854
1854
1854
Note: The dependent variable is listed at the top of each column. Additional controls
include pre-period asthma costs, dummies for age group of mother, calendar month
dummies corresponding to month of diagnosis (or assigned diagnosis), and county fixed
effects . + denotes significant at 10%; * denotes significant at 5%; ** denotes significant
at 1%
43
Table 11: Asthma Costs, excluding kids who share mom's doctor
Diagnosed
Pre-Period
Hispanic
Black
Age 2-5
Age 6-10
Female
Observations
% Inpatient
-334.196
(174.101)+
-0.119
(0.132)
-45.558
(35.965)
-79.509
(35.448)*
-1.974
(38.269)
27.492
(36.471)
51.748
(24.134)*
ER
-94.946
(49.875)+
0.262
(0.188)
6.181
(7.500)
8.87
(6.194)
7.028
(8.993)
4.446
(7.612)
-6.055
(4.761)
Outpatient
-68.717
(67.805)
0.101
(0.068)
8.828
(10.660)
-9.169
(9.799)
-5.516
(17.960)
-11.378
(15.327)
-12.217
(7.362)+
Pharmacy
-4.635
(27.153)
0.403
(0.088)**
-0.775
(3.747)
-7.186
(4.107)+
-1.599
(4.634)
-0.687
(4.421)
-0.666
(2.556)
Total
-527.464
(237.871)*
0.098
(0.081)
-31.473
(43.994)
-74.913
(41.899)+
3.866
(49.205)
22.889
(46.635)
30.835
(29.264)
1854
1854
1854
1854
1854
Note: The dependent variable is listed at the top of each column. Additional controls include pre-period
asthma costs, dummies for age group of mother, calendar month dummies corresponding to month of
diagnosis (or assigned diagnosis), and county fixed effects . + denotes significant at 10%; * denotes
significant at 5%; ** denotes significant at 1%
44
Number of PCPs
Figure 1: PCP Diagnosing Propensity
350
300
305
265
250
200
150
100
50
0
99
20
0%
1-10%
11-20%
21-30%
23
9
31-40%
>40%
% Patients with Depression Diagnosis
45
Figure 2: Map of Florida Counties
46
Figure 3: Risk of Top Carcinogens in Major Metropolitan Areas in Florida
Names of highest risk counties in state appear unboxed. City names, in boxes, are provided for geographic orientation.
KEY
The U.S. Environmental Protection Agency has developed benchmarks for "safe" concentrations of toxics in air, levels
at which one person in one million is at risk of developing cancer. Key represents the risk factor for a combination of 31
toxics as a multiple of EPA's safe level (e.g. darkest shade indicates levels more than 400 times EPA's safe
concentrations). See the chart in the introduction for a list of the 31 toxics.
HIGHEST RISK COUNTIES IN THE STATE
COUNTY
RISK
Putnam
Escambia
Duval
Santa Rosa
Dade
Hillsborough
Leon
Broward
Bay
344
182
144
133
129
123
113
112
104
Source: Natural Resources Defense Council, available at http://www.nrdc.org/air/pollution/cep/sfl.asp.
47