Abstract
Objective
The Patient Protection and Affordable Care Act (ACA) increases Medicaid physician fees for preventive care up to Medicare rates for 2013 and 2014. The purpose of this paper is to model the relationship between Medicaid preventive care payment rates and the use of US Preventive Services Task Force (USPSTF) recommended preventive care use among Medicaid enrollees.
Data sources / study session
We used data from the 2003 and 2008 Medical Expenditure Panel Survey (MEPS), a national probability sample of the U.S. civilian, noninstitutionalized population, linked to Kaiser state Medicaid benefits data, including the state Medicaid-to-Medicare physician fee ratio in 2003 and 2008.
Study design
Probit models were used to estimate the probability that eligible individuals received one of five USPSF recommended preventive services. A difference-in-difference model was used to separate out the effect of changes in the Medicaid payment rate and other factors.
Data collection / extraction methods
Data were linked using state identifiers.
Principal findings
Although Medicaid enrollees had a lower rate of use of the five preventive services in univariate analysis, neither Medicaid enrollment nor changes in Medicaid payment rates had statistically significant effects on meeting screening recommendations for the five screenings. The results were robust to a number of different sensitivity tests. Individual and state characteristics were significant.
Conclusions
Our results suggest that although temporary changes in primary care provider payments for preventive services for Medicaid enrollees may have other desirable effects, they are unlikely to substantially increase use of these selected USPSTF recommended preventive care services among Medicaid enrollees.
Keywords: Medicaid, payment, preventive care
One of the goals of Patient Protection and Affordable Care Act (ACA) was to increase the provision of preventive care, particularly in low income populations (Doherty, 2010). This is based on the belief that American society is being “penny-wise and pound-foolish,” creating a situation where low income individuals cannot afford to obtain relatively inexpensive preventive care, with the public then later paying for more expensive acute care. Preventive care can lead to earlier diagnoses of illnesses, particularly cancer, and can help prevent potentially fatal illnesses such as heart disease and stroke. Prevention is important for individuals with chronic illnesses, such as diabetes and hypertension.
Although previous research has found that insurance is a key factor in the provision of preventive care (Ayanian et al., 2003; Manning et al., 1987; Lurie et al., 1984; Davidoff et al., 2003; Broyles et al., 2002; Adams et al., 2003; Clancy et al., 2003; Beckles et al., 1998; Ayanian et al., 2000; Schiff et al., 1998), state Medicaid programs generally are not required to cover preventive services and federal Medicaid law does not define preventive care (GAO, 2009). Overall, the use of preventive care among Medicaid enrollees is lower than for commercially insured individuals, although higher than for the uninsured (Mortensen and Atherly, 2012). The reason for the discrepancy between Medicaid enrollees and the privately insured is unclear. It could be that Medicaid enrollees place a lower value on preventive care than commercially insured individuals. Alternatively, it could be that Medicaid enrollees have poorer access to primary care providers due to program characteristics such as low provider payment rates.
Currently, there are substantial differences between Medicaid and Medicare physician fees in many states. On average, Medicaid fees are 72 percent of Medicare fees overall, but only 66 percent for primary care services. The Medicaid payment rate was at or above Medicare fees in 11 states and below in the other 39 (Zuckerman et al., 2009). This ranges from a low of 37% in New Jersey to a high of 143% in Wyoming for overall fees and from a low of 36% in New York and Rhode Island for preventive care to a high of 140% in Alaska (with Alaska being an outlier). The ACA attempts to increase the use of preventive (and other) services by expanding eligibility for Medicaid, increasing payments to providers, encouraging public programs to cover preventive services, and limiting the use of cost sharing for preventive services. The ACA increases Medicaid payment rates for primary care providers for preventive services and immunizations from the current level to Medicare levels for 2013 and 2014.
A key factor in access to care is primary care physicians’ (PCPs) willingness to see Medicaid patients. PCPs who serve Medicaid beneficiaries most actively are also the most willing to accept new Medicaid patients (Kaiser Family Foundation, 2011). However, they also face capacity constraints to serve more of this population. To increase access, it is necessary to convince more doctors to accept Medicaid patients. Nationally, 42% of primary care physicians are willing to accept new Medicaid patients (in 2008), which is considerably lower than for Medicare (61%) or privately insured (84%) (Cunningham, 2011).
Many factors influence physician’s decisions regarding whether or not to accept a Medicaid patient. For example, delays in reimbursement and high administrative burden can offset the effects of high Medicaid fees, thereby lowering participation in high payment states to levels that are closer to those in states with relatively low payment rates (Cunningham and O’Malley, 2009). Indeed, high Medicaid acceptance rates by physicians in a community have been found to be more important than overall fee levels for Medicaid enrollees’ access to medical care (Cunningham and Nichols, 2005). Although high fee levels do increase the probability that individual physicians will accept Medicaid patients, they do not necessarily lead to high levels of physician Medicaid acceptance in an area. Other factors, such as physician practice structure, health system characteristics, and other community factors are more important.
Yet there is support in the literature for the notion that higher payment rates will increase access; higher Medicaid-to-Medicare fee ratios increase both the fraction of Medicaid patients seen by physicians and the number of private physicians who see Medicaid patients (Shen and Zuckerman, 2005). Physicians in states with the lowest Medicaid fees are less willing to accept new Medicaid patients (Long, Settle and Stuart, 1986). Higher payments have been found to increase the probability of having a usual source of care and the probability of having at least one visit to a doctor for Medicaid adults, and produce more positive assessments of the health care received by adults and children (Kenney et al., 2011).
Although the studies cited above do show a relationship between physician access and overall Medicaid payment rates, they do not look at service specific payment rates. Indeed, although Kenney (2011) found higher payments had positive effects on access, it also found no effect on the probability of receiving preventive care or having unmet health needs. It may be that lower physician reimbursement simply leads to a shift in location where care is delivered (Cunningham, 2011; Decker, 2007). There have also been several other papers finding no relationship between Medicaid payment levels and receipt of preventive care (Cunningham and O’Malley, 2009; Cunningham and Nichols, 2005). Decker (2009) found that payment level cuts shift care away from physician offices to emergency and outpatient departments. However, Decker (2009) also found that the baseline number of visits was far higher for Medicaid recipients than for the privately insured – potentially due to lower copayments – so that lower payment levels do not clearly reduce access to levels below the privately insured, depending on what type of services are foregone due to the payment cuts.
In this paper, we focus on the effect of state level Medicaid policies in effect before the ACA payment increase on the use of US Preventive Services Task Force (USPSTF) recommended A and B preventive services among Medicaid enrollees in the 10 largest states. USPSTF is an independent panel of non-Federal experts in prevention and evidence-based medicine that conducts scientific evidence reviews of a broad range of clinical preventive health care services to develop recommendations for primary care clinicians and health systems services that are recommended grade A services have a high certainty that the net benefit is substantial, and B services have high certainty that the net benefit is moderate or moderate certainty that the net benefit is moderate to substantial (USPSTF, 2012). We empirically estimate the impact of preventive care services payment changes on the use of USPSTF recommended A and B preventive services. This paper adds to the existing literature on the subject by looking at the effect of payment changes on the delivery of specific services. It is hoped that Medicaid primary care payment increases will lead to higher rates of delivery of preventive care; the indirect evidence in literature to date is mixed on whether this strategy is likely to succeed. By directly examining the link between payment rates and preventive care, we provide evidence on the viability of the strategy.
Methods
The underlying theory behind the Medicaid payment increases is that Medicaid recipients are not provided preventive services due to low payment rates. The precise mechanism is unclear; it could be that higher payment rates induce more physicians to accept Medicaid thus increasing access and thereby leading to higher screening rate. Alternatively, it could be that physicians who could, but do not, perform preventive screenings among their Medicaid patients at lower payment rates would do so at higher payment rates. In either case, it is hoped that an increase in provider payments for primary care services will lead to an increase in the quantity of primary care services supplied. The approach is supported by both standard economic theory and by a substantial literature finding a relationship between Medicaid payment rates and outcome variables such as physician participation in Medicaid (Decker, 2009; Zuckerman et al, 2004; Gruber, Adams and Newhouse, 1997).
In our empirical model, the main explanatory variable is Medicaid payment rate (discussed below), with control variables included for individual attributes related to the individual’s need and likelihood of seeking preventive care, plus year and state fixed effects to control for time invariant state and temporal changes in the use of preventive care.
Data
Our primary data source is the Medical Expenditure Panel Survey (MEPS) Household Component, a nationally representative sample of families and individuals across the nation(Cohen et al., 1996). The survey is sponsored by the Agency for Healthcare Research and Quality and the National Center for Health Statistics and contains data on a variety of health status measures, health insurance coverage and health care usage, including the use of preventive services. The survey employs a panel design, with several rounds of interviews generating data for 2 full calendar years. MEPS provides variables relating to health status, chronic diseases, income, race, gender, and education primary plus our dependent variables -- the use of different types of preventive services. The Medicaid physician fees and the Medicaid to Medicare fee index are published by the Kaiser Family Foundation, described in detail in Zuckerman, Williams and Stockley (2009). Creation of the analytic variables is described below. We use data from two different years – 2003 and 2008. Total sample size varies depending on the particular condition (described below).
Analytic Approach
The target population for our analysis is Medicaid enrollees who are eligible for the different preventive services. Because individuals were not assigned randomly to states (and thus to primary care payment rates – our variable of interest), we face the possibility of omitted variable bias –unobserved factors correlated with both the Medicaid primary care payment rate and the subsequent probability that a person in the target population received preventive care. Our main strategy to address this issue is to use the well-known difference-in-differences model.
The standard difference-in-differences model utilizes two dichotomous variables – typical a pre-post time variable and a variable indicating the treatment and control groups. In our model, we follow previous literature and set the “treatment” group as enrollment in Medicaid, while the control group is individuals with private insurance (Decker, 2009; Decker, 2011). The pre year is 2003 and the post 2008. However, our intervention is a change in a continuous variable – the Medicaid primary care payment rate. Thus in our difference-in-differences model is somewhat akin to dose /response models in pharmaceutical studies. Our approach is similar to that used by Decker (2011).
In our results, the coefficient of the “Medicaid” variable represents differences in rates of use of preventive services associated with Medicaid enrollment. This coefficient thus captures differences between Medicaid enrollees and privately insured individuals in underlying propensity to use preventive services across the two time periods. The “Medicaid Payment Rate” variable controls for differences based on overall Medicaid payment levels (described below). The payment effect, is represented by the coefficient on the interaction term (Medicaid Enrollment*Payment Rate). That coefficient is the “difference-in-differences” term.
We estimate equation 1 with a probit model. We report the marginal effects of each of our explanatory variables on the dependent variable. Although the marginal effects represented by many types of interaction terms in non-linear models (like our probit equation) vary with the values of the explanatory variables in the model (Ai and Norton, 2003), Puhani (2008) has shown that the difference-in-difference model is a special case and the treatment effect is given by the coefficient on the interaction term.
The control variables represent factors that have been found to be associated with both the probability of receiving preventive care and the probability of being enrolled in Medicaid. We include variables in our model for:
Health (self-rated and presence of chronic illness),
Marital status,
Insurance (Medicaid, Medicare)
Employment characteristics (employed full time, part time, self employed),
Individual socio-demographic characteristics (income, education, race, marital status and age),
A fixed effect for our initial year (2003).
We also include a state fixed effect to capture time-invariant differences in state-level variables associated with the use of preventive services. Our model controls for the complex survey design of the MEPS by using longitudinal strata and primary sampling unit identifiers and survey weights. The estimated coefficients are representative of the U.S. civilian, non-institutionalized, adult population.
The key independent variable – the Medicaid primary care payment rate – represents a ratio of the weighted average of state Medicaid payments to Medicare payments for the twelve most frequent office based primary care services, such as office visit, new patient, 30 minutes; office visit, established patient, 15 minutes’ office visit, established patient, 25 minutes (Decker, 2009). Although this ratio does not include comprehensive inventory of all Medicaid payment rates, Medicaid payment rates for similar services within a state tend to be highly correlated (Decker, 2009). The use of the Medicaid to Medicare payment ratio is relatively standard in the existing literature, even in examining differences in utilization between Medicaid and private insurance utilization (e.g. Decker, 2009; Zuckerman et al, 2004; Cunningham and Nichols, 2004; Cunningham and O’Malley, 2008; Gray, 2001). There are a number of reasons for the use of the ratio, rather than a ratio of Medicaid to private insurance payments. Pragmatically, private insurance payment rates are often not available and are potentially endogenous due to reverse causation. One advantage of the Medicaid to Medicare ratio is that payment rate changes for Medicaid are often made in response to budget considerations, rendering them relatively exogenous to our model (e.g., Kaiser Health News, 2012). However, Medicare payment rates are highly correlated with private insurance rates (Hogan, 2005).
Further, because this mechanism—increasing Medicaid payments relative to Medicare – is the one envisioned and operationalized by the ACA to increase the use of preventive care in the Medicaid population, it is logical to examine the effect of increases in Medicaid payments relative to Medicare payments.
Defining Preventive Services
Although there are a wide variety of preventive services recommended for Americans by various health plans, government agencies and physicians groups, we are focusing this analysis on grade “A” and “B” preventive services recommended by the USPSTF. These include tests for colorectal, cervical and breast cancer, hypertension, and high cholesterol.
For our analysis, we created five separate binary indicators to reflect whether the USPSTF recommendation was met. Our measures are all drawn from the MEPS interview. Because the sample varies depending on the particular preventive procedure, the sample size varies accordingly. It ranges from a low of 7,468 for breast cancer screening to a high of 24,446 for blood pressure screening
The cervical cancer screening measure (also known as a “Pap smear”) is recommended every three years for women age 21 to 65 with an intact uterus. Mammography screening (breast cancer) is recommended for women age 40 to 74 every two years in the ACA. Colorectal cancer screening is recommended for men and women between ages 50 and 75. The guidelines for colorectal cancer screening include: fecal occult blood test every year, sigmoidoscopy every 5 years combined with a fecal occult blood test, or colonoscopy every 10 years.
USPSTF recommends checking blood pressure for adults age 18 and over every one to two years. Screening for lipid disorders in adults is strongly recommended every five years for men over age 35 regardless of health history. There is a B recommendation for men age 20 to 35 if they are at increased risk for coronary heart disease (CHD, defined as the presence of diabetes, previous personal history of CHD or non-coronary atherosclerosis, a family history of cardiovascular disease before age 50 in male relatives or age 60 in female relatives, tobacco use, hypertension, and obesity (BMI =30)). For women, the screen is strongly recommended every 5 years for women over age 45 if they are at increased risk for coronary heart disease and for women aged 20 to 45 if they are at increased risk.
For lipid disorders, we are unable to determine accurately an individual’s increased risk primarily due to a lack of family history in the data, thus we include all men over age 35 and all women over age 45 as having met the guideline if they had a cholesterol test in the past five years. This is consistent with the recommendation during much of the study period which was to screen all women over 45 years of age.
Our measure of meeting the recommendation for colorectal cancer screening is an indicator for if the individual had a sigmoidoscopy or colonoscopy within the past 5 years, or if they had a fecal occult blood test in the last year. This is because the 2003 to 2008 MEPS data do not distinguish between sigmoidoscopy or colonoscopy (this is distinguished in the 2009 data), and the question is truncated at 5 or more years. Our blood pressure measure indicates whether these adults have had their blood pressure checked in the last two years, which is consistent with other guidelines.
Results
Overall, screening rates for four of the five conditions are quite high and were relatively unchanged between 2003 and 2008 (Table 1). Overall, 91% of those eligible received cervical cancer screening in both 2003 and 2008, as did 80% of those eligible for breast cancer screenings and 94% of those eligible for blood pressure screenings. The screening rates for cholesterol and colorectal cancer increased slightly – from 91% to 93% and 49% to 55% respectively.
Table 1.
Screening Rates, 2003–2008 for Eligible Individuals
| Cervical Cancer Screening (Pap Smear) | Breast Cancer Screening | Cholesterol Screening | Blood Pressure Screening | Colorectal Cancer Screening | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 2003 | 2008 | 2003 | 2008 | 2003 | 2008 | 2003 | 2008 | 2003 | 2008 | |
| Overall | 91% | 91% | 80% | 80% | 91% | 93% | 94% | 94% | 49% | 55% |
| Private Insurance | 92% | 92% | 81% | 81% | 89% | 91% | 93% | 93% | 46% | 52% |
| Medicaid | 87% | 86% | 69% | 60% | 85% | 91% | 93% | 96% | 27% | 30% |
| Medicare | 80% | 79% | 96% | 96% | 97% | 97% | 53% | 59% | ||
| Employed Full Time | 93% | 93% | 82% | 81% | 88% | 90% | 92% | 93% | 43% | 50% |
| Employed Part Time | 91% | 92% | 81% | 81% | 93% | 94% | 95% | 94% | 51% | 54% |
| Not Employed | 88% | 87% | 78% | 77% | 95% | 96% | 96% | 96% | 52% | 58% |
| Self Employed | 92% | 87% | 76% | 80% | 90% | 91% | 93% | 92% | 45% | 54% |
| Self Rated Health: Excellent | 91% | 92% | 81% | 79% | 87% | 89% | 91% | 91% | 47% | 51% |
| Self Rated Health: Very Good | 92% | 92% | 81% | 81% | 90% | 93% | 94% | 94% | 49% | 56% |
| Self Rated Health: Good | 92% | 89% | 80% | 81% | 92% | 94% | 95% | 95% | 49% | 57% |
| Self Rated Health: Fair | 90% | 90% | 76% | 75% | 95% | 96% | 97% | 97% | 47% | 55% |
| Self Rated Health: Poor | 92% | 86% | 75% | 71% | 97% | 95% | 99% | 99% | 55% | 53% |
| No Chronic Illness | 91% | 91% | 76% | 76% | 83% | 85% | 90% | 90% | 38% | 46% |
| Any Chronic Illness | 93% | 92% | 83% | 82% | 96% | 96% | 98% | 98% | 53% | 57% |
| Income: Less than 100% of FPL | 90% | 86% | 71% | 73% | 89% | 93% | 94% | 95% | 41% | 49% |
| Income: 100%–200% of FPL | 88% | 90% | 71% | 72% | 91% | 92% | 92% | 93% | 42% | 49% |
| Income: 200%–400% of FPL | 91% | 89% | 78% | 74% | 89% | 90% | 93% | 93% | 49% | 51% |
| Income: Over 400% of FPL | 93% | 93% | 84% | 85% | 92% | 94% | 95% | 95% | 52% | 59% |
| Race: Black Non-Hispanic | 92% | 94% | 82% | 83% | 92% | 94% | 96% | 95% | 44% | 56% |
| Race: Other Non-White | 85% | 85% | 74% | 75% | 92% | 90% | 91% | 83% | 32% | 44% |
| Race: Hispanic | 90% | 90% | 73% | 78% | 87% | 91% | 89% | 90% | 34% | 42% |
| Race White | 92% | 91% | 80% | 80% | 91% | 93% | 94% | 94% | 51% | 56% |
| Education: Less than High School | 82% | 85% | 69% | 68% | 90% | 92% | 92% | 93% | 39% | 47% |
| Education: High School Grad | 90% | 88% | 80% | 77% | 90% | 91% | 94% | 95% | 48% | 50% |
| Education: Some College | 90% | 89% | 81% | 79% | 90% | 93% | 93% | 95% | 51% | 58% |
| Education: College Graduate | 94% | 94% | 84% | 85% | 93% | 95% | 95% | 94% | 54% | 60% |
| Married | 94% | 93% | 81% | 82% | 92% | 93% | 94% | 94% | 50% | 58% |
| Widowed/Divorced/Separated | 92% | 87% | 77% | 76% | 91% | 94% | 95% | 96% | 45% | 50% |
| Never Married | 85% | 87% | 74% | 74% | 86% | 89% | 90% | 90% | 48% | 44% |
| Age: Under 21 | 67% | 65% | 89% | 88% | ||||||
| Age: 21–34 | 92% | 92% | 90% | 90% | ||||||
| Age: 35–39 | 93% | 93% | 70% | 70% | 73% | 79% | 92% | 93% | ||
| Age: 40–44 | 93% | 94% | 81% | 77% | 79% | 86% | 93% | 94% | ||
| Age: 45–49 | 93% | 89% | 84% | 84% | 88% | 91% | 95% | 94% | ||
| Age: 50–64 | 91% | 90% | 79% | 80% | 94% | 93% | 96% | 95% | 45% | 51% |
| Age: 65–74 | 95% | 95% | 97% | 96% | 55% | 62% | ||||
| Age: Over 74 | 96% | 97% | 98% | 98% | 50% | 55% | ||||
| Sample Size | 4,358 | 4,077 | 3,899 | 3,569 | 8,056 | 7,591 | 12,690 | 11,756 | 5,825 | 5,623 |
Medicaid recipients generally had lower screening rates than either privately insured individuals or Medicare beneficiaries. In 2008, Medicaid recipients had lower screening rates for cervical cancer (86% vs. 92% for private insurance), breast cancer (60% vs. 81% for private insurance and 79% for Medicare), cholesterol screening (91% (Medicaid and private) and 96% (Medicare)) and colorectal cancer screening (30% vs. 52% (private) vs. 59% (Medicare)). However, with the exception of colorectal cancer screening, none of the screening rates significantly changed 2003–2008 within insurance category. Colorectal cancer screening increased for all payers, including Medicaid (27% to 30%), private insurance (46% to 52%) and Medicare (53% to 59%).
In the multivariate analysis (Tables 2–6), the Medicaid payment rate is negative in all four models, with the effect reaching statistical significance in three of the model (breast cancer, cholesterol and blood pressure screening). In contrast, Medicaid enrollment is insignificantly related to the use of screening in all five models. This suggests that, controlling for other factors, Medicaid enrollees are equally likely to receive preventive services as individuals in the reference group (privately insured) and that states with higher Medicaid primary care payment rates had lower use of preventive services. For the models including Medicare beneficiaries (all but cervical cancer), Medicare enrollment is also insignificantly related to the probability of receiving preventive services.
Table 2.
Marginal Effect of Medicaid Payment Rates on Cervical Cancer Screening (“Pap Smear”)
| Coefficient | Std Err | t statistic | p value | |
|---|---|---|---|---|
| Medicaid Payment Rate | −0.048 | 0.0377 | −1.28 | 0.201 |
| Medicaid Enrollee | 0.009 | 0.0238 | 0.39 | 0.693 |
| Payment × Enrollee | −0.012 | 0.0383 | −0.31 | 0.757 |
| Year 2008 | −0.005 | 0.0068 | −0.69 | 0.494 |
| Age: 21–34 | 0.073 | 0.0334 | 2.19 | 0.029 |
| Age: 35–39 | 0.068 | 0.0324 | 2.08 | 0.038 |
| Age: 40–44 | 0.070 | 0.0332 | 2.10 | 0.037 |
| Age: 45–49 | 0.047 | 0.0288 | 1.63 | 0.103 |
| Age: 50–64 | 0.042 | 0.0275 | 1.53 | 0.126 |
| Race: Black | 0.032 | 0.0145 | 2.17 | 0.030 |
| Race: Other Non-White | −0.058 | 0.0241 | −2.39 | 0.017 |
| Race: Hispanic | 0.008 | 0.0088 | 0.86 | 0.388 |
| Income: 100%–200% of FPL | −0.012 | 0.0110 | −1.10 | 0.273 |
| Income: 200%–400% of FPL | −0.020 | 0.0135 | −1.49 | 0.138 |
| Income: Over 400% of FPL | −0.002 | 0.0107 | −0.20 | 0.840 |
| Employed Part Time | −0.007 | 0.0074 | −0.90 | 0.369 |
| Not Employed | −0.030 | 0.0138 | −2.16 | 0.031 |
| Self Employed | −0.027 | 0.0168 | −1.61 | 0.108 |
| Widowed/Divorced/Separated | −0.026 | 0.0134 | −1.95 | 0.052 |
| Never Married | −0.064 | 0.0253 | −2.51 | 0.012 |
| Self Rated Health: Very Good | 0.005 | 0.0076 | 0.67 | 0.506 |
| Self Rated Health: Good | −0.002 | 0.0072 | −0.25 | 0.805 |
| Self Rated Health: Fair | 0.003 | 0.0111 | 0.31 | 0.755 |
| Self Rated Health: Poor | 0.004 | 0.0153 | 0.28 | 0.779 |
| Any Chronic Illness | 0.021 | 0.0103 | 2.09 | 0.037 |
| Education: High School Grad | 0.035 | 0.0171 | 2.07 | 0.039 |
| Education: Some College | 0.047 | 0.0208 | 2.25 | 0.025 |
| Education: College Graduate | 0.068 | 0.0277 | 2.46 | 0.014 |
| Alabama | −0.001 | 0.0367 | −0.02 | 0.987 |
| Arizona | 0.015 | 0.0337 | 0.45 | 0.650 |
| California | 0.019 | 0.0136 | 1.43 | 0.154 |
| Colorado | 0.034 | 0.0278 | 1.22 | 0.223 |
| Connecticut | 0.059 | 0.0148 | 3.99 | 0.000 |
| Florida | 0.009 | 0.0165 | 0.53 | 0.598 |
| Georgia | 0.031 | 0.0221 | 1.43 | 0.154 |
| Illinois | 0.005 | 0.0202 | 0.27 | 0.789 |
| Indiana | 0.018 | 0.0211 | 0.85 | 0.398 |
| Kentucky | 0.007 | 0.0224 | 0.31 | 0.757 |
| Louisiana | −0.006 | 0.0335 | −0.18 | 0.857 |
| Maryland | 0.040 | 0.0226 | 1.75 | 0.080 |
| Massachusetts | 0.044 | 0.0197 | 2.25 | 0.025 |
| Michigan | 0.039 | 0.0173 | 2.25 | 0.025 |
| Minnesota | 0.031 | 0.0201 | 1.56 | 0.119 |
| Missouri | −0.001 | 0.0223 | −0.02 | 0.980 |
| New Jersey | −0.004 | 0.0165 | −0.22 | 0.826 |
| North Carolina | 0.046 | 0.0211 | 2.16 | 0.031 |
| Ohio | 0.035 | 0.0212 | 1.67 | 0.096 |
| Oklahoma | 0.032 | 0.0255 | 1.25 | 0.213 |
| Oregon | 0.049 | 0.0268 | 1.82 | 0.069 |
| Pennsylvania | 0.002 | 0.0182 | 0.10 | 0.918 |
| South Carolina | 0.054 | 0.0224 | 2.39 | 0.017 |
| Tennessee | −0.016 | 0.0222 | −0.71 | 0.480 |
| Texas | −0.001 | 0.0209 | −0.05 | 0.960 |
| Virginia | 0.026 | 0.0218 | 1.21 | 0.225 |
| Washington | 0.043 | 0.0240 | 1.81 | 0.071 |
| Wisconsin | 0.033 | 0.0199 | 1.64 | 0.102 |
Table 6.
Marginal Effect of Medicaid Payment Rates on Colorectal Cancer Screening
| Coefficient | Std Err | t statistic | p value | |
|---|---|---|---|---|
| Medicaid Payment Rate | −0.215 | 0.1395 | −1.54 | 0.123 |
| Medicaid Enrollee | −0.081 | 0.1147 | −0.71 | 0.479 |
| Payment × Enrollee | −0.152 | 0.1850 | −0.82 | 0.410 |
| Medicare Enrollee | −0.001 | 0.0428 | −0.03 | 0.975 |
| Year 2008 | 0.063 | 0.0131 | 4.84 | 0.000 |
| Age: 65–74 | 0.074 | 0.0413 | 1.78 | 0.075 |
| Age: Over 74 | 0.017 | 0.0436 | 0.39 | 0.698 |
| Race: Black | 0.009 | 0.0193 | 0.44 | 0.658 |
| Race: Other Non-White | −0.147 | 0.0271 | −5.41 | 0.000 |
| Race: Hispanic | −0.094 | 0.0209 | −4.51 | 0.000 |
| Income: 100%–200% of FPL | −0.020 | 0.0238 | −0.83 | 0.406 |
| Income: 200%–400% of FPL | 0.042 | 0.0244 | 1.73 | 0.084 |
| Income: Over 400% of FPL | 0.104 | 0.0243 | 4.29 | 0.000 |
| Employed Part Time | 0.052 | 0.0235 | 2.21 | 0.027 |
| Not Employed | 0.101 | 0.0186 | 5.40 | 0.000 |
| Self Employed | 0.008 | 0.0229 | 0.36 | 0.718 |
| Widowed/Divorced/Separated | −0.045 | 0.0133 | −3.35 | 0.001 |
| Never Married | −0.060 | 0.0263 | −2.27 | 0.023 |
| Self Rated Health: Very Good | 0.033 | 0.0177 | 1.89 | 0.060 |
| Self Rated Health: Good | 0.046 | 0.0180 | 2.54 | 0.011 |
| Self Rated Health: Fair | 0.033 | 0.0212 | 1.55 | 0.122 |
| Self Rated Health: Poor | 0.083 | 0.0268 | 3.11 | 0.002 |
| Any Chronic Illness | 0.128 | 0.0142 | 9.04 | 0.000 |
| Education: High School Grad | 0.036 | 0.0189 | 1.93 | 0.054 |
| Education: Some College | 0.099 | 0.0239 | 4.15 | 0.000 |
| Education: College Graduate | 0.127 | 0.0201 | 6.31 | 0.000 |
| Gender: Female | −0.022 | 0.0106 | −2.08 | 0.038 |
| Alabama | −0.001 | 0.0788 | −0.01 | 0.989 |
| Arizona | 0.108 | 0.0993 | 1.09 | 0.276 |
| California | −0.004 | 0.0366 | −0.11 | 0.914 |
| Colorado | 0.153 | 0.0658 | 2.32 | 0.021 |
| Connecticut | 0.107 | 0.0745 | 1.44 | 0.150 |
| Florida | 0.081 | 0.0433 | 1.86 | 0.064 |
| Georgia | 0.060 | 0.0712 | 0.84 | 0.403 |
| Illinois | −0.068 | 0.0509 | −1.33 | 0.185 |
| Indiana | −0.079 | 0.0593 | −1.32 | 0.186 |
| Kentucky | 0.047 | 0.0759 | 0.62 | 0.537 |
| Louisiana | −0.024 | 0.0909 | −0.27 | 0.790 |
| Maryland | 0.133 | 0.0745 | 1.79 | 0.075 |
| Massachusetts | 0.151 | 0.0729 | 2.07 | 0.039 |
| Michigan | 0.065 | 0.0539 | 1.21 | 0.229 |
| Minnesota | 0.033 | 0.0604 | 0.55 | 0.584 |
| Missouri | −0.053 | 0.0487 | −1.09 | 0.276 |
| New Jersey | −0.006 | 0.0381 | −0.17 | 0.866 |
| North Carolina | 0.109 | 0.0895 | 1.22 | 0.224 |
| Ohio | −0.008 | 0.0616 | −0.13 | 0.897 |
| Oklahoma | −0.085 | 0.0891 | −0.96 | 0.339 |
| Oregon | 0.012 | 0.0752 | 0.15 | 0.877 |
| Pennsylvania | −0.048 | 0.0536 | −0.90 | 0.367 |
| South Carolina | 0.111 | 0.0737 | 1.51 | 0.131 |
| Tennessee | −0.153 | 0.0809 | −1.89 | 0.060 |
| Texas | −0.029 | 0.0527 | −0.56 | 0.576 |
| Virginia | 0.078 | 0.0719 | 1.09 | 0.278 |
| Washington | 0.148 | 0.0778 | 1.90 | 0.058 |
| Wisconsin | 0.043 | 0.0627 | 0.68 | 0.494 |
The key variable in the analysis is the interaction term which represents the differential effect of increased Medicaid payment rates on use of preventive services in the Medicaid population. In all five models, this coefficient is statistically insignificant; the coefficients are greater than zero in only three of the five models. This suggests that higher Medicaid payment rates had no association with the use of these five preventive services in the Medicaid population.
However, there are many significant factors that do predict having a screening test, even if Medicaid enrollment and Medicaid payment rates are not significant predictors of receiving a screening. For both cholesterol and blood pressure screening, self-reported health status was negatively related to screening probabilities. Relative to excellent health, the probability of receiving a cholesterol test increased from 1.1 percentage points more likely for Very Good health to 2.6 percentage points for Poor health. For blood pressure, the increase in the probability of screening ranged from 0.9 to 2.5 percentage points. Similarly, any chronic illness increased the probability of all the screening tests, with the effect ranging from a low of 2.1 percentage points (cervical cancer) to a high of 12.8 percentage points (colorectal cancer). Finally, education was strongly and consistently positively associated with screening. For all five screening tests, the effect increased with greater education, with the lowest range (blood pressure) going from a low of 1.0 percentage points for high school graduate (relative to non-high school graduates) to 1.9 percentage points (college graduate) and the highest range (colorectal cancer) going from 3.6 percentage points for high school graduate to 12.7 percentage points (college graduate).
Other individual factors were not significant. Race was largely insignificant except for colorectal cancer, where minorities were less likely to receiving screening. Marital status was largely insignificant, as was employment.
A second set of factors which was significant is the state fixed effects. Connecticut had significantly higher screening rates than the reference state (New York) for four of the five measures: cervical cancer (5.9 percentage points higher), breast cancer (9.5 percentage points higher), cholesterol screening (2.5 percentage points higher) and blood pressure (1.8 percentage points higher). Massachusetts had higher screening rates for three (cervical cancer, blood pressure and colorectal cancer) as did North Carolina (cervical cancer, blood pressure and cholesterol). In contrast, Tennessee was significantly worse than New York on three measures: cholesterol screening (3.3 percentage points lower), blood pressure (1.9 percentage points lower) and colorectal cancer (15.3 percentage points lower).
Discussion
This paper explored the relationship between Medicaid primary care payment rates and the use of five preventive care services (breast cancer, cervical cancer, cholesterol, blood pressure and colorectal cancer) recommended by the USPSTF. The descriptive statistics suggest only a slight relationship between Medicaid enrollment and use of preventive services and no relationship between Medicaid payment rates and the same. The multivariate analysis reinforces this finding, with no statistically significant relationships between Medicaid enrollment or Medicaid primary care payment rates and the use of preventive services.
There are a number of potential threats to our causal inference. First, we use the ratio of Medicare to Medicaid payment rates. To test the sensitivity of our model to this specification, we estimated the models with the Medicaid sample only and estimated the effect of changes in payment rates on changes in the use of preventive services and found a similar result. We also limited our analysis to only the 10 largest states to ensure that the results weren’t associated with smaller sample sizes in smaller states. Finally, we limited the data to only individuals with physician visits to test whether the results were somehow driven by lack of use. Our findings were robust to all of those specifications.
One further complication is that many Medicaid programs use managed care, which is a potential confounder in our data. The MEPS data identified whether Medicaid enrollees are in a HMO / managed care plans. To address this concern we controlled for managed care enrollment, both through indicator variables and also through stratification. Our results were robust to these specifications.
We also rely on self-report of the use of preventive care. Although MEPS self-report has been validated with claims data and is an accepted source for data on use of preventive services, our results could be biased if self-report of services is correlated with changes in Medicaid payment rates.
A final limitation of our analysis is that the difference-in-differences model assumes that there are not any other significant shocks that are correlated with the changes in payments. Such shocks seem unlikely, partly because payment rates increased, decreased and remained unchanged and partly because of the lack of changes in overall prevention use (Table 1).
The results of this analysis appear, at first blush, to be counterintuitive. The conventional wisdom is that low payment rates are a key obstacle to Medicaid enrollees accessing care. What this paper suggests is that incremental changes in particular payment rates are insufficient to increase use of services. The idea behind the payment increases is straightforward: if you want more Medicaid enrollees to receive preventive care, pay doctors more to provide preventive screenings. However, this straightforward idea runs into two practical challenges. First, after controlling for demographic factors, there isn’t a statistically significant difference in the receipt of preventive services between Medicaid, Medicare and privately insured individuals. Second, changes in payment rates for preventive services are unlikely to motivate providers who are not accepting Medicaid enrollees to suddenly change their practice and begin to accept Medicaid enrollees and provide preventive screenings. Indeed, it is difficult to convince providers who don’t accept Medicaid patients to change their practice and accept them with broad based Medicaid payment increases. Service specific payment increases are unlikely to motivate these providers to accept patients and provide preventive services. The results of this paper suggest that piecewise increases in the Medicaid preventive care payment rate are not an effective strategy to increase the receipt of preventive services in the Medicaid population.
Supplementary Material
Table 3.
Marginal Effect of Medicaid Payment Rates on Breast Cancer Screening (“Mammogram”)
| Coefficient | Std Err | t statistic | p value | |
|---|---|---|---|---|
| Medicaid Payment Rate | −0.134 | 0.0542 | −2.47 | 0.014 |
| Medicaid Enrollee | −0.077 | 0.0580 | −1.33 | 0.185 |
| Payment × Enrollee | 0.064 | 0.0931 | 0.69 | 0.494 |
| Medicare Enrollee | 0.003 | 0.0377 | 0.07 | 0.947 |
| Year 2008 | −0.002 | 0.0083 | −0.27 | 0.790 |
| Age: 45–49 | 0.065 | 0.0183 | 3.55 | 0.000 |
| Age: 50–64 | 0.094 | 0.0224 | 4.22 | 0.000 |
| Age: 65–74 | 0.086 | 0.0407 | 2.12 | 0.035 |
| Race: Black | 0.043 | 0.0138 | 3.09 | 0.002 |
| Race: Other Non-White | −0.033 | 0.0195 | −1.66 | 0.097 |
| Race: Hispanic | 0.016 | 0.0135 | 1.15 | 0.252 |
| Income: 100%–200% of FPL | −0.027 | 0.0183 | −1.46 | 0.145 |
| Income: 200%–400% of FPL | −0.014 | 0.0180 | −0.80 | 0.426 |
| Income: Over 400% of FPL | 0.034 | 0.0179 | 1.90 | 0.058 |
| Employed Part Time | −0.004 | 0.0134 | −0.27 | 0.786 |
| Not Employed | −0.019 | 0.0120 | −1.60 | 0.110 |
| Self Employed | −0.031 | 0.0196 | −1.58 | 0.116 |
| Widowed/Divorced/Separated | −0.033 | 0.0128 | −2.58 | 0.010 |
| Never Married | −0.054 | 0.0208 | −2.61 | 0.009 |
| Self Rated Health: Very Good | 0.002 | 0.0108 | 0.15 | 0.878 |
| Self Rated Health: Good | 0.009 | 0.0110 | 0.85 | 0.394 |
| Self Rated Health: Fair | −0.021 | 0.0158 | −1.33 | 0.185 |
| Self Rated Health: Poor | −0.027 | 0.0283 | −0.96 | 0.340 |
| Any Chronic Illness | 0.060 | 0.0161 | 3.74 | 0.000 |
| Education: High School Grad | 0.061 | 0.0199 | 3.05 | 0.002 |
| Education: Some College | 0.068 | 0.0240 | 2.83 | 0.005 |
| Education: College Graduate | 0.098 | 0.0265 | 3.68 | 0.000 |
| Alabama | −0.012 | 0.0485 | −0.25 | 0.803 |
| Arizona | −0.021 | 0.0635 | −0.33 | 0.740 |
| California | −0.016 | 0.0220 | −0.72 | 0.471 |
| Colorado | −0.013 | 0.0478 | −0.27 | 0.789 |
| Connecticut | 0.095 | 0.0291 | 3.26 | 0.001 |
| Florida | 0.001 | 0.0239 | 0.03 | 0.977 |
| Georgia | 0.007 | 0.0411 | 0.17 | 0.862 |
| Illinois | −0.035 | 0.0290 | −1.20 | 0.229 |
| Indiana | −0.035 | 0.0431 | −0.82 | 0.414 |
| Kentucky | 0.034 | 0.0352 | 0.97 | 0.331 |
| Louisiana | −0.008 | 0.0514 | −0.15 | 0.877 |
| Maryland | 0.012 | 0.0412 | 0.29 | 0.771 |
| Massachusetts | 0.049 | 0.0351 | 1.40 | 0.163 |
| Michigan | 0.029 | 0.0265 | 1.10 | 0.273 |
| Minnesota | 0.028 | 0.0317 | 0.88 | 0.378 |
| Missouri | −0.089 | 0.0399 | −2.23 | 0.026 |
| New Jersey | −0.025 | 0.0281 | −0.90 | 0.369 |
| North Carolina | 0.025 | 0.0484 | 0.52 | 0.600 |
| Ohio | 0.000 | 0.0318 | 0.01 | 0.995 |
| Oklahoma | −0.006 | 0.0481 | −0.13 | 0.895 |
| Oregon | −0.003 | 0.0418 | −0.07 | 0.944 |
| Pennsylvania | −0.050 | 0.0345 | −1.45 | 0.149 |
| South Carolina | 0.038 | 0.0493 | 0.76 | 0.445 |
| Tennessee | −0.093 | 0.0300 | −3.10 | 0.002 |
| Texas | −0.050 | 0.0371 | −1.36 | 0.175 |
| Virginia | −0.050 | 0.0531 | −0.94 | 0.350 |
| Washington | −0.011 | 0.0503 | −0.23 | 0.819 |
| Wisconsin | −0.034 | 0.0412 | −0.82 | 0.414 |
Table 4.
Marginal Effect of Medicaid Payment Rates on Cholesterol Screening
| Coefficient | Std Err | t statistic | p value | |
|---|---|---|---|---|
| Medicaid Payment Rate | −0.055 | 0.0091 | −5.97 | 0.000 |
| Medicaid Enrollee | −0.004 | 0.0171 | −0.22 | 0.822 |
| Payment × Enrollee | 0.001 | 0.0260 | 0.04 | 0.969 |
| Medicare Enrollee | −0.015 | 0.0168 | −0.91 | 0.361 |
| Year 2008 | 0.007 | 0.0032 | 2.24 | 0.026 |
| Age: 40–44 | 0.024 | 0.0131 | 1.83 | 0.068 |
| Age: 45–49 | 0.043 | 0.0174 | 2.49 | 0.013 |
| Age: 50–64 | 0.055 | 0.0212 | 2.60 | 0.009 |
| Age: 65–74 | 0.072 | 0.0313 | 2.30 | 0.022 |
| Age: Over 74 | 0.077 | 0.0326 | 2.38 | 0.018 |
| Race: Black | 0.012 | 0.0054 | 2.22 | 0.027 |
| Race: Other Non-White | −0.005 | 0.0077 | −0.65 | 0.513 |
| Race: Hispanic | 0.006 | 0.0046 | 1.35 | 0.177 |
| Income: 100%–200% of FPL | 0.009 | 0.0090 | 0.96 | 0.336 |
| Income: 200%–400% of FPL | 0.018 | 0.0102 | 1.74 | 0.082 |
| Income: Over 400% of FPL | 0.035 | 0.0144 | 2.44 | 0.015 |
| Employed Part Time | 0.005 | 0.0062 | 0.84 | 0.402 |
| Not Employed | 0.011 | 0.0060 | 1.80 | 0.072 |
| Self Employed | 0.002 | 0.0050 | 0.41 | 0.679 |
| Widowed/Divorced/Separated | −0.012 | 0.0055 | −2.13 | 0.034 |
| Never Married | −0.011 | 0.0065 | −1.75 | 0.082 |
| Self Rated Health: Very Good | 0.011 | 0.0054 | 1.97 | 0.049 |
| Self Rated Health: Good | 0.016 | 0.0068 | 2.40 | 0.017 |
| Self Rated Health: Fair | 0.023 | 0.0086 | 2.65 | 0.008 |
| Self Rated Health: Poor | 0.026 | 0.0115 | 2.29 | 0.023 |
| Any Chronic Illness | 0.050 | 0.0169 | 2.94 | 0.003 |
| Education: High School Grad | 0.014 | 0.0078 | 1.74 | 0.082 |
| Education: Some College | 0.020 | 0.0098 | 2.09 | 0.037 |
| Education: College Graduate | 0.035 | 0.0142 | 2.43 | 0.015 |
| Gender: Female | 0.014 | 0.0060 | 2.31 | 0.021 |
| Alabama | −0.004 | 0.0171 | −0.25 | 0.800 |
| Arizona | 0.019 | 0.0153 | 1.25 | 0.211 |
| California | −0.004 | 0.0109 | −0.33 | 0.740 |
| Colorado | −0.005 | 0.0170 | −0.31 | 0.759 |
| Connecticut | 0.025 | 0.0113 | 2.24 | 0.025 |
| Florida | 0.003 | 0.0114 | 0.23 | 0.820 |
| Georgia | 0.005 | 0.0145 | 0.32 | 0.752 |
| Illinois | −0.016 | 0.0140 | −1.14 | 0.255 |
| Indiana | −0.033 | 0.0201 | −1.62 | 0.106 |
| Kentucky | −0.005 | 0.0154 | −0.30 | 0.762 |
| Louisiana | −0.010 | 0.0186 | −0.55 | 0.581 |
| Maryland | 0.036 | 0.0145 | 2.48 | 0.014 |
| Massachusetts | 0.031 | 0.0188 | 1.62 | 0.105 |
| Michigan | −0.009 | 0.0135 | −0.63 | 0.529 |
| Minnesota | −0.010 | 0.0134 | −0.71 | 0.477 |
| Missouri | −0.023 | 0.0163 | −1.41 | 0.159 |
| New Jersey | −0.007 | 0.0120 | −0.58 | 0.562 |
| North Carolina | 0.031 | 0.0125 | 2.44 | 0.015 |
| Ohio | −0.005 | 0.0133 | −0.40 | 0.689 |
| Oklahoma | −0.001 | 0.0192 | −0.05 | 0.962 |
| Oregon | 0.001 | 0.0147 | 0.10 | 0.924 |
| Pennsylvania | −0.019 | 0.0138 | −1.37 | 0.171 |
| South Carolina | 0.021 | 0.0131 | 1.62 | 0.105 |
| Tennessee | −0.033 | 0.0150 | −2.20 | 0.029 |
| Texas | −0.008 | 0.0133 | −0.63 | 0.531 |
| Virginia | 0.010 | 0.0152 | 0.64 | 0.521 |
| Washington | 0.005 | 0.0159 | 0.34 | 0.732 |
| Wisconsin | −0.017 | 0.0169 | −1.03 | 0.305 |
Table 5.
Marginal Effect of Medicaid Payment Rates on Blood Pressure Screening
| Coefficient | Std Err | t statistic | p value | |
|---|---|---|---|---|
| Medicaid Payment Rate | −0.040 | 0.0029 | −13.83 | 0.000 |
| Medicaid Enrollee | 0.005 | 0.0084 | 0.64 | 0.521 |
| Payment × Enrollee | 0.001 | 0.0127 | 0.05 | 0.961 |
| Medicare Enrollee | 0.009 | 0.0104 | 0.89 | 0.376 |
| Year 2008 | −0.001 | 0.0017 | −0.31 | 0.759 |
| Age: 21–34 | −0.009 | 0.0050 | −1.81 | 0.071 |
| Age: 35–39 | −0.005 | 0.0044 | −1.13 | 0.261 |
| Age: 40–44 | −0.005 | 0.0042 | −1.27 | 0.204 |
| Age: 45–49 | −0.002 | 0.0036 | −0.66 | 0.510 |
| Age: 50–64 | −0.004 | 0.0037 | −0.96 | 0.335 |
| Age: 65–74 | −0.012 | 0.0130 | −0.94 | 0.346 |
| Age: Over 74 | 0.001 | 0.0079 | 0.08 | 0.933 |
| Race: Black | 0.006 | 0.0032 | 2.01 | 0.045 |
| Race: Other Non-White | −0.009 | 0.0048 | −1.96 | 0.051 |
| Race: Hispanic | −0.006 | 0.0035 | −1.56 | 0.120 |
| Income: 100%–200% of FPL | −0.005 | 0.0041 | −1.26 | 0.210 |
| Income: 200%–400% of FPL | 0.001 | 0.0033 | 0.43 | 0.668 |
| Income: Over 400% of FPL | 0.009 | 0.0050 | 1.90 | 0.059 |
| Employed Part Time | 0.003 | 0.0028 | 1.16 | 0.246 |
| Not Employed | 0.000 | 0.0026 | 0.01 | 0.992 |
| Self Employed | −0.002 | 0.0032 | −0.62 | 0.535 |
| Widowed/Divorced/Separated | −0.005 | 0.0030 | −1.74 | 0.082 |
| Never Married | −0.008 | 0.0038 | −2.13 | 0.033 |
| Self Rated Health: Very Good | 0.009 | 0.0041 | 2.28 | 0.023 |
| Self Rated Health: Good | 0.012 | 0.0050 | 2.32 | 0.021 |
| Self Rated Health: Fair | 0.017 | 0.0069 | 2.53 | 0.012 |
| Self Rated Health: Poor | 0.025 | 0.0099 | 2.48 | 0.013 |
| Any Chronic Illness | 0.027 | 0.0101 | 2.65 | 0.008 |
| Education: High School Grad | 0.010 | 0.0048 | 2.08 | 0.038 |
| Education: Some College | 0.017 | 0.0069 | 2.49 | 0.013 |
| Education: College Graduate | 0.019 | 0.0075 | 2.51 | 0.012 |
| Gender: Female | 0.027 | 0.0098 | 2.74 | 0.006 |
| Alabama | 0.010 | 0.0062 | 1.64 | 0.101 |
| Arizona | 0.008 | 0.0090 | 0.88 | 0.377 |
| California | −0.004 | 0.0054 | −0.72 | 0.472 |
| Colorado | 0.005 | 0.0087 | 0.62 | 0.533 |
| Connecticut | 0.018 | 0.0056 | 3.29 | 0.001 |
| Florida | −0.004 | 0.0071 | −0.52 | 0.605 |
| Georgia | 0.003 | 0.0085 | 0.36 | 0.718 |
| Illinois | −0.004 | 0.0067 | −0.58 | 0.564 |
| Indiana | −0.001 | 0.0068 | −0.14 | 0.890 |
| Kentucky | 0.010 | 0.0053 | 1.84 | 0.066 |
| Louisiana | 0.009 | 0.0085 | 1.07 | 0.286 |
| Maryland | 0.011 | 0.0072 | 1.53 | 0.127 |
| Massachusetts | 0.027 | 0.0086 | 3.20 | 0.001 |
| Michigan | 0.006 | 0.0051 | 1.09 | 0.277 |
| Minnesota | 0.003 | 0.0069 | 0.45 | 0.654 |
| Missouri | −0.003 | 0.0082 | −0.31 | 0.757 |
| New Jersey | 0.003 | 0.0052 | 0.63 | 0.529 |
| North Carolina | 0.021 | 0.0055 | 3.76 | 0.000 |
| Ohio | 0.004 | 0.0061 | 0.58 | 0.561 |
| Oklahoma | 0.019 | 0.0070 | 2.66 | 0.008 |
| Oregon | −0.002 | 0.0089 | −0.22 | 0.824 |
| Pennsylvania | 0.000 | 0.0058 | −0.08 | 0.934 |
| South Carolina | 0.000 | 0.0103 | −0.02 | 0.981 |
| Tennessee | −0.019 | 0.0057 | −3.29 | 0.001 |
| Texas | 0.002 | 0.0060 | 0.37 | 0.709 |
| Virginia | 0.019 | 0.0045 | 4.15 | 0.000 |
| Washington | 0.006 | 0.0074 | 0.86 | 0.392 |
| Wisconsin | 0.002 | 0.0076 | 0.26 | 0.797 |
Acknowledgments
Joint Acknowledgment
This project is supported by Grant Number 1R21HD071550 from the National Institute of Child Health and Human Development.
Footnotes
Disclosure Statement
‘Disclosures: None’ or ‘No Other Disclosures’.
Contributor Information
Adam Atherly, Associate Professor, Department of Health Systems, Management & Policy, Colorado School of Public Health, 13001 E. 17th Place, MS B117, Aurora, CO 80045, (303) 724-4471 (phone), (303) 724-4495 (fax)
Karoline Mortensen, Email: karoline@umd.edu, Assistant Professor, Department of Health Services Administration, 3310 School of Public Health Building, University of Maryland, College Park, MD 20742-2611, 301 405-6545
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