BACKGROUND AND OBJECTIVES:

Thirty million children are currently covered by public insurance; however, the future funding and structure of public insurance are uncertain. Our objective was to determine the number, estimated costs, and demographic characteristics of hospitalizations that would become ineligible for public insurance reimbursement under 3 federal poverty level (FPL) eligibility scenarios.

METHODS:

In this retrospective cohort study using the 2014 State Inpatient Databases, we included all pediatric (age <18) hospitalizations in 14 states from January 1, 2014, to December 31, 2014, with public insurance as the primary payer. We linked each patient’s zip code to the American Community Survey to determine the likelihood of the patient being below 3 different public insurance income eligibility thresholds (300%, 200%, and 100% of the FPL). Multiple simulations were used to describe newly ineligible hospitalizations under each threshold.

RESULTS:

In 775 460 publicly reimbursed hospitalizations in 14 states, reductions in eligibility limits to 300%, 200%, or 100% of the FPL would have resulted in large numbers of newly ineligible hospitalizations (∼155 000 [20% of hospitalizations] for 300%, 440 000 [57%] for 200%, and 650 000 [84%] for 100% of the FPL), equaling $1.2, $3.1, and $4.4 billion of estimated child hospitalization costs, respectively. Patient demographics differed only slightly under each eligibility threshold.

CONCLUSIONS:

Reducing public insurance eligibility limits would have resulted in numerous pediatric hospitalizations not covered by public insurance, shifting costs to families, other insurers, or hospitals. Without adequately subsidized commercial insurance, this reflects a potentially substantial economic hardship for families and hospitals serving them.

What’s Known on This Subject:

The availability of public insurance expanded health care access for children, including many in low-income working families. States are dependent on federal financial support for public insurance; decreased federal funding may push states to limit public insurance enrollment for children.

What This Study Adds:

If public insurance eligibility limits were reduced to 300%, 200%, or 100% of the federal poverty level, numerous publicly reimbursed hospitalizations would become ineligible (∼155 000, 440 000, and 650 000 annually, respectively), resulting in shifting costs to families, other insurers, or hospitals.

Medicaid and the Children’s Health Insurance Program (CHIP) provide health care to over 30 million children. Income eligibility limits for Medicaid have historically directed Medicaid-funded health care to children in poverty. In 1997, the introduction of CHIP (hereafter, Medicaid and CHIP will be referred to simply as “public insurance”) expanded health care access for children, including many in low-income working families.1,2 Increases in child health care access resulted in more consistent primary care use, decreases in avoidable hospitalizations, and decreases in child mortality.3,4 Rollbacks in public insurance eligibility criteria may potentially result in large increases in both noninsurance and underinsurance owing to the cost of obtaining commercial insurance coverage for low income families.5 

Currently, Medicaid spends $100 billion per year in health care payments for children.6,7 Several recent proposals for cost-saving mechanisms (including block grant and per-capita caps) are aimed at controlling rising federal Medicaid expenditures.8,9 These financing options are typically adjusted by annual growth rates, with a goal of adequate health care access and controlled costs.10 However, if annual growth rates are set inappropriately low (eg, global budgetary pressures, unanticipated increases in disease burden, or care pricing), these models may fail to account for year-to-year increases in health care costs and may leave states to absorb a greater financial burden.11 

States are dependent on federal financial support for public insurance; the federal share of public insurance costs exceeds 50% in all states and is >70% in one-quarter of states.12 A decrease in federal funding may push states to employ fiscal reduction strategies for children covered by Medicaid, including limitation of public insurance enrollment, covered services, or access to preventive and acute care services. One likely method for reducing expenditures would be to decrease enrollment by lowering income eligibility thresholds. Public insurance eligibility thresholds for children vary by state between 152% and 405% of the federal poverty level (FPL). If income eligibility thresholds were reduced from their current levels, some children currently insured by public insurance would lose public coverage and would be eligible only for commercial insurance. If they were unable to obtain commercial insurance, some combination of 3 events would occur: (1) families would become directly responsible for health care expenses, (2) hospital systems would become responsible for unpaid expenses, or (3) care would be forgone.

Inpatient hospitalization represents 1 of the highest health care costs for children with public insurance.13 Therefore, our primary objective was to describe the impact of decreasing public insurance eligibility threshold on insurance coverage of pediatric hospitalizations. We sought to provide the number and estimated costs of hospitalizations that would become ineligible on the basis of 3 hypothetical eligibility scenarios for public insurance (maximums of 300% of the FPL, 200% of the FPL, and 100% of the FPL). Our secondary objective was to describe the demographic characteristics of children currently covered by public insurance whose hospitalizations would become ineligible under the 3 eligibility threshold scenarios.

We conducted a retrospective cohort study using the 2014 Agency for Healthcare Research and Quality’s State Inpatient Databases (SID). The SID are a set of state-based all-payer inpatient databases for all hospitalized patients.14 The SID do not include information about individual family income. Therefore, states were included only if the zip code for patients’ home residence was available (14 states; Table 1). To obtain the percentage of families living at various thresholds of the FPL (<100%, 100%–199%, and 200%–299%), and thus the percentage of children insured by public insurance, we geocoded each patient’s zip code to the US Census Bureau’s 2014 American Community Survey (ACS).15 This procedure is similar to previous work in which zip codes were used as a proxy for household income to estimate the effects of income on pediatric health services use.16 The FPL threshold for state-level Medicaid and CHIP eligibility in 2014 was collected from the Kaiser Family Foundation’s Commission on Medicaid and the Uninsured.17 

TABLE 1

State-Level Public Insurance Eligibility Limits Based on the FPL and Hospital Use for Children Enrolled in Public Insurance in 2014

StateState-Level Public Insurance Eligibility (% FPL) and Enrollment, N (%)Hospitalizations for Children Enrolled in Public Insurancea
Childrenb, NPublic Insurance Eligibility, % FPLcChildren Enrolled in Public Insuranceb, % NTotal Costs ($, in Millions)
Arizona 1 618 368 152 37 64 938 395.4 
Colorado 1 232 503 266 30 34 166 295.1 
Florida 4 011 668 215 40 200 986 1164.9 
Iowa 723 081 317 33 39 934 114.3 
Kentucky 1 016 118 218 40 41 764 287.0 
North Carolina 2 281 113 216 41 80 172 436.2 
Nebraska 461 286 218 30 8717 45.4 
New Jersey 2 031 951 355 29 49 605 291.3 
New York 4 263 500 405 39 155 834 1174.0 
Oregon 858 892 305 37 25 282 173.7 
Rhode Island 217 046 266 35 8490 74.2 
Vermont 124 685 318 46 2960 21.1 
Washington 1 588 492 305 36 42 013 437.0 
Wisconsin 1 314 966 306 33 40 566 310.0 
StateState-Level Public Insurance Eligibility (% FPL) and Enrollment, N (%)Hospitalizations for Children Enrolled in Public Insurancea
Childrenb, NPublic Insurance Eligibility, % FPLcChildren Enrolled in Public Insuranceb, % NTotal Costs ($, in Millions)
Arizona 1 618 368 152 37 64 938 395.4 
Colorado 1 232 503 266 30 34 166 295.1 
Florida 4 011 668 215 40 200 986 1164.9 
Iowa 723 081 317 33 39 934 114.3 
Kentucky 1 016 118 218 40 41 764 287.0 
North Carolina 2 281 113 216 41 80 172 436.2 
Nebraska 461 286 218 30 8717 45.4 
New Jersey 2 031 951 355 29 49 605 291.3 
New York 4 263 500 405 39 155 834 1174.0 
Oregon 858 892 305 37 25 282 173.7 
Rhode Island 217 046 266 35 8490 74.2 
Vermont 124 685 318 46 2960 21.1 
Washington 1 588 492 305 36 42 013 437.0 
Wisconsin 1 314 966 306 33 40 566 310.0 
a

The total number of hospitalizations and costs were taken from the Agency for Healthcare Research and Quality’s 2014 SID.

b

The number of children and percent of children enrolled in public insurance was taken from the 2014 ACS.

c

Public insurance income eligibility requirements (% of the FPL) were taken from the Kaiser Family Foundation. Public insurance includes Medicaid and CHIP.

We included all pediatric (age <18 years) hospitalizations in the 14 states from January 1, 2014, to December 31, 2014, with public insurance as the primary payer. Hospitalizations with missing or erroneous zip codes were excluded (0.1%).

The main exposure was lowering public insurance eligibility thresholds to 300%, 200%, and 100% of the FPL for children currently insured through public insurance.

The main outcomes were the number, estimated costs, and demographic characteristics of children whose hospitalizations would become ineligible under each of the 3 different public insurance income eligibility thresholds.

Normal newborns were defined as any hospitalization with a principal diagnosis of birth (International Classification of Diseases, Ninth Revision, Clinical Modification) and a length of stay of 3 days or less. Age groups were then defined as normal newborn, other infants <1 year, children 1 to 4 years, 5 to 9 years, and 10 to 17 years. Race and ethnicity were categorized as non-Hispanic white, non-Hispanic African American, Hispanic, other, and missing. Hospitalizations missing race and/or ethnicity were primarily from Nebraska, which does not report race and/or ethnicity. Hospitalizations for children with complex chronic conditions (CCCs) were determined by using a previously established set of International Classification of Diseases, Ninth Revision, Clinical Modification codes and represent conditions that are expected to last longer than 12 months and are associated with high rates of morbidity and/or mortality.18,19 Rurality for each patient was determined by linking each patient’s home zip code to the rural-urban commuting area code.20 Hospitalization costs were estimated from billed hospital charges by using hospital-specific cost-to-charge ratio files.21 

For states where the public insurance eligibility limit exceeded 300% of the FPL (n = 6 of 14 states), we lowered the FPL limit to 300%. Next, we calculated the number, percent, estimated costs, and characteristics of hospitalizations in 2014 that would have become ineligible on the basis of that new public insurance income eligibility threshold. We repeated this procedure for public insurance income eligibility limits of 200% of the FPL (lowering eligibility limits for 13 of 14 states) and 100% of the FPL (lowering eligibility limits for all included states).

Categorical variables were summarized with frequencies and percentages, whereas continuous variables were summarized with medians with interquartile ranges (IQRs). Linear relationships between state-level measures were assessed with linear regression. The actual family household income level for each patient was unknown. However, the ACS provided the percentage of households living at different levels of the FPL (<100%, <200%, and <300%) in each patient’s residential zip code. We performed 1000 simulations for each FPL eligibility scenario (100%, 200%, and 300%), and during each simulation, we randomly set each hospitalization to be from a child living above or below the eligibility limit using a Bernoulli trial, with the probability being the proportion of children in the patient’s zip code living at less than the eligibility limit (Supplemental Fig 2). After each simulation, the number and percent of 2014 hospitalizations with public insurance as the primary payer that would have been ineligible for public insurance (ie, over each FPL scenario limit) and the characteristics of these hospitalizations (estimated costs, clinical characteristics, and demographics) were determined as stated above. The results of simulations were summarized with medians and IQRs.

All statistical analyses were performed by using SAS v 9.4 (SAS Institute, Inc, Cary, NC), and P values <.05 were considered statistically significant.

The Office of Research Integrity at Children’s Mercy Hospital deemed this study exempt from institutional board review.

The 14 states examined in this study included 30.6% of family households within the United States. Of included families, 43.1% lived below 300% of the FPL, 27.2% below 200% of the FPL, and 11.2% below 100% of the FPL (Supplemental Fig 3). Six states had public insurance eligibility limits >300% of the FPL, 13 states had limits >200% of the FPL, and all 14 states had limits >100% of the FPL. Public insurance eligibility limits differed considerably across states (Table 1). Public insurance FPL eligibility limits tended to be lower in states with a greater percentage of families below 300% of the FPL (Fig 1; P = .01).

FIGURE 1

Percentage of families living <300% of the FPL and 2014 public insurance eligibility limits. The percent of families with incomes <300% of the FPL was taken from the 2014 ACS. Public insurance income eligibility requirements (% of the FPL) were taken from the Kaiser Family Foundation. Public insurance includes Medicaid and CHIP. AZ, Arizona; CO, Colorado; FL, Florida; IA, Iowa; KY, Kentucky; NC, North Carolina; NE, Nebraska; NJ, New Jersey; NY, New York; OR, Oregon; RI, Rhode Island; VT, Vermont; WA, Washington; WI, Wisconsin.

FIGURE 1

Percentage of families living <300% of the FPL and 2014 public insurance eligibility limits. The percent of families with incomes <300% of the FPL was taken from the 2014 ACS. Public insurance income eligibility requirements (% of the FPL) were taken from the Kaiser Family Foundation. Public insurance includes Medicaid and CHIP. AZ, Arizona; CO, Colorado; FL, Florida; IA, Iowa; KY, Kentucky; NC, North Carolina; NE, Nebraska; NJ, New Jersey; NY, New York; OR, Oregon; RI, Rhode Island; VT, Vermont; WA, Washington; WI, Wisconsin.

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After 1000 simulations, we identified 7 994 484 children as eligible for public insurance in the 14 included states, equaling 21.3% of publicly insured US children. For the 795 427 pediatric hospitalizations for this sample of children, total estimated hospitalization costs were calculated at $5.2 billion.

In 7 states, 2014 public insurance eligibility exceeded 300% of the FPL. By reducing public insurance eligibility to 300% of the FPL in those states, the number of hospitalizations currently reimbursed by public insurance that would no longer be eligible was ∼155 000 (45.5%–58.3% of hospitalizations by state; Table 2, Supplemental Table 4). These reductions would exclude at least 1 hospitalization for ∼144 000 children and their families. Median per-hospitalization estimated costs associated with these ineligible hospitalizations ranged from $5991 to $10 634, accumulating $1.2 billion in estimated costs.

TABLE 2

Public Insurance Hospitalizations in 2014 for Children in 3 Public Insurance FPL Limit Eligibility Scenarios

Percent of Current Public Insurance Hospitalizations That Would Become Ineligible, Median (IQR)Cost per Hospitalization That Would Become Ineligible, $, Median (IQR)
Eligibility limit reduced to 100% FPL 
 Arizona 79.8 (79.7–79.9) 6093 (6061–6125) 
 Colorado 86.7 (86.6–86.8) 8660 (8617–8706) 
 Florida 83.5 (83.4–83.6) 5790 (5773–5806) 
 Iowa 89.2 (89.1–89.3) 2859 (2840–2879) 
 Kentucky 82.6 (82.5–82.7) 6882 (6834–6930) 
 North Carolina 84.0 (83.9–84.1) 5436 (5412–5458) 
 Nebraska 86.5 (86.2–86.7) 5176 (5122–5225) 
 New Jersey 84.0 (83.9–84.1) 5910 (5887–5931) 
 New York 81.0 (80.9–81.1) 7469 (7450–7490) 
 Oregon 86.1 (86.0–86.3) 6872 (6831–6908) 
 Rhode Island 82.4 (82.1–82.6) 8810 (8697–8928) 
 Vermont 90.6 (90.2–90.9) 7153 (7049–7238) 
 Washington 88.0 (87.9–88.2) 10 434 (10 389–10 475) 
 Wisconsin 85.2 (85.1–85.3) 7581 (7534–7629) 
Eligibility limit reduced to 200% FPL 
 Arizona Limit is <200% 
 Colorado 67.6 (67.4–67.7) 8672 (8603–8752) 
 Florida 61.3 (61.3–61.4) 5778 (5748–5806) 
 Iowa 72.3 (72.1–72.4) 2854 (2817–2889) 
 Kentucky 62.1 (62.0–62.3) 6915 (6839–6997) 
 North Carolina 63.3 (63.1–63.4) 5440 (5403–5474) 
 Nebraska 66.8 (66.5–67.2) 5137 (5048–5221) 
 New Jersey 65.6 (65.4–65.7) 5956 (5922–5990) 
 New York 61.2 (61.1–61.3) 7397 (7363–7431) 
 Oregon 67.2 (67.0–67.4) 6883 (6811–6957) 
 Rhode Island 62.2 (61.9–62.5) 8879 (8701–9063) 
 Vermont 75.1 (74.6–75.6) 7200 (7054–7363) 
 Washington 71.1 (71.0–71.3) 10 515 (10 441–10 589) 
 Wisconsin 67.2 (67.0–67.3) 7536 (7455–7618) 
Eligibility limit reduced to 300% FPL 
 Arizona Limit is <300% 
 Colorado Limit is <300% 
 Florida Limit is <300% 
 Iowa 54.0 (53.8–54.1) 2859 (2806–2909) 
 Kentucky Limit is <300% 
 North Carolina Limit is <300% 
 Nebraska Limit is <300% 
 New Jersey 50.0 (49.9–50.2) 5991 (5944–6039) 
 New York 45.5 (45.4–45.6) 7360 (7314–7408) 
 Oregon 49.3 (49.1–49.5) 6872 (6775–6974) 
 Rhode Island Limit is <300% 
 Vermont 58.3 (57.7–58.9) 7308 (7057–7538) 
 Washington 54.7 (54.5–54.8) 10 634 (10 525–10 749) 
 Wisconsin 49.6 (49.5–49.8) 7514 (7407–7625) 
Percent of Current Public Insurance Hospitalizations That Would Become Ineligible, Median (IQR)Cost per Hospitalization That Would Become Ineligible, $, Median (IQR)
Eligibility limit reduced to 100% FPL 
 Arizona 79.8 (79.7–79.9) 6093 (6061–6125) 
 Colorado 86.7 (86.6–86.8) 8660 (8617–8706) 
 Florida 83.5 (83.4–83.6) 5790 (5773–5806) 
 Iowa 89.2 (89.1–89.3) 2859 (2840–2879) 
 Kentucky 82.6 (82.5–82.7) 6882 (6834–6930) 
 North Carolina 84.0 (83.9–84.1) 5436 (5412–5458) 
 Nebraska 86.5 (86.2–86.7) 5176 (5122–5225) 
 New Jersey 84.0 (83.9–84.1) 5910 (5887–5931) 
 New York 81.0 (80.9–81.1) 7469 (7450–7490) 
 Oregon 86.1 (86.0–86.3) 6872 (6831–6908) 
 Rhode Island 82.4 (82.1–82.6) 8810 (8697–8928) 
 Vermont 90.6 (90.2–90.9) 7153 (7049–7238) 
 Washington 88.0 (87.9–88.2) 10 434 (10 389–10 475) 
 Wisconsin 85.2 (85.1–85.3) 7581 (7534–7629) 
Eligibility limit reduced to 200% FPL 
 Arizona Limit is <200% 
 Colorado 67.6 (67.4–67.7) 8672 (8603–8752) 
 Florida 61.3 (61.3–61.4) 5778 (5748–5806) 
 Iowa 72.3 (72.1–72.4) 2854 (2817–2889) 
 Kentucky 62.1 (62.0–62.3) 6915 (6839–6997) 
 North Carolina 63.3 (63.1–63.4) 5440 (5403–5474) 
 Nebraska 66.8 (66.5–67.2) 5137 (5048–5221) 
 New Jersey 65.6 (65.4–65.7) 5956 (5922–5990) 
 New York 61.2 (61.1–61.3) 7397 (7363–7431) 
 Oregon 67.2 (67.0–67.4) 6883 (6811–6957) 
 Rhode Island 62.2 (61.9–62.5) 8879 (8701–9063) 
 Vermont 75.1 (74.6–75.6) 7200 (7054–7363) 
 Washington 71.1 (71.0–71.3) 10 515 (10 441–10 589) 
 Wisconsin 67.2 (67.0–67.3) 7536 (7455–7618) 
Eligibility limit reduced to 300% FPL 
 Arizona Limit is <300% 
 Colorado Limit is <300% 
 Florida Limit is <300% 
 Iowa 54.0 (53.8–54.1) 2859 (2806–2909) 
 Kentucky Limit is <300% 
 North Carolina Limit is <300% 
 Nebraska Limit is <300% 
 New Jersey 50.0 (49.9–50.2) 5991 (5944–6039) 
 New York 45.5 (45.4–45.6) 7360 (7314–7408) 
 Oregon 49.3 (49.1–49.5) 6872 (6775–6974) 
 Rhode Island Limit is <300% 
 Vermont 58.3 (57.7–58.9) 7308 (7057–7538) 
 Washington 54.7 (54.5–54.8) 10 634 (10 525–10 749) 
 Wisconsin 49.6 (49.5–49.8) 7514 (7407–7625) 

The number of hospitalizations with public insurance as a primary payer as well as costs were taken from the Agency for Healthcare Research and Quality’s 2014 SID. The ACS provided the percentage of households living at different levels of the FPL (<100%, <200%, and <300%) in each patient’s residential zip code. We then performed 100 simulations for each FPL eligibility scenario (100%, 200%, and 300%). After each simulation, the number and percent of 2014 hospitalizations with public insurance as the primary payer that would have been ineligible for public insurance (over each FPL scenario limit) is reported.

The 2014 eligibility limits exceeded 200% of the FPL in 13 of the 14 states. (Only 1 state included in the study, Arizona, has an eligibility level below 200% of the FPL.) If eligibility limits were reduced to 200% of the FPL, ∼440 000 hospitalizations would no longer be covered by public insurance (61.2%–75.1% of hospitalizations by state). Nearly 412 000 children and their families would be responsible for at least 1 hospitalization under these changes. Median estimated costs of these ineligible hospitalizations varied from $2854 to $10 515, totaling $3.1 billion in estimated costs.

The 2014 eligibility limits of all 14 included states exceeded 100% of the FPL. If the FPL eligibility threshold of those states were reduced to 100% of the FPL, the number of hospitalizations currently reimbursed by public insurance that would no longer be eligible would increase to ∼650 000 (79.8%–90.6% of hospitalizations by state). Close to 608 000 children and their families would be affected 1 or more times by these reductions. The median per-hospitalization estimated costs of ineligible hospitalizations remained relatively stable ($2859–$10 434), but the total estimated costs summed $4.4 billion.

The entire study population of hospitalized, publicly insured children was generally young, with a plurality (38.0%) being non-Hispanic white (20.2% were non-Hispanic African American and 24.5% were Hispanic). Most hospitalizations were for public insurance recipients living in urban settings (84.2%; Table 3). The demographics of patients who would lose public insurance under lowered eligibility thresholds differed only slightly across thresholds. For instance, when eligibility levels were reduced to 300% of the FPL, newly ineligible hospitalizations were 40.1% non-Hispanic white (compared with 38.0% under current thresholds).

TABLE 3

Characteristics of Hospitalized Children Covered by Public Insurance in 2014 Who Would Become Ineligible for Public Insurance on the Basis of 3 FPL Limit Eligibility Scenarios

Characteristics of All Public Insurance Hospitalizations, Mean %Characteristics of Hospitalized Children Over Various FPL Eligibility Scenarios, Mean %
100% FPL200% FPL300% FPL
Age     
 Normal newborn 53.2 53.3 53.1 54.8 
 Other infants, y     
  <1 18.6 18.5 18.6 18.4 
  1–4 8.7 8.6 8.6 8.5 
  5–9 5.8 5.8 5.8 5.6 
  10–17 13.8 13.8 13.9 12.7 
Race and/or ethnicity     
 Non-Hispanic white 38.0 39.4 41.2 40.1 
 Non-Hispanic African American 20.2 19.3 19.5 15.1 
 Hispanic 24.5 23.8 21.1 20.2 
 Other 11.2 11.0 11.2 16.2 
 Missing 6.2 6.4 7.1 8.3 
Patient residence     
 Urban 84.2 84.0 83.5 85.5 
 Rural 15.8 16.0 16.5 14.5 
CCC 12.9 12.9 12.9 12.4 
Characteristics of All Public Insurance Hospitalizations, Mean %Characteristics of Hospitalized Children Over Various FPL Eligibility Scenarios, Mean %
100% FPL200% FPL300% FPL
Age     
 Normal newborn 53.2 53.3 53.1 54.8 
 Other infants, y     
  <1 18.6 18.5 18.6 18.4 
  1–4 8.7 8.6 8.6 8.5 
  5–9 5.8 5.8 5.8 5.6 
  10–17 13.8 13.8 13.9 12.7 
Race and/or ethnicity     
 Non-Hispanic white 38.0 39.4 41.2 40.1 
 Non-Hispanic African American 20.2 19.3 19.5 15.1 
 Hispanic 24.5 23.8 21.1 20.2 
 Other 11.2 11.0 11.2 16.2 
 Missing 6.2 6.4 7.1 8.3 
Patient residence     
 Urban 84.2 84.0 83.5 85.5 
 Rural 15.8 16.0 16.5 14.5 
CCC 12.9 12.9 12.9 12.4 

The number of hospitalizations with public insurance as a primary payer were taken from the Agency for Healthcare Research and Quality’s 2014 SID. The ACS provided the percentage of households living at different levels of the FPL (<100%, <200%, and <300%) in each patient’s residential zip code. We then performed 100 simulations for each FPL eligibility scenario (100%, 200%, and 300%). After each simulation, the demographic characteristics of 2014 hospitalizations with public insurance as the primary payer that would have been ineligible for public insurance (over each FPL scenario limit) is reported.

Hospitalization types also differed only slightly for each reduction in public insurance eligibility limits. For example, when eligibility levels were reduced to 300% of the FPL, the greatest proportion of hospitalizations were for normal newborns (54.8% compared with 53.2% under current eligibility thresholds). The proportion of hospitalizations for children with CCCs was also relatively equal (12.4% compared with 12.9%) to current eligibility thresholds (12.4% compared with 12.9%). These proportions did not change substantially with reductions of public insurance eligibility to 200% of the FPL and 100% of the FPL.

Normal newborns who are currently publicly insured represent the majority of children affected by reductions of income eligibility requirements at all levels (300%, 200%, and 100% of the FPL). For example, the number of normal newborn hospitalizations covered by public insurance would decrease by 96 375 (23%) if income eligibility levels were decreased to 300% of the FPL (7 states), 249 317 (59%) if decreased to 200% of the FPL (13 states), and 355 828 (84%) if reduced to 100% of the FPL (14 states; Supplemental Tables 5 and 6). Estimated costs associated with normal newborn hospitalizations would equal $149 million, $303 million, and $423 million for reductions to 300% of the FPL, 200% of the FPL, and 100% of the FPL, respectively.

In this study of 795 427 hospitalizations of children with public insurance across 14 geographically diverse states, we found that public insurance would no longer reimburse half or more of currently covered hospitalizations and their associated estimated costs under each of 3 scenarios of lowered income eligibility criteria and would result in up to $4.4 billion of estimated hospital costs ineligible for public insurance reimbursement. With these findings, we suggest that reducing public insurance eligibility may potentially result in a large number of children who are currently publicly insured having to either purchase commercial insurance or become uninsured. With these findings, we also predict substantial shifts in costs to lower income families, commercial insurance (if obtainable by families), and/or the health care institutions that serve them.

Under 3 scenarios of decreased income eligibility criteria, loss of public insurance would disproportionately affect healthy newborns. Although they vary by state, existing income eligibility criteria for newborns greatly exceeds that for any other age group, and current law (Children’s Health Insurance Program Reauthorization Act of 2009, Public Law 111-3) allows for fast-tracking public insurance coverage for newborn infants until 12 months provided that their mothers received or were eligible for Medicaid or CHIP coverage during pregnancy.22,24 Newborn hospitalization is 1 of the fastest-rising costs in pediatric care.25 In our study, hospitalization costs for normal newborns ranged from ∼$700 to $2000. Depending on whether a family can obtain commercial insurance for their child after losing public insurance eligibility, the cost of the newborn hospitalization (or potentially the charges, which generally exceed the costs) may represent a significant financial strain for those families. Across all types of hospitalizations, however, the estimated costs associated with a hospitalization ranged from ∼$3000 to $10 000. Given that 200% of the FPL for a family of 4 is $59 640, the cost of a single hospitalization may represent ∼5% to 15% of a family’s annual income.26,27 This may represent an untenable financial burden.

Decreases in health care coverage for children and families would also have implications for health care institutions serving lower income children. The specific impact of those changes would depend on whether newly ineligible children would be able to obtain commercial insurance as well as the specific health benefits of those commercial plans. What is clear is that a large amount of reimbursement (albeit reimbursement that is typically below costs) would be at stake. For the 14 states examined in this study, estimated hospitalization costs for children who would lose public insurance eligibility equaled $1.2 billion when income eligibility was reduced to 300% of the FPL, $3.1 billion at 200% of the FPL, and $4.4 billion at 100% of the FPL. Consequently, changes in public insurance income eligibility criteria may place health care institutions at greater financial risk, especially safety-net hospitals, which already operate at narrower financial margins,28,29 and other hospitals serving large numbers of newborns and low-income families.30 

Funding of public insurance comprises a large proportion of state budgets.12 Outside of our analysis of public insurance eligibility changes, we found that state-level public insurance FPL eligibility criteria were indirectly proportional to the percentage of state population near poverty (ie, states with higher proportions of children in poverty had lower public insurance eligibility thresholds). It is likely that higher income eligibility thresholds in states with many near-poor residents would result in higher marginal costs compared with higher thresholds in states with fewer near-poor residents. State government budgets are more restrictive, including legal requirements for a balanced budget and debt limits in many states.31 Therefore, any reductions in state-level funding for pediatric public insurance programs may result in lowering of eligibility thresholds.

This study has several limitations. First, our sample was limited to the 14 states in the SID that reported zip codes. However, these states represented approximately one-third of US families and were diverse in their geography, population characteristics, and public insurance FPL eligibility criteria. Second, we did not account for public insurance eligibility for people with disabilities that may have resulted in some children retaining coverage. Although small in number, children with special health care needs represent a large fraction of child health care spending and may have more frequent and complex admissions. The costs specifically attributable to medical complexity are relevant but difficult to validly assess within this data set. Third, CHIP income eligibility limits equaled or exceeded age-specific limits for Medicaid and/or Title XXI CHIP for children of all ages in all states except Iowa (separate CHIP limits 317% of the FPL versus age-specific Title XXI CHIP funding for infants aged 0–1 year 380% of the FPL). As a result, we likely underestimate the effects of a reduction in benefits for infants aged 0 to 1 year in Iowa but likely not in other states. Fourth, although we used cost-to-charge ratio files provided specifically for the SID from the Healthcare Cost and Utilization Project, estimated hospitalization costs varied significantly on the basis of states. Information about hospitalizations within the SID is collected by states but may include different entities within states, which may or may not include observation status.32,33 It is also likely that differences in cost of living contribute to differences in hospitalization costs (eg, the wage index for Washington is among the highest at 1.13, whereas Iowa is much lower at 0.89).34 Last, given the nature of our data set, individual child-level income characteristics are unknown and were based on zip codes. Zip codes have been shown to be a reasonable proxy for socioeconomic factors but may not fully reflect the characteristics of all individuals within a given area.35,38 

Eligibility scenarios in which reductions in public insurance eligibility thresholds for children are simulated resulted in the loss of public insurance coverage for a large number of pediatric hospitalizations. If families of children losing public insurance were unable to obtain commercial insurance, the health of those children and the economic well-being of their families and health care institutions would be greatly impacted.

     
  • ACS

    American Community Survey

  •  
  • CCC

    complex chronic condition

  •  
  • CHIP

    Children’s Health Insurance Program

  •  
  • FPL

    federal poverty level

  •  
  • IQR

    interquartile range

  •  
  • SID

    State Inpatient Databases

Dr Bettenhausen proposed the study idea, participated in the study design, analysis, and interpretation of the data, and was the primary author of the manuscript; Drs Hall, Colvin, Puls, and Chung participated in the study design, analysis, and interpretation of the data and were authors of the manuscript; and all authors provided critical intellectual content in the revision of the manuscript and approved the final manuscript as submitted.

FUNDING: No external funding.

1
Strane
D
,
French
B
,
Eder
J
,
Wong
CA
,
Noonan
KG
,
Rubin
DM
.
Low-income working families with employer-sponsored insurance turn to public insurance for their children.
Health Aff (Millwood)
.
2016
;
35
(
12
):
2302
2309
[PubMed]
2
Kreider
AR
,
French
B
,
Aysola
J
,
Saloner
B
,
Noonan
KG
,
Rubin
DM
.
Quality of health insurance coverage and access to care for children in low-income families.
JAMA Pediatr
.
2016
;
170
(
1
):
43
51
[PubMed]
3
Howell
EM
,
Kenney
GM
.
The impact of the Medicaid/CHIP expansions on children: a synthesis of the evidence.
Med Care Res Rev
.
2012
;
69
(
4
):
372
396
[PubMed]
4
Howell
E
,
Decker
S
,
Hogan
S
,
Yemane
A
,
Foster
J
.
Declining child mortality and continuing racial disparities in the era of the Medicaid and SCHIP insurance coverage expansions.
Am J Public Health
.
2010
;
100
(
12
):
2500
2506
[PubMed]
5
Hudson
JL
,
Hill
SC
,
Selden
TM
.
If rollbacks go forward, up to 14 million children could become ineligible for public or subsidized coverage by 2019.
Health Aff (Millwood)
.
2015
;
34
(
5
):
864
870
[PubMed]
6
The Henry J. Kaiser Family Foundation
. Total Medicaid spending. 2016. Available at: http://kff.org/medicaid/state-indicator/total-medicaid-spending/. Accessed April 11, 2017
7
Medicaid and CHIP Payment and Access Commission
. Trends in Medicaid spending. 2016. Available at: https://www.macpac.gov/wp-content/uploads/2016/06/Trends-in-Medicaid-Spending.pdf. Accessed March 31, 2017
8
The Henry J. Kaiser Family Foundation
. Data note: estimated Medicaid savings in the House Budget Resolution from March 2016. 2017. Available at: http://kff.org/medicaid/issue-brief/data-note-estimated-medicaid-savings-in-the-house-budget-resolution-from-march-2016/. Accessed March 21, 2017
9
Congressional Budget Office
. How repealing portions of the Affordable Care Act would affect health insurance coverage and premiums. 2017. Available at: https://www.cbo.gov/publication/52371. Accessed April 11, 2017
10
The Henry J. Kaiser Family Foundation
. Overview of Medicaid per capita cap proposals. 2016. Available at: http://kff.org/medicaid/issue-brief/overview-of-medicaid-per-capita-cap-proposals/. Accessed March 16, 2017
11
Goodman-Bacon
AJ
,
Nikpay
SS
.
Per capita caps in Medicaid - lessons from the past.
N Engl J Med
.
2017
;
376
(
11
):
1005
1007
[PubMed]
12
Medicaid and CHIP Payment and Access Commission
. Medicaid’s share of state budgets. 2017. Available at: https://www.macpac.gov/subtopic/medicaids-share-of-state-budgets/. Accessed March 21, 2017
13
Kuo
DZ
,
Hall
M
,
Agrawal
R
, et al
.
Comparison of health care spending and utilization among children with Medicaid insurance.
Pediatrics
.
2015
;
136
(
6
). Available at: www.pediatrics.org/cgi/content/full/136/6/e1521
[PubMed]
14
Agency for Healthcare Research & Quality, Healthcare Cost and Utilization Project
. Overview of the State Inpatient Databases (SID). 2017. Available at: https://www.hcup-us.ahrq.gov/sidoverview.jsp. Accessed May 19, 2016
15
US Census Bureau
. American Community Survey (ACS): technical documentation. 2016. Available at: https://www.census.gov/programs-surveys/acs/technical-documentation.html. Accessed March 16, 2017
16
Fieldston
ES
,
Zaniletti
I
,
Hall
M
, et al
.
Community household income and resource utilization for common inpatient pediatric conditions.
Pediatrics
.
2013
;
132
(
6
). Available at: www.pediatrics.org/cgi/content/full/132/6/e1592
[PubMed]
17
The Henry J. Kaiser Family Foundation
. Medicaid and CHIP income eligibility limits for children as a percent of federal poverty level. 2017. Available at: http://kff.org/health-reform/state-indicator/medicaid-and-chip-income-eligibility-limits-for-children-as-a-percent-of-the-federal-poverty-level/. Accessed March 16, 2017
18
Feudtner
C
,
Hays
RM
,
Haynes
G
,
Geyer
JR
,
Neff
JM
,
Koepsell
TD
.
Deaths attributed to pediatric complex chronic conditions: national trends and implications for supportive care services.
Pediatrics
.
2001
;
107
(
6
). Available at: www.pediatrics.org/cgi/content/full/107/6/e99
[PubMed]
19
Feudtner
C
,
Feinstein
JA
,
Satchell
M
,
Zhao
H
,
Kang
TI
.
Shifting place of death among children with complex chronic conditions in the United States, 1989-2003.
JAMA
.
2007
;
297
(
24
):
2725
2732
[PubMed]
20
Hailu
A
. Guidelines for using rural-urban classification systems for community health assessment. 2016. Available at: https://www.doh.wa.gov/Portals/1/Documents/1500/RUCAGuide.pdf. Accessed April 11, 2017
21
Healthcare Cost and Utilization Project
. Cost-to-charge ratio files. 2016. Available at: https://www.hcup-us.ahrq.gov/db/state/costtocharge.jsp#files. Accessed April 21, 2017
22
Centers for Medicare & Medicaid Services
. Federal policy guidance on the implementation of CHIPRA public law 111-3. 2009. Available at: https://www.medicaid.gov/federal-policy-guidance/downloads/sho-08-31-09b.pdf. Accessed June 26, 2017
23
The Henry J. Kaiser Family Foundation
. Medicaid and CHIP income eligibility limits for pregnant women as a percent of the federal poverty level. 2017. Available at: www.kff.org/health-reform/state-indicator/medicaid-and-chip-income-eligibility-limits-for-pregnant-women-as-a-percent-of-the-federal-poverty-level/. Accessed July 4, 2017
24
The Henry J. Kaiser Family Foundation
. State adoption of 12-month continuous eligibility for children’s Medicaid and CHIP. 2017. Available at: www.kff.org/health-reform/state-indicator/state-adoption-of-12-month-continuous-eligibility-for-childrens-medicaid-and-chip/. Accessed July 4, 2017
25
Bui
AL
,
Dieleman
JL
,
Hamavid
H
, et al
.
Spending on children’s personal health care in the United States, 1996-2013.
JAMA Pediatr
.
2017
;
171
(
2
):
181
189
[PubMed]
26
The Pew Charitable Trusts
. Household expenditures and income. 2016. Available at: http://pew.org/1VBBiDF. Accessed April 11, 2017
27
Office of the Assistant Secretary for Planning and Evaluation, US Department of Health and Human Services
. 2014 poverty guidelines. 2014. Available at: https://aspe.hhs.gov/2014-poverty-guidelines. Accessed April 11, 2017
28
Mohan
A
,
Grant
J
,
Batalden
M
,
McCormick
D
.
The health of safety net hospitals following Massachusetts health care reform: changes in volume, revenue, costs, and operating margins from 2006 to 2009.
Int J Health Serv
.
2013
;
43
(
2
):
321
335
[PubMed]
29
Colvin
JD
,
Hall
M
,
Berry
JG
, et al
.
Financial loss for inpatient care of Medicaid-insured children.
JAMA Pediatr
.
2016
;
170
(
11
):
1055
1062
[PubMed]
30
Colvin
JD
,
Hall
M
,
Gottlieb
L
, et al
.
Hospitalizations of low-income children and children with severe health conditions: implications of the patient protection and affordable care act.
JAMA Pediatr
.
2016
;
170
(
2
):
176
178
[PubMed]
31
National Conference of State Legislatures
. NCSL fiscal brief: state balanced budget provisions. 2010. Available at: www.ncsl.org/documents/fiscal/StateBalancedBudgetProvisions2010.pdf. Accessed June 26, 2017
32
Healthcare Cost and Utilization Project
. Characteristics of Medicaid and uninsured hospitalizations, 2012. Available at: https://www.hcup-us.ahrq.gov/reports/statbriefs/sb182-Medicaid-Uninsured-Hospitalizations-2012.pdf. Accessed January 10, 2018
33
Healthcare Cost and Utilization Project
. HCUP methods series: identifying observation services in the Healthcare Cost and Utilization Project (HCUP) state databases, report # 2015-05. Available at: https://www.hcup-us.ahrq.gov/reports/methods/2015-05_public.pdf. Accessed January 10, 2018
34
Centers for Medicare & Medicare Services
. Details for title: FY 2014 wage index home page. 2017. Available at: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Wage-Index-Files-Items/FY_2014_Wage_Index_Home_Page.html?DLPage=1&DLEntries=10&DLFilter=2014&DLSort=1&DLSortDir=descending. Accessed January 19, 2018
35
Liberatos
P
,
Link
BG
,
Kelsey
JL
.
The measurement of social class in epidemiology.
Epidemiol Rev
.
1988
;
10
:
87
121
[PubMed]
36
Krieger
N
.
Overcoming the absence of socioeconomic data in medical records: validation and application of a census-based methodology.
Am J Public Health
.
1992
;
82
(
5
):
703
710
[PubMed]
37
Krieger
N
,
Chen
JT
,
Waterman
PD
,
Soobader
M-J
,
Subramanian
SV
,
Carson
R
.
Choosing area based socioeconomic measures to monitor social inequalities in low birth weight and childhood lead poisoning: the Public Health Disparities Geocoding Project (US).
J Epidemiol Community Health
.
2003
;
57
(
3
):
186
199
[PubMed]
38
Geronimus
AT
,
Bound
J
.
Use of census-based aggregate variables to proxy for socioeconomic group: evidence from national samples.
Am J Epidemiol
.
1998
;
148
(
5
):
475
486
[PubMed]

Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data