Abstract
Objective:
This study estimates the cost and length of stay (LOS) of pediatric asthma hospitalizations in the US; examines disparities in LOS and cost according to income; and quantifies the proportion of this relationship mediated by individual, environmental, and community factors.
Methods:
Cross-sectional data from the US National Inpatient Sample (2016–2019) were used to examine the hospital service delivery cost (USD 2020) and LOS (days) of pediatric (5–17 years) asthma admissions, N = 150,845. Disparities in cost and LOS according to income were estimated. Causal mediation analysis was used to quantify the role of potential mediators (asthma severity, comorbidity, tobacco/smoke exposure, insurance, or hospital capacity) in explaining disparities.
Results:
The total cost of admissions was $260 million annually. The average cost and LOS per admission were $6884 (95 %CI: 6682, 7086) and 2 days (95 %CI: 2.09, 2.15). Individuals living in lower income areas had a 3 % increase in LOS and a 3 % decrease in costs per admission compared to those who live in higher-income areas. Severe persistent asthma and exposure to smoke/tobacco mediated the largest proportion of disparities between the income-LOS (20 % & 17 %) and income-cost (−29 % & −21 %) relationships. Hospital capacity was the only positive mediator between income and cost (10 %).
Conclusion:
Pediatric patients from lower income areas have longer LOS and lower costs compared to those from higher income areas. Interventions to eliminate income-related differences in asthma severity and smoke/tobacco exposure may narrow LOS disparities while widening cost disparities; revealing even lower costs among lower income patients.
Keywords: Asthma, Economic, Pediatric, Hospital, Socioeconomic, Mediation, Inpatient
1. Introduction
Asthma affects 6.5 % of U.S. children under 18, making it one of the most common chronic conditions in this age group [1,2]. It accounted for 7.9 million missed school days among 5-17 year-olds in 2018 and approximately $5.9 billion (2015 USD) in annual direct costs among those aged 6–17 years [3,4]. Asthma-related hospitalizations drive much of this economic impact, accounting for $502 million in 2010, with a relatively stable cost per admission of $3600 (USD 2010) between 2000 and 2010 [4,5]. Asthma hospitalizations are in decline [3,6] but are still considered potentially preventable through primary and specialist care, and medication adherence [6,7]. Further reducing the reliance on hospital services is a priority of the US Dept. of Health and Human Services [8]. Understanding resource use within hospitals and the factors that drive it are key to reducing the substantial economic and humanistic burden of asthma among children and adolescents.
Hospitalizations for children with asthma are significantly higher among those from lower socioeconomic backgrounds [9-13]. Factors like health literacy contribute to increased exacerbations and greater reliance on secondary care rather than primary services [9,12]. Among adults, socioeconomic disparities persist even after hospital admission. Khalid et al. (2024) showed that lower income patients have shorter lengths of stay (LOS) and lower costs for asthma hospitalizations compared to higher income patients [10]. However, there is little evidence on socioeconomic disparities in LOS and cost among pediatric patients and what factors may drive them. Mitchell et al. (2021) note a relative dearth of evidence on the social determinants of health (SDOH) among pediatric critical care patients, especially for respiratory conditions, in the U.S. compared to adults [14]. These authors also note issues in available evidence where studies have been restricted to a few sites, despite large regional variations in SDOH, or a lack of focus on outcomes other than mortality, such as length of stay (LOS) [14]. This study investigates whether socioeconomic disparities among pediatric patients persist after hospital admission by examining admission cost and LOS.
Efforts to reduce disparities and ultimately reliance on hospital services require identification of potential targets for intervention. The CDC’s EXHALE package, for example, outlines strategies like removing environmental triggers, such as smoke exposure, as those which may influence asthma control and healthcare costs [15]. Recent reviews of the SDOH among children and adolescents have highlighted several societal factors, particularly in relation to respiratory diseases, which operate together to produce disparities. These include: 1) Community-level factors, such as healthcare access or hospital quality; 2) Environmental factors, such as air pollution and smoke exposure; and 3) Socioeconomic factors, such as health insurance [14,16-18]. For example, lower income children and adolescents are more likely to have public insurance or to be uninsured, which is associated with delayed service use and worse in-hospital outcomes [14]. Furthermore, living in lower income areas can increase exposure to environmental triggers with less means to avoid such triggers [16,17]. Finally, neighborhood socioeconomic disadvantage is also associated with lower quality hospital access [19]. These present a suite of potential mediators which may explain socioeconomic disparities and serve as potential intervention targets.
This study uses a large nationally representative dataset (2016–2019) of pediatric hospitalizations in the U.S. to: 1) provide up-to-date estimates of LOS and cost associated with asthma admissions among children in the US; 2) examine disparities in LOS and cost per admission according to SES (median household income by zip code); and 3) quantify the role of individual, environmental, and community factors in explaining disparities. We hypothesized that we would observe significant differences in LOS and cost according to SES and that this relationship would be mediated, in part, by differences in observed individual, environmental, and community factors.
2. Methods
2.1. Data
This study used the National Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality discharge data from 2016 to 2019. Beginning from the first full year that the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Clinical Modification (ICD-10-CM) coding system [20] was used in NIS, and ending in the last year preceding the first wave of the COVID-19 pandemic (to avoid potential misclassification with respiratory admissions related to COVID-19). NIS is a representative dataset of hospital inpatient admissions (approximately 7 million admissions annually) and is the largest publicly available all-payer inpatient care database in the US; full dataset documentation has been published elsewhere [21].
The full data covers 28 million admissions over 4 years and contains information on inpatient records not individual patients. As such, it is not possible to see whether a patient had multiple visits. To focus on pediatric asthma admissions, we included records for patients aged 5–17 years - given differences in diagnostic and treatment procedures and difficulties in making reliable diagnoses in younger age-groups [22,23] - with a primary diagnosis of asthma (ICD-10 codes beginning J45). We excluded elective admissions, those with non-primary diagnoses for other respiratory disorders, which may be associated with complications requiring admission [24] (Appendix A), and records with missing exposure or outcome data (<1 % total missing cells; Appendix B).
2.2. Measures and variables
The directed acyclic graph outlining our model is presented in Fig. 1.
Fig. 1.
Directed acyclic graph outlining the model and variables used for causal mediation analysis.
X = Exposure: lower income quartile (Y/N)
m = Mediators: patient insurance status, asthma status, comorbidity, smoke/tobacco exposure, and hospital capacity
y = Outcomes: admission length of stay and cost
z = Confounders: patient age category, sex, race/ethnicity, year, and location; hospital region, ownership, location and teaching status; and admission season, and weekend
2.2.1. Exposure
The exposure was whether the admission was for a patient from the lowest median household income quartile [by zip code], henceforth ‘income’.
2.2.2. Outcomes
Length of stay (LOS) in days during the inpatient admission; and
Inpatient service delivery cost per admission, henceforth ‘cost’.
Hospital charges are provided in NIS instead of costs so these were converted using year-specific cost-to-charge ratios available from HCUP [25] and adjusted to USD 2020 using the Personal Consumption Expenditures – Hospital Care Index from the Bureau of Economic Analysis [26].
2.2.3. Potential confounders
They included: patient age category (5–12, 13–17), sex (male/female), race/ethnicity (Asian/Pacific Islander, Black, Hispanic, American Indian, White, Other), and location (urban/rural classification); hospital region, ownership, urban/rural classification, and teaching status; and admission year, season, and weekend (Yes/No).
2.2.4. Potential mediators
They included whether the patient had a severe persistent asthma diagnosis (ICD-10 code beginning J45.5), one or more comorbidities (identified using the Elixhauser comorbidity index [27,28]), exposure to tobacco/smoke (ICD-10 codes: Z7722, Z87891, Z5731, Z720), no insurance (no record of public or private insurance), or was admitted to a hospital with a large (vs small/medium) bed-size capacity. These mediators were chosen to capture potentially important health (severe asthma, comorbidity), community (hospital bed-size capacity; a marker of hospital quality [29,30]), and environmental (smoke/tobacco exposure) characteristics which were available in NIS and which may explain socioeconomic disparities in inpatient outcomes.
2.3. Statistical analysis
Missing data accounted for <1 % of cells. Variables with missing data (race/ethnicity, insurance, sex; see Appendix B and Table 1) were imputed using multiple imputation. Five datasets were imputed with 20 iterations per dataset using chained equations with random forests and the miceRanger package in R [31]. All results were estimated per imputed dataset and combined using Rubin’s rules [32].
Table 1.
Sociodemographic characteristics of admissions for pediatric patients hospitalized with asthma (2016–2019) in total and across income groups (N = 150,845).
| Variable | Overall, N = 150,845 |
Upper three income quartiles, N = 87,375 |
Lowest income quartile, N = 63,470 |
|---|---|---|---|
| Age categories (years), n (%) | |||
| 5–12 | 124,785 83 %) | 72,195 (83 %) | 52,590 (83 %) |
| 13–17 | 26,060 (17 %) | 15,180 (17 %) | 10,880 (17 %) |
| Female, n (%) | 62,760 (42 %) | 36,125 (41 %) | 26,635 (42 %) |
| N missing | 5 (<0.1 %) | 0 (0 %) | 5 (<0.1 %) |
| Hospital region, n (%) | |||
| Northeast | 36,295 (24 %) | 20,195 (23 %) | 16,100 (25 %) |
| Midwest | 26,445 (18 %) | 14,185 (16 %) | 12,260 (19 %) |
| South | 56,845 (38 %) | 31,055 (36 %) | 25,790 (41 %) |
| West | 31,260 (21 %) | 21,940 (25 %) | 9320 (15 %) |
| Hospital location/teaching status, n (%) | |||
| Rural | 5945 (3.9 %) | 2395 (2.7 %) | 3550 (5.6 %) |
| Urban, nonteaching | 12,395 (8.2 %) | 8195 (9.4 %) | 4200 (6.6 %) |
| Urban, teaching | 132,505 (88 %) | 76,785 (88 %) | 55,720 (88 %) |
| Control/ownership of hospital, n (%) | |||
| Government, nonfederal | 19,300 (13 %) | 9500 (11 %) | 9800 (15 %) |
| Private, not-profit | 116,350 (77 %) | 70,240 (80 %) | 46,110 (73 %) |
| Private, invest-own | 15,195 (10 %) | 7635 (8.7 %) | 7560 (12 %) |
| Patient Location: NCHS Urban-Rural Code, n (%) | |||
| Central counties of metro areas of≥1 million population | 66,970 (44 %) | 33,075 (38 %) | 33,895 (53 %) |
| Fringe counties of metro areas of≥1 million population | 33,775 (22 %) | 27,660 (32 %) | 6115 (9.6 %) |
| Counties in metro areas of 250,000–999,999 population | 28,740 (19 %) | 15,845 (18 %) | 12,895 (20 %) |
| Counties in metro areas of 50,000–249,999 population | 9625 (6.4 %) | 6065 (6.9 %) | 3560 (5.6 %) |
| Micropolitan counties | 7165 (4.7 %) | 3285 (3.8 %) | 3880 (6.1 %) |
| Not metropolitan or micropolitan counties | 4570 (3.0 %) | 1445 (1.7 %) | 3125 (4.9 %) |
| Year, n (%) | |||
| 2016 | 40,285 (27 %) | 22,985 (26 %) | 17,300 (27 %) |
| 2017 | 38,940 (26 %) | 21,950 (25 %) | 16,990 (27 %) |
| 2018 | 37,885 (25 %) | 22,560 (26 %) | 15,325 (24 %) |
| 2019 | 33,735 (22 %) | 19,880 (23 %) | 13,855 (22 %) |
| Season (Northern Hemisphere), n (%) | |||
| Spring | 44,395 (29 %) | 25,870 (30 %) | 18,525 (29 %) |
| Summer | 21,580 (14 %) | 12,555 (14 %) | 9025 (14 %) |
| Autumn | 48,375 (32 %) | 27,690 (32 %) | 20,685 (33 %) |
| Winter | 36,495 (24 %) | 21,260 (24 %) | 15,235 (24 %) |
| Weekend (Y/N), n (%) | 42,540 (28 %) | 24,765 (28 %) | 17,775 (28 %) |
| Race/ethnicity of patient, n (%) | |||
| White | 38,495 (26 %) | 29,155 (33 %) | 9340 (15 %) |
| Black | 58,350 (39 %) | 25,105 (29 %) | 33,245 (52 %) |
| Hispanic | 33,055 (22 %) | 18,500 (21 %) | 14,555 (23 %) |
| Asian/Pacific Islander | 4365 (2.9 %) | 3625 (4.1 %) | 740 (1.2 %) |
| Native American | 1085 (0.7 %) | 550 (0.6 %) | 535 (0.8 %) |
| Other | 8055 (5.3 %) | 5130 (5.9 %) | 2925 (4.6 %) |
| N missing | 7440 (4.9 %) | 5310 (6.1 %) | 2130 (3.4 %) |
| Severe persistent asthma, n (%) | 17,685 (12 %) | 9195 (11 %) | 8490 (13 %) |
| Exposure to smoke/tobacco, n (%) | 15,215 (10 %) | 7280 (8.3 %) | 7935 (13 %) |
| 1 or more comorbidities, n (%) | 23,470 (16 %) | 13,140 (15 %) | 10,330 (16 %) |
| No insurance, n (%) | |||
| Public/Private | 142,540 (94 %) | 82,075 (94 %) | 60,465 (95 %) |
| No insurance (Self-pay/No charge/Other) | 8020 (5.3 %) | 5155 (5.9 %) | 2865 (4.5 %) |
| N missing | 285 (0.2 %) | 145 (0.2 %) | 140 (0.2 %) |
| Large hospital bedsize capacity, n (%) | |||
| Small/Medium | 64,985 (43 %) | 39,080 (45 %) | 25,905 (41 %) |
| Large | 85,860 (57 %) | 48,295 (55 %) | 37,565 (59 %) |
Next, we estimated the average cost and LOS per pediatric asthma hospitalization, as well as the annual cost for all pediatric asthma hospitalizations in the US between 2016 and 2019. Secondly, using generalized linear regression analysis, we estimated the total effect (i.e. excluding mediators) of SES separately on LOS (Poisson family and log link) and cost (Gamma family and log link) with and without adjustment for confounders (patient [age, sex, race/ethnicity, and location], hospital [region, ownership, urban/rural classification, and teaching status], and admission characteristics [year, season, weekend]). The total effect (TE) estimates the incidence rate ratios (IRR), that is the proportionate change in the outcome (cost or LOS) for differences in income. These analyses accounted for NIS complex survey weights using the ‘svy’ package in R [33].
2.3.1. Causal mediation analysis
Parallel mediation analysis quantified the role of each mediator in explaining differences in cost and LOS according to income (indirect effects) and pathways not involving the mediators (direct effects) [34,35]. The same models used to estimate the TE were used for mediation, this time sequentially including each mediator and an exposure-mediator interaction. We also regressed each potential mediator on each exposure, using generalized linear models with a binomial family distribution and a log link - given the dichotomous nature of the mediators - while adjusting for confounders. Each model pair (exposure-mediator model and exposure-outcome model including the mediator) was used to estimate: 1) total natural direct effects (TDE) – the proportionate change (IRR) in the outcome if everyone had low income vs high income, while holding the mediator at the level it would have been if everyone had low income; and 2) the pure natural indirect effect (PIE) – the proportionate change in the outcome for a change in the mediator if everyone had low vs high income, while setting the exposure to high income for everyone. The product of these equals the total effect (TDE*PIE = TE). Finally, we calculated the proportion of the IRR that is due to the PIE, i.e. how much of the exposure-outcome relationship is due to the exposure’s effect on the mediator and the subsequent effect of the mediator on the outcome.
To conduct this analysis, we use the parametric g-formula [35,36] with 500 bootstrapped iterations, while accounting for the complex survey structure of the data by randomly sampling from 100 bootstrapped replicate weights [37] with each bootstrapped iteration of our model. To decompose effects using the g-formula algorithm, we assumed no confounding in the exposure-mediator, mediator outcome, and exposure-outcome relationships after adjustment and no exposure-induced mediator-outcome confounding [38]. Where threats to the validity of these assumptions may occur, for example residual confounding due to unmeasured differences in health, we outline these in the discussion and limitations sections. We also assumed positivity and consistency, and no interference, model misspecification, selection bias or measurement error [39]. The study conforms with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies [40]. Institutional Review Board approval was waived because this study used de-identified data and as such did not qualify as human subject research. Raw data were formatted in Stata (2016) [41] using HCUP Stata load programs [21] and all other analyses were conducted using R, version 4.4.0 [42].
2.4. Sensitivity analysis
Though we selected ages 5–17 for the main analysis, for comparison with other literature, we also estimated total and average cost and LOS per admission across a wider set of pediatric age-groups (0–2, 3–4, 5–12, 13–17). Bivariate analysis was used to examine differences in cost and LOS according to the exposure and each mediator without adjustment for confounders. We also examined disparities when comparing the lowest vs. the highest income quartile (N = 85,305), instead of lowest income quartile (Y/N) in the main analysis. A complete-case analysis was also conducted to examine the impact of excluding missing data compared to the use of multiple imputation in our main analysis. Finally, indirect effects of public vs private insurance were estimated (via the PIE) among admissions for insured individuals only.
3. Results
The final sample of 30,169 admission records represented 150,845 pediatric asthma admissions nationally between 2016 and 2019. Table 1 outlines the sociodemographic characteristics of the records in total and according to the exposure (lowest income quartile [Y/N]). The income variable is derived by the data providers, thus when examining pediatric asthma only, we found that 42 % (N = 63,470) of records were in the lowest income quartile. Most records occurred: in the South region (38 %); urban, teaching hospitals (88 %); central counties of metro areas of≥1 million population (44 %); private, not-for-profit hospitals (77 %); in the Autumn season (32 %); and among Black patients (39 %), males (58 %), and those aged 5–12 years (83 %). In relation to potential mediators, admissions from lower income groups were more likely to report: severe asthma (13 % vs 11 %); comorbidity (16 % vs 15 %); exposure to smoke/tobacco (13 % vs 8 %); and attendance at a hospital with a large capacity (59 % vs 55 %). Patient admissions from lower income groups were less likely to have no insurance (5 % vs 6 %).
The total cost of admissions from 2016 to 2019 was $1,038,351,761; approximately $260 million annually. The average cost and LOS per admission during this period were $6884 (95 % CI: 6682, 7086) and 2 days (95 % CI: 2.09, 2.15). Both cost and LOS were higher among older compared to younger age-groups (Appendix D). In bivariate comparisons, unadjusted cost and LOS were higher among records for patients with severe asthma, comorbidity, and exposure to smoke/tobacco, and lower for patients with no insurance. LOS was higher for admissions recorded from lower income groups and in large hospitals, while costs were lower for these groups (Appendix C).
In adjusted regression analysis (Table 2), we found that those in the lower income group had longer LOS (adjusted IRR [aIRR] = 1.03 [95 % CI: 1.00, 1.05]) per admission, equating to approximately 0.06 days. Costs were lower among lower income groups (aIRR = 0.97 [95 % CI: 0.94, 1.00]), costing $207 less per admission compared to those in higher income groups. Disparities widened when comparing the lowest to the highest income quartile (Appendix E); suggesting a dose-response relationship: LOS was 0.12 days longer (aIRR = 1.05 [95 % CI: 1.01, 1.1]) and costs were $572 lower (aIRR = 0.93 [95 % CI: 0.88, 0.98]).
Table 2.
Total effects for each exposure-outcome combination.
| Exposure - Outcome | Unadjusted Model |
Adjusted Model |
|---|---|---|
| IRR (95 % CI) | IRR (95 % CI)a | |
| Low income - Cost | 0.93 [0.89, 0.97] | 0.97 [0.94, 1.00] |
| Low income - LOS | 1.03 [1.01, 1.06] | 1.03 [1.00, 1.05] |
IRR – Incidence Rate Ratio; CI – Confidence Interval; LOS – Length of Stay.
Adjustments were made for age category, female, hospital region, hospital location/teaching status, patient location, year, hospital control, season, weekend, race/ethnicity.
Asthma severity and smoke/tobacco exposure mediated (via the PIE) the largest proportion of disparities between income and LOS (20 % & 17 %; Table 3). The largest proportion of disparities between income and cost were also mediated by severe persistent asthma and smoke/tobacco exposure (−29 % and −21 %). In this case, the negative proportion mediated in the income-cost relationship suggests that these mediators suppress rather than explain disparities [43]. The only positive proportion mediated in the income-cost relationship was for hospital bed-size capacity (10 %). Here, “negative mediation” means that exposure-outcome disparities would be larger if the indirect effect from the mediator could be removed, while “positive mediation” means that disparities would be smaller if the indirect effect could be removed.
Table 3.
Estimates of the direct and indirect effects of income on hospital length of stay and cost among pediatric patients hospitalized with asthma through potential mediators.
| Outcome/ Mediator |
Total Direct Effect - Adjusted IRR (95 % CI) |
Pure Indirect Effect (PIE) - Adjusted IRR (95 % CI) |
Proportion of the excess ratio due to PIE (%) |
|---|---|---|---|
| Outcome 1: Length of Stay (days) | |||
| One or more comorbidities | 1.025 (0.999, 1.051) | 1.002 (0.999, 1.005) | 9 |
| Large hospital bed-size capacity | 1.024 (1.000, 1.050) | 1.002 (1.000, 1.004) | 8 |
| No insurance | 1.026 (1.001, 1.053) | 1.001 (1.000, 1.001) | 3 |
| Severe persistent asthma | 1.021 (0.996, 1.046) | 1.005 (1.002, 1.008) | 20 |
| Exposure to smoke/tobacco | 1.022 (0.996, 1.048) | 1.005 (1.002, 1.007) | 17 |
| Outcome 2: Cost (USD 2020) | |||
| One or more comorbidities | 0.967 (0.928, 1.006) | 1.004 (0.999, 1.008) | - 12 |
| Large hospital bed-size capacity | 0.975 (0.938, 1.014) | 0.997 (0.994, 1.000) | 10 |
| No insurance | 0.971 (0.933, 1.010) | 1.000 (0.999, 1.001) | -1 |
| Severe persistent asthma | 0.963 (0.926, 1.001) | 1.009 (1.004, 1.014) | - 29 |
| Exposure to smoke/tobacco | 0.965 (0.928, 1.002) | 1.006 (1.002, 1.010) | - 21 |
| Adjustments were made for: Age categories (years), Female (Y/N), Hospital region, Hospital location/teaching status, Patient Location: NCHS Urban-Rural Code, Year of admission, Control/ownership of hospital, Season (Northern Hemisphere), Weekend (Y/N), Race/ethnicity of patient | |||
IRR – Incidence Rate Ratio; CI – Confidence Interval; PIE – Pure (Natural) Indirect Effect; USD – United States Dollars; Y/N – Yes/No; NCHS – National Center for Health Statistics.
We did not find evidence of mediation for the absence of health insurance. However, we did observe mediation (i.e., PIE) between income and LOS when comparing those with public vs private insurance (N = 142,540). Those from lower income groups were more likely to have public insurance which was associated with longer LOS (Appendix F). Lastly, we found little difference in our mediation results when using a complete-case analysis (N = 143,170; Appendix G).
4. Discussion
This study estimated the average length of stay (LOS) and cost per admission for pediatric patients hospitalized with asthma; disparities in these outcomes according to household income (by zipcode); and potential individual, environmental, and community factors which may mediate these disparities. The average LOS per admission of 2 days was the same as previous research examining LOS among pediatric in-patients from 2010 [44]. The annual economic impact of hospitalizations ($260m [ages 5–17]; $432m [ages 0–17]) was lower than a 2010 estimate of $502m (ages 0–17) [4]. The average cost per admission ($6884 [ages 5–17]; $6290 [ages 2–17]) was almost double that of the average estimate between 2001 and 2010 ($3600 [ages 2–17]) [4]. While asthma hospitalizations have been decreasing over time [3,6], the increasing cost per admission may have partially offset reductions in their annual economic impact.
Although disparities in cost and LOS persisted after admission, they were relatively small (3 %) compared to the disparities observed in ED and hospital admission and readmission rates [9-13]. Redmond et al. (2022), for example, found that hospitalization rates were 66 % higher among lower SES pediatric patients with asthma compared to their higher SES counterparts [9]. Khalid et al. (2024), when examining asthma hospitalizations for adults (NIS 2016–2019), found that both LOS and cost were lower among those from lower income areas. The authors suggest that providers may discharge patients based on factors other than medical readiness, perhaps working to limit the cost of a hospital stay for those least able to pay or taking the symptoms of lower income patients less seriously [10]. This is supported by our finding that costs were lower among lower vs higher income groups, but, in contrast with our finding that LOS was longer.
To better understand these disparities mediation analysis was conducted. Severe persistent asthma and exposure to smoke/tobacco were identified as important mediators explaining LOS disparities. Targeting these mediators could help to reduce the longer LOS among lower income groups by 20 % or 17 % respectively. We observed negative mediation in relation to cost disparities, particularly for severe persistent asthma and exposure to smoke/tobacco. This means that these mediators suppress rather than explain disparities [43]. In other words, targeting these mediators could widen cost disparities (by 29 % and 21 % respectively) but for good reason: The effects of smoke/tobacco exposure and severe asthma in driving up costs are removed, revealing even lower costs among lower vs higher income patients. As suggested by Khalid et al. (2024), this may be due to providers consideration of social stressors or medical bias [10].
Another possible explanation relates to hospital quality; a key factor underlying SDOH among children and adolescents [16,17]. Patients from lower income areas are more likely to be admitted to lower quality hospitals [19], which can be associated with both lower costs per admission [45] and greater inefficiencies resulting in longer lengths of stay. We found that lower income patients tend to receive care in hospitals with larger capacity, which partially explained (10 %) their lower hospital costs. This likely reflects greater economies of scale in larger hospitals rather than differences in the quality of care. More nuanced measures of hospital quality—such as those evaluating patient experience, care effectiveness, and timeliness [19]—may shed further light on why lower income patients incur lower costs, though such measures were not available for our study.
Access to higher quality healthcare is related to insurance status [46]. We did not find evidence that the absence (vs presence) of insurance mediated our outcomes. However, when examining only those with insurance, we found that public vs private insurance mediated the relationship between income and LOS. Those from lower income groups were more likely to have public insurance which was associated with longer LOS. Insurance status is likely to differ by SES [47] but the reasons by which this may lead to longer LOS are less clear. There may be residual confounding in relation to background health or differential coverage of services, which can be highly heterogeneous even among those with public insurance.
This distinction in insurance coverage becomes especially meaningful in the context of interventions for conditions such as childhood asthma. Outside the hospital, the coverage of home visits for children with asthma - which may differ according to insurance type - is an important policy tool to improve health literacy. This can help to make carers of children and adolescents more aware of environmental triggers such as tobacco/smoke exposure [9,12]. Within the hospital, the development of appropriate action plans offers a clinical intervention strategy to reduce potentially preventable hospital admissions through improved medication adherence, especially among those with severe disease [48-50].
Thus, insurance coverage may still offer an important tool by which policymakers can intervene on other mediators. While smoke exposure and asthma severity highlight key mediators which could be targeted directly by clinicians, e.g. action plans to improve medication adherence and to educate carers on environmental triggers. Such interventions may help to reduce the disparate pediatric asthma admission rates according to socioeconomic status. They may also help to reduce the disparate lengths of stay following hospitalization that we observe in this study. Finally, by helping to reduce the deleterious effects of severe asthma and smoke exposure – more common among lower socioeconomic groups – such interventions may widen cost disparities. Further research is required to understand which factors, other than hospital capacity, explain the lower costs observed among lower income children and adolescents hospitalized with asthma.
4.1. Limitations
Limitations in our analysis are that, firstly, our data may be subject to measurement error. For example, the ICD-10 coding system includes measures of SDOH, however documentation of these codes may vary according to hospital type, size and location, and patient demographics and health [51]. More detailed and consistent screening of SDOH across hospitals and regions would help in identifying other individual, environmental, and community factors for intervention [16,52]; extending the potential for mediation analyses using discharge data while also reducing the potential for measurement error. As a corollary, we were limited in our analysis by the availability of a single SES variable (household income by zipcode) in the NIS. Additional information on SES, such as parental occupation or education, would allow a more comprehensive understanding of SES disparities.
Secondly, we conducted a parallel mediation analysis which examines each mediator in isolation. This allowed us to quantify (and rank) the magnitude of each mediator in explaining differences between SES and inpatient outcomes but not pathways among mediators. Thirdly, the differences in our outcomes according to income are small but precise. Given the substantial economic burden associated with pediatric asthma hospitalizations, small changes at the record level may have large impacts at the aggregate level. Fourthly, our mediation results in relation to hospital capacity were imprecise. As such, our confidence in its mediating potential is low. Additionally, although we adjust for confounding in relation to geography and urbanicity, residual confounding may still be present as lower income patients and large capacity hospitals are more often located in urban areas. Finally, NIS provides information on admissions not individual patients so we are unable to observe whether a patient had multiple visits in one year, which may help in understanding factors which mediate readmissions.
5. Conclusion
Pediatric patients from lower income areas have longer LOS and lower costs compared to those from higher income areas. Inpatient stays are longer, in part, because exposure to smoke/tobacco and asthma severity are more prevalent in lower income areas. Inpatient costs are lower despite exposure to smoke/tobacco and severe persistent asthma being more prevalent in lower income areas. Overall, our results suggest that interventions to reduce smoke/tobacco exposure and severe persistent asthma among lower income patients may help to reduce costs and LOS per admission. As cost per admission is lower and LOS is longer for lower compared to higher income patients, such interventions could widen cost disparities and narrow LOS disparities. While this may be intuitive in the case of LOS, further research is required to understand what is driving the lower costs among lower income groups.
Supplementary Material
Acknowledgment
We would like to acknowledge all of the HCUP data partners: Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, District of Columbia, Florida, Georgia, Hawaii, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin, Wyoming.
Funding
Luke Barry and Mina Habib were partially supported by the National Library of Medicine of the National Institutes of Health under Award Number T15LM013976. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The corresponding author had full access to all the data in the study and takes final responsibility for the paper.
Footnotes
CRediT authorship contribution statement
Luke E. Barry: Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Mina Habib: Writing – review & editing, Validation, Software, Resources, Formal analysis. Liam G. Heaney: Writing – review & editing, Supervision. Roch A. Nianogo: Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A.: Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.rmed.2025.108501.
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