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. Author manuscript; available in PMC: 2021 May 1.
Published in final edited form as: J Trauma Acute Care Surg. 2020 May;88(5):619–628. doi: 10.1097/TA.0000000000002611

Benchmarking the Value of Care: Variability in Hospital Costs for Common Operations and Its Association with Procedure Volume

Cheryl K Zogg 1,2, Andrew C Bernard 3, Sameer A Hirji 2, Joseph P Minei 4, Kristan L Staudenmayer 5, Kimberly A Davis 1
PMCID: PMC7802807  NIHMSID: NIHMS1553978  PMID: 32039972

Abstract

Background:

Efforts to improve healthcare value (quality/cost) have become a priority in the US. While many seek to increase quality by reducing variability in adverse outcomes, less is known about variability in costs. In conjunction with the AAST Healthcare Economics Committee, the objective of this study was to examine the extent of variability in total hospital costs for two common procedures: laparoscopic appendectomy (LA) and cholecystectomy (LC).

Methods:

Nationally-weighted data for adults ≥18y was obtained for patients undergoing each operation in the 2014 and 2016 National Inpatient Sample. Data were aggregated at the hospital-level to attain hospital-specific median index hospital costs in 2019 USD and corresponding annual procedure volumes. Cost variation was assessed using caterpillar-plots and risk-standardized observed/expected cost-ratios. Correlation analysis, variance decomposition, and regression analysis explored costs’ association with volume.

Results:

In 2016, 1563 hospitals representing 86,170 LA and 2276 hospitals representing 230,120 LC met inclusion criteria. In 2014, the numbers were similar (1602, 2259 hospitals). Compared to a mean of $10,202, LA median costs ranged from $2,850 to $33,381. LC median costs ranged from $4,406 to $40,585 with a mean of $12,567. Differences in cost strongly associated with procedure volume. Volume accounted for 9.9% (LA) and 12.4% (LC) of variation between hospitals, after controlling for the influence of other hospital (8.2%, 5.0%) and patient (6.3%, 3.7%) characteristics and in-hospital complications (0.8%, 0.4%). Counterfactual modeling suggests that were all hospitals to have performed at or below their expected median cost, one would see a national cost savings >$301.9 million/year (95%CI: $280.6 to $325.5 million).

Conclusions:

Marked variability of median hospital costs for common operations exists. Differences remained consistent across changing coding structures and database years and were strongly associated with volume. Taken together, the findings suggest room for improvement in EGS and a need to address large discrepancies in an often-overlooked aspect of value.

Level of evidence:

Epidemiological, III

Keywords: Cost, Volume, Benchmarking, Appendectomy, Cholecystectomy, Acute Care Surgery, Emergency General Surgery

Introduction

Economic burden of emergency general surgery and its association with volume

National health expenditures per capita in the United States (US) have grown steadily since the 1960s, amounting to $3.7 trillion in 2017 and projected to reach $6.0 trillion by 2027.1 Acute care surgery (ACS)—a field encompassing the disciplines of trauma, emergency general surgery (EGS), and surgical critical care—accounts for a large and growing proportion of US spending on direct medical care.2,3 As the US population ages, costs for EGS alone are expected to increase from $33.3 billion in 2010 (total hospital costs in 2019 US dollars [USD]) to $48.3 billion by 2060,4 making EGS the single most expensive cause of emergency hospitalization.5 Perceptions of surgical providers and evidence-based queries of administrative claims suggest that EGS as a field is “characterized by an exceptionally high level of variability”6 and that it carries a burden >3 million hospitalizations per year, comprising 7.2% of all US hospital admissions.7-9

In the face of mounting and presumably variable EGS costs, research has begun to focus on standardization of care in order to improve outcomes, thus, decreasing costs of care. External benchmarking offers a viable solution to help understand what, if anything, can and should be done about expensive EGS care and how we can determine the extent to which variability in EGS costs and “excess” costs occur. Early work on benchmarking in EGS suggests an inverse association between operative volume and adverse patient outcomes.10-16 Whether similar volume-based patterns exist for total hospital costs is less clear, largely owing to a lack of knowledge about the extent of variability between hospitals in terms of cost.

Cost as a part of value in healthcare

First described in 2009,17 the healthcare value equation, defined as value = quality/cost, was intended to unite patients, providers, and payers.18 It suggests that improvements in value and corresponding efforts to promote “value-based” care should be defined by both increases in quality and reductions in cost. Such a conceptualization has underlain many changes taking place as a part of healthcare reform in the US and served as a guiding framework for the development of programs as diverse as the Centers for Medicare & Medicaid Services’ (CMS) “value-based” pay-for-performance, American College of Surgeons’ National Surgical Quality Improvement Program, and ongoing calls for development of an EGS registry19,20—all of which focus on the improvement of quality with little direct attention paid to reductions in cost.

The objective of this study was to take a different approach, examining the extent of variability in total index hospital costs for two common procedures known to substantially contribute to the overall burden and cost of EGS care21: laparoscopic appendectomy (LA) and cholecystectomy (LC). In conjunction with the American Association for the Surgery of Trauma (AAST) Healthcare Economics Committee whose mission is to focus AAST membership and leadership on economic factors that relate to the practice of ACS,22 we hypothesized that significant variability in total hospital costs for specific EGS operations exists and that hospitals with higher volumes of each procedure would perform more efficiently and, thereby, at lower total hospital cost.

Methods

Data source and study population

Nationally-weighted data for urgently/emergently admitted adults aged ≥18 years were obtained for patients with primary procedure codes corresponding to each operation in the 2014 (last full year of ICD-9-CM codes: 47.01, 51.23) and 2016 (first full year of ICD-10-CM codes: 0DTJ4ZZ, 0FT44ZZ) Agency for Healthcare Research and Quality (AHRQ) Hospital Cost and Utilization Project (HCUP) National Inpatient Sample (NIS). The NIS is the largest publicly available all-payer inpatient database in the US that when weighted yields national estimates of hospital stays. It includes information on patient encounters for patients aged ≥18 years with all forms of insurance coverage and contains data on up to 15 ICD-9/10-CM procedure and 25 ICD-9/10-CM diagnosis codes in addition to information on patient demographic and hospital characteristics. Information on total hospital costs is reported as total hospital charges in the corresponding year USD amount. To convert hospital charges (the amount billed by the hospital) into total hospital costs (the expense to the hospital itself) in 2019 USD, reported charges were multiplied by HCUP-provided annual hospital-specific hospitalwide cost-to-charge (CCR) ratios and adjusted for inflation. This approach, recommended by HCUP for the evaluation of hospital costs in NIS,23 yields approximations of cost as “CCR-derived total hospital costs.”

Annual CCR files for NIS are constructed by AHRQ using information from the Healthcare Cost Report Information System files submitted by hospitals to the Centers for Medicare & Medicaid Services.23 The files provide an estimate of all-payer inpatient CCR ratios for all reporting hospitals with available American Hospital Association links. In 2014, they included 4,411 hospitals nationwide. In 2016, they included 4,575 hospitals nationwide, of which approximately one-third (LA) and one-half (LC) were eligible for inclusion in this study due to adequate procedure volumes and the type of procedure performed.23 Recognizing potential discrepancies between the use of hospitalwide CCR and those derived from diagnosis related group (DRG)-specific billing claims, in 2009 (updated 2012) HCUP released a methods series report containing DRG-based hospitalwide CCR adjustment factors for each DRG.24 In this study, DRG adjustment factors were applied to calculated costs as a sensitivity analysis in order to determine the extent to which their use altered calculations of the extent of variability in total hospital costs.

Explanatory and outcome variables

Data for each procedure in each calendar year were aggregated separately at the hospital-level in order to attain hospital-specific median index hospital costs and corresponding annual procedure volumes. Hospitals were excluded if they performed <20 weighted operations in a given year in order to ensure stability of the estimate. Resultant median index total hospital costs served as the study’s primary outcome. As a secondary outcome, observed/expected median cost ratios were also calculated for each hospital based on “expected costs” derived from risk-adjusted mixed-effects quantile regression models (hierarchical models with random intercepts for each hospital) that accounted for known variations in patient- and hospital-level risk factors (see below).

All models, including those used in the calculation of expected costs, were risk-adjusted for: hospital bedsize (categorized by NIS based on geographic region and teaching status as small, medium, and large), hospital teaching status/rurality (categorized as urban teaching, urban non-teaching, and rural), hospital geographic region, continuous patient age, patient gender, ordinal patient APR-DRG risk of mortality (modeled as a series of categorical predictors), ordinal patient APR-DRG severity (modeled as a series of categorical predictors), and ordinal patient Elixhauser Comorbidity Index (calculated as the sum of individual comorbidity indicators present based on secondary ICD-9/10-CM diagnosis codes; modeled as a series of categorical predictors). Given controversy surrounding the use of socioeconomic predictors in benchmarking models, variables describing variations in patient-level insurance status (insured versus uninsured), race/ethnicity (non-Hispanic White versus all other races/ethnicities), and income (HCUP-provided ordinal income quartiles based on patient residential ZIP code) were omitted from the main analysis but were included in a second model the results of which are reported in SFigure 1.

Statistical analysis: Variability in hospital costs

Variability in median total hospital costs and interquartile ranges (IQR) was visualized for each procedure in each year using caterpillar plots and histograms of ln-transformed median total hospital costs with vertical lines representing the mean of the medians and the percentage of hospitals one to three standard deviations (SD) above and below the mean. Similar plots were constructed for observed/expected median cost ratios in 2016. Observed/Expected ratios >1.0 indicate higher median costs than expected, while ratios <1.0 indicate lower median costs than expected. Counterfactual models were used to determine anticipated cost savings were all hospitals to perform at their expected median level. Hospitals already outperforming expectation (observed/expected ratio <1.0) did not have their costs changed.

Statistical analysis: Accounting for variability in costs

Variation partitioning with calculation of pseudo R2 was used to determine the extent of variability between ln-transformed hospital costs explained by differences in ln-transformed procedure volume, included hospital characteristics (see above), included patient characteristics (see above), and the occurrence of in-hospital complications. Complications were assessed using secondary diagnosis codes.25 They included: pneumonia, pulmonary embolism, renal failure, cardiovascular accident, myocardial infarction, cardiac arrest, acute respiratory distress syndrome, sepsis, and severe sepsis.

Statistical analysis: Relationship between volume and cost

To further quantify the relationship between volume and cost, correlations were calculated between ln-transformed procedure volume and ln-transformed median total hospital costs using Spearman’s rank correlation coefficients to account for the imperfectly normal nature of the ln-transformed data. Differences in median total hospital costs based on variations in procedure volume were then compared using: (1) ln-transformed linear regression of continuous procedure volume, (2) linear regression of categorized procedure volume based on quintile of annual procedures performed, and (3) quantile regression comparing differences in costs based on volume at the 50/60/70/80/90th percentile of median total hospital costs.

All models were run using mixed-effects regression that accounted for clustering of patients within hospitals and national design weights in NIS. They were assessed using robust standard errors. Two-sided p-values<0.05 were considered significant. Missing cost data were excluded (<1.0% for each procedure in each year). Missing covariate data had missing values imputed using multiple imputation with chained equations in order to account for the non-multivariate normal nature of the data (<3.0% for each procedure in each year). Data were analyzed using R and Stata Statistical Software: Version 16. The Yale Human Investigation Committee deemed the study exempt from full review.

Results

In 2014, a total of 1602 hospitals representing a nationally-weighted total of 89,765 hospitalizations for LA and 2259 hospitals representing a nationally-weighted total of 222,875 hospitalizations for LC meet inclusion criteria based on ICD-9-CM codes. In 2016, based on ICD-10-CM codes, the numbers were similar with 1563 hospitals representing 86,170 LA admissions and 2276 hospitals representing 230,120 LC admissions.

Variation in total hospital costs: Difference in median costs

For both procedures in both years (Figure 1A: 2014; Figure 1B: 2016), there was substantial variation in median total index hospital costs. Relative to a 2014 global mean of the medians for LA of $9,505 (median: $9,073), median total hospital costs ranged from $1,928 to $24,719 (absolute difference: $22,791). In 2016 under the ICD-10-CM coding scheme, results for LA were similar with a global mean of the medians of $10,202 (median: $9,643) and overall results which ranged from $2,850 to $33,381 (absolute difference: $30,531). For LC in 2014, total median index hospital costs averaged $13,321 (median: $12,658) and ranged from $4,671 to $43,020 (absolute difference: $38,349). In 2016, results ranged from $4,406 to $40,585 (absolute difference: $36,179) with a global mean of $12,567 (median: $11,941). Similar results were seen on sensitivity analysis using DRG adjustment factors (Figure 1A-B) where absolute differences between extremes differed by $22,080 for LA in 2014 ($29,591 in 2016) and $37,225 for LC in 2014 ($35,119 in 2016). Differences in total hospital charges for 2016 are reported in SFigure 1A; expected (risk-adjusted) total hospital costs with and without the inclusion of patient-level socioeconomic predictors are presented in SFigure 1B and 1C (all values have been adjusted for inflation and are reported in 2019 USD).

Figure 1.

Figure 1.

Figure 1.

Figure 1.

Figure 1.

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Plot of median total hospital costs in 2019 USD (IQR) for laparoscopic appendectomy and cholecystectomy, (A) NIS 2014 and (B) NIS 2016

Variation in total hospital costs: Difference in observed/expected median cost ratios

Differences in 2016 observed/expected median cost ratios are presented in Figure 2. Consistent with the results of the non-standardized median total index hospital costs (Figure 1A-B), variation in observed/expected median cost ratios was pronounced for both LA and LC. Relative to respective means (medians) of 1.01 (0.97) and 1.01 (0.98) indicating no difference from expectation, within the upper quartile of LA median cost ratios, differences from expectations rose dramatically, climbing to 1.15 at the 75th percentile and 3.00 at the upper extreme. Likewise, for LC, within the upper quartile of observed/expected median cost ratios, results sharply climbed to 1.15 at the 75th percentile and 2.75 at the upper extreme.

Figure 2.

Figure 2.

Figure 2.

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Plot of observed/expected (O/E) median cost ratios for laparoscopic appendectomy and cholecystectomy, NIS 2016

Implications of hospitals falling above the mean

Pragmatic interpretations of these trends are depicted in Figure 3, where histograms for each procedure in each year indicate the percentage of hospitals with median total hospital costs falling one, two, and three standard deviations (SD) above and below the mean. In 2014 (Figure 3A), 15.0% of hospitals had median total hospital costs for LA >1 SD above the mean (median values >$12,000 per patient); 2.7% had median costs >2 SD above the mean (>$15,800 per patient). In 2016 (Figure 3B), proportions were similar (14.9% and 2.8%) with median costs for LA totaling >$13,100 per patient 1 SD above the mean and >$17,500 per patient 2 SD above the mean. Combined, these hospitals resulted in >$61.6 million in “excess” median LA costs in 2016, of which >$19.2 million was due to just 44 hospitals (2.8% of n=1,563). For LC in 2014 (Figure 3C), 14.8% of hospitals had median costs >1 SD above the mean (>$14,900 per patient); 3.1% had costs >2 SD above the mean (>$19,500 per patient). In 2016 (Figure 3D), median costs for LC totaled >$16,000 per patient 1 SD above the mean (14.8% of hospitals) and >$21,200 2 SD above the mean (3.1% of hospitals). Combined, these hospitals resulted in >$181.1 million in “excess” median LC costs in 2016, of which >$103.2 million was due to 71 hospitals (3.1% of n=2,276).

Figure 3.

Figure 3.

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Histogram of ln-transformed median total hospital costs in 2019 USD, NIS 2014 (A-LA; C-LC) and NIS 2016 (B-LA; D-LC). Lines represent the mean and 1, 2, and 3 standard deviations (SD) above/below the mean.

Counterfactual modeling suggests that were all hospitals in 2016 to have performed at or below their expected median cost, one would expect an average cost savings for LA of $940/patient (95%CI: $880 to $1,000), resulting in a national reduction in total hospitalized costs of $81.0 million/year (95%CI: $75.8 million to $86.2 million). For LC, counterfactual models predicted an average savings of $960/patient (95%CI: $890 to $1,040), resulting in a national estimated reduction in total index hospital costs of $220.9 million/year (95%CI: $204.8 to $239.3 million).

Association of median total hospital costs with procedure volume

After accounting for the influence of the other considered factors (included hospital characteristics, included patient characteristics, and the occurrence of in-hospital complications) differences in ln-transformed procedure volume accounted for 9.9% of the between hospital variance in ln-transformed median total hospital costs for LA. For LC, ln-transformed volume accounted for 12.4% of the variance between hospitals. The other considered factors accounted for 8.2% (LA) and 5.0% (LC) (hospital characteristics), 6.3% and 3.7% (patient characteristics), and 0.8% and 0.4% (in-hospital complications), respectively (LA total: 25.2%, LC total: 21.5%).

For both LA and LC, increases in median total hospital costs were negatively associated with procedure volume. SFigure 2A (2014) and SFigure 2B (2016) depict scatterplots of the association between ln-transformed annual procedure volume and ln-transformed total median hospital cost. Corresponding Spearman’s rank correlation coefficients suggest moderate associations for LA of −0.404 (95%CI: −0.435 to −0.372) in 2014 and −0.366 (95%CI: −0.397 to −0.333) in 2016. For LC, the association was similar with Spearman’s rank correlation coefficients of −0.398 (95%CI: −0.427 to −0.368) in 2014 and −0.335 (95%CI: −0.366 to −0.303) in 2016.

Unadjusted and risk-adjusted mixed-effect regression results for volume are presented in STable 1. Under each of the three models (categorical quintiles, ln-transformed cost, and quantile regression) in each calendar year, a similar inverse relationship between cost and procedure volume emerged. For example, for LA in 2016, hospitals managing the highest volume of hospitalized LA (Quintile 5) had median risk-adjusted total index hospital costs that were on average $2,984 lower than hospitals managing the lowest volume (Quintile 1), 95%CI: $3,670 to $2,277 (Figure 4A). This corresponded to a risk-adjusted reduction in exponentiated mean differences in median hospital costs of $146 per 10 additional operations per year (95%CI: $207 to $85) and in a similar risk-adjusted median difference of $108 per 10 additional operations per year (95%CI: $165 to $51) that stepwise increased to $174 (95%CI: $313 to $37) at the 90th percentile. Similar trends were observed for LC costs in 2016 (Figure 4B), which resulted in a risk-adjusted average difference of $4,852 (95%CI: $5,606 to $4,097) in median total hospital costs between the highest (Quintile 5) and the lowest (Quintile 1) quintile of procedure volume.

Figure 4.

Figure 4.

Figure 4.

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Differences in median total hospital costs based on quintile of procedure volume. Plots show unadjusted and risk-adjusted differences in median total costs among volume quintiles when compared to patients treated at hospitals with the lowest annual procedure volume. Error bars show 95%CI; all models were significant based on two-sided p-values<0.001 (see STable 1).

Discussion

The results of this study demonstrate the extent of variability in median total index hospital costs for two common operations known to substantially contribute to the overall burden and cost of EGS care.21 The data also demonstrate a clear inverse relationship between operative volume and cost. For both LA and LC in 2014 and 2016 under both ICD-9-CM and ICD-10-CM coding schemes, marked variability in median hospital costs exists, resulting in significant excess costs per patient. Variation in observed/expected median cost ratios tell a similar story. Counterfactual modeling suggests that were all hospitals to have performed at or below their expected median cost, one would expect to see a national cost savings >$300 million/year for these two operations. Differences in volume accounted for 9.9% (LA) and 12.4% (LC) of variance in costs between hospitals, after controlling for the influence of other included hospital and patient characteristics and in-hospital complications. It was by far the strongest driver, resulting in average risk-adjusted savings of $150 and $200/patient for each additional 10 operations (a difference between the highest and lowest quintile of procedure volume of $3,000 and $5,000, respectively, per patient).

The finding of a significant association between volume and median total index hospital costs among EGS patients is in keeping with expectations. Prior research has shown a similar inverse association between volume and adverse outcomes (particularly in-hospital mortality)10-16—a finding which has led some researchers to call for establishing designated centers of EGS care.11,26 While the decision to centralize EGS care remains controversial,27 the benefit potentially limited to the sickest subset of critically ill patients,16 and the exact association between volume and mortality among older adults mired by questions of whether it is hospital versus surgeon volume14 or volume versus the proportion of older adult patients15 that ultimately matters, the resulting assertion is largely the same. Differences in the number of patients and operations performed have an important impact on the quality of EGS care. The results of our study show that they also have a driving influence on the cost of EGS care for two common ACS operations.

As noted in the literature on EGS volume and mortality,10-16 volume does not act alone. The results of our study agree, pointing toward important differences in hospital characteristics (including teaching status, bedsize, and extent of rurality), patient characteristics (including age, pre-existing comorbidities, and severity), the occurrence of in-hospital complications, and random chance/residual confounding that all contribute toward differences in costs. Nevertheless, despite this influence and after accounting for the extent of variance explained by other observed risk factors, procedure volume remained the largest driver of cost, alone accounting for 39.3% (LA) and 57.7% (LC) of the total between hospital variance explained.

Such results underscore the reality that cost is not a simple proxy for quality nor volume a unilateral mechanism that affects quality and, thereby, influences cost. For although it is certainly true that lower volume can lead to higher rates of complications and resultantly higher total hospital costs, additional mechanisms are clearly at play. Future studies are needed to elucidate the specific mechanism(s) through which volume influences cost and whether there are differences between, for example, for profit versus not for profit hospital systems. Likely possibilities include lower operating room (OR) costs in terms of increased experience-directed efficiency that results in shorter OR times and/or less equipment being used/misused, shorter overall lengths of stay, and/or the presence of effective protocol-directed ACS care that helps eliminate waste.

Extensive variation in cost, whether related to procedure volume or not, speaks to room for improvement in EGS care. Outside of EGS, use of external benchmarking has emerged as the pre-eminent methodology by which measure variation in value. Its widespread use in quality improvement and health policy programs to rank hospitals by their relative performance on agreed upon measures (e.g. observed/expected in-hospital mortality [American College of Surgeons’ Trauma Quality Improvement Project], 30-day risk-standardized all-cause mortality [CMS]) attests to the growing and recognized need to curb healthcare expenditure in the US. ACS is not exempt from these concerns.2,3 Providing care to high-risk emergency patients can incur significant financial and human costs. The development of multiple successful ACS care models and quality improvement initiatives over the past 10 years proves that ACS can be clinically and financially viable, providing higher quality care to patients at lower hospital cost. 2,3 It can also be widely variable with room for improvement that has the potential to better the lives of its surgeons, patients, and staff. Dedication to ensuring access to high value care is tantamount to success—both by promoting increases in quality through the reduction of adverse outcomes and by curbing unwarrantedly high variation in and limiting patient/hospital exposure to excess hospital costs. Ongoing calls for development of an EGS benchmarking registry19,20 and parallel efforts to define appropriate process/outcome measures6 provide an important first step on which future value-conscience research and quality improvement development (reflecting both outcomes and cost) are encouraged to build.

The study is not without limitations. Most reflect its reliance on administrative claims where the completeness of information and the potential for absent or misreporting of events can be concerns. Data are limited to available information on clinical, patient, and hospital characteristics. In conducting the study, hospital-specific CCR ratios were used to convert hospital charges to cost, and hospitals with a weighted annual volume <20 cases per year were excluded in order to ensure estimate stability. The smallest hospitals are among those expected to have the highest and most variable hospital cost. While their exclusion was deemed necessary for methodological rigor, the results must be interpreted in light of their exclusion. What use of NIS data did offer was a large national assessment of hospital claims, inclusive of CCR-derived cost information for all US patients aged ≥18 years regardless of insurance payer. No other database enables such assessment.

Marked variability of median hospital costs for two common ACS operations exists. Differences remained consistent across consideration of changing coding structures and database years and were strongly associated with variations in volume, even after controlling for differences in other hospital and patient characteristics and in-hospital complications. Taken together, the findings suggest room for improvement in EGS care and a need to address large discrepancies in an often-overlooked aspect of the value of care. As efforts to benchmark EGS continue to develop, surgeons caring for ACS patients need “to consider all of these factors.”3 The future of ACS patients and the economic realities of the US health system require an understanding of and improvements in value as conceptualized by both quality and cost.3

Supplementary Material

Supplemental Data File (.doc, .tif, pdf, etc.)

Acknowledgments

Conflicts of interest and sources of funding: The authors declare that we have no conflicts of interest relevant to the analysis to report. Cheryl K Zogg, MSPH, MHS, is supported by NIH Medical Scientist Training Program Training Grant T32GM007205. She is the PI of an F30 award through the National Institute on Aging F30AG066371 entitled “The ED.TRAUMA Study: Evaluating the Discordance of Trauma Readmission And Unanticipated Mortality in the Assessment of hospital quality.”

Footnotes

This work was previously presented at the 78th Annual Meeting of the American Association for the Surgery of Trauma, September 18-21, 2019, in Dallas, TX.

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