Characteristics Driving Higher Diabetes-Related Hospitalization Charges in Pennsylvania

Objectives

To evaluate the characteristics driving higher diabetes-related hospitalization charges.

Methods

Hospital discharge data on 216,858 hospitalizations from 2001 to 2011 with primary discharge diagnosis of diabetes were linked to the Pennsylvania death registry. Multiple linear regression analysis was used to evaluate the association between inpatient hospitalization charges and complications, sociodemographic status, comorbidities, readmission, length of stay, admission type, region, procedures, payer type, and hospitalization misadventures.

Results

Diabetes-related adjusted hospitalization charges were higher for those with long-term complications [renal manifestations (75%), peripheral circulatory disorders (38%), neurological manifestations (30%), ophthalmic manifestations (16%)], acute complications [ketoacidosis (48%), hyperosmolarity (41%), coma (40%)], amputations (91%) or other medical procedure(s) (70%), emergency/urgent admissions (7%), comorbidity (3% per modified Charlson Comorbidity Index score item), medical misadventure (20%), different regions [Philadelphia (135%), and Pittsburgh (8%)], and for minorities [non-Hispanic black (12%), Hispanic (21%), and non-Hispanic other (14%)]. Readmission adjusted charges were not different from the initial admission charges.

Conclusions

• Diabetes complications, medical procedures, region, race/ethnicity, and medical misadventures are major drivers of diabetes-related hospitalization charges. Age, admission type, payer type, and comorbidities are also independent drivers of these charges.

• Evidence-based clinical pathways, policy changes that reduce medical misadventures and assess discharge readiness, and improving quality of follow-up care after discharge should reduce unnecessary charges and readmissions.

• Future research is needed to investigate why hospital charges differ by region, payer type, and race/ethnicity, after simultaneously taking into account 16 contributing factors.

To reduce diabetes-related hospitalizations and curb hospitalization charges, public health and healthcare policy makers should be cognizant of high-impact drivers: diabetes-related complications, unnecessary procedures, race/ethnicity, and region. Inpatient are should focus on preventing unnecessary readmissions and misadventures. These findings are timely as the Affordable Care Act reduces Medicare payments to hospitals with high readmission rates, and as states consider Medicaid expansion.Multiple linear regression analyses of factors driving diabetes-related hospitalization charges are sparse, but essential when making managed care decisions regarding healthcare reform.Diabetes is an important public health problem, affecting an estimated 25.8 million Americans (8.3% of the US population) as of 2010.¹ In Pennsylvania, our area of focus, the prevalence of adults diagnosed with diabetes has considerably increased, almost doubling from an estimated 500,000 (5.5%) in 1994 to 941,000 (9.7%) in 2010.^2,3 In addition to the morbidity and mortality burden, the economic burden of diabetes in the United States was estimated as $174 billion in 2007, including direct medical costs of $116 billion and indirect disability, work loss, and premature mortality costs of $58 billion.¹ The average medical expenditures for those with diagnosed diabetes are 2.3 times higher than the expenditures for those without diabetes.¹

Hospitalization costs are a major contribution to the healthcare costs of those with diabetes,⁴ exacerbated by the economic burden of poor management. Data regarding the characteristics that drive higher hospitalization charges are sparse, but they are crucial; the Affordable Care Act (ACA) began reducing Medicare payments to hospitals with high readmissions rates during Fiscal Year 2013.⁵ Comprehensive evaluations that quantify the multiple patient and hospital factors that independently drive the variation in diabetes hospital charges are scarce. Studies designed to address this gap in knowledge can facilitate the rendering of more informed data-driven decisions. The data reported herein add to the scientific evidence by providing such adjusted diabetes hospitalization charges. We simultaneously consider complications, sociodemographic status, comorbidities, readmission, length of stay (LOS), admission type, region, procedures, payer type, and medical misadventures.^4,6-8

By investigating this question—“What patient and discharge characteristics independently drive hospitalization charges related to poor diabetes management in Pennsylvania?”—the study’s objective was to provide much-needed relevant data for states as the ACA reduces Medicare payments to hospitals with high readmissions, and as states consider expansion of Medicaid. The analytic approach simultaneously takes into account all patient and hospitalization factors associated with hospitalization charges: acute and chronic complications, comorbidity, sociodemographic status, readmission, LOS, admission type, region, procedures, payer type, and medical misadventure. Such an approach allows healthcare policy makers and decision makers to develop evidence-based approaches that target the high-impact drivers of hospitalization charges.

MATERIALS AND METHODSStudy Population

A diabetes-related hospitalization was defined as a patient hospitalized with a primary diagnosis of diabetes (all 250 codes in the International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM]) at discharge in the Pennsylvania Health Care Cost Containment Council (PHC4) data set during the period of 2001 to 2011, with a total hospitalization room charge greater than $0 and an LOS less than 90 days. PHC4 data were linked to Pennsylvania vital statistics death certificate data by PHC4, using patient identity match algorithms to get death information.

Description of Hospitalization Charge Outcome (dependent variable)

Hospitalization charge was defined as the total charge related to the hospital stay with a primary diagnosis of diabetes excluding professional fees. This includes room and board charges, ancillary charges, drug charges, equipment charges, specialty charges, and miscellaneous charges.

Description of Patient and Discharge Characteristics (independent variables)

Complications: Derived from the fourth digit of the primary and all 8 secondary diagnoses ICD-9-CM codes, and were grouped into the following categories: no complication, coma, hyperosmolarity, ketoacidosis, renal manifestations, neurological manifestations, ophthalmic manifestations, peripheral circulatory disorders, other complications, and 2 or more complications. These are based on the Agency for Healthcare Research Quality (AHRQ) Prevention Quality Indicators (PQI) technical specifications, version 4.4.⁹

Year: Inpatient hospitalization year at date of admission, from 2001 to 2011.

Age: Patient age in years at time of hospitalization. Categorized as <20, 20 to 34, 35 to 44, 45 to 64, ≥65 years.

Race/Ethnicity: Patients were categorized as non-Hispanic white (NHW), non-Hispanic black (NHB), non-Hispanic other race (NHO), and Hispanic. If race/ethnicity was missing for a record, it was inputted from a previous or subsequent record in the data set.

Gender: If gender was missing for a record, it was inputted from another record in the dataset.

Diabetes type: Type 1 diabetes (T1DM) or type 2 diabetes (T2DM) was defined based on the last digit of the primary ICD-9-CM diagnosis code; an even number is T2DM, an odd number is T1DM.

Readmission: Readmission days were calculated from the previous hospitalization discharge date with a primary discharge diagnosis of diabetes. Readmissions were categorized as 1 to 7 days, 8 to 14 days, 15 to 30 days, after 30 days, and first admission. Year 2000 data were consulted to ensure that all hospitalizations could be assessed as a new hospitalization or readmission for a minimum of 1 year.

Mortality status: Patients with discharge status of “expired” or having the same discharge date and death date on their death certificate were defined as “death at discharge.” Based on the difference between death date and the discharge date, others were categorized as either death between 1 and 7 days after discharge, and alive after 7 days of discharge.

Primary payer type: Categorized as commercial, Medicaid, Medicare, uninsured, and other.

Admission type: Defined as the hospital admission’s level of urgency; 1) Emergency department (ED)/urgent admissions were defined as such when the patient required immediate medical intervention as a result of severe, life-threatening, or potentially disabling conditions; urgent admissions were noted when the patient required immediate attention for the care and treatment of a physical or mental disorder; 2) Elective admissions were defined as cases in which the patient’s condition permitted adequate time to schedule the services.

Admission day of week: Categorized as weekday versus weekend.

Region: Facility region was categorized as Philadelphia (includes the surrounding 4 counties of Chester, Montgomery, Bucks and Delaware); Pittsburgh (includes Pittsburgh city, Allegheny County, and the 7 surrounding counties of Armstrong, Beaver, Butler, Fayette, Greene, Washington, Westmoreland), or other (the rest of the state of Pennsylvania).

Procedures: Defined based on the primary and 5 secondary procedure codes, then further categorized as no procedure, amputation, and procedures other than amputation.

Medical misadventure: Defined as the patient having external causes of injury and poisoning based on the presence of one or more codes between E870 and E879. These include misadventures to patients during surgical and medical care (E870-E876), and surgical and medical procedures as the cause of abnormal reaction of patient or later complication, without mention of misadventure at the time of procedure (E878-E879).

LOS: The number of hospitalization days with a maximum of 90 days; LOS of 0 was recoded to 0.5 to avoid counting partial-day admissions as 0.

Modified Charlson Comorbidity Index (CCI): Predicts 10-year mortality based on weighted scores ranging from 0 to 35 for 19 conditions.^10,11 All 8 secondary diagnoses were considered in the calculation of the CCI. Diabetes was excluded from the weighted score so the impact of diabetes would not be double-counted; all the hospitalizations we assessed were related to diabetes.

Statistical Analyses

Descriptive analysis: Total number of hospitalizations and average hospitalization charges were calculated by year, complication, age group, race/ethnicity, gender, diabetes type, readmission status, mortality status, primary payer type, admission type, admission day of the week, region, procedure(s), and medical misadventure. Average LOS and modified CCI were also calculated.

Multiple linear regression modeling: Hospitalization charge was the dependent variable and the following characteristics were categorical independent variables: complication, age, race/ethnicity, gender, diabetes type, readmission status, mortality status, primary payer type, admission type, admission day, region, procedures, and medical misadventure. Year, LOS, and modified CCI were continuous independent variables. Initial model fitting diagnosis revealed a funnel-shaped residual plot, which indicated nonconstant variance. Natural log transformation of the hospitalization charge was used to correct heteroskedasticity.¹² Multicollinearity was examined in the final transformed hospitalization charge model for year, LOS, modified CCI, and medical misadventure. All variance inflation factors and condition numbers were less than 2 and 5, respectively, indicating multicollinearity was not an issue. SAS version 9.3 (SAS Institute, Cary, North Carolina) was used for all analyses. A P value of <.05 was considered statistically significant.

RESULTS

Table 1

Table 2

To clearly illustrate the merit of our analytic approach, we will first describe the average unadjusted hospitalization charges for 216,858 diabetes-related hospitalizations from 2001 to 2011, depicted in . This was followed by the more informative adjusted hospitalization charges that simultaneously take into account all 16 factors in the multiple linear regression model depicted in .

Year: The overall average unadjusted hospital charges consistently increased every year without adjustment for inflation from $19,167 in 2001 to $41,246 in 2011. On average there was an 8.6% increase in adjusted hospitalization charges every year.

Patient complications: Average unadjusted hospitalization charges were 158% higher for those with any type of complication ($34,577) than for those without complications ($13,417), ranging from 73% higher for those with ophthalmic manifestations to 431% higher for those with renal manifestations, than for those who did not have complications. However, after adjusting for the other 15 factors the increase in charges for those with complications reduced substantially, ranging from 16% higher for those with ophthalmic complications or other complications, to 75% higher for those with renal manifestations.

Age: The average unadjusted hospitalization charge for patients aged 45 to 64 years ($37,572) was 166% higher than for those younger than 20 years ($14,147) and 18% higher than for those 65 years and older ($31,854). Adjusted hospitalization charges for patients aged 45 to 64 years was 17% higher than for patients younger than 20 years, a significant reduction compared with the unadjusted difference of 166%.

Race/ethnicity: Average unadjusted hospitalization charges to NHBs ($43,117) were nearly 60% higher than the charges to NHWs ($27,156), but this difference reduced to 12% after adjusting for the other factors. The average unadjusted hospitalization charge to Hispanics ($40,943) was 51% higher than to NHWs, but reduced to 21% higher after adjusting for the other factors.

Gender: Men’s average unadjusted hospitalization charges were $4331, 15% more than for women, but there was no gender difference in adjusted hospitalization charges (P = .0520).

Diabetes type: Average unadjusted hospitalization charge for those with T1DM was 19% less than for those with T2DM, but was 5% higher after adjusting for the other factors.

Readmission: Average unadjusted hospitalization charge for readmission ranged from 27% to 35% higher than for the first admission, but was no longer significantly different after adjusting for the other factors. In other words, adjusted hospitalization charges for readmissions were about the same as the initial hospitalization charges.

Mortality status: Average unadjusted hospitalization charges for patients who died in the hospital or on the same date that they were discharged were 202% higher, and for those who died within a week after discharge were 55% higher, than for those who were alive after 7 days of discharge. After adjusting for the other factors, the difference reduced to 6% for death at discharge, and was no longer significant for those who died within a week after discharge.

Primary payer type: Average unadjusted hospitalization charges to those with commercial, Medicare, Medicaid, and other types of medical insurance were, respectively, 32%, 62%, 48%, and 20% higher than the charges to uninsured. This reduced to 11% for commercially insured, 13% for Medicare, 10% for Medicaid, and 8% for other types of insurances after adjusting for other factors.

Admission type: Average unadjusted hospitalization charges to those who were ED or urgent admissions were 10% less than those with elective admissions, but the adjusted charges were 7% higher for ED/urgent admissions.

Admission day of week: Average unadjusted hospitalization charges for weekday admissions ($32,196) were 9% higher than for weekend admissions ($29,677), but the adjusted charges were 3% higher for weekend admissions.

Region: Average unadjusted hospitalization charges in the Philadelphia region were 158% higher than in the rest of the state (other than Pittsburgh); after adjustment, the figure was 135% higher.

Procedures: Average unadjusted hospitalization charges for amputations ($64,548) and for other procedures ($47,833) were 317% and 209% higher, respectively, than for those with no procedure ($15,494). Adjusted charges were 91% higher for those with amputations and 70% higher for those with other procedures, than for those without procedures.

Medical misadventure: Average unadjusted hospitalization charges for those who had a medical misadventure during surgical and medical care ($99,745) were 233% higher than the charges for those who did not have medical misadventure ($29,937), but reduced to 20% higher after adjusting for the other factors.

LOS and CCI: The average LOS was 5.7 days, and the average CCI was 1.3. After adjusting for the other factors, each additional day of hospitalization was associated with a 9% increase in hospitalization charges, and each 1-unit increase in modified CCI was associated with an increase of approximately 3% in the hospitalization charge.

DISCUSSION

Given the current changes to Medicare reimbursement, and conceivably future Medicaid expansion, it is essential that policy makers and other decision makers understand what is driving the substantial economic burden of potentially avoidable hospitalizations for those with poorly controlled diabetes. Total hospitalization charges for patients in Pennsylvania with potentially avoidable diabetes-related hospitalizations more than doubled over the past 11 years, from approximately $353 million in 2001 to more than $802 million in 2011, with little change in the number of such hospitalizations.

Our study builds upon the CMS report of potentially avoidable hospitalizations¹³ by using multiple linear regression modeling with 16 known contributing factors to determine the characteristics driving higher charges for potentially avoidable diabetes-related hospitalizations. The findings that the charges are driven higher by particular factors provides healthcare policy makers and decision makers with data-driven support for evidence-based clinical pathways and policy changes that assess discharge readiness and quality of follow-up care after discharge.

Complications are major drivers of hospitalization charges. Over 86% of the diabetes-related hospitalizations in Pennsylvania had complications, and the average unadjusted charge for hospital stays with any type of complication was 158% ($34,577) more than the charge for hospitalizations without any complications ($13,417). This is consistent with previous reports,^6,7 and we provide pertinent quantitative data to address the gap in knowledge regarding the impact of specific types of complications. When compared to those who did not have complications, the adjusted hospitalization charge was substantially higher for those who had the following long-term complications: renal manifestations (75%), peripheral circulatory disorders (38%), neurological manifestations (30%), and ophthalmic manifestations (16%). The adjusted hospitalization charge was also higher for acute complications—ketoacidosis (48%), hyperosmolarity (41%), and coma (40%)&mdash;than for those without complications.

Medical procedures are also high-impact drivers of hospitalization charges related to diabetes progression. Of all diabetes-related hospitalizations, 44% included costly procedures. A distressing finding was that amputations were the most frequent procedure; amputations resulted in 91% higher adjusted hospitalization charges, on average, than for those without procedures. The 12% of diabetes-related hospitalizations with amputations accounted for 25% of all hospitalization charges.

As healthcare policy makers and decision makers direct efforts to improve quality of care and decrease hospitalization charges, it is important to consider medical misadventures. Approximately 2.5% of diabetes-related hospitalizations encountered medical misadventures; these accounted for more than 7.5% of all hospitalization charges. It is somewhat remarkable that medical misadventures increased adjusted charges by only 20%, substantially less than a 233% increase in unadjusted charges. That is, when hospitals develop and implement evidence-based standard operating procedures and policies to reduce or eliminate hospital misadventures, the expected reduction should be about 20% rather than 233%. This finding adds to the current scientific evidence and is pertinent should hospitals consider financial incentives to reduce surgery-related complications.¹⁴

In addition to the aforementioned drivers, being hospitalized through an emergency/urgent admission increased adjusted hospitalization charges nearly 7%, and adjusted charges for weekend admissions were 3% higher than weekday admissions, rather than the higher weekday unadjusted hospitalization charges.¹⁵ Healthcare policy makers and decision makers should be aware that each hospitalization day contributes only an additional 9% to the total adjusted hospitalization charge. Thus, a cost effective data-driven recommendation is for patients to stay a few extra days to optimize the management of their condition, rather than prematurely discharge patients and then readmit them with a corresponding hospitalization charge that is nearly the same as the charge for the first admission.

Region and race/ethnicity are additional drivers of hospitalization charges worthy of further investigation, as indicated in the recently released Medicare data.¹⁶ The adjusted hospitalization charges were 135% higher in the Philadelphia region than in the other regions of the state, and were higher for NHB (12%), Hispanic (21%), and NHO (14%) than for NHW patients. Despite the substantially improved fit of our model, which explains 68% of inpatient hospitalization charge variation, compared with the recently reported prediction model that explained 10%,¹⁷ the basis for these race/ethnicity and regional differences remains uncertain. This new knowledge that adjusted hospital charges were 21% higher for Hispanics is contrary to a recent report that diabetes hospitalization charges were lower for Hispanics than other races/ethnicities.⁴ Therefore, as public health and healthcare policy makers consider expansion of Medicaid coverage under the ACA, it is recommended that these racial/ethnicity and regional differences in hospitalization charges be investigated further to better understand these perplexing findings and the potential impact on healthcare costs.

Our study addressed the previously identified gap in knowledge regarding the need to evaluate whether the trend of lower charges for women is due to women having less complicated hospitalizations than men.⁴ We found no gender difference in adjusted hospitalization charges after simultaneously taking into account the other 15 patient and discharge characteristics.

The factors driving higher healthcare charges, reported herein, are timely and especially relevant as states contemplate expanding Medicaid coverage under the ACA, and as the ACA progressively reduces Medicare payments to hospitals with high rates of 30-day readmissions.⁵ The findings from our modeling approach, combined with the potential substantial loss of revenues from Medicare, point to possibly dire indications for hospitals’ fiscal stability. The loss of Medicare dollars related to high 30-day readmission rates, along with the evidence that readmission charges and first admission charges are basically the same, with no substantial decrease, will likely lead to hospitals suffering fiscal deficits.

There are several limitations of our analysis of hospital discharge data. First, there may be some inconsistency and inaccuracy in the diagnosis and procedure codes entered by the various providers. This may be due to errors in providers’ understanding of diagnostic coding/groupings (eg, ICD-9-CM, CPT), which leads to misclassification, or due to purposeful attempts to alter coding in order to maximize reimbursement.^18,19 Such errors, however, are reduced by PHC4’s standard operating procedures to check for data quality and data consistency. Second, hospital inpatient charges are not the reimbursed inpatient costs. This limitation should be minimal because hospital inpatient charges are generally proportional to the reimbursed inpatient costs. And furthermore, external data sources (eg, Medicare Cost Reports) can be used to estimate the actual cost-to-charge ratios; however, this approach for estimating inpatient costs is not always clear and may vary among different providers and healthcare systems. Third, hospital discharge data are not available for some potential drivers of hospitalization charges such as the duration of diabetes, sociodemographic variables (eg, income, education), and quality of primary care management and control information. While these factors may in fact be related to the likelihood of patients with diabetes developing complications, and thus in turn affect hospitalization charges, the excellent model fit suggests this limitation has been adequately addressed by the 16 factors in the full model. To cite another example of how the factors addressed possible limitations, death after discharge provided a proxy measure for disease severity during hospitalization which is similar to, but may not be consistent with, the disease severity index.²⁰ The resulting model fit indicates that this potential difference was sufficiently remedied by incorporating CCI, diabetes complication(s), inpatient procedure(s), and admission type in the full model.

In addition to addressing the aforementioned limitations, a strength of our study design is the evaluation of inpatient charges for patients with a principal diagnosis of diabetes. The inclusion of diabetes patients hospitalized for reasons other than a principle diagnosis of diabetes, such as admissions for general medical or surgical reasons not attributable to diabetes (eg, trauma, elective surgical procedures, rehabilitation) is not consistent with the aim of our study to evaluate the characteristics driving diabetes-related hospitalization charges. Had all diabetes admissions been included in the analysis, this would have added unnecessary “noise” to the data and resulted in substantial misclassification bias because hospitalizations not related to diabetes would have been inappropriately classified as diabetes-related hospitalizations.

In conclusion, we found the following independent drivers of higher hospitalization charges for Pennsylvanians with poor management of diabetes: diabetes-related complication(s), amputation or other procedure(s), being admitted to a hospital in the Philadelphia region, medical misadventure, minority status, age, T1DM, ED/urgent admission, comorbidities, longer LOS, and having insurance. These data support the need for the development of evidence-based clinical pathways and policy changes that improve discharge readiness assessment and quality of follow-up care after discharge,^21-27 to ultimately decrease healthcare costs.^28,29 A public health focus on primary care and secondary prevention of diabetes is essential to curb hospitalization charges, by reducing diabetes-related complications, unnecessary procedures, and comorbidity. These findings are timely, given the possible expansion of Medicaid, and reductions in Medicare payments to hospitals with high readmissions due to the implementation of the ACA. A better understanding of the drivers of hospitalization charges may be gleaned from further assessments of complications and procedures, especially for insured minority patients living in Philadelphia.Author Affiliations: Bureau of Epidemiology, Pennsylvania Department of Health, Harrisburg (ZQM); and Central Michigan University Medical School (MAF).

Source of Funding: This study was funded by a CDC Coordinated Chronic Disease Prevention grant, CDC-RFA-DP09-9010301PPHF11, and a National Association of Chronic Disease Directors Epidemiology Capacity Building grant, NACDD-0612012.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (ZQM, MAF); acquisition of data (ZQM); analysis and interpretation of data (ZQM); drafting of the manuscript (ZQM, MAF); critical revision of the manuscript for important intellectual content (ZQM, MAF); statistical analysis (ZQM).

Address correspondence to: Zhen-qiang Ma, MD, MPH, MS, Pennsylvania Department of Health, Bureau of Epidemiology, 625 Forster St, Harrisburg, PA 17120. E-mail: zma@pa.gov.REFERENCES

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