Do Hospitals that Serve a High Percentage of Medicaid Patients Perform Well on Evidence-based Guidelines for Colon Cancer Care?
Background. Previous work suggests hospitals serving high percentages of patients with Medicaid are associated with worse colon cancer survival. It is unclear if practice patterns in these settings explain differential outcomes. Hypothesis: High Medicaid hospitals (HMH) have lower compliance with evidence-based care processes (examining 12 or more lymph nodes (LN) during surgical staging and providing appropriate chemo-therapy). Methods. Retrospective analysis of stage I–III colon cancers from California Cancer Registry (1996–2006) linked to discharge abstracts and hospital profiles predicted hospital compliance with guidelines and trends in compliance over time. Results. Cases (N=60,000) in 439 hospitals analyzed. High Medicaid hospital settings had lower odds of compliance with the 12 LN exam (ORHMH0.91, CIHMH[0.85, 0.98]) and with the delivery of appropriate chemotherapy (ORHMH0.76, CIHMH[0.67, 0.86]). Conclusions. High Medicaid hospital status is associated with poor performance on evidence-based colon cancer care. Policies to improve the quality of colon cancer care should target these settings.
Colon cancer, quality of care, Medicaid hospitals, guideline compliance colon cancer, quality of care, Medicaid hospitals, guideline compliance. [End Page 1180]
Disparities in colon cancer are well documented in the literature1–13 and addressing these disparities is a national priority.14–15 While individual factors such as poor access to care and poorly managed co-morbidities have been shown to play a role in poor cancer outcomes for racial and ethnic minorities,6,16–17 there is mounting evidence to suggest that the type of hospital where care is delivered18 and quality of the treatment hospital19 also predict health outcomes.18,20–27 Correlations have been demonstrated between select hospital characteristics such as hospital volume, payer mix, and the quality of care26–28 and outcomes in both benign25 and malignant disease.20,28 In previous work, we have shown that hospitals serving high percentages of Medicaid patients were independently associated with poor colon cancer outcomes, even after adjusting for comorbidities and cancer stage.20 Despite the growing literature describing the association between hospital characteristics, outcomes and disparities,20,29 little work has been conducted to understand the processes and quality of care in high Medicaid hospitals (HMHs).
In 2002, the Institute of Medicine released Unequal Treatment, Confronting Racial and Ethnic Disparities in Health.15 In it, the panel suggested that due to Medicaid’s low reimbursement rates hospitals and clinics serving high proportions of patients with Medicaid insurance (largely constituted of minorities and the poor) develop alternative processes of care. The panel suggested that this might be due in part to resource constraints. Other hospitals with limited resources, so-called safety-net hospitals, have been shown to face challenges in obtaining and delivering high-quality30 specialty care31 and in complying with evidence-based guidelines in certain medical diseases.32
The National Comprehensive Cancer Network (NCCN), a not-for-profit alliance of leading cancer centers, reviews and updates resources for health care providers and other stakeholders, including guidelines for cancer treatment.33 The first set of NCCN guidelines was released in 1995. These recommendations are revised annually and describe processes of care which have been shown in randomized trials and other studies to improve outcomes for each specified type of malignancy. Recommendations for the care of colon cancer include examination of a minimum of 12 lymph nodes (LN) after colon cancer resection and chemotherapy for patients with stage III disease.
Understanding the quality of care delivered in settings serving high concentrations of poor and minority patients, such as HMHs, may help to elucidate a mechanism for cancer disparities. If there is variation in the processes of care known to affect outcomes positively, rational and targeted quality improvement interventions can be developed to improve care delivery in these settings and mitigate cancer disparities. The purpose of the current study is to characterize the level of compliance with NCCN guidelines for colon cancer in high Medicaid hospitals and compare this to performance in other settings. The hypotheses are that hospitals serving high proportions of patients with Medicaid insurance have low rates of compliance with NCCN guidelines for colon cancer: examination of at least 12 LN and chemotherapy for stage III disease.
Sources of data
After obtaining institutional review board (IRB) approval from the State of California and Stanford University, we analyzed a large statewide, all-payer [End Page 1181] administrative dataset comprising information from the California Cancer Registry (CCR), the California Office of Statewide Health Planning and Development (OSHPD) Patient Discharge Database (PDD), and the OSHPD Hospital Annual Financial Data (HAFD). Colon cancer cases diagnosed between the years of 1996 and 2006 (inclusive) were included. The California Cancer Registry is a statewide database containing specific clinical, demographic and treatment details about all cancers treated in the State of California. By state mandate, all providers treating patients with a primary diagnosis of cancer are required to report care delivered for a primary diagnosis of cancer to the registry. The CCR is recognized as one of the most comprehensive and complete cancer registries in the country. There are fewer than 3% missing race data and fewer than 2% of the records are collected through death records. Because reporting occurs regardless of the treatment modality and whether or not the patient receives care at multiple facilities in the state, the loss to follow up rate is low. Demographic variables contained in the CCR include age, gender, and race/ethnicity. Socioeconomic status variables include income; education; employment; and a validated composite socioeconomic status (SES) score.33,34 These variables are reported at the census block group level in the CCR, in contrast to other administrative datasets that report variables at the ZIP code, census tract, or county level. These higher-level measures (ZIP code, census tract, and county) are more likely to misclassify SES because clusters of low-SES populations may live within high SES ZIP codes, census tracts, and counties. Clinical variables include number of lymph nodes examined; the number of lymph nodes containing cancer; tumor stage; receipt of chemotherapy; coding for the reasons why chemotherapy may not have been received; and survival time in months.
The OSHPD-PDD is an all-payer database containing records for every discharge from a general acute, non-federal facility in California. Each record contains International Classification of Disease 9th Clinical Modification (ICD9-CM) codes indicating the primary diagnosis for the index admission and coding for the primary procedure performed during the hospitalization. The database also includes up to 24 secondary diagnoses and an indicator for whether the condition was present on admission (CPOA). This allows for the distinction of co-morbid disease from hospital acquired conditions and facilitates risk adjustment for predicting mortality. The dataset also contains an indicator for scheduled versus unscheduled admissions and a unique hospital identification number indicating where care was delivered. This allows for calculation of annual colon cancer volume. Records from the PDD were linked to the cancer database by the CCR staff using a probabilistic linkage algorithm based on patient’s date and year of birth, and social security number (SSN). The matching variables were stripped from the dataset and the data was disclosed to the investigators.
Using the unique hospital identifier contained in the CCR-OSHPD PDD, the dataset was linked to the California Hospital Annual Financial Data (HAFD). The HAFD is a publicly available data source containing detailed information about each hospital as defined by the state of California. This includes information about hospital teaching status and the proportion of patients with Medicaid insurance served. Hospitals serving more than one standard deviation above the mean for statewide Medicaid utilization were classified as HMHs. Because the hospital characteristics analyzed for the purposes [End Page 1182] of this paper are relatively stable across time, and typically do not change with short term shifts in the population, we used financial data from 2001.
Patients with ICD9 coding for a primary diagnosis of colon cancer (ICD9 codes: 153.0–4 and 153.6–9) were included. Patients with cancer of the appendix and rectum were excluded because the treatment algorithms and NCCN guidelines are different from those for colon cancer. Patients who did not undergo surgical resection were also excluded since lymph node examination can only be assessed after surgical resection. Patients with stage IV disease were excluded because lymph node counts have no bearing on treatment in this group. Patients with missing lymph node data were also excluded.
The hospital characteristics of interest were chosen based on published literature that shows correlation between these characteristics and cancer outcomes. The mean Medicaid utilization rate (MUR) for the state was calculated based on data contained in the HAFD. Those hospitals with MUR more than one standard deviation above the mean were classified as high Medicaid hospitals (HMH). This definition is based on the Medicaid utilization rate cut point that was historically used to help define Disproportionate Share Hospitals (DSH) in the state of California. Teaching status was defined by the state of California in the HAFD. Annual hospital volume for colon cancer was calculated based on the average number of patients with a primary diagnosis of colon cancer discharged on an annual basis. In the analytical models, hospital volume was treated continuously with incremental increases by 5 cases per year.
The primary outcome was rates of compliance with NCCN guidelines in HMH versus other settings. Evidence-based guidelines include examination of at least 12 lymph nodes after resection and delivery of adjuvant chemotherapy to patients with documented stage III, or node positive, disease. In order to depict hospital performance accurately, rather than reflecting patient preference or behavior on the chemo-therapy measure, hospitals were considered guideline-compliant if the chemotherapy was recommended, even if the patient did not actually receive it. The indicator for chemotherapy delivery in the CCR dataset has multiple possible responses, including: none given; one agent; multiple agents; recommended not received; patient refused; recommended unknown if given; unknown; and records collected by death record. Records were designated as having received appropriate chemotherapy for all responses except none given. Records with unknown’ (<100 records) and those identified by death record (<100 records) were excluded from the analysis.35
Multilevel (hierarchical) logistic regression models were built to predict the odds of receiving guideline-based care in each setting over the period under study. These models were adjusted for patient demographic and clinical characteristics (including age, gender, and co-morbidities). Co-morbid illness was assessed using the Deyo-Modified Charlson co-morbidity index.36 Each model was also adjusted for the year of admission to account for differing rates of adopting evidence-based practice over time. The models were subsequently adjusted for high versus low colon cancer volume and teaching status, at the hospital level, in order to assess for interaction of these hospital characteristics. Hierarchical models were used in order to adjust for the effects of clustering of patients with similar characteristics within hospitals. Joinpoint [End Page 1183] analysis (version 3.5.0 Statistical Research and Applications Branch, National Cancer Institute; Information management services, Inc. Silver Spring, MD) was used to evaluate trends in compliance over time. All tests of significance were two-tailed. Results were considered significant if the associated p-value was less than .05 or if the odds ratio (OR) was not equal to 1 and the 95% confidence interval (95% CI) excluded 1.
There were 62,184 cases of colon cancer, excluding patients with stage IV disease, identified in the data base. There were 59 hospitals (13.9%) with missing data on MUR or teaching status, resulting in the exclusion of 1,538 (2.5%) cases that were treated in these hospitals. Another 1,993 (3.2%) cases were excluded for missing stage of disease, leaving 58,653 cases, treated in 378 hospitals, retained for analysis. The vast majority (n=317) of hospitals in California are classified as non-high Medicaid (non-HMH). There was some overlap of the characteristics of HMH settings with teaching and volume status. Of the 61 HMH settings, 10 were also teaching institutions. In contrast, only three HMH settings were also classified as high-volume; the remainder (n=58) were defined as low-volume.
Table 1 shows the distribution of patients across high Medicaid and other hospital settings. White patients constituted only 36.4% of patients in HMH settings; in contrast, they made up 75.0% of patients in non-HMH settings. In comparison, the percent of Black, Hispanic, and Asian/Pacific Islander (API) patients in HMH settings was at least twice that found in non-HMH settings: (13.1% versus 6.0%; 27.7% versus 10.4%; 22.8% versus 8.7%, respectively). Patients with private insurance constituted a small fraction of those served in HMH settings (18.9%) compared with other types of settings. There was a much higher proportion of patients under 65 (44.2 % versus 26.8%), and patients with Medicaid (20.2% versus 2.8%) or no insurance (10.6% versus 0.7%), who received care in the HMH settings compared with other hospitals.
High Medicaid hospitals and compliance with evidence-based guidelines
Statewide, the overall rate of 12 LN examination was 45.8%. The rate of compliance with recommendations for chemotherapy in patients with stage III disease was slightly higher at 58.8%. In multivariable analysis (Table 2), there was a negative association between HMH status and guideline compliance. On the whole, patients treated in HMH settings were (8%) less likely to receive an adequate lymph node (LN) examination than patients in non-HMH settings (p=.032). Adjusting the models for the type of admission (unscheduled or scheduled) did not significantly alter this result. There were significant differences by patient characteristics, including decreasing odds of compliance with increasing age and lower rates of meeting the 12 LN standard in patients of non-White race/ethnicity. Seemingly paradoxically, patients with Medicaid and those without insurance were more likely to have 12 lymph nodes examined. Though this would seem to contradict our assertion, further stratified analysis revealed support for our hypothesis about the effect of hospital characteristics. However, in stratified analysis assessment of the odds of compliance with guidelines for chemotherapy showed similarly low level performance in HMH settings. High Medicaid hospitals were 21% less likely to recommend [End Page 1184] chemotherapy for stage III disease [OR 0.79; 95%CI (0.69–0.91)]. The odds also decreased with increasing age, but there were no significant differences by race.
Teaching status, increasing hospital volume, and high Medicaid hospitals’ performance
Multivariable models predicting guideline compliance, as described above, were subsequently adjusted for other hospital characteristics including teaching status and increasing colon cancer hospital volume (Table 3). The negative association between HMH and 12 LN examination worsened when the models were adjusted for hospital teaching status [ORhmh 0.87; 95%CI (0.80–0.93)], despite the positive independent correlation between teaching status and guideline compliance [ORteach1.51; 95%CI (1.43–1.59)]. Adjustment for increasing hospital volume (in five-case increments per annum) was independently associated with a significant increase in the odds of compliance with 12 lymph node examination [ORvol 1.03; 95%CI (1.03–1.04)]; and this [End Page 1185]
neutralized the negative effect of HMH on compliance with lymph node examination. Similar effects were observed in predicting compliance with the recommendation for chemotherapy in stage III disease. Accounting for teaching status lowered the odds of HMHs recommending chemotherapy; and adjustment for hospital volume neutralized the negative association between HMH and delivery of adjuvant therapy. [End Page 1186]
Trends in HMH compliance over time
Figure 1 shows the trends in compliance with evidence-based care in HMH settings, compared with both teaching and high volume hospitals. While there is improvement in the lymph node measure over time in all three settings (p <.05 for all), HMH is the last type of setting to show improvement. Table 4 details changes in trends with the exact year of change in performance and associated p-values. Changes in HMH performance trends on the 12 LN recommendation lag two years behind higher volume hospitals (HVH) and one year behind teaching hospitals. A similar trend showing low level compliance and failure to improve over time is observed in HMH settings for appropriate chemotherapy. Finally, although there was no significant improvement in the overall trend for any of the settings (all p values >.05) for compliance with guidelines for chemotherapy, it is important to note that compliance on both measures begins and ends at the lowest levels in HMH settings.
The results of the current investigation show that unadjusted compliance with NCCN guidelines for colon cancer care is relatively low in high Medicaid settings in California. Overall compliance with 12 LN examination was less than half for all patients across the state. The rates for compliance with chemotherapy for stage III colon cancer was slightly better, occurring about 60% of the time across all settings. We also found that responsiveness to changes in recommendations over time is slower and lower in [End Page 1187] HMH settings. Despite the positive correlation between teaching hospital status and compliance, adjusting for teaching status had no significant effect on the impact of HMH status. In comparison, adjusting for volume made HMH status a non-significant predictor of performance.
The results of this study are in line with the work of others who have shown variation in performance in hospitals serving large percentages of minority patients.18,37,38 Studies assessing the responsiveness of under-resourced institutions to incentive-based quality improvement programs suggest a limited ability to improve over time compared with other care settings.32 These investigators showed that low-resource settings would [End Page 1188]
suffer financial penalties under certain incentive-based improvement programs due to poor performance. Our results are also in line with the vast literature showing the relevance of high-volume hospitals and cancer outcome quality in that adjustment for volume status neutralized the effect of HMH status. This finding is consistent with the predominance of low-volume hospitals among the group of HMH settings in this study, and may suggest a lack of familiarity with the treatment of colon cancer. However, NCCN guidelines for treating colon cancer are readily available and free of charge for any clinician with access to the Internet.33 An alternative explanation for the lapse in achieving the standard may relate to the qualifications or availability of the faculty and staff that play critical roles in meeting the standard. Thus, we propose that lower compliance with guidelines in HMH settings may relate to limited resources, including key personnel, which is independent of a hospital’s volume for a particular disease. However, because the majority of HMH settings in California are not high-volume, and this is not easily remediable, this correlation may offer no leverage for improving care and outcomes for the patients who use HMH settings. Further research is needed to elucidate the underlying mechanisms of the patterns we have observed.
Although our results are consistent with prior research, the current investigation is also novel because the bulk of studies correlating hospital characteristics with quality of care have focused on mortality as the outcome of interest.20,29,39–42 This study, in contrast, [End Page 1189] attempts to understand variation in process quality and adherence to evidence-based care. In the few studies that have addressed process of care disparities, including our own prior work, most have found similar results.18,26,27 However, these studies have not focused on the treatment of cancer. Ours is the first study to focus process measures in cancer care looking at settings that also serve high proportions of Medicaid patients and complements prior literature showing a strong association between HMH status and mortality rates after colon cancer care. This work further supports the idea that HMH hospitals may benefit from focused quality improvement efforts in order to improve cancer care and outcomes. While we did not directly study the effects of the use of HMH on disparities in outcomes, the fact that minority patients cluster for care in HMH settings suggests that addressing variations in care might have an impact on differences in outcome by both race and SES.
This study must be interpreted with some limitations in mind. The study relies on cross-sectional administrative data. Therefore, we cannot, and do not, directly establish any causal links between the hospital characteristics and performance on these various measures. It may be the case that performance on either measure is related to the performance of individual clinicians in each setting. For example, some clinicians may not offer chemotherapy to patients they feel lack social support or who have low performance status. They may even be reluctant to offer chemotherapy to patients in low SES categories due to concern that they may face challenges obtaining care. That said, it is important to note that adjusting the models for these characteristics had little effect on the negative correlation between HMH and delivery of guideline-based care. Thus, despite our limited ability to attribute causation, use of hierarchical models provides confidence in the independent association between system-level (hospital) factors and performance. In doing this, our work has identified system-level targets where interventions may be applied to smooth this variation in care.
Despite its limitations, the current study demonstrates low levels of compliance with colon cancer processes of care in high Medicaid hospitals. These are settings where many California minorities cluster for care. It is unclear if the lack of compliant care is unique to HMH settings, or if it occurs in all settings serving high proportions of minorities. Future studies should investigate the delivery of guideline-based care in what could be called ‘minority serving institutions’. Nonetheless, the results of this work are important because, by assessing performance on two specific guidelines, we have identified salient targets for quality improvement initiatives. In order to be successful; however, any improvement program targeting HMH settings must be designed to enhance compliance while avoiding the potential for further resource deprivation and potential exacerbation of negative outcomes.
Dr. Rhoads is an assistant professor of Surgery and the Director of the Community Partnership Program for the Stanford Cancer Institute. Ms. Justine V. Ngo is a research assistant in the Department of Surgery at Stanford. Mr. Yifei Ma is a statistical analyst in the Department of Surgery and the Stanford Cancer Institute. Dr. Lyen huang is a post-doctoral fellow in the Department of Surgery, Stanford University School of Medicine. Dr. Mark Welton is the Harry A. Oberhelman Professor and Chief of Colon and Rectal Surgery in the Department of Surgery at Stanford University School of Medicine, and Dr. R. Adams Dudley is Professor of Medicine and Health Policy at the University of California, San Francisco School of Medicine and Institute for Health Policy Studies.
This work was introduced in a podium presentation at the Disparities Interest Group at the Academy Health Annual Research Meeting, Seattle Washington, June 12–14, 2011. [End Page 1190] Dr. Rhoads’ work on this project was supported by a grant from the Harold Amos Medical Faculty Development Program of the Robert Wood Johnson Foundation, Princeton, New Jersey.
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