Medicaid-insured type 2 diabetes mellitus patients, just like the uninsured, are more likely to be hospitalized through emergency/urgent admissions.
Objectives: To evaluate the associations between potentially avoidable diabetes-related emergency/urgent hospital admissions and different health insurance status (ie, Medicaid, Medicare,
uninsured, private), along with other characteristics including sociodemographic status (age, race/ethnicity, gender, region), hospitalization status (previous hospitalizations, weekend admissions), and health status (complications, comorbidities), among patients with type 2 diabetes mellitus (T2DM).
Study Design: The 2011 data set of all inpatient discharge records with a primary diagnosis of T2DM from all hospitals in Pennsylvania were included in the analyses.
Methods: Multivariable logistic regression modeling with diabetes-related emergency/urgent hospitalizations as the dependent outcome variable and health insurance status as the main exposure independent variable, adjusting for age, race/ethnicity, gender, region, previous hospitalizations, weekend admissions, complications, and comorbidity. Hosmer and Lemeshow
goodness-of-fit test was used for logistic model fit analysis.
Results: Nearly 91% of 17,097 potentially avoidable diabetes-related hospitalizations were emergency/urgent admissions for T2DM patients in Pennsylvania during 2011. Uninsured and Medicaidinsured patients were 2.1 (adjusted odds ratio [AOR], 2.11; 95% CI, 1.23-3.61) and 1.8 (AOR, 1.78; 95% CI, 1.44-2.20) times more likely than privately insured patients, respectively, to be admitted through emergency/urgent admissions. There was no statistically significant difference in emergency/urgent admissions between Medicaid and uninsured (AOR, 0.85; 95% CI, 0.49-1.47).
Conclusions: Medicaid-insured T2DM patients, like the uninsured, are more likely to be hospitalized through emergency/urgent admissions. The presumption that insured individuals with diabetes are more likely than the uninsured to manage and control the progression of their condition, and receive care in the right setting, is not supported for those with Medicaid coverage.
Am J Manag Care. 2015;21(5):e312-e319
Multivariate analyses of medical insurance status affecting diabetes-related emergency/urgent hospitalization charges are sparse, but essential when making managed care decisions regarding health coverage expansion.
Diabetes and diabetes-related comorbidity are important public health problems due to the substantial disease and cost burdens.1,2 Approximately 22.3 million Americans, or 7% of the US population, were diagnosed with diabetes in 2012; individuals with diabetes account for more than 20% of US healthcare dollars spent, with hospital inpatient care incurring 43% of the $176 billion total direct diabetes medical cost.1 Because hospitalizations account for the largest share of total healthcare costs, the Agency for Healthcare Research and Quality (AHRQ) developed Prevention Quality Indicators (PQIs) to identify hospital admissions that were potentially avoidable with timely access to primary care and appropriate disease management.3-6 Using AHRQ’s PQIs as the recognized metric, an estimated 12.4% of the hospitalizations in Pennsylvania are potentially avoidable, and of that 12.4%, 1 in 8 are diabetes-related,7 despite 96% of the diabetes-related hospitalizations being preventable.8
Hospitalization costs related to poor management of diabetes are major contributors to the healthcare costs of those with diabetes. This is especially important when considering Medicaid expansion under the Affordable Care Act (ACA), because Medicaid and Medicare are the primary payers for 49% of the diabetes-related emergency/urgent hospital admissions.9 Studies assess factors related to avoidable hospitalizations as indicators of access to primary healthcare and corresponding healthcare costs.3-6,9,10 Health insurance is a key determinant of access to care and health outcomes,11 such that health insurance status can significantly impact effective management and control of diabetes. For instance, both uninsured and Medicaid-insured individuals have less favorable health outcomes than those who are privately insured and/or Medicare-insured,12,13 including avoidable hospitalizations.14-18 In addition to health insurance status, having a previous hospital visit or readmission is typically assessed in studies of potentially avoidable hospitalizations.19-26
However, not all avoidable hospitalizations are the same. In particular, whether a hospital visit is an emergency admission or non-emergency admission for ambulatory care—sensitive conditions (eg, diabetes) is an indicator of process and coordination of care.16,27-29 That is, unplanned emergency/urgent hospital admissions are an indicator of poorly managed acute diabetes conditions, whereas elective or planned admissions generally suggest that patients received diabetes management care in the right setting such that their conditions permitted adequate time to schedule the services. Due to the paucity of scientific evidence, studies are especially needed to gain a better understanding of the effect of health insurance status on emergency/urgent admissions, because 94% of potentially avoidable diabetes-related hospitalizations fall into this category.9
To better understand the factors influencing the potentially avoidable emergency/urgent diabetes-related hospitalizations, the objective of this study is to quantify the independent association between potentially avoidable diabetes-related emergency/urgent hospital admissions and different health insurance status (ie, Medicaid, Medicare, uninsured, private), along with other characteristics including sociodemographic status (ie, age, race/ethnicity, gender, region), hospitalization status (ie, previous hospitalizations, weekend admissions), and health status (ie, complications, comorbidities), among those with the most common type of diabetes, type 2 diabetes mellitus (T2DM).
The Pennsylvania Health Care Cost Containment Council (PHC4)—an independent state agency that collects approximately 1.9 million inpatient discharge records annually from all hospitals in Pennsylvania—provided the 2011 data set of inpatient discharge records from all the state’s hospitals, which was then analyzed. Potentially avoidable diabetes-related hospitalizations were defined based on AHRQ’s PQIs that are used with inpatient hospital discharge data to identify conditions that should be preventable with proper primary care.3-6 Potentially avoidable T2DM-related hospitalizations were defined as all hospitalizations with a primary discharge diagnosis of T2DM (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes of 250.00-250.92 with a last digit of 0 or 2).
Description of outcome (dependent variable). The outcome, emergency/urgent hospital admission, was dichotomized (yes/no) based on the admission type that defined the hospital admission’s level of urgency. An emergency/urgent admission was defined as the patient requiring immediate medical intervention as a result of a severe, life-threatening, or potentially disabling condition; or the patient required immediate attention for the care and treatment of a physical or mental disorder. A non-emergency/urgent admission was defined as an elective/planned admission, with the patient’s condition permitting adequate time to schedule the services.
Description of main exposure (independent variable). Health insurance status was based on the primary payer type and categorized as Medicaid, Medicare, uninsured, and private.
Description of Other Explanatory Factors (Independent Variables)
Sociodemographic characteristics. The 4 sociodemographic characteristics that were assessed include age, race/ethnicity, gender, and region: 1) age: the patient’s age in years at the time of hospitalization, categorized as <20, 20-44, 45-64, or ≥6530; 2) race/ethnicity: patients were categorized as non-Hispanic white (NHW), non-Hispanic black, non-Hispanic other race, or Hispanic based on the combination of race and ethnicity information; 3) gender: if gender information was missing, it was imputed from prior (2000-2011) records; 4) region: facility region code was categorized as Philadelphia (includes 4 surrounding counties), Pittsburgh (includes Pittsburgh city, Allegheny County and 7 surrounding counties), and the rest of the state (other 53 counties).
Hospitalization and Health Status. The 4 hospitalization and health-related statuses that were assessed include previous hospitalization, weekend admission, complications, and comorbidities: 1) previous hospitalization: categorized as “yes” or “no” and defined as any prior hospital admission with primary diagnosis of diabetes from 2000 to 2011; 2) weekend admission: categorized as “yes” or “no” based on the date of admission; 3) complications: defined based on the AHRQ’s PQI #1 Diabetes Short-Term Complications Admission Rate, and PQI #3 Diabetes Long-Term Complications Rate, along with PQI #14 Uncontrolled Diabetes Admission Rates.3-6 Complications were derived from the fourth digit of the primary diagnosis ICD-9-CM codes and categorized as: a) no complication; b) acute: coma, hyperosmolarity, ketoacidosis; c) chronic: renal manifestations, neurological manifestations, ophthalmic manifestations, peripheral circulatory disorders, and other complications.3-6 Thus, complications are a proxy measure for the progression of diabetes; 4) comorbidities as defined by the modified Charlson comorbidity index (CCI): the CCI predicts 10-year mortality based on weighted scores ranging from 0 to 35 for 17 conditions.31 All 8 secondary diagnoses were considered in the calculation of the CCI. Diabetes was excluded from CCI calculation so as not to double-count the impact of diabetes, because all assessed hospitalizations were diabetes-related. The CCI distribution ranged from 0 to 12, with 24% having a score of 3 or more. CCI categories were defined as 0, 1, 2, and 3 or more in order to keep the distribution (or data) balanced.
Descriptive summary. First, descriptive analyses were performed to report the overall and characteristic-specific (independent variables) proportion of potentially avoidable diabetes-related hospitalizations that were emergency/urgent admissions for Pennsylvanians in 2011. This was followed by the corresponding unadjusted association or crude odds ratio (COR) between emergency/urgent hospitalizations and insurance status, sociodemographic status, hospitalization, and health status.
Multivariable logistic regression modeling. The independent association was calculated as the adjusted odds ratio (AOR) for potentially avoidable diabetes-related emergency/urgent hospitalizations and health insurance status, while simultaneously taking into account sociodemographic status, hospitalization status, and health status. Hosmer and Lemeshow’s goodness-of-fit test was used for logistic model fit analysis. The observed event rates matched expected event rates in all subgroups of the population in the final model.
Descriptive summary (Table 1). Among 17,097 potentially avoidable diabetes-related hospitalizations in Pennsylvania in 2011, 15,538 (90.9%) were emergency/urgent admissions. When considering only a single characteristic, the following associations with having potentially avoidable diabetes-related emergency/urgent admissions were quantified: 1) health insurance status: uninsured and Medicaid-insured patients were 3.1 and 2.4 times more likely, respectively, than privately insured patients; and Medicare-insured patients were approximately one-eighth less likely; 2) sociodemographics: younger patients were 1.3 to 2.7 times more likely than older patients aged ≥65 years, and minority patients were 2.2 to 2.9 times more likely than NHW patients. Hospitalizations in Philadelphia and Pittsburgh were approximately 3.1 times and 1.2 times, more likely, respectively, than admissions in the rest of the state; 3) hospitalization status: weekend admissions were 4.7 times more likely than weekday admissions; and first hospitalizations were 1.4 times more likely than those with a previous diabetes-related hospitalization(s); 4) health status: those with acute complications were 3.4 times more likely, and those with chronic complications were 2.6 times less likely, than those without complications, respectively. Patients with comorbidities were one-half to one-third as likely as patients with no comorbidity.
Multivariable logistic regression modeling (Table 2). After simultaneously taking into account all 9 characteristics listed in Table 2, the following independent associations with having potentially avoidable diabetes-related emergency/urgent admissions were quantified: 1) health insurance status: uninsured and Medicaid-insured patients were 2.1 and 1.8 times more likely, respectively, than privately insured patients. There was no statistical difference between Medicaid-insured and uninsured (AOR, 0.85; 95% CI, 0.49-1.47 using uninsured as the reference group in the model); or between Medicare-insured and privately insured; 2) sociodemographics: younger patients aged <20 years were approximately half as likely as patients aged ≥65 years, and minority patients were 1.5 times more likely than NHW patients. Hospitalizations in Philadelphia and Pittsburgh were 2.6 times and 1.2 times, respectively, more likely than admissions in the rest of state; 3) hospitalization status: first hospitalizations were 1.4 times more likely than those for patients with a previous diabetes-related hospitalization(s), and weekend admissions were 4.3 times more likely than weekday admissions; 4) health status: those with acute complications were 3.1 times more likely, and those with chronic complications were half as likely, as those with no complications. Patients with comorbidities were approximately one-fifth as likely as patients with no comorbidity.
Hospitalization costs related to poor management of diabetes are major contributors to the healthcare costs of those with diabetes. This study was designed to investigate the factors influencing the substantial disease and cost burden of potentially avoidable and expensive diabetes-related emergency/urgent hospital admissions, indicative of poor diabetes management and control. It is noteworthy that most previous studies of potentially avoidable hospitalizations did not differentiate between emergency and non-emergency admissions, resulting in misclassification bias, because it is the emergency admissions that are most amenable to interventions that improve ambulatory disease management and control. By addressing this misclassification bias, our study provides relevant scientific evidence regarding factors (eg, health insurance status, sociodemographic characteristics, hospitalization, and health status) related to the 94% of potentially avoidable diabetes-related hospitalizations that are emergency/urgent admissions.9 Analyses such as these, focusing on ambulatory care—sensitive conditions, illustrate the opportunity to reduce potentially avoidable hospital admissions and redirect resources to other types of healthcare services.
Furthermore, as states consider Medicaid expansion, they should be cognizant of the issues surrounding Medicaid recipients’ access to care, utilization of services, and compliance with self-management recommendations, not just health insurance status. As indicated in our analysis, Medicaid-insured diabetes-related hospitalizations for those with T2DM were similar to those of the uninsured. Their admissions were more likely to be emergency/urgent admissions than were those of the privately insured or Medicare-insured, after simultaneously taking into account other factors. This key finding—that having Medicaid insurance is not much different from being uninsured—necessitates further investigation, especially when publicly provided or publicly subsidized health insurance coverage for all is being considered. That is, when attempting to understand the lack of routine healthcare utilization, and to understand issues related to better management and control of diabetes, ultimately, it is important to consider some things other than simply having insurance. For example, financial constraints most likely influence healthcare-seeking behaviors of both uninsured and Medicaid recipients. In addition, lapses in Medicaid coverage have been associated with increased inpatient and emergency department (ED) services in the post lapse period.19 Hence, one should be cognizant that previously uninsured individuals who are later covered by publicly provided or subsidized health insurance may continue to use healthcare services differently from those who were privately insured or Medicare-insured, and may continue to use emergency/urgent care rather than obtain care through primary care providers.32
More specifically, simply having insurance coverage most likely will not resolve the issue of increasing healthcare costs because of the multitude of intertwined factors related to preventive care, having a medical home, committing to self-management (eg, through diet, exercise, medication adherence), access to care, and utilizing health services in the right setting (eg, seeking primary care when appropriate, being able to make and keep appointments, being able to get time off from work, taking into account a possibly long wait time for an appointment). These and other factors may mean that poor and working-poor Medicaid-insured and uninsured individuals may not be able to optimally seek and utilize preventive care services in an ambulatory care setting. Instead, they may end up in the ED, being admitted to the hospital for potentially avoidable diabetes-related conditions.
For example, it is commendable to focus on person-centered care through a medical home model,33 and to create Teaching Health Center Graduate Medical Education programs in ambulatory care centers in underserved areas through the ACA.34 However, these efforts will not ultimately improve health outcomes and decrease healthcare costs if patients with chronic conditions such as T2DM do not have regular preventive visits in an ambulatory care setting at the right time, to manage and control the progression of their conditions.19 To give another example, quality improvement approaches that focus on educational interventions have reportedly improved medication adherence and decreased acute care services (ie, hospitalization, emergency visits).35,36 However, Cochrane Systematic Reviews reports that the effect of quality improvement programs for managing diabetes, which include financial incentives to directly reward primary care physicians, has not been clearly established.25,37,38
These seemingly inconsistent findings suggest that while changes in clinical care pathways seem to make sense, the translation of knowledge to behavioral change is complex, and there is no easy solution. Additional research that builds upon the currently available science—such as applying a version of the “5 As” approach that is a cornerstone of some smoking cessation plans39-41—could provide some helpful insight when counseling patients in how to improve their diabetes self-management behaviors. While the desire to change behavior is necessary, it is not sufficient—one also needs concrete knowledge regarding appropriate self-care/management.
Some additional barriers to behavioral change, especially among the poor and working poor, include food insecurity, which can impede the dietary component of diabetes self-management,42 and health insurance status, which has been associated with access to appointments, timely appointments, and affordability of ambulatory care. When out-of-pocket payments were limited to less than $50, only 14% of uninsured people in one study were able to schedule an appointment, whereas without this payment limitation 73% of uninsured, 71% of privately insured, 62% of Medicare-insured, and 37% of Medicaid-insured people were able to do so. These percentages are further reduced for an appointment within 7 days with a $50 out-of-pocket cost.43 Intervention strategies that modify the primary care physician financial incentive program,38 such as financial incentives to individual patients, may be worth consideration, especially for uninsured or Medicaid-insured individuals who have substantial financial constraints.
Strengths and Limitations
This study of the association between potentially avoidable diabetes-related emergency/urgent hospitalizations and health insurance status among those with T2DM had several advantages. First of all, of interest to insurance providers (ie, private, Medicaid, Medicare), the more rigorous multivariable model has stronger data-driven policy implications for disease prevention, management, and control than simple descriptive and/or unadjusted crude associations. More specifically, the diabetes-related hospitalizations with acute complications were over 3 times more likely to have emergency/urgent hospital admissions than those without complications. Thus, considerable cost savings are feasible when informed policies impede the development and progression of T2DM, and ultimately prevent or substantially decrease emergency/urgent hospital admissions.
Secondly, comorbidities were also taken into account, based on the weighted composite CCI. Diabetes-related hospitalizations were less likely to be emergency/urgent admissions for those with T2DM and comorbidity. This apparent paradox may be partially explained by the speculation that those with 1 or more comorbidities may see multiple providers/specialists; cumulatively, those providers may do a better job at monitoring and controlling diabetes, so the patient is less likely to have an emergency/urgent hospital admission. Alternatively, patients who have T2DM alone may not believe it’s as important to manage and control their condition as do those with multiple comorbidities. They may be lackadaisical about scheduling primary care appointments to manage diabetes alone, with the result being the need for emergency/urgent acute care services requiring hospitalization. Further research of the role(s) of specific components of the CCI measure may provide relevant insight when developing potential recommendations and policy changes that address the utilization of primary care versus ED services.
Thirdly, the fact that those who had previous diabetes-related hospitalization(s) were less likely to have a subsequent emergency/urgent admission suggests that the hospital experience may have changed the patterns of care for these patients. Additional insight may be gleaned from investigations of the effectiveness of improved clinical pathways that focus on multifaceted patient-oriented interventions such as education and compliance with treatment,25,37 financial incentives to primary care physicians,38 discharge preparation,26 and post discharge programs that assess compliance with insulin therapy after hospitalization.44
We purposefully restricted our analyses to the more common type 2 form of diabetes because types 1 and 2 are very different diseases medically. We selected diabetes hospitalization with primary diagnosis of diabetes only in our analyses. This is consistent with standard definitions used by AHRQ’s PQI and Healthcare Cost and Utilization Project to identify conditions that should be preventable with proper primary care.3-6 Hence, this approach eliminated the potential misclassification bias that is present when admissions with a secondary diagnosis of diabetes, such as hospitalizations for joint replacement surgery or for trauma related to a car accident, are inappropriately considered to be potentially avoidable diabetes-related hospitalizations. It is also noteworthy that among patients with T2DM, diabetes-related hospitalizations in the Philadelphia region were significantly more likely to have expensive emergency/urgent admissions than in Pittsburgh or the rest of the state. This finding is consistent with the report that patients who present to the ED were more likely to be admitted to the hospital in metropolitan areas (13.6%) than in rural areas (8.3%).45
We believe the aforementioned strengths of this study far outweigh the following limitations. It would have been informative if data were available on income or education; that might partially explain the results that Medicaid-insured and uninsured diabetes-related hospitalizations were more likely to have begun with emergency/urgent admissions.21 Medicaid-insured and uninsured populations were similar in that both were more likely to have emergency/urgent hospitalizations than those privately insured or Medicare-insured; this suggests that having insurance coverage is not sufficient when considering associated costly health behaviors. As discussed earlier, different financial constraints may affect the poor and working poor, to limit their access to, and utilization of, primary care services that would help manage and control the progression of diabetes.
The presumption that insured individuals with T2DM are more likely than the uninsured to manage and control the progression of their condition, and to receive the needed care at the right time in the right setting, is not supported for those insured by Medicaid. These data have potential implications for states considering Medicaid expansion under the ACA. Further research is needed to assess the services provided by Medicaid and the health insurance exchanges that promote compliance with self-care and management recommendations, and to determine if access to care and utilization of appropriate ambulatory/primary care services is improved.
The authors acknowledge the support, review, and insightful comments provided by Stephen M. Ostroff, MD, director of the Bureau of Epidemiology at the Pennsylvania Department of Health at the time this study was initiated. Support from Judith Good at Pennsylvania Health Care Cost Containment Council for extracting the hospital discharge data for this analysis is also acknowledged. The Pennsylvania Health Care Cost Containment Council (PHC4) is an independent state agency responsible for addressing the problem of escalating health costs, ensuring the quality of healthcare, and increasing access to healthcare for all citizens. While PHC4 has provided data for this study, PHC4 specifically disclaims responsibility for any analyses, interpretations or conclusions.
Author Affiliations: College of Medicine, Central Michigan University (MAF), Mount Pleasant, MI; Pennsylvania Department of Health, Bureau of Epidemiology (ZM), Harrisburg, PA.
Source of Funding: This study was funded by the CDC Coordinated Chronic Disease Prevention grant, CDC-RFA-DP09-9010301PPHF11 and 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 (MAF, ZM); acquisition of data (ZM); analysis and interpretation of data (MAF, ZM); drafting of the manuscript (MAF, ZM); critical revision of the manuscript for important intellectual content (MAF, ZM); statistical analysis (ZM).
Address correspondence to: Zhen-qiang Ma, MD, MPH, MS, Pennsylvania Department of Health, Bureau of Epidemiology, 625 Forster St, Harrisburg, PA 17120. E-mail: firstname.lastname@example.org.
1. American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care. 2013;36(4):1033-1046.
2. Mayes R, Oliver TR. Chronic disease and the shifting focus of public health: is prevention still a political lightweight? J Health Polit Policy Law. 2012;37(2):181-200.
3. Prevention Quality Indicators Technical Specifications—version 4.5. Agency for Healthcare Research and Quality website. http://www.qualityindicators.ahrq.gov/Archive/PQI_TechSpec_V45.aspx. Published May 2013. Accessed April 29, 2015.
4. Healthcare Cost and Utilization Project. Statistical Brief #36: Trends in potentially preventable hospitalizations among adults and children, 1997-2004. Agency for Healthcare Research and Quality website. http://www.hcup-us.ahrq.gov/reports/statbriefs/sb36.pdf. Published August 2007. Accessed April 29, 2015.
5. Nationwide frequency and costs of potentially preventable hospitalizations 2006. Agency for Healthcare Research and Quality website. http://www.nonprofithealthcare.org/uploads/AHRQ_Releases_Report_on_Potentially_Preventable_Hospitalizations.doc. Published May 1, 2009. Accessed April 29, 2015.
6. AHRQ Quality Indicators—Guide to Prevention Quality Indicators: Hospital admission for ambulatory care sensitive conditions [AHRQ Pub. No. 02-R0203]. Agency for Healthcare Research Quality website. http://www.ahaphysicianforum.org/resources/appropriate-use/ACSC/content/AHRQ-pqiguide.pdf. Published October 2001, Revised April 2002. Accessed April 29, 2015.
7. Potentially preventable hospitalizations in Pennsylvania, 2010. Pennsylvania Health Care Cost Containment Council website. http://www.phc4.org/reports/preventable/10/docs/preventable2010report.pdf. Published June 2012. Accessed April 29, 2015.
8. Chronic health conditions in Pennsylvania. Pennsylvania Health Care Cost Containment Council website. http://www.phc4.org/reports/chroniccare/10/docs/chroniccare2010report.pdf. Published June 2010. Accessed April 29, 2015.
9. Kim S. Burden of hospitalizations primarily due to uncontrolled diabetes: implications of inadequate primary health care in the United States. Diabetes Care. 2007;30(5):1281-1282.
10. Garg SK, Hirsch IB. Self-monitoring of blood glucose. Int J Clin Pract Suppl. 2012;(175):2-7.
11. Fowler RA, Noyahr LA, Thornton JD, et al; American Thoracic Society Disparities in Healthcare Group. An official American Thoracic Society systematic review: the association between health insurance status and access, care delivery, and outcomes for patients who are critically ill. Am J Respir Crit Care Med. 2010;181(9):1003-1011.
12. Halpern MT, Ward EM, Pavluck AL, Schrag NM, Bian J, Chen AY. Association of insurance status and ethnicity with cancer stage at diagnosis for 12 cancer sites: a retrospective analysis. Lancet Oncol. 2008;9(3):222-231.
13. McWilliams JM. Health consequences of uninsurance among adults in the United States: recent evidence and implications. Milbank Q. 2009;87(2):443-494.
14. Weissman JS, Gatsonis C, Epstein AM. Rates of avoidable hospitalization by insurance status in Massachusetts and Maryland. JAMA. 1992;268(17):2388-2394.
15. Pappas G, Hadden WC, Kozak LJ, Fisher GF. Potentially avoidable hospitalizations: inequalities in rates between US socioeconomic groups. Am J Public Health. 1997;87(5):811-816.
16. Oster A, Bindman AB. Emergency department visits for ambulatory care sensitive conditions: insights into preventable hospitalizations. Med Care. 2003;41(2):198-207.
17. Gusmano MK, Rodwin VG, Weisz D. A new way to compare health systems: avoidable hospital conditions in Manhattan and Paris. Health Aff (Millwood). 2006;25(2):510-520.
18. Chang CF, Pope RA. Potentially avoidable hospitalizations in Tennessee: analysis of prevalence disparities associated with gender, race, and insurance. Public Health Rep. 2009;124(1):127-137.
19. Hall AG, Harman JS, Zhang J. Lapses in Medicaid coverage: impact on cost and utilization among individuals with diabetes enrolled in Medicaid. Med Care. 2008;46(12):1219-1225.
20. McCall N, Cromwell J. Results of the Medicare Health Support disease-management pilot program. N Engl J Med. 2011;365(18):
21. Booth GL, Hux JE. Relationship between avoidable hospitalizations for diabetes mellitus and income level. Arch Intern Med. 2003;163(1): 101-106.
22. Chandra S, Agarwal D, Hanson A, et al. The use of an electronic medical record based automatic calculation tool to quantify risk of unplanned readmission to the intensive care unit: a validation study [published online June 28, 2011]. J Crit Care. 2011;26(6):634.e9-634.e15.
23. Cherry-Bukowiec JR, Miller BS, Doherty GM, et al. Nontrauma emergency surgery: optimal case mix for general surgery and acute care surgery training. J Trauma. 2011;71(5):1422-1426; discussion 1426-1427.
24. Flynn D, Knoedler MA, Hess EP, et al. Engaging patients in health care decisions in the emergency department through shared decision-making: a systematic review. Acad Emerg Med. 2012;19(8):959-967.
25. Rotter T, Kinsman L, James E, et al. Clinical pathways: effects on professional practice, patient outcomes, length of stay and hospital costs. Cochrane Database Syst Rev. 2010;(3): CD006632. Review.
26. Weiss ME, Yakusheva O, Bobay KL. Quality and cost analysis of nurse staffing, discharge preparation, and postdischarge utilization. Health Serv Res. 2011;46(5):1473-1494.
27. Bankart MJ, Baker R, Rashid A, et al. Characteristics of general practices associated with emergency admission rates to hospital: a cross-sectional study. Emerg Med J. 2011;28(7):558-563.
28. Kim H, Helmer DA, Zhao Z, Boockvar K. Potentially preventable hospitalizations among older adults with diabetes. Am J Manag Care. 2011;17(11):e419-e426.
29. Freund T, Campbell SM, Geissler S, et al. Strategies for reducing potentially avoidable hospitalizations for ambulatory care-sensitive conditions. Ann Fam Med. 2013;11(4):363-370.
30. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. healthy people statistical notes, no. 20. Hyattsville, MD: National Center for Health Statistics; 2001.
31. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130-1139.
32. Decker SL, Doshi JA, Knaup AE, Polsky D. Health service use among the previously uninsured: is subsidized health insurance enough? Health
33. Gulley SP, Rasch EK, Chan L. The complex web of health: relationships among chronic conditions, disability, and health services. Public Health Rep. 2011;126(4):495-507.
34. Chen C, Chen F, Mullan F. Teaching health centers: a new paradigm in graduate medical education. Acad Med. 2012;87(12):1752-1756.
35. Khanna R, Pace PF, Mahabaleshwarkar R, Basak RS, Datar M, Banahan BF. Medication adherence among recipients with chronic diseases enrolled in a state Medicaid program. Popul Health Manag. 2012;15(5):253-260.
36. Iyer R, Coderre P, McKelvey T, et al. An employer-based, pharmacist intervention model for patients with type 2 diabetes. Am J Health Syst Pharm. 2010;67(4):312-316.
37. Renders CM, Valk GD, Griffin SJ, Wagner EH, Eijk van JT, Assendelft WJ. Interventions to improve the management of diabetes mellitus in primary care, outpatient and community settings. Diabetes Care. 2001;24(10):1821-1833. Review.
38. Scott A, Sivey P, Ait Ouakrim D, et al. The effect of financial incentives on the quality of health care provided by primary care physicians. Cochrane Database Syst Rev. 2011;(9): CD008451.
39. Five A’s of counseling patients. ucanquit2.org website. http://www.ucanquit2.org/HelpHeroQuit/HealthProfessionals/Cessation%20Resources/FiveAs.aspx. Accessed April 29, 2015.
40. Fiore MC, Jaen CR, Baker TB, et al. Treating tobacco use and dependence: 2008 update. Rockville, MD: HHS, Public Health Service. Agency for Healthcare Research and Quality website. http://www.ahrq.gov/professionals/clinicians-providers/guidelines-recommendations/tobacco/clinicians/update/treating_tobacco_use08.pdf. Published May 2008. Accessed April 29, 2015.
41. Reid RD, Mullen KA, Slovinec D’Angelo ME, et al. Smoking cessation for hospitalized smokers: an evaluation of the “Ottawa Model.” Nicotine Tob Res. 2010;12(1):11-18.
42. Seligman HK, Davis TC, Schillinger D, Wolf MS. Food insecurity is associated with hypoglycemia and poor diabetes self-management in a low-income sample with diabetes. J Health Care Poor Underserved. 2010;21(4):1227-1233.
43. Blanchard J, Ogle K, Thomas O, Lung D, Asplin B, Lurie N. Access to appointments based on insurance status in Washington, D.C. J Health Care Poor Underserved. 2008;19(3):687-696.
44. Wu EQ, Zhou S, Yu A, et al. Outcomes associated with insulin therapy disruption after hospital discharge among patients with type 2 diabetes mellitus who had used insulin before and during hospitalization. Endocr Pract. 2012;18(5):651-659.
45. CDC. QuickStats: hospital admission after emergency department (ED) visits, by type of locality-United States, 2007-2009. MMWR. 2012;61(24):454.