DISSERTATION. Jordan Phil Harrop. Graduate Program in Public Health. The Ohio State University. Dissertation Committee: Thomas M.

Transcription

1 Hospital and Community Characteristics Associated with Pediatric Appendectomy Outcomes DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Jordan Phil Harrop Graduate Program in Public Health The Ohio State University 2012 Dissertation Committee: Thomas M. Wickizer, Advisor Deena J. Chisolm Abigail B. Shoben

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3 Copyright by Jordan Phil Harrop 2012

4 Abstract Acute appendicitis is the most common cause for emergent surgery among children in the United States. More than 70,000 cases of confirmed appendicitis are diagnosed in the pediatric population in the United States per year, accounting for some $3 billion in hospital charges annually. Potential complications associated with suspected acute appendicitis include misdiagnosed or negative appendectomy (i.e. the surgical removal of a healthy appendix) and perforated appendicitis (i.e. the rupturing of the appendix). The objective of this dissertation was to examine hospital and community characteristics associated with pediatric appendectomy outcomes. The first study examined negative appendectomy in a cross-sectional analysis of pediatric patients, including a secondary analysis involving adult and pediatric appendectomy volumes; the second study focused on the change in negative appendectomy rates over time in a longitudinal study of pediatric patients; and the third study involved perforated appendicitis among pediatric patients, incorporating county-level healthcare resource and socioeconomic variables into the analysis, in a cross-sectional study. Secondary data from the Healthcare Cost and Utilization Project (HCUP) Kids Inpatient Database (KID), the Nationwide Inpatient Sample (NIS), and the Area Resource File (ARF) were analyzed in logistic regression models employing generalized estimating ii

5 equations in order to determine associations between pediatric appendectomy outcomes and hospital and community characteristics. Study one results showed children s hospitals were associated with lower odds of negative appendectomy, especially for very young patients. Health system membership was not found to be associated with negative appendectomy outcomes generally; however, an exploratory analysis found children s hospitals have higher odds of negative appendectomy when part of multi-hospital health systems. Appendectomy volume (including adult and pediatric surgical volume) was not found to be associated with negative appendectomy rates. Results of the longitudinal study showed general hospitals achieved the greatest decrease in negative appendectomy rates from 2000 to Similarly, urban hospitals were found to have greater decreases in the odds of negative appendectomy over the same time period when compared with rural hospitals. Results for hospital teaching status were mixed. Teaching hospitals appear to have had a greater reduction in odds of negative appendectomy from 2000 to 2003 and 2003 to 2006, but both teaching and nonteaching hospitals had meaningful reductions from 2006 to Perforation rates were not found to be associated with physician density in study three results. However, post-study analysis revealed that increased density of pediatricians is associated with a decrease in the odds of perforated appendicitis. High density of healthcare resource availability, namely, emergency department and operating room density, was found to be associated with increased odds of perforation. Conversely, iii

6 high median income (regardless of patient income) was found to be associated with decreased odds of perforation. iv

7 Dedication To my dear wife, Heidi, and our children. v

8 Acknowledgments Numerous individuals, more than can be mentioned here, deserve recognition for enabling me to reach this academic pinnacle and personal goal. My mother, Tookie, and father, Brad, instilled in my siblings and me desires to learn and to work diligently. My wife, Heidi, and our children sacrificed the last several years in terms of opportunity costs associated with my leaving full-time employment in pursuit of full-time doctoral work. The past several months in particular have been challenging; I thank Heidi for being with our five children and managing the various details of our family s transition while I have begun a new job and completed my dissertation. Eunice Cox (Berkeley High School) and Ted Stoddard (Brigham Young University) improved my capacity for written communication. Several faculty members from The Ohio State University have been of tremendous help along the way. Ann McAlearney, Sharon Schweikhart, Electra Paskett and Shad Morris provided important opportunities and feedback that aided me in the dissertation development process. Tom Wickizer, Abby Shoben and Deena Chisolm have provided the most direct influence on my dissertation, for which I am very grateful. Lastly, I would like to thank Steve Loebs for his overall influence and support, especially for his encouragement to get it done. vi

9 Vita Berkeley High School, Moncks Corner, SC B.S. Business Management Finance, Brigham Young University M.B.A., M.H.A., The Ohio State University Director, Center for Cancer Care, Indiana University Health Goshen Hospital Executive Director, John Stoddard Cancer Center Graduate Research Associate, The Ohio State University Medical Center Present... Executive Director of Operations, Saint Alphonsus Medical Center - Nampa Fields of Study Major Field: Health Services Management and Policy Minor Field: Organizational Behavior and Human Resources vii

10 Publications Harrop JP, Nelson DE, Kuratani DG, Dolan Mullen P, Paskett ED. Translating Cancer Prevention and Control Research into the Community Setting: Workforce Implications. Journal of Cancer Education. February Harrop JP, Dean JA, Paskett ED. Cancer Survivorship Research: A Review of the Literature and Summary of Current NCI-Designated Cancer Center Projects. Cancer Epidemiology, Biomarkers and Prevention. October Paskett ED, Harrop JP, Wells KJ. Patient Navigation: An Update on the State of the Science. CA: A Cancer Journal for Clinicians. July/August Paskett ED, Harrop JP. Patient Navigation Across the Continuum of Care. In Cancer Rehabilitation and Survivorship: Transdisciplinary Approaches to Personalized Care. Eds. Lester J and Schmitt P. ONS Publishing Division, Pittsburgh, PA, McAlearney AS, Oliveri JM, Post DM, Song PH, Jacobs E, Waibel J, Harrop JP, Steinman K, Paskett EP. Trust and Distrust Among Appalachian Women Regarding Cervical Cancer Screening: A Qualitative Study. Patient Education and Counseling. April McAlearney AS, Robbins J, Hirsch A, Jorina M, Harrop JP. Perceived Efficiency Impacts Following Electronic Health Record Implementation: An Exploratory Study of an Urban Community Health Center Network. International Journal of Medical Informatics. December McAlearney AS, Song PH, Rhoda DA, Tatum C, Lemeshow S, Ruffin M, Harrop JP, Paskett ED. Ohio Appalachian Women s Perceived Cost of Cervical Cancer Screening. Cancer. October viii

11 Table of Contents Abstract... ii Dedication...v Acknowledgments... vi Vita... vii Table of Contents... ix Chapter 1: Introduction...1 Research Aims...2 Background and Significance...3 Chapter 2: Hospital Characteristics Associated with Pediatric Negative Appendectomy: A Cross-Sectional Study Introduction Hypotheses Design and Methods Results Discussion ix

12 Chapter 3: Hospital Characteristics Associated with Pediatric Negative Appendectomy: A Longitudinal Study Introduction Hypotheses Design and Methods Discussion Chapter 4: Community Characteristics Associated with Perforated Appendicitis among Pediatric Patients Introduction Hypotheses Design and Methods Results Discussion Chapter 5: Conclusion Objective and Results Summary Policy Implications Future Research Conclusion References x

13 Appendix: Supplemental Data Figures Tables xi

14 Chapter 1: Introduction Acute appendicitis is the most common cause for emergent surgery among children and adults in the United States [1-3]. More than 70,000 cases of confirmed appendicitis are diagnosed in the pediatric population in the United States per year [4], accounting for some $3 billion in hospital charges annually [5]. Potential complications associated with suspected acute appendicitis include misdiagnosed or negative appendectomy (i.e. the surgical removal of a healthy appendix) and perforated appendicitis (i.e. the rupturing of the appendix). Patients with perforated or rupture appendicitis experience more complications [6-10], higher rates of morbidity [7, 8, 11-13] and mortality [14-16], and longer hospital stays [6, 7, 17-19] and higher costs [6, 7] when compared to patients with non-perforated appendicitis. Patients experiencing negative appendectomy have likewise been shown to have higher rates of mortality, longer hospital stays and higher costs when compared to patients with appendicitis [20]. The objective of this dissertation was to examine hospital and community characteristics associated with pediatric appendectomy outcomes. The first study examined negative appendectomy in a cross-sectional analysis of pediatric patients, including a secondary analysis involving adult and pediatric appendectomy volumes; the second study focused on the change in negative appendectomy rates over time in a 1

15 longitudinal study of pediatric patients; and the third study involved perforated appendicitis among pediatric patients, incorporating county-level healthcare resource and socioeconomic variables into the analysis, in a cross-sectional study. This chapter outlines the overall research aims for this dissertation as well as the background and significance of pediatric appendectomy research, including sections discussing the human appendix; appendicitis (presentation and diagnosis); perforated appendicitis; negative appendectomy; physician and hospital impact on appendectomy outcomes; and disparities (patient and geographic) in appendicitis outcomes. Research Aims Study 1: Hospital characteristics associated with pediatric negative appendectomy: a cross-sectional study Aim 1: to determine whether hospital type (i.e. children s hospital, general hospital, children s unit on a general hospital) and patient age interaction is associated with the odds of negative appendectomy Aim 2: to determine whether health system membership is associated with the odds of negative appendectomy Aim 3: to determine whether hospital volume of appendectomies (pediatric and adult) is associated with the odds of negative appendectomy Study 2: Hospital characteristics associated with pediatric negative appendectomy: a longitudinal study 2

16 Aim 4: to determine whether hospital type (i.e. children s hospital, general hospital, children s unit on a general hospital) is associated with a change in the odds of negative appendectomy rates over time Aim 5: to determine whether hospital location (i.e. rural vs. urban) is associated with a change in the odds of negative appendectomy rates over time Aim 6: to determine whether hospital teaching status (i.e. teaching vs. nonteaching) is associated with a change in the odds of negative appendectomy rates over time Study 3: Community characteristics associated with perforated appendicitis among pediatric patients Aim 7: to determine whether availability of healthcare resources (e.g. primary care physician density, etc. at the county level) is associated with the odds of perforated appendicitis Aim 8: to determine whether socioeconomic/environmental variables (e.g. median income, at county level) are associated with the odds of perforated appendicitis Background and Significance Human Appendix The human appendix (also vermiform appendix, cecal appendix, and caecal appendix) is an appendage of the cecum measuring approximately 10cm by 7-8mm, with a luminal (internal) diameter of 1-3mm [21]. While the function of the appendix in humans in not clearly understood, Darwin suggested the appendix is an evolutionary remnant of a cecum that would have been used to digest leaves and other plant materials [22]. More recent epidemiological findings suggest the appendix may play a role in 3

17 immune function [23, 24], a belief advanced over 110 years ago by Berry [25]. As evidenced by the frequency of appendectomies performed in the United States and elsewhere, the appendix is obviously not a vital organ necessary for the survival of modern humans. Appendicitis As noted previously, acute appendicitis is the most common cause for emergent surgery among children and adults in the United States [1-3]. The principal cause of appendicitis is obstruction of the appendiceal lumen; the result of the obstruction is twofold. First, intraluminal fluid builds up and causes the appendix to become distended or swollen. Second, the lack of appropriate venous and lymphatic drainage allows bacteria to grow within the appendix, leading to potential ischemia and necrosis and, if untreated, perforation and peritonitis [26]. Appendicitis most commonly occurs in the year old age range [27] and rarely occurs in children under the age of two; when appendicitis occurs in those under the age of two, the appendix is often perforated [26]. Appendicitis is more common among males than females, and whites have a 1.5 times greater likelihood of developing appendicitis when compared to non-whites [27]. Heredity has been shown to play a role in pediatric appendicitis [28, 29]. In one study, children with appendicitis were twice as likely to have a family history of appendicitis as children with right iliac fossa (RIF) pain but no appendicitis [30]. Additionally, family history of appendicitis has been correlated with an increased risk for appendiceal perforation [31]. 4

18 In additional to genetics, there is evidence of seasonality being positively associated with appendicitis. A study by the Centers for Disease Control and Prevention (CDC) found the incidence of appendicitis in the United States to be 11.3% higher in summer than in winter months [27]. Further, in a study analyzing the rates of perforated appendicitis among pediatric patients in the United States, rupture rates were found to be highest in the fall (OR=1.12; 95% CI= ) and winter months (OR=1.11; 95% CI ) [32]. Trauma and appendicitis are two of the most common pediatric emergencies [33]. It has long been debated whether trauma causes appendicitis [33]. One retrospective study found that 5 out of 29 patients who experienced abdominal trauma were found to have acute appendicitis [34]. However, such small sample sizes leave this question of the association between trauma and appendicitis largely unanswered. While direct causation has not been clinically proven, it is suggested that practitioners be mindful of potential appendicitis in cases of abdominal trauma given an apparent correlation between the two [33, 34]. Clinical Presentation The diagnosis of appendicitis in children is difficult [35] and is generally considered to be more challenging than diagnosing appendicitis in adults [36]. The rate of missed appendicitis in children (i.e. not correctly diagnosing appendicitis when a child presents with appendicitis) ranges from 7.5%-37% [36-38]. Since textbook presentations of pediatric appendicitis are uncommon, diagnosis may be more difficult at the early stages of appendicitis [39]. 5

19 Young children are more difficult to diagnose than are older children [26] not only because they may not be able to verbally describe their symptoms but also because their symptoms are different than those in older children and adults. While abdominal pain, low-grade fever, and tenderness and guarding of the RIF are common symptoms in older children and adults, children less than two years of age may present with irritability, vomiting, grunting, abdominal distension, diarrhea, and right hip pain or limp, thus complicating diagnosis [40]. Moreover, many other ailments mimic RIF pain and are sometimes mistaken for appendicitis. The following is a list of such confusing diagnoses as provided by a recent review: Acute gastroenteritis Constipation Henoch-Schonlein purpura Intussusception Lobar pneumonia Meckel s diverticulum Mesenteric lymphadenitis Mittelschmerz Torted ovarian cyst Right-sided pyelonephritis Pelvic inflammatory disease Ectopic pregnancy Renal calculi Urinary tract infection [26] Diagnosis Despite the common occurrence of appendicitis, [n]o single historical variable or physical examination finding has a good ability to predict appendicitis [39]. Various 6

20 clinical tools are employed by physicians in attempts to diagnose appendicitis, including clinical examination, laboratory tests and imaging studies. Clinical Examination The first line of defense in diagnosing appendicitis is clinical or physical examination. In children presenting with abdominal pain, the most reliable sign of appendicitis is fever (likelihood ratio [LR], 3.4; 95% confidence interval [CI], ). For children without a fever, the likelihood of appendicitis is dramatically reduced (LR, 0.32; 95% CI, ). Additionally, abdominal pain migrating to the RIF (LR range, ) increases the likelihood of appendicitis more than RIF pain alone (summary LR, 1.2; 95% CI, ). Rebound tenderness (i.e. pain upon the removal of pressure as opposed to pain at the application of pressure) also increases the likelihood of appendicitis (summary LR, 3.0; 95% CI, ), while its absence reduces the odds (summary LR, 0.28; 95% CI, ) [41]. The sensitivity of clinical examination alone (i.e. not in combination with other diagnostic modalities) ranges from 54% to 70% in children, as compared to 70-87% in adults [17, 42-45]. Clearly, physical examination by itself is less effective in children than in adults and does not always provide sufficient information for an accurate diagnosis of appendicitis. Laboratory Tests A variety of laboratory tests have been utilized when appendicitis is suspected. A review of the clinical utility of white blood count (WBC) in the diagnosis of acute appendicitis in children concluded that an elevated WBC alone is neither sensitive nor 7

21 specific enough to diagnosis appendicitis in children [46]. The combination of an elevated WBC and a left shift (increased number of immature leukocytes), however, has a positive likelihood ratio of 9.8 and specificity of 94% [47]. When appendicitis is suspected clinically, C-reactive protein (CRP) > 10 mg/dl has a specificity of 52-82% and a sensitivity of 48-75% [48-50]. Normal CRP and WBC counts, however, do not rule out appendicitis in children [26]. Urinalysis is not useful in diagnosing appendicitis, though a urinary human chorionic gonadotropin (HCG) test is recommended in order to rule out ectopic pregnancy (which mimics appendicitis insofar as RIF pain is concerned) in female patients [26]. Imaging Studies Ultrasound (US) was first suggested for evaluation of appendicitis in 1986 [51] and has since become a mainstay in the evaluation of children with possible appendicitis in the United States due to its affordability [52], safety, and wide availability [53]. Relative to computed tomography (CT), US is also a less invasive test (especially when CT involves the use of contrast). Many experts consider US to be the preferred imaging test in children with appendicitis due to radiation exposure risk from CT [54, 55]. Ultrasonography used for the diagnosis of acute appendicitis in the pediatric population has shown sensitivity and specificity rates of 85-90% and %, respectively [56]. An editorial by Taylor [53] cites 14 studies [56-69] involving more than 10,000 children who received US for the evaluation of potential appendicitis (also cited in [39]). 8

22 The range of specificity for US in these studies is 88%-99%, and the range for US accuracy is 82%-99%; both ranges, according to Taylor, are acceptable [53]. Such accuracy may be why radiologists have reported feeling more confident with their interpretations of CT versus US study results when evaluating children with suspected appendicitis [76]. However, the range for US sensitivity has been widely variable: 50% - 100%. A major criticism of US is that negative US findings do not rule out appendicitis visualization of the appendix is needed and visualization rates vary widely (22%-98%) [53]. Advantages of CT include high accuracy and the ability to make alternative diagnoses. One study conducted with adult and pediatric appendicitis patients at Massachusetts General showed that appendiceal CT lowered the rates of negative appendectomy from 20% to 7% and lowered the rates of perforation from 22% to 14% [70]. A similar study reported that CT utilization potentially reduced the negative appendectomy rate from 50% to 17% (P=.03) [71]. Another study reported significantly better preoperative diagnosis due to CT scan utilization, thus drastically reducing the negative appendectomy rate while having no impact on the perforation rate [72]. An ongoing debate exists regarding the risk harmful radiation exposure related to CT scans has in causing cancer, especially among pediatric patients. For example, in a study wherein the authors concluded CT did not reduce negative appendectomy rates at their hospital, the authors provided this warning: Potentially harmful radiation exposure should prompt pediatric surgeons to reevaluate the role of CT scanning in the 9

23 management of children with suspected appendicitis [77]. More recently, during a November 2010 meeting of the Radiological Society of North America, participants were polled as to whether cancer risks should be taken into consideration when ordering CT scans; the attendees were split in their responses [78]. Proponents of CT scanning suggest is would be an astounding failure of mission and an unconscionable abdication of responsibility to our patients to eschew CT and allow the current radiation scare to return us to the dismal days of uncertain diagnoses, error, and patient misfortune [79]. Whether or not CT is harmful is a question beyond the scope of this dissertation; the important understanding is that CT utilization has been called into question, despite what, by all accounts, are superior results over US in diagnosing appendicitis. Kaiser et al. report that overall perforation rates dropped from 32% in 1991 to 7.3% in 2000 due to the introduction and utilization of CT and US [80]. However, Flum et al. found the utilization of advanced radiologic imaging such as CT and ultrasound during the late 1980s and 1990s was not associated with a decline in perforation rates in a population-based analysis conducted in the state of Washington [81]. Lee et al. arrived at similar conclusions, adding that CT and US may increase delays in diagnosis [82]. Therefore, the benefits of CT relative to perforated appendix rates are equivocal at best. Additional factors associated with perforation are discussed in the following section. A Markov-based decision model study concluded that ultrasound is more cost effective than CT in the diagnosis of pediatric appendicitis [52]. While important 10

24 considerations, the economic analysis of healthcare services provided in connection with appendicitis is beyond the scope of this dissertation. Non-Operative Management While operative management has been the standard treatment of suspected pediatric appendicitis, it should be noted that non-operative management (i.e. antibiotic therapy) for simple, uncomplicated appendicitis is seemingly gaining acceptance [83]. However, there is a risk of elevated complication rates in the event of failure of nonoperative management [84]. This dissertation does not address the changes or differences in outcomes between operative vs. non-operative management of suspected appendicitis. Perforated Appendicitis Perforated appendicitis occurs when the appendix ruptures. Rates of perforated or ruptured appendicitis in children range from 23-73% [85]. Patients who were correctly diagnosed with appendicitis versus those whose appendicitis diagnosis was initially missed (i.e. the patient was sent home) have been shown to have lower perforation rates [27, 38, 86]. Ponsky et al. s study of 36 children s hospitals found the perforation or rupture rate to be 70.5% in children aged 4 years and younger and 37.1% in children aged 5-17 years [87], results similar to previous work by Rothrock et al. [37]. The mean rupture rate among the 36 hospitals in Ponsky s study was 35.1% (with a range of 22-62%). Ponsky et al. further report that radiological testing was not associated with lower rupture 11

25 rates, and that hospital volume of appendectomies was not associated with rupture rates even when adjusted for race, sex, age and patient insurance [87]. In a study including adult and pediatric patients, delays in outpatient and inpatient care were attributed to higher rates of perforation. Approximately 52% of the patients developed perforations before their first encounter with the outpatient service of the health system, and roughly 68% of the perforations occurred prior to surgical evaluation and admission [88]. The main point of this study is that perforation occurs nearly 70% of the time before a surgeon has the opportunity to properly evaluate a patient with suspected appendicitis; in other words, perforation is largely a factor beyond the hospital s sphere of influence. Yardeni et al. found that delaying surgery of patients who presented during the night until daytime hours did not significantly impact perforation rates [89]. This supports the assertion that Narsule et al. and others have advanced, namely that perforation rates have more to do with pre-hospital versus intra-hospital delays [90]. Kaiser et al., as cited earlier, report that overall perforation rates dropped from 32% in 1991 to 7.3% in 2000 due to the introduction and utilization of CT and US [80]. As previously referenced, a study conducted with adult and pediatric appendicitis patients at Massachusetts General showed that appendiceal CT lowered the rates of perforation from 22% to 14% [70]. An additional study from Children s Hospital Boston showed that implementation of a US-CT imaging protocol reduced the perforation rate from 35.4% to 15.5% [91]. However, as noted earlier, Flum et al. found the utilization of 12

26 advanced radiologic imaging such as CT and US during the late 1980s and 1990s was not associated with a decline in perforation rates in a population-based analysis conducted in the state of Washington [81]. In a study wherein the odds of perforated appendix was analyzed by quartiles, perforation rates were found to decrease with an increase in the density of pediatricians, with a significant result in the highest density level quartile (OR=0.88; 95% CI= ). A decreasing trend was also found for high density of general surgeons (OR=0.91; 95% CI= ) [92]. Since the study did not include pediatric surgeons, the results may be different for pediatric surgeons. This same study found the density of emergency department physicians and radiologists, as well as number of hospitals, number of hospitals with an ED, number of hospitals with a CT, and number of surgical procedures performed in a county, were not associated with perforation rates [92]. The impact of hospital surgical volume on perforation rates is equivocal. One study showed a positive relationship between volume and perforation rates[93], while two studies showed no statistically significant relationship [4, 94]. Among pediatric patients, perforation rates have been shown to be higher among minorities [93, 94] and among those without insurance or with public insurance [4, 87]. However, Lee et al. found that lower socioeconomic and minority patients in Southern California did not have higher rupture rates or longer length of hospital stays than patients in higher socioeconomic or non-minority groups when access to care was equal. The authors conclude that disparities reported in the literature regarding pediatric 13

27 appendicitis outcomes can be eliminated with equal access to care [95]. This is supported by a study that found that preventive visits to primary care physicians by Medicaid patients have been shown to be inversely associated with the rate of perforation, suggesting the importance of access to primary care [96]. However, such a conclusion may not be universally applicable. For example, equal access to care did not remove disparities in care in Bratu et al. s study of Canadian pediatric appendicitis patients [97]. Some studies have found no impact of socioeconomic indicators, such as Camp et al. s study that included a handful of county-level socioeconomic factors (household size, median household income, married-couple families, children aged 0-17 in poverty, and people 25 years or older with at least a high school diploma) that were not found to be significantly related to perforation rates and for which data were not presented [92]. Among pediatric patients, perforated appendicitis has been shown to correspond with delays in presentation, diagnosis, and treatment [31, 98]. Brender et al. found that treatment delays of 36 hours or more were associated with a 65% increased odds of perforation, that professional delay was significantly longer in the perforated group versus the normal appendicitis group, and that parental delays were not significantly different between groups [31]. This suggests professional delays in outpatient and inpatient care indeed play a role in timeliness of care and in prevention of perforation. As summed up by authors of a comparable study, The primary-care physician and consulting surgeon have crucial roles in diagnosing the disease [appendicitis] early in its course [98]. 14

28 Conversely, a four-year retrospective review of pediatric appendectomy patients at Children s Hospital of Buffalo showed that patients with Medicaid or no insurance had higher rates of perforation (44% for Medicaid/uninsured vs. 27% for HMO, 23% for private, P<.05); longer presentation of symptoms (Medicaid/uninsured, 47.3+/-4.1 hours; HMO /- 1.9 hours; private, / hours, P<.01); and longer hospital stay (Medicaid/uninsured 7.9, +/- 0.9 days; HMO, 4.8 +/ days; private, 4.6 +/ days; P,.01). The authors conclude that primary care gatekeepers did not appear to add to delay, but parents of Medicaid or uninsured patients may have failed to recognize the seriousness of their children s symptoms or did not seek care in a timely fashion due to financial or logistical reasons [99]. In a 12 institution-study (2 teaching, 10 non-teaching), Lee et al. found higher perforation rates at teaching hospitals versus non-teaching hospitals (37% vs. 30%, P<.0001) [100]. While the reasons for this difference are not entirely clear, it is interesting to note that the teaching hospitals in this study were more prone to nonoperative management of perforated appendicitis (7.4% vs. 12.8%, p = ) [100]. This may suggest that teaching hospitals are more prone to delay surgery at the outset for a patient with suspected appendicitis, which may be a partial explanation for the higher perforation rates in teaching vs. non-teaching hospitals. Negative Appendectomy Negative appendectomy is the term referring to an appendectomy being performed when, in fact, the appendix is healthy. Negative appendectomy is also referred 15

29 to as misdiagnosed appendicitis. Traditional understanding regarding the relationship between negative appendectomy and perforation, as reflected by Velanovich et al. s 1992 report, has been that the two are inversely related (i.e., increasing negative appendectomy rates would, ceteris paribus, reduce perforation rates) [15]. However, this assumption has been questioned in the literature as perforation is a function of delay in seeking/receiving care while negative appendectomy is a function of accurate diagnostic capabilities [88]. Therefore, it is important to conduct independent analyses of negative appendectomy rates and perforation rates as opposed to drawing conclusions about one via results from the other. A study of the impact of negative appendectomy using 1997 nationwide data found a negative appendectomy rate of 15.3% that resulted in an estimated $741.5 million in hospital charges. Further, patients undergoing a negative appendectomy, as opposed to patients who had appendicitis, experienced longer lengths of stay (5.8 vs. 3.6 days, P<.001), had higher case fatality rates (1.5% vs. 0.2%, P<.001), and had greater rates of infectious complications (2.6% vs. 1.8%, P<.001). These results prompted the study authors to conclude that negative appendectomy is not, as oftentimes historically considered, a benign intervention [20]. Ponsky et al. reported the highest negative appendectomy rates, excluding children under 5 years of age, occur in females aged years [87]. A nationwide study of negative appendectomy rates among patients of all ages similarly found that 16

30 being female was strongly associated with negative appendectomy (OR=2.7; 95% CI= ) [20]. Unlike perforation, delays in outpatient or inpatient settings have not been found to be significantly associated with a change in negative appendectomy rates [88]. Rather, negative appendectomies are related to the overlap of symptoms of diseases that mimic appendicitis [88]. High rates of negative appendectomy are probably a function of limitations in diagnostic capacities [20]. Such capacities may be improving, as several studies have attributed reduced negative appendectomy rates to CT utilization. As mentioned previously, a study conducted with adult and pediatric appendicitis patients at Massachusetts General showed that appendiceal CT lowered the rates of negative appendectomy from 20% to 7% [70]. Another study reported that CT utilization potentially reduced the negative appendectomy rate from 50% to 17% (P=.03) [71]. Further, Kaiser et al. report that negative appendectomy rates dropped from 23% in 1991 to 4% in 2000 due to the introduction and utilization of CT and US [80]. Another study from Children s Hospital Boston showed that implementation of a US-CT imaging protocol reduced the negative appendectomy rate from 14.7% to 4.1% [91]. However, such findings from study environments (i.e. individual hospitals) have not always been sustained at the population level. As noted earlier, Flum et al. found the utilization of advanced radiologic imaging such as CT and ultrasound during the late 1980s and 1990s was not associated with a decline in negative appendectomy rates in a population-based analysis conducted in the state of Washington [81]. However, a 10-17

31 year review of national negative appendectomy rates in adults found a steady decline in the negative appendectomy rate, from 14.7% in 1998 to 8.5% in The authors of the study concluded the favorable trend may be attributable to better diagnostics [101]. A comparable study of pediatric negative appendectomy rate trends shows a decreasing trend from 8.1% in 2000 to 5.2% in 2006 [102]. The evidence, therefore, suggests that advances in imaging technology have likely reduced negative appendectomy rates. The odds of negative appendectomy have been reported to be higher for teaching hospitals (OR=1.1; 95% CI = ) and rural hospitals (OR=1.2; 95% CI= ) and among patients in the 0-4 age group (OR=3.0; 95% CI= ) [20]. Oyetunji et al. found that children s hospitals had lower negative appendectomy rates (OR=0.60; 95% CI= ) when compared with general hospitals. Curiously, negative appendectomy rates were significantly higher for children s units in general hospitals when compared with general hospitals (OR=1.46; 95% CI= ) [102]. Physician Impact on Appendectomy Outcomes Primary care physicians (pediatricians and family practice physicians); emergency department physicians; surgeons (general and pediatric); and radiologists (general and pediatric) all play key roles in the accurate and timely diagnosis of appendicitis [92]. Green et al. reported that approximately 50% of children presenting with abdominal pain who received a surgical consult were referred by a primary care physician [103]. Therefore, the role of primary care physicians in providing timely referral is important. 18

32 Few studies of pediatric and general surgeons, as it relates to pediatric appendectomy outcomes, exist in the extant literature. One such study showed that children with perforated appendicitis have less complications and shorter lengths of hospital stay when their care is managed by pediatric surgeons versus HMO adult general surgeons. In this study, the pediatric surgeons followed clinical guidelines and protocols while the adult general surgeons were found to have not practiced in such a uniform manner. Further, the authors of the study suggest that, by virtue of their training and experience, pediatric surgeons have assumed a leading role in the treatment of children with appendicitis [104]. A recently published study observed, Children can be managed either by general surgeons or pediatric surgeons depending on the organization of the emergency service; there may be a higher incidence of removal of a normal appendix in non-specialized services [83]. Somme et al. confirmed such a hypothesis in their study of pediatric appendectomy in Ontario, Canada, namely, that pediatric surgeons had lower negative appendectomy rates (8.3%) than general surgeons (13.4%) when performing appendectomies on pediatric patients; this difference was significant at the P<.0001 level [105]. As previously noted, Camp et al. found perforation rates decreased in areas with high density of pediatricians (OR=0.88; 95% CI= ). A decreasing trend was also found for high density of general surgeons (OR=0.91; 95% CI= ) [92]. Since the study did not include pediatric surgeons, results may be different for pediatric surgeons. 19

33 A single-facility study conducted at a children s hospital found that clinical outcomes for pediatric and general surgeons were not significantly different and the rates of negative appendectomy were comparable [106]. However, it is unclear to what degree institutional guidelines and personnel knowledge may have impacted these results. The authors note that general surgeons with low-volume pediatric appendectomy may have high volume in adult appendectomy, and that the actual surgical procedure is comparable (except in the very young). While this seems plausible, there is clear evidence that successful and timely diagnosis of appendicitis in children is more difficult than for adults [36]. Therefore, it may well be that the lack of a difference in negative appendectomy rates reported by Emil and Taylor is attributable to the children s hospital staff being in a better position to make a correct and timely diagnosis of pediatric appendicitis than general hospital staff. Pediatric surgeons have been shown to treat younger patients with more severe appendicitis when compared to general surgeons [106]. In a study analyzing differences in outcomes between a teaching and a non-teaching hospital, study authors note that at both hospitals children 5 years of age and younger are operated on by pediatric surgeons [107]. Hospital Impact on Appendectomy Outcomes Hospital characteristics have been considered to be an important factor in pediatric healthcare outcomes [4, ]. Chisolm et al. studied the 2000 HCUP KID database and found that laparoscopic appendectomy, considered to be an innovative 20

34 surgical procedure when compared to traditional open appendectomy, was utilized significantly more at children s hospitals than general hospitals. The authors conclude that children s hospitals may be more likely to adopt innovative surgical procedures than general hospitals [108]. As Chisolm et al. note, HCUP data do not provide information regarding physician specialty. However, it is common knowledge that children s hospitals are primarily staffed with pediatric surgeons, whereas most general hospitals rely upon general surgeons [108, 112]. Similarly, the majority of pediatric radiologists practice in children s hospitals [113]. The increased training received by pediatric subspecialists in children s hospitals may be attributable to improved healthcare outcomes. Pediatric patients receiving appendicitis care in a UK general hospital had a greater risk of complications and readmission compared to patients being managed in a pediatric surgical unit. However, the pediatric surgical unit implemented a care pathway that was credited with improving outcomes; outcomes prior to the care pathway s implementation were comparable for both facilities [114]. It is quite possible that pediatric hospitals and, in the case of the aforementioned study, pediatric units, are more inclined to implement care pathways in a similar vein to how Chisolm et al. theorize that children s hospitals adopt innovative surgical techniques sooner than their general hospital counterparts. Children s hospitals are more likely to have access to pediatric radiologists who are familiar with pediatric imaging techniques; Taylor credits availability of such 21

35 expertly trained professionals in part to successes in lowering the negative appendectomy and perforation rates at Children s Hospital Boston from 14.7% to 4.1% and 35.4% to 15.5%, respectively [53, 91]. In explaining their finding that lower negative appendectomy rates are associated with children s hospitals, Oyetunji et al. suggest their findings may be related to volume and expertise [102]. Smink et al. found that higher hospital volumes of pediatric appendectomy indeed were associated with lower negative appendectomy rates, which supports Oyetunji et al. s assertion [109] and is consistent with the conclusion Ponsky et al. draw, namely, that negative appendectomy rates decrease with increased volume of appendectomy procedures among pediatric patients [87]. However, no study to date has combined hospital type (i.e. children s versus general versus children s unit in a general hospital) with appendectomy volume either among pediatric or inclusive of adult and pediatric patients in analyzing the impact each has on negative appendectomy rates and perforation rates. Multiple studies have shown that hospital surgical volumes are associated with improved patient outcomes such as decreased mortality, complications, length of stay, and misdiagnosis of appendicitis [109, ]. Smink et al. did not control for children s hospital versus general hospital status due to collinearity in the 1997 HCUP data; children s hospitals had on average higher volumes of pediatric appendectomy cases when compared with general hospitals [109]. However, Chisolm et al. included both children s hospital status and volume in their analysis of 2000 HCUP data [108]. 22

36 As noted previously, a single-facility study conducted at a children s hospital found that clinical outcomes for pediatric and general surgeons were not significantly different and the rates of negative appendectomy were comparable [106]. However, it is unclear to what degree institutional guidelines and personnel knowledge may have impacted these results. The authors note that general surgeons with low-volume pediatric appendectomy may have high volume in adult appendectomy and that the actual surgical procedure is comparable across age groups [106]. Alexander et al. s study of the differences in outcome for patients being managed by pediatric and general surgeons was conducted on the same pediatric hospital floor, which was part of an academic medical center, with patients receiving care from the same medical, nursing and support staff [104]. While the study was helpful in examining the potential differences in outcomes in care provided by pediatric and general surgeons, it does not shed light on the potential impact hospital type (i.e. pediatric vs. general, teaching vs. nonteaching, rural vs. urban) has on outcomes. One study mentioned earlier analyzed whether the density of certain providers was associated with perforation rates. Of the four types of physicians included in the analysis (pediatrician, emergency medicine physician, radiologist, surgeon), the only significant finding was that as the density of pediatricians increased the perforation rate decreased. However, this study only looked at general surgeons, not pediatric surgeons, and did not include children s hospital versus general acute hospital as a control variable. It did control for teaching/nonteaching and rural/urban hospital type [92]. 23

37 In a nationwide study of adult and pediatric patients, Flum et al. found the odds of negative appendectomy were higher for teaching hospitals (OR=1.1; 95% CI = ) [20]. Lee et al. studied two California medical centers one teaching, one nonteaching and found that children treated in the presence of medical trainees (i.e. at the teaching hospital) experienced no adverse outcomes. Specifically, the authors found that children with non-perforated appendicitis had similar postoperative morbidity and shorter lengths of stay at the teaching hospital when compared with the nonteaching hospital and that patients with perforated appendicitis had similar postoperative morbidity and length of stay at the two centers [107]. In a follow-up study expanded to 12 hospitals (2 teaching and 10 nonteaching) Lee et al. report additional evidence that surgical trainees had no adverse impact on pediatric appendectomy outcomes in terms of wound infection rates, readmission, and length of stay [100]. Further, Lee et al. s expanded study reported greater perforation rates at the teaching hospitals (37% vs. 30%, P<.0001) and a higher rate of laparoscopic appendectomy at the nonteaching hospital (39% vs. 52%, P<.0001).[100]. Impact of Disparities on Appendectomy Outcomes As cited in Nwomeh et al. [119], significant racial and ethnic disparities have been reported in the pediatric literature [ ]. Disparities related to perforated appendicitis are also well documented [4, 94, 97, 125]. However, not all studies have found disparities; for example, a study of acute appendicitis in Latino children found no differences in perforation rates based upon socioeconomic or ethnic differences [126]. 24

38 In a nationwide study of adult and pediatric patients, Flum et al. found the odds of negative appendectomy were higher for rural hospitals than for urban hospitals (OR=1.2; 95% CI= ) [20]. This finding was supported by To and Langer s analysis of Canadian data that showed a higher negative appendectomy rate in non-metropolitan populations [127]. Geographic disparities experienced by pediatric patients living in rural areas who have higher rates of perforated appendicitis are also documented in the literature [128]. As mentioned previously, a four-year retrospective review of pediatric appendectomy patients at Children s Hospital of Buffalo showed that patients with Medicaid or no insurance had higher rates of perforation (44% for Medicaid/uninsured vs. 27% for HMO, 23% for private, P<.05); longer presentation of symptoms (Medicaid/uninsured, 47.3+/-4.1 hours; HMO /- 1.9 hours; private, / hours, P<.01); and longer hospital stay (Medicaid/uninsured 7.9, +/- 0.9 days; HMO, 4.8 +/ days; private, 4.6 +/ days; P,.01). The authors conclude that primary care gatekeepers did not appear to add to delay, but parents of Medicaid or uninsured patients may have failed to recognize the seriousness of their children s symptoms or did not seek care in a timely fashion due to financial or logistical reasons [99]. Conversely, a previously-referenced state-wide study involving Maryland Medicaid patients found no significant difference in terms of insurance status and perforation rates [96]. A similar study of pediatric patients conducted at Children s Hospital (Columbus, OH) over a three year period found no significant results in 25

39 perforation rates by insurance status. Further, the authors found no significant difference between race, educational level or income status; additionally, radiologic imaging use was comparable among all socioeconomic and race groups [119]. A study of 102,692 Canadian patients and 276,890 American patients (both adults and children) found that perforation rates in Canada were not associated with socioeconomic status (measured by postal code income quintiles) while there was a significant, inverse relationship between SES and rupture rate for American patients in the lowest versus the highest income levels (OR=1.20; 95% CI= ). The authors of this study suggest that access to emergency surgical care is related to SES in the United States but not in Canada, potentially reflecting concerns over ability to pay in the United States (where universal care was not provided during the time of the study) or lack of a relationship with a primary care provider [129]. Contrasting the results of the aforementioned study, another Canadian study reviewing 20 years of pediatric patient outcome data found that lower SES was associated with increase appendiceal rupture rates in Canada, despite pediatric patients having free access to care [97]. This suggests that perforation rates among pediatric patients may not be entirely attributable to access to care, but may be influenced by other factors. Bickell and Siu noted in an editorial column that delay in the diagnosis and treatment of appendicitis can result in perforation, peritonitis, and death, and that such delays can be the result of a combination of patient, physician and health system factors 26

40 [130]. Patient-related delays may be attributable to knowledge, beliefs, coping strategies, symptom severity, financial constraints, and past experiences with symptoms and the health care delivery system [130]. Physician-related delays may include, availability, diagnostic certainty, and understanding of new technologies and medical effectiveness [130]. System-level factors may include, availability of diagnostic and treatment technologies, access to care, gatekeeping and referral policies, use of guidelines and protocols and triage approaches [130]. Further, Bickell and Siu cite studies wherein delays are primarily attributed to the patients prior to presentation for care at a hospital [7, 8] as well as studies wherein delays are attributed to the physicians post patient presentation [19, 31]. Gadomski and Jenkins find that lower perforation rates are associated with more preventative primary care visits [96], potentially indicating that patients with access to primary care have reduced delays in seeking care for possible appendicitis. A study analyzing the rates of perforated appendicitis among pediatric patients in the United States found rupture rates were highest on Mondays (OR=1.16; 95% CI= ), presumably due to delays in seeking care, diagnosis, and treatment associated with the weekend [32]. Conclusion The purpose of this chapter was to provide an overview of the research aims for this dissertation as well as a review of the background and significance of pediatric appendectomy research. Sections discussing the human appendix; appendicitis 27

41 (presentation and diagnosis); perforated appendicitis; negative appendectomy; physician and hospital impact on appendectomy outcomes; and disparities (patient and geographic) in appendicitis outcomes were presented. In sum, pediatric appendicitis is a clinical phenomenon uniquely different from adult appendicitis in its presentation. Many strides have been made to improve overall pediatric appendectomy outcomes. The research aims set forth earlier in this chapter summarize how this dissertation will favorably add to the body of work in this field. 28

42 Chapter 2: Hospital Characteristics Associated with Pediatric Negative Appendectomy: A Cross-Sectional Study Introduction Hospital characteristics have been considered to be an important factor in pediatric healthcare outcomes [4, ], including pediatric appendectomy. One of the major characteristics of a hospital is its status as a children s versus a general hospital. It is common knowledge that children s hospitals are primarily staffed with pediatric surgeons, whereas most general hospitals rely upon general surgeons [108, 112]. Similarly, the majority of pediatric radiologists practice in children s hospitals [113]. The increased training received by pediatric subspecialists may lead to improved healthcare outcomes for the treatment of pediatric appendectomy in children s hospitals as opposed to general hospitals. An additional hospital characteristic that may lead to improved quality of care is membership in a health system, as health systems are able to share information and resources among hospital members [131]. Overall declines in the rate of negative appendectomy are widely attributable to advances in imaging, CT in particular [70-72]. However, hospital type appears to have a role to play. In explaining their finding that lower negative appendectomy rates are associated with children s hospitals, Oyetunji et al. suggest their findings may be related 29

43 to volume and expertise [102]. Smink et al. found that higher hospital volumes of pediatric appendectomy indeed were associated with lower negative appendectomy rates, which supports Oyetunji et al. s assertion [109] and is consistent with the conclusion Ponsky et al. draw that negative appendectomy rates decrease with increased volume of appendectomy procedures among pediatric patients [87]. Multiple studies have shown that hospital surgical volumes are associated with improved patient outcomes such as decreased mortality, complications, length of stay, and misdiagnosis of appendicitis [109, ]. Smink et al. did not control for children s hospital versus general hospital status due to collinearity in the 1997 HCUP data; children s hospitals had on average higher volumes of pediatric appendectomy cases when compared with general hospitals [109]. However, Chisolm et al. included both children s hospital status and volume in their analysis of 2000 HCUP data [108], thus indicating that possible collinearity issues in the data may have been limited to A single-facility study conducted at a children s hospital found that clinical outcomes for pediatric and general surgeons were not significantly different and the rates of negative appendectomy were comparable [106]. However, it is unclear to what degree institutional guidelines and personnel knowledge may have impacted these results. The authors note that general surgeons with low-volume pediatric appendectomy may have high volume in adult appendectomy and that the actual surgical procedure is comparable across age groups (except in the very young). Like Camp et al. [92], I hypothesize that certain health system factors and infrastructure may influence the timely diagnosis of appendicitis, thus potentially impacting perforation rates. Further, I theorize these same 30

44 health system factors and infrastructure may impact the accurate diagnosis of appendicitis, thus potentially impacting misdiagnosis rates. No study to date has combined hospital type (i.e. children s versus general versus children s unit in a general hospital), appendectomy volume (either among pediatric or inclusive of adult and pediatric patients), and health system membership in analyzing the impact each has on negative appendectomy rates. Additionally, no study has evaluated volume and hospital type and hospital type and age interaction. This study addresses these gaps in the pediatric appendectomy literature. The conceptual framework that guided this study is Avedis Donabedian s structure, process, outcome framework of quality assessment (see Figure 1) [132]. This framework is widely used by health services researchers to categorize variables and was used to categorize variables in this study. Structure refers to the environment in which care is provided by caregivers and received by the patient, and can be further broken up into patient-level factors impacting structure (e.g. socioeconomic status, insurance coverage, age, gender, race, geographic location, etc.); physician-level factors (e.g. education/training, specialty, expertise, volume, referral patterns, etc.); and hospital-level factors (e.g. equipment, facilities, staffing expertise, specialty designation, volume, system participation, etc.). Process refers to the actual provision of care by caregivers and reception of care by the patient. Again, patient-level factors (e.g. managing symptoms, seeking out care, scheduling/preference constraints, compliance with medical advice, etc.), physician-level factors (e.g. clinical judgment, diagnostic procedures/tests; 31

45 diagnosis and treatment decisions) and hospital-level factors (e.g. care protocols and guidelines, diagnostic procedures and tests, accessibility to facilities, etc.) all contribute to the process of care. Finally, outcome refers to health outcomes, which in this case is negative appendectomy. Upon first observation, the structure, process, outcome framework may appear to suggest rigid causal relationships. However, Donabedian felt causality could only be inferred, perhaps due to the vast number of factors contributing to a patient s overall health status and the difficulty in isolating the impact of treatment effects alone [133]. Hypotheses Hypothesis 1: children s hospitals will be associated with lower odds of negative appendectomy, especially for very young children, when compared to general hospitals, including children s units in general hospitals Hypothesis 2: hospitals participating in health systems will have lower odds of negative appendectomy Hypothesis 3: volume of appendectomies (including pediatric and adult appendectomies) will be inversely associated with the odds of negative appendectomy Design and Methods Data Sources This study utilized data from the Healthcare Cost and Utilization Project (HCUP) Kids Inpatient Database (KID) and the Nationwide Inpatient Sample (NIS) for the year The 2009 KID contains data for inpatient pediatric hospital stays from 4,121 hospitals in 44 states, while the 2009 NIS contains data from 1,050 hospitals in 44 states. 32

46 Over 100 clinical and nonclinical variables for each hospital discharge are available for both the KID and NIS, including diagnoses, procedures, patient demographics (e.g. gender, race, median income for ZIP code, etc.), and hospital characteristics (e.g. hospital type, teaching status, etc.). Eligibility For this study, eligibility was limited to patients 17 years of age and younger at the time of hospital admission that underwent an appendectomy. Further, observations were limited to those with hospital type and health system membership reported, for a total of 46,169 observations being included in the study (see Figure 2). The secondary analysis including adult appendectomy volumes includes patients of all ages, with pediatric patients defined as patients 17 years of age or younger at the time of admission. A total of 10,773 patient observations were included in the secondary analysis; this represents the total number of appendectomy cases (adult and pediatric) in hospitals that are included in both the 2009 NIS and 2009 KID. Negative appendectomy was defined by a combination of diagnosis and procedure codes. Specifically, International Classification of Diseases, Ninth Revision procedure codes of 47.0 (appendectomy), (laparoscopic appendectomy), or (other appendectomy) were used to determine if a patient had a non-incidental appendectomy procedure performed; patients undergoing an incidental appendectomy were excluded from analysis, consistent with prior work on this subject [109]. A diagnosis of appendicitis was defined by an International Classification of Diseases, Ninth Revision diagnosis code of (acute appendicitis with generalized 33

47 peritonitis), (acute appendicitis with peritoneal abscess), (acute appendicitis without mention of peritonitis), 541 (appendicitis, unqualified), or 542 (other appendicitis). Following prior studies, the assumption is that patients with discharge diagnosis codes reflecting appendicitis were accurately diagnosed at the time they underwent appendectomy; conversely, patients who underwent appendectomy but did not have a discharge diagnosis code of appendicitis were misdiagnosed or underwent a negative appendectomy [109]. Dependent, Independent, and Control Variables The dependent variable of interest for this study is misdiagnosed or negative appendectomy. Independent variables include hospital type and age interaction as well as health system membership; total volume of appendectomies performed in adult and pediatric patients were also be included as an independent variable in a secondary analysis. Control variables for this study include patient gender, race, and primary payer as well as hospital teaching status and location. Data Analysis As the outcome of interest negative appendectomy is binary, logistic regression was used. The unit of analysis for this study is the patient. Analyses were performed using Stata Intercooled 11 [134] and SAS software version 9.2 [135]. Since cases from the same hospital are not independent observations, generalized estimating equation (GEE) models with a working exchangeable correlation matrix and robust standard errors were used to adjust for hospital case clustering [136]. No adjustments were made for multiple comparisons. Descriptive statistics of key patient and hospital 34

48 characteristics, unadjusted and age-adjusted negative appendectomy rates, and logistic regression analyses were performed as part of the data analysis for this study. Missing data categories were created for several variables (see Table 1). Age-adjusted rates were calculated standardizing to 2010 population data reported by the U.S. Census Bureau [137]. The unadjusted empirical model tested for the first hypothesis is as follows: g(µ) = β 0 + β 1 (age x children s hospital) + β 2 (age x children s unit) + β 3 (age x general hospital) + β 4 (age x children s hospital) + β 5 (age x children s unit) + β 6 (age 5-9 x general hospital) + β 7 (age 5-9 x children s hospital) + β 8 (age 5-9 x children s unit) + β 9 (age 0-4 x general hospital) + β 10 (age 0-4 x children s hospital) + β 11 (age 0-4 x children s unit). The reference category is year old patients treated in general hospitals, so the exponentiated value of each coefficient (β 1- β 11 ) is the odds ratio comparing that category to the reference. For example, e^ β 1 should be interpreted as the odds of negative appendectomy for patients aged years old treated in children s hospital when compared with patients aged years old treated in general hospitals. Partial F-tests were run for the fully-adjusted model in order to determine if any of the age groups by hospital type interaction outcomes were significantly different from one another. As an example, the null hypothesis of no difference among hospital type by patients aged 5-9 would be Ho: β 6 = β 7 = β 8. The second and third hypotheses were tested by similar models that included coefficients representing health system membership and hospital appendectomy volume. 35

49 Models were adjusted for race, gender, teaching status, hospital location, and payer. Final models were tested for collinearity by calculating the variance inflation factor (vif command in Stata) to minimize multicollinearity concerns [138]. Variables were excluded if they were found to possess a variance inflation factor value above 10. Results The number and percentage of appendectomy cases by hospital type among a variety of patient and hospital demographic areas are depicted in Table 1. The largest number of appendectomy cases occurred in the group of patients aged (39.6%), followed by patients aged (29.4%) and patients aged 5-9 (27.2%). Patients aged 0-4 accounted for the smallest number of appendectomy cases (4.8%). As shown in Table 1, younger patients were treated more frequently in children s hospitals and children s units at general hospitals than they were at general hospitals, while older patients were more frequently treated at general hospitals. The majority of patients were male (56.5%). The largest race group represented was white (43.5%), followed by Hispanic (25.1%), and black (4.5%). Other races accounted for 6.4% of the patients in the study, and 20.5% of the patients did not have a race reported. Sensitivity analysis showed study results were comparable when patients without a reported race were removed from the dataset. Reported race categories, however, may not be accurate; thus, interpretations based upon race are to be made cautiously. The dominant primary payer was private insurance (55.7%), followed by Medicaid (35.8%). Other payers (including self pay, no charge, and Medicare) comprised 8.5% of the total. 36

50 In terms of hospital characteristics, the majority of patients were treated in hospitals that were part of a multiple hospital system (59.9%), in urban settings (88.8%), and in large hospitals (62.3%). Roughly half of the patients were treated in teaching facilities (48.2%). Nearly all children s hospitals and children s units on general hospitals were teaching (25 out of 28 and 91 out of 94, respectively), while 411 general hospitals (out of 2,364) were classified as teaching. All of the children s hospitals and all but one of the children s units on general hospitals were considered urban; of the general hospitals, 1,551 were urban. Unadjusted and age-adjusted negative appendectomy rates are presented in Tables 2.2 and 2.3. The overall unadjusted negative appendectomy rate was 2.5% compared to an age-adjusted overall rate of 2.8%. Children s hospitals had the lowest rates, unadjusted and adjusted, followed by general hospitals and children s units on general hospitals. There were no meaningful differences between negative appendectomy rates for Children s hospitals and general hospitals. It is interesting to note that, among the unadjusted rates, each of the age groups with the exception of the oldest category had the lowest rates in children s hospitals; general hospitals had the lowest unadjusted rates for the oldest age group, though not meaningfully different than children s hospitals. Logistic regression results are provided in Tables 2.4 (primary study) and 2.5 (secondary analysis). The first hypothesis for this study, children s hospitals will be associated with lower odds of negative appendectomy, especially for very young children, was tested using the interaction of hospital type and patient age. Partial F-tests showed statistically significant findings for the two youngest age groups (p< for 37

51 the 0-4 year old age group; p= for the 5-9 year old age group); the two oldest age groups did not have statistically significant differences from one hospital type to another (p=0.40 for the oldest age group and p=0.39 for the second oldest age group). Thus, the odds of negative appendectomy were different by hospital type among the 0-4 and 5-9 age groups, while they were not different for patients in the and age groups. As shown in Table 4, the oldest patients (15-17) treated at general hospitals served as the reference group. When compared with this group, patients in the 5-9 year old age group treated at children s hospitals experienced 42% lower odds of misdiagnosed or negative appendectomy (OR=0.58, 95% CI= ). When the model was adjusted for race and gender, this result was unchanged (OR=0.54, 95% CI= ). When teaching status and location (model 3) and payer (model 4) were added to the other variables, the difference became statistically insignificant but may hold practical relevance (OR=0.61, 95% CI= for model 3; OR=0.61, 95% CI= for model 4). Compared with the oldest patient group treated at general hospitals, the youngest age group treated at children s hospitals had statistically significant higher odds of negative appendectomy. However, the odds of negative appendectomy for the youngest age group were lowest for patients receiving treatment in children s hospital versus general hospitals and children s units on general hospitals. Therefore, support for the first hypothesis was found. Younger children in this study experienced better outcomes in children s hospitals. The second hypothesis, hospitals participating in health systems will have lower odds of negative appendectomy, was supported in the unadjusted (OR=0.85, 95% 38

52 CI= ) and race- and gender-adjusted models (OR=0.85, 95% CI= ), but did not maintain statistical significance when adjusted for hospital teaching status and location and primary payer (OR=0.88, 95% CI= ). Interestingly, when observing the adjusted rates for health system status in Table 3, patients treated at children s hospitals had statistically significant higher negative appendectomy rates when the children s hospitals were members of a multi-hospital health system, whereas general hospitals and children s units on general hospitals had lower negative appendectomy rates when participating as members of health systems versus when acting as freestanding hospitals. This result was further examined in as an exploratory post-hoc analysis that will be described below. The final hypothesis, volume of appendectomies (including pediatric and adult appendectomies) will be inversely associated with the odds of negative appendectomy, was not supported (results presented in Table 5). Although the overall odds of negative appendectomy increases with decreasing volume in the unadjusted logistic model, such trends do not hold in the adjusted models. Based on these results, the notion that greater volume of appendectomies is inversely associated with odds of negative appendectomy is not supported in either the unadjusted model or the fully adjusted model (after adjustment for sex, race, age-hospital type interaction, health system membership, hospital teaching status, and hospital location). An exploratory analysis of health system membership and hospital type was performed after the study was completed. It is important to note that 1,410 general hospitals were part of a health system (compared to 954 that were not), 11 of the 28 39

53 children s hospitals were part of a health system, and 62 of the 94 children s units on general hospitals were part of multi-hospital health systems. This exploratory analysis found that, when compared with general hospitals that are not members of a health system (reference category), general hospitals that are part of a health system have lower odds of negative appendectomy when adjusting for age and hospital type interaction, gender, race, hospital teaching status, hospital location, and payer (OR=0.87; 95% CI= ). Children s units on general hospitals also had lower odds of negative appendectomy when part of a multi-hospital health system as opposed to when an independent general hospital (OR=4.11; 95% CI= for nonmembers and OR=3.12; 95% CI= for members). However, children s hospitals that are part of a health system have higher odds of negative appendectomy compared to those that are not part of health systems (OR=2.00; 95% CI= for members; OR=1.18; 95% CI= for nonmembers). Discussion The results of this study are consistent with prior work in this field, namely, that children s hospitals have lower negative appendectomy rates and children s units at general hospitals have higher negative appendectomy rates when compared with general hospitals [102]. Study results show that children s hospitals are doing particularly better in correctly diagnosing appendicitis in the youngest children. It is not surprising that facilities specializing in the care and treatment of pediatric patients would have more favorable outcomes for pediatric appendectomy misdiagnosis rates. However, the fact that children s units at general hospitals underperform general hospitals in this pediatric 40

54 outcome is curious. Prior work in this area attributes such a finding to surgical volume [87, 102]. However, the secondary analysis conducted with total pediatric and adult surgical volume found no support for the hypothesis that increased appendectomy volume corresponds to decreased negative appendectomy rates when controlling for hospital type. While it appears pediatric units at general hospitals do not provide comparable clinical performance outcomes compared to children s hospitals, the rationale for why children s units at general hospitals maintain poorer performance vis-à-vis general hospitals remains unclear. It may be that pediatric units on general hospitals, with limited expertise in pediatric patient care, are overextended when treating patients that would have fared better at dedicated children s hospitals. Certain contributions of this study, including the just-referenced secondary analysis of appendectomy volume and hospital type, are unique. No prior study has examined the interaction of patient age and hospital type, and this study provides some evidence that younger patients have better outcomes at children s hospitals; those in the 5-9 year old category have better outcomes, while patients aged 0-4 treated in children s hospitals experienced much better results than children treated in children s units on and qualitatively better outcomes that patients treated in general hospitals. Additionally, no study has previously looked at the impact hospital membership in a multi-hospital health system may have on negative appendectomy outcomes. While this finding was not statistically significant, it may hold practical relevance. As the exploratory analysis results indicated, children s hospitals that are part of health systems 41

55 have significantly worse negative appendectomy outcomes vis-à-vis their independent counterparts. Potential benefits of being a member of a health system include the ability to share clinical expertise and process knowledge among health system participants. However, it appears that a potential drawback for children s hospitals participating in multi-hospital health systems is reduced ability to generate favorable pediatric negative appendectomy outcomes. This may be attributable to a diffused focus on a variety of adult and children s health issues as opposed to a sole focus on children s health outcomes. Further study of the reasons for these outcomes differences among hospital type and health system membership is warranted. It is important to keep in perspective the relative meaning of the results of this study. Since the overall negative appendectomy rate was 2.5%, statistically significant reductions in the odds of negative appendectomy may not translate into large numbers of pediatric patients avoiding unnecessary appendectomy procedures. For example, one of the logistic models showed a 15% decrease in the odds of negative appendectomy for patients treated in hospitals that were part of multiple-hospital health systems compared with stand-alone hospitals. A 15% relative reduction in the odds of an overall negative appendectomy rate of 2.5% is roughly 3.75 patients out of 1,000 patients. Given the high incidence of appendicitis among pediatric patients, however, the absolute impact made on potential patient populations is meaningful. Limitations While advances in imaging, previously mentioned, are widely attributed with the overall decrease in negative appendectomy rates, the data source used for this study did 42

56 not include information regarding the availability of imaging modalities in the facilities where patients were treated. The data allowed for study of overall hospital and patient characteristics, however, which was the focus of this study. A potential limitation of this study is the use of administrative data. Administrative data consist of billing codes that, it is hoped, are accurately reflective of the initial clinical observations. As Flum et al. note [20], Addiss et al. [27] were the first researchers to use administrative data in the study of pediatric appendectomy outcomes, and their findings are consistent with robust clinical datasets [7, 70]. Limitations specific to the Healthcare Costs and Utilization Project, of which both the KID and NIS databases are a part, have been well documented [139, 140]. Study results may not be generalizable to non-pediatric patient populations as appendicitis presents differently in children and adults [36]. Additionally, the study population consisted of patients exclusively from the United States. Hence, results may not be generalizable to pediatric populations residing in other parts of the world. 43

57 Chapter 3: Hospital Characteristics Associated with Pediatric Negative Appendectomy: A Longitudinal Study Introduction Hospital characteristics have been considered to be an important factor in pediatric healthcare outcomes [4, ], including pediatric appendectomy. One of the major characteristics of a hospital is its status as a children s versus a general hospital. It is common knowledge that children s hospitals are primarily staffed with pediatric surgeons, whereas most general hospitals rely upon general surgeons [108, 112]. Similarly, the majority of pediatric radiologists practice in children s hospitals [113]. The increased training received by pediatric subspecialists may lead to improved healthcare outcomes for the treatment of pediatric appendectomy in children s hospitals as opposed to general hospitals. Taylor credits availability of such expertly trained professionals in part to successes in lowering the negative appendectomy and perforation rates at Children s Hospital Boston from 14.7% to 4.1% and 35.4% to 15.5%, respectively [53, 91]. Overall declines in the rate of negative appendectomy are widely attributable to advances in imaging, CT in particular [70-72]. However, hospital type appears to have a role to play. In explaining their finding that lower negative appendectomy rates are associated with children s hospitals, Oyetunji et al. suggest their findings may be related 44

58 to volume and expertise [102]. Smink et al. found that higher hospital volumes of pediatric appendectomy indeed were associated with lower negative appendectomy rates, which supports Oyetunji et al. s assertion [109] and is consistent with the conclusion Ponsky et al. draw that negative appendectomy rates decrease with increased volume of appendectomy procedures among pediatric patients [87]. Chisolm et al. studied the 2000 HCUP KID database and found that laparoscopic appendectomy (LA), considered to be an innovative surgical procedure when compared to traditional open appendectomy, was utilized significantly more at children s hospitals than general hospitals. The authors conclude that children s hospitals may be more likely to adopt innovative surgical procedures than general hospitals [108]. This theory may also hold true regarding the adoption and implementation of innovative care protocols and guidelines. Similarly, I theorize that hospitals that are located in urban environments and that are teaching facilities may be more prone to adopt and implement innovative technology and care protocols and guidelines that might improve rates of negative appendectomy over time vis-à-vis hospitals that are rural and that are non-teaching facilities. Like Camp et al., I hypothesize that certain health system factors and infrastructure may influence the timely diagnosis of appendicitis, thus potentially impacting perforation rates. Further, I theorize that these same health system factors and infrastructure may impact the capability for accurate diagnosis of appendicitis, thus potentially impacting misdiagnosis rates over time. While studies have looked at trends of negative appendectomy rates among pediatric patients [102], no study to date has 45

59 looked at the potential impact hospital type (i.e. children s versus general versus children s unit in a general hospital) has on the change in negative appendectomy rates among pediatric patients over time. This study addresses this gap in the pediatric appendectomy literature. The conceptual framework that guided this study is Avedis Donabedian s structure, process, outcome framework of quality assessment (see Figure 1) [132]. This framework is widely used by health services researchers to categorize variables and was used to categorize variables in this study. Structure refers to the environment in which care is provided by caregivers and received by the patient, and can be further broken up into patient-level factors impacting structure (e.g. socioeconomic status, insurance coverage, age, gender, race/ethnicity, geographic location, etc.); physician-level factors (e.g. education/training, specialty, expertise, volume, referral patterns, etc.); and hospitallevel factors (e.g. equipment, facilities, staffing expertise, specialty designation, volume, etc.). Process refers to the actual provision of care by caregivers and reception of care by the patient. Again, patient-level factors (e.g. managing symptoms, seeking out care, scheduling/preference constraints, compliance with medical advice, etc.), physician-level factors (e.g. clinical judgment, diagnostic procedures/tests; diagnosis and treatment decisions) and hospital-level factors (e.g. care protocols and guidelines, diagnostic procedures and tests, accessibility to facilities, etc.) all contribute to the process of care. Finally, outcome refers to health outcomes; in this case, negative appendectomy. Upon first observation, this structure, process, outcome framework may appear to suggest rigid causal relationships. However, Donabedian felt causality could only be 46

60 inferred, perhaps due to the vast number of factors contributing to a patient s overall health status and the difficulty in isolating the impact of treatment effects alone [133]. Hypotheses Hypothesis 1: children s hospitals will be associated with a greater reduction in odds of negative appendectomy rates over time when compared to general hospitals, including children s units in general hospitals Hypothesis 2: urban hospitals will be associated with a greater reduction in odds of negative appendectomy rates over time when compared to rural hospitals Hypothesis 3: teaching hospitals will be associated with a greater reduction in odds of negative appendectomy rates over time when compared to non-teaching hospitals Design and Methods Data Sources This study utilized data from the Healthcare Cost and Utilization Project (HCUP) Kids Inpatient Database (KID) for the years 2000, 2003, 2006 and The 2009 KID contains data for inpatient pediatric hospital stays from 4,121 hospitals in 44 states; the 2000, 2003 and 2006 KID contain data for 27, 36 and 38 states, respectively. Over 100 clinical and nonclinical variables for each hospital discharge are available in each KID database, including diagnoses, procedures, patient demographics (e.g. gender, race, median income for ZIP code, etc.), and hospital characteristics (e.g. hospital type, teaching status, etc.). Eligibility 47

61 For this study, eligibility was limited to patients 17 years of age and younger at the time of hospital admission that experienced negative appendectomy. Further, observations were limited to those with hospital type reported and hospitals participating in the KID datasets. Negative appendectomy was defined by a combination of diagnosis and procedure codes. Specifically, International Classification of Diseases, Ninth Revision procedure codes of 47.0 (appendectomy), (laparoscopic appendectomy), or (other appendectomy) were used to determine if a patient had an non-incidental appendectomy procedure performed; patients undergoing an incidental appendectomy were excluded from analysis, consistent with prior work on this subject [109]. A diagnosis of appendicitis was defined by an International Classification of Diseases, Ninth Revision diagnosis code of (acute appendicitis with generalized peritonitis), (acute appendicitis with peritoneal abscess), (acute appendicitis without mention of peritonitis), 541 (appendicitis, unqualified), or 542 (other appendicitis). Following prior studies, the assumption is that patients with discharge diagnosis codes reflecting appendicitis were accurately diagnosed at the time they underwent appendectomy; conversely, patients who underwent appendectomy but did not have a discharge diagnosis code of appendicitis were misdiagnosed or underwent a negative appendectomy [109]. Further, data were limited to those 1,573 hospitals that reported data in each of the 2000, 2003, 2006 and 2009 KID databases, which represents 64.5% of the hospitals reporting data in 2000; 53.4% of the hospitals reporting data in 2003; 52.2% of the 48

62 hospitals reporting data in 2006; and 56.8% of the hospitals reporting data in This limitation of hospitals participating in each of the four years includes 134,697 or 71.8% of the 187,718 appendectomy observations included in these four databases. The final restriction on data utilized for this study involved the inclusion of hospitals that did not change type, location or teaching status over the four reporting years. The final figures used for the study include 803 hospitals and 74,166 patient observations (see Figure 3). Dependent, Independent, and Control Variables The dependent variable of interest for this study is negative appendectomy. Independent variables include hospital type and year interaction; hospital teaching status and year interaction; and hospital location and year interaction. Control variables for this study include patient age, gender, race, and primary payer as well as hospital teaching status and location (if teaching status and location interactions were not part of the model). Data Analysis The unit of analysis for this study is the patient. Logistic regression was used to determine the change in negative appendectomy over time. Analyses were performed using Stata Intercooled 11 [134], SAS software version 9.2 [135], and R statistical software version [141]. Since cases from the same hospital are not independent observations, generalized estimating equation (GEE) models with a working exchangeable correlation matrix and robust standard errors were used to adjust for hospital case clustering [136]. Additionally, negative appendectomy rates (unadjusted 49

63 and age-adjusted) were calculated. Age-adjusted rates were calculated standardizing to 2010 population data reported by the U.S. Census Bureau [137]. The unadjusted empirical model used to test the first hypothesis is as follows: g(µ) = β 0 + β 1 (2000 x children s hospital) + β 2 (2000 x children s unit) + β 3 (2003 x general hospital) + β 4 (2003 x children s hospital) + β 5 (2003 x children s unit) + β 6 (2006 x general hospital) + β 7 (2006 x children s hospital) + β 8 (2006 x children s unit) + β 9 (2009 x general hospital) + β 10 (2009 x children s hospital) + β 11 (2009 x children s unit). The reference category is patients treated in 2000 at general hospitals, so the exponentiated value of each coefficient (β 1- β 11 ) is the odds ratio comparing that category to the reference. For example, e^ β 1 should be interpreted as the odds of negative appendectomy for patients treated in 2000 in children s hospital when compared with patients treated in 2000 in general hospitals. In terms of the first hypothesis, if point estimates for β 1, β 4, β 7, β 10 are consistent that would indicate little change in negative appendectomy rates over time for children s hospitals. Hypotheses 2 and 3 were tested with similar models and included interactions terms for general hospital location and year (to test hypothesis 2) and interaction terms for general hospital teaching status and year (to test hypothesis 3). Final models were tested for collinearity by calculating the variance inflation factor (vif command in Stata) to minimize multicollinearity concerns [138]. Variables were excluded if they were found to possess a variance inflation factor value above 10. Results 50

64 Descriptive statistics reflecting the number and percentage of appendectomy cases by year among a variety of patient and hospital demographic areas are depicted in Table 6. The largest number of appendectomy cases (n=20,042) occurred in 2009, with each of the other three years fairly evenly distributed at around 18,000 cases. Patients treated by hospital type followed distinct trends. The percentage of cases at general hospitals declined in each of the four years from a high of 69% in 2000 to a low of nearly 57% in 2009, while the percentage of cases at children s hospitals and children s units at general hospitals increased in each of the four years from lows of 14.7% and 16.1%, respectively, in 2000 to highs of roughly 22% apiece in The majority of cases were treated at teaching hospitals (70-80%) and in urban facilities (83-88%). In terms of patient demographics, the largest age group represented in each of the years was the year old group (with around 40% of the volume), followed by the 5-9 year old group (with 27-30% of the total), the year old group (with roughly onequarter of the volume), and finally the 0-4 year old age group had around 6% of the appendectomy volume. The majority of the patients were male (57-60%). The largest race group represented was white (39-46%), followed by Hispanic (24-29%) and black (5-6%). Other races accounted for around 6-7% of the patients in the study, and anywhere from 15-24% of the patients in the study did not have a race reported. Sensitivity analysis showed study results were comparable when patients without a reported race were removed from the dataset. Reported race categories, however, may not be accurate; thus, interpretations based upon race are to be made cautiously. The dominant primary payer reported was private insurance, which showed a decreasing trend 51

65 (high of 62% in 2000 to a low of 54% in 2009). Conversely, Medicaid, which was the second most commonly reported primary payer, increased from 28% in 2000 to 38% in Unadjusted and age-adjusted negative appendectomy rates by year are presented in Tables 3.2 and 3.3. The overall unadjusted negative appendectomy rates for each of the four years (2000, 2003, 2006, 2009) were 6.0%, 4.6%, 3.3% and 2.8%, compared to overall age-adjusted rates of 6.3%, 5.0%, 3.6%, and 3.2%, respectively. Meaningful differences in these overall rates (adjusted and unadjusted) exist for each of the years except for 2006 and The overall unadjusted negative appendectomy rates showed consistent downward trends for all age categories with the exception of the youngest age group, which held steady at around % for the years 2006 and Different rates were observed for the 5-9 and year old groups from 2000, 2003 and 2006, but no meaningful differences were observed from 2006 to The oldest age group experienced reductions in the rate of negative appendectomy for each of the reporting periods. When evaluating age-adjusted rates by hospital type, each of the hospital types showed downward trends except for children s units at general hospitals, which had a spike in 2009 to 6.5% after values of 5.4% in 2003 and 4.3% in The lowest overall negative appendectomy rates for each of the years occurred in children s hospitals. Teaching hospitals reported lower negative appendectomy rates in each of the years save Similarly, urban hospitals reported lower negative appendectomy rates in each of the four years. 52

66 Age-adjusted rates were lower for males than females in each of the years reported, with each gender showing consistent downward trends in rates. Differences in rates by race are most noticeable for Hispanic patients, which had lower rates when compared with blacks and whites in each of the reporting years. No real patterns exist for rates by median income, but patients with Medicaid experienced lower negative appendectomy rates in each of the years when compared with privately insured patients; both payer groups experienced consistent downward trends in each of the four years. Logistic regression results are provided in Table 9. The first hypothesis for this study, children s hospitals will be associated with a greater reduction in odds of negative appendectomy rates over time when compared to general hospitals (including children s units in general hospitals), was tested using the interaction of year and hospital type with general hospital in the year 2000 as the reference. The unadjusted logistic model results indicate mixed results for children s units on general hospitals; for the years 2000 and 2009, there is no difference in the odds of negative appendectomy among patients treated at children s units on general hospitals compared with patients treated at a general hospital in The lowest odds of negative appendectomy occurs for children s hospitals in 2006 and 2009, both of which are 73% lower than the odds of negative appendectomy for patients treated at general hospitals in However, the confidence intervals for each year of the children s hospitals overlap and are not significantly different over time. General hospitals not only have lower odds in each of the subsequent years, but the confidence intervals do not overlap, thus indicating that general hospitals are likely to have had a greater decrease in the rate of negative appendectomy over time 53

67 vis-à-vis other hospital types. Three different models are also reported; the first adjusts for age, race and gender; the second adds payer to the other previously mentioned variables; and the third controls for hospital location and teaching status (in addition to other variables in the model). The results of the full model (model 3) are consistent with the results for the unadjusted model, namely, that the odds of general hospital patients having negative appendectomy appear to be significantly reduced in each of the subsequent years, while the odds for children s hospital patients did not show this change. Additionally, model 3 shows the odds of patients being treated at a children s unit on a general hospital in 2003 do not have significantly different odds of negative appendectomy. In short, support for hypothesis one was not found; alternatively, general hospitals, not children s hospitals, experienced a greater reduction in negative appendectomy rates over time. The second hypothesis (urban hospitals will be associated with a greater reduction in odds of negative appendectomy rates over time when compared to rural hospitals) was tested using the interaction of rural/urban status and year. Since no children s hospitals were considered rural and few children s units on general hospitals were considered rural, analyses could only be performed for general hospitals. Results of the fully adjusted logistic model (model 3) indicate general rural hospitals had similar odds in 2003 as general rural hospitals in For 2006 and 2009, however, patients treated in rural general hospitals had lower odds of negative appendectomy (OR=0.71; 95% CI= for 2006; OR=0.39; 95% CI= for 2009). In contrast, the odds of negative appendectomy for patients treated in urban general hospitals were lower in each of the 54

68 study years compared with Thus, there is evidence supporting the second hypothesis of this study, that urban hospitals will be associated with a greater reduction in odds of negative appendectomy rates over time when compared to rural hospitals. We tested the third hypothesis for this study using the interaction of teaching/nonteaching status and year. Similar to the prior hypothesis regarding urban/rural status, analyses could only be performed for general hospitals since an insufficient number of children s hospitals and children s units on general hospitals were nonteaching. It was expected that teaching hospitals would show some improvement over non-teaching hospitals. Results for the fully-adjusted model (model 3) show the odds of negative appendectomy in the year 2000 are no different between patients treated at non-teaching and teaching hospitals. Curiously, there is no difference between the odds of negative appendectomy for patients treated in 2003 at teaching hospitals when compared with patients treated at non-teaching facilities in 2000 (reference group). The odds of negative appendectomy for non-teaching facilities and teaching hospitals are comparable in 2006 and Thus, support for the third hypothesis is mixed. Discussion The general results of this study are consistent with prior work in this field, namely, that negative appendectomy rates over time have decreased [102]. While support for the hypothesis that children s hospitals would have greater reduction in negative appendectomy rates over time was not found, it is clear when evaluating the data in Tables 3.2 and 3.3 that children s hospitals had significantly lower negative appendectomy rates at the beginning of the study time period (3.2% when compared with 55

69 6.3% for general hospital and 8.1% for children s units on general hospitals). Thus, it is not surprising that, when testing for changes over time, children s hospitals would be less likely to demonstrate a reduction in the rate of negative appendectomies. In other words, children s hospitals already had relatively low negative appendectomy rates in 2000; general hospitals (with and without children s units) had higher negative appendectomy rates and thus had a greater opportunity for improvement. Given the focused nature of children s hospitals on pediatric outcomes as well as the increased likelihood of being teaching, urban facilities, it may well be that improvements made in care procedures and protocols, including adoption of CT in the evaluation of suspected appendicitis, were put in place in children s hospitals prior to general hospitals. That said, it should be noted that, when evaluated linearly throughout the four reporting years of data, each one year increase in time resulted in decreased odds of negative appendectomy of 11% for general hospitals versus 8% for children s hospitals. Thus, it is clear that general hospitals achieved a greater reduction in odds, but children s hospitals experienced a decrease in the odds of negative appendectomy as well. The contribution of this study in evaluating the change in rates over time should not be mistaken for portraying children s hospitals in a negative light; to the contrary, it reflects the fact that children s hospitals had lower negative appendectomy rates at an earlier point in time. Children s units in general hospitals are a different matter; negative appendectomy outcomes on children s units in general hospitals were no different in 56

70 2009 than they were in 2000 (despite improvements in 2003 and 2006). When analyzed linearly, the odds of negative appendectomy for patients treated at children s units on general hospitals did not achieve a statistically significant change. Reasons for this backslide are unknown and warrant further attention. The study s findings supporting the second hypothesis (urban facilities will have greater reduction in the rate of negative appendectomy when compared with rural hospitals) may be attributable to specialized staff and equipment being more readily available in urban facilities compared with rural facilities. Given the positive impact CT has made in reducing negative appendectomy rates [70-72], it may be adoption of the use of CT in evaluating suspected appendicitis patients in urban facilities outpaced that of rural facilities. Of course, it should be recognized that negative appendectomy rates have improved for patients treated in rural hospitals, in particular for the year Thus, adoption and application of technology may be catching up in rural hospitals as it relates to properly diagnosing appendicitis. Given the absence of data regarding the availability of CT at the study hospitals, the aforementioned theories stand to be tested with empirical data. Lastly, the mixed results for the third hypothesis regarding teaching hospitals is best summarized by the observation that the negative appendectomy rates among patients seen in both facilities is essentially identical for 2006 and Again, this may well be a function of the imaging technology and protocols for use in diagnosing pediatric 57

71 appendicitis being disseminated widely among all hospital types, teaching and nonteaching alike. Limitations While advances in imaging, previously mentioned, are widely attributed with the overall decrease in negative appendectomy rates, the data source used for this study did not include information regarding the availability of imaging modalities in the facilities where patients were treated. The data allowed for study of overall hospital and patient characteristics, however, which was the focus of this study. A potential limitation of this study is the use of administrative data. Administrative data consist of billing codes that, it is hoped, are accurately reflective of the initial clinical observations. As Flum et al. note [20], Addiss et al. [27] were the first researchers to use administrative data in the study of pediatric appendectomy outcomes, and their findings are consistent with robust clinical datasets [7, 70]. Limitations specific to the Healthcare Costs and Utilization Project, of which the KID databases are a part, have been well documented [139, 140]. An additional limitation of this study may lie in the fact that data were restricted to include only those hospitals that participated in each of the study years and that reported consistently on hospital type, location, and teaching status. It was felt that, in order to obtain clearly interpretable results, such restrictions in the data were important. For example, accounting for hospitals that change teaching status over time would have introduced a level of complexity that would have taken away from the intent of the 58

72 original study s design and purpose. It should be acknowledged there exists a potential for study results to be limited as a result of decisions to restrict the study dataset. It is the author s belief that the results provided, however, are beneficial both in terms of being clearly interpreted and in terms of being produced by a conservative study design. 59

73 Chapter 4: Community Characteristics Associated with Perforated Appendicitis among Pediatric Patients Introduction Perforated appendicitis is an Agency for Healthcare Research and Quality (AHRQ) Pediatric Quality Indicator [142]. Two key factors that lead to perforation of the appendix are delayed seeking of care by the patient, which is seen as the primary factor, [6, 9, 12, 17, 143, 144] as well as delayed provision of care by healthcare providers [6, 12-14, 88, ]. Factors impacting delays in the seeking of care are numerous. Patient insurance status may be related to access to care and delays in seeking care, thus impacting perforation rates. However, studies of insurance status and perforated appendicitis are mixed. For example, the risk of complicated appendix disease (e.g. perforation or abscess) in a study of pediatric patients from Washington state was found to be increased for children with Medicaid as the primary payer versus children with commercial health insurance as the primary payer; however, the risk of perforation was not elevated for uninsured children [147]. Conversely, in a study of avoidable hospitalizations in Maryland and Massachusetts, Medicaid patients (age 65 and younger) and patients without insurance had an elevated relative risk of admission for ruptured appendix, adjusting for age and sex, while privately insured patients had a reduced relative risk [148]. Thus, primary payer alone does not provide a clear picture. However, 60

74 lower socioeconomic status has been found to be associated with higher odds for a variety of pediatric health outcomes including asthma, migraine/severe headaches, and ear infections [149] and is largely attributed to lack of access to care. Therefore, socioeconomic status, of which payer status is a measure, may be a helpful variable to consider. Another series of variables helpful in understanding delays in seeking care and access to care are the availability of healthcare resources. Camp et al. studied provider density and healthcare facility factors at the county level and found increasing density of pediatricians in a geographic area was associated with decreased odds of perforated appendicitis [92]. In a related finding, Gadomski et al. found the odds of perforation were inversely related to the number of preventive visits pediatric patients had in Maryland [96]. Given the numerous factors potentially impacting access to and delays in seeking care, a broad versus narrow framework was sought out to guide this study. Anderson and Aday s work on access to medical care in the United States provides such a broad-based view of health-seeking behavior and was thus adopted as the guiding framework for this study. Anderson and Aday identified three important areas connected with healthseeking behaviors: population, health system, and environmental characteristics [150]. Population characteristics include predisposing variables such as patient demographics (e.g. age, race, gender), enabling factors such as income and insurance status, and need factors such as the severity and duration of illness. Health system factors include the availability and type of healthcare resources, and environmental characteristics include 61

75 economic, environmental and geopolitical factors (see also Young s review on illness behavior) [151]. The idea that the broader environment in which a person resides impacts health outcomes is illustrated by Dahlgren and Whitehead s social determinants of health model (see Figure 4) [152]. In short, social determinants of health include social and economic factors that impact the health of individuals and communities [153]. In order to appreciate the social determinants of health more completely, consider the context of the factors commonly recognized as the five determinants of health for a given population: genes and biology; health behaviors; social/societal characteristics; physical environment/total ecology; and medical care [154]. While the exact contribution each of these areas has on overall health isn t known, it is estimated that approximately 25% of overall health is attributed to genetics and health behaviors, roughly 20% to medical care, and the remaining to social and environmental risk factors (see Figure 5) [155]. Since medical care is considered to be part of the broader social determinants of health, a brief glance at Figure 5 indicates some 75% of health outcomes are attributable to the societal risk factors as opposed to 25% being attributable to individual risk factors. Connecting the social determinants of health construct with health-seeking behavior, including delays in seeking care, one may begin to sense how social factors may impact access to healthcare and delays in seeking care. For example, while personal income may affect modes of transportation available to the parent/guardian of a child experiencing symptoms associated with suspected appendicitis, the overall wealth of a given population may impact the degree to which reliable public transportation is an 62

76 option for those without other means. While transportation is just one example, the broader concept is that community-level socioeconomic factors and individual-level socioeconomic factors might interact with one another as related to healthcare access and outcomes [156, 157]. A study of the interactions between community-level socioeconomic factors and individual-level socioeconomic factors, as related to healthcare access, found that individuals with lower incomes fared better in terms of access to healthcare when they were found to be residing in lower income communities versus higher income communities [158]. The rational for such a finding is that poorer individuals may benefit most, in terms of healthcare access, by being in communities that resemble their individual level of income because they may find it easier to network with similar others in finding ways to cope with accessing the healthcare system versus when the same lower income individuals live in wealthier communities [158]. Through the lens of Anderson and Aday s work, such social networks would be included as an enabling factor in healthseeking behavior. Following Anderson and Aday, this study combined factors of county-level access to healthcare resources measures (e.g. physician density, healthcare facility factors) with county-level socioeconomic variables (e.g. income, education level) and patient level factors (e.g. primary payer) in order to identify factors associated with perforated appendicitis. Like Camp et al., I hypothesize that certain health system factors and infrastructure may influence the timely diagnosis of appendicitis, ostensibly due to the impact on health-seeking behavior of guardians on behalf of the pediatric patients 63

77 they care for, thus potentially impacting perforation rates. From a social perspective, I would expect educational attainment, the unemployment rate, single parent status and family size to influence the timely seeking of care by guardians on behalf of their children. For example, single parents or parents of larger families may have more difficulty finding time to take children to seek care. Educational attainment also may impact the degree to which guardians understand and are able to act on appropriate care seeking. Hypotheses Hypothesis 1: patients in counties with a higher density of primary care physicians, surgeons, emergency physicians and radiologists will have lower odds of perforated appendicitis when compared with patients in counties with a lower density of primary care physicians, surgeons, emergency physicians and radiologists Hypothesis 2: the number of hospitals with emergency departments and number of operating rooms in a county (per population) will be inversely associated with the odds of perforation Hypothesis 3: patients with lower socioeconomic status in counties with higher median income levels will be more likely to have increased odds of perforated appendix when compared with patients with lower socioeconomic in counties with lower median income levels Design and Methods Data Sources 64

78 This cross-sectional study of pediatric perforated appendicitis utilized data from the Healthcare Cost and Utilization Project (HCUP) Kids Inpatient Database (KID) for the year 2009 and data from the Area Resource File (ARF) [159], including Census data. Area Resource File data for were linked to the HCUP KID 2009 data at the county level of the hospital. Eligibility Patients included in this study are patients 17 years of age and younger who were diagnosed with acute appendicitis or perforated appendicitis in the 2009 KID in states that provided county level identification for the hospitals. Further, observations were limited to those with hospital type reported (4,732 or 8.48% of 55,818 observations were removed). Observations were limited to those for which county code data was provided by HCUP KID The final total patient observations and number of hospitals included in this study are 39,865 and 1,890, respectively (see Figure 6). Patients with perforated appendicitis are indicated by an International Classification of Diseases, Ninth Revision diagnosis code of (acute appendicitis with generalized peritonitis) or (acute appendicitis with peritoneal abscess). Patient with acute appendicitis are indicated by an International Classification of Diseases, Ninth Revision diagnosis code of (acute appendicitis without mention of peritonitis). The approach of using diagnosis codes, not procedure codes, for identifying pediatric patients with appendicitis is consistent with previous work by Kokoska et al. [125] and Camp et al. [92]. Dependent, Independent, and Control Variables 65

79 The dependent variable of interest is appendiceal perforation. Independent variables include the interaction of median household income and patient zip code median income. Additionally, availability of healthcare resources as measured by densities of the following physician specialties at the county level are independent variables: pediatricians and family practice physicians (as a measure of primary care physician density), emergency department physicians, radiologists, and surgeons. Lastly, the number of hospitals with emergency departments and volume of surgical operations at the county level were included as independent variables. Control variables at the patient level include age, gender, race, and primary payer. Since perforation is largely attributable to factors outside of the hospital, it is not suspected that systematic differences in perforation outcomes will be associated with hospital characteristics; however, to remain consistent with the theme of the overall dissertation, hospital characteristics, including teaching status, children s vs. general hospital designation, and rural/urban location, were controlled for in this study. Control variables at the county level include educational attainment (measured by the percent of adults 25 years and older having accomplished the educational variable threshold), unemployment rate (for those 16 years of age and older), density of single parent households, average family size, total population, and population per square mile. In order to minimize collinearity issues with healthcare resource availability measures, the density of pediatricians and family practice physicians were combined into one primary care physician density measure and certain measures of hospital resources (such as hospitals in the county, hospitals with CT in the county, etc.) were not included 66

80 in lieu of the two primary variables hospitals with emergency departments and total surgical volume. Total primary care provider density, for purposes of this study, was calculated by adding the number of family medicine physicians and pediatricians together and dividing by total county population. Similarly, all density values were calculated by dividing the variable values by total population. Data Analysis As the outcome of interest perforated appendicitis is binary, logistic regression was utilized. The unit of analysis for this study is the patient. Analyses were performed using Stata Intercooled 11 [134] and SAS software version 9.2 [135]. Since cases from the same hospital are not independent observations, generalized estimating equation (GEE) models with a working exchangeable correlation matrix and robust standard errors were used to adjust for clustering at the hospital level [136]. Descriptive statistics as well as unadjusted and age-adjusted perforated appendicitis rates by patient, hospital, and county-level characteristics were calculated. County-level variables are reported in terms of categories representing the lowest, mid, and highest values for the various variables. The low category reflects the bottom quartile, the high category the highest quartile, and the mid category the middle two quartiles. Age-adjusted rates were calculated standardizing to 2010 population data reported by the U.S. Census Bureau [137]. Lastly, four types of logistic regression models were run: unadjusted, adjusted for patient-level covariates, adjusted for patient and hospital covariates, and adjusted for patient-level, hospital-level, and county-level covariates. 67

81 The unadjusted empirical model used to test the first hypothesis is as follows: g(µ) = β 0 + β 1 (mid primary care density) + β 2 (high primary care density). Models for each physician type (primary care, surgeon, emergency medicine and radiologist) were adjusted for variables at the patient, hospital and county levels (see Table 12). The reference is low primary care density, so the exponentiated value of each coefficient is the odds ratio comparing that category to patients in low primary care density counties. For example, e^ β 2 should be interpreted as the odds of appendiceal perforation for patients receiving care in high primary care density counties when compared with patients receiving care in low primary care density counties. The null hypothesis of no difference in perforation for patients receiving care in areas of high primary care density compared with low primary care density would be β 2 = 0. Hypothesis 2 was tested with similar models that included coefficients for the number of hospitals with emergency departments and number of operating rooms in the county. Hypothesis 3 was likewise tested with a similar model that included coefficients for the interaction term of patient SES by county SES. Low, medium, and high categories for each of the two SES measures were used, for a total of nine interaction categories. The unadjusted empirical model used to test the third hypothesis is as follows: g(µ) = β 0 + β 1 (low zip median income x mid county median income) + β 2 (low zip median income x high county median income) + β 3 (mid zip median income x low county median income) + β 4 (mid zip median income x mid county median income) + β 5 (mid zip median income x high county median income) + β 6 (high zip median income x low county median income) + β 7 (high zip median income x mid county median income) 68

82 + β 8 (high zip median income x high county median income). The reference category is patients in low zip median income and low county median income. Final models were tested for collinearity by calculating the variance inflation factor (vif command in Stata) to minimize multicollinearity concerns [138]. Variables were excluded if they were found to possess a variance inflation factor value above 10 (two of the three education variables were removed from the final model due to multicollinearity). Results Descriptive statistics as well as unadjusted and age-adjusted perforated appendicitis rates by patient- and hospital-level characteristics are summarized in Table 10. The majority of patients in this study were males (56%). The year old age group represented 39% of the study observations, followed by approximately 28% each for the groups immediately older and younger; the youngest age group represented 5% of the observations. In terms of race, white patients represented 43% of the observations, followed by Hispanic (26%), black (5%) and other (7%). Twenty percent of patients did not have a race reported. Sensitivity analysis showed study results were comparable when patients without a reported race were removed from the dataset. Reported race categories, however, may not be accurate; thus, interpretations based upon race are to be made cautiously. The majority of patients in this study were privately insured patients (56%). The rate of appendiceal perforation was by far the highest for the youngest age group, which experienced perforation rates of 65%. The lowest rates were experienced 69

83 by the oldest patients in the year old age group (23%). After adjusting for age, male and female patients had similar rates of perforation (around 36% for males and 38% for females). After adjusting for age, black patients were found to have the highest rate of perforation (42%); other race groups had similar perforation rates (35-37%). The ageadjusted perforation rate was higher for Medicaid patients (38%) compared with privately insured patients (35%). There was not a meaningful difference in perforation rates among patients in the lowest three quartiles for median household income for zip code; the highest quartile, however, had lower perforation rates. In terms of hospitals characteristics, the majority of patients were treated in general hospitals (67%), followed by children s units in general hospitals (18%) and children s hospitals (14%). The age-adjusted perforation rates were similar for children s hospitals and children s units on general hospitals (40%), but were lower for general hospital patients (34%). The percentage of patients treated in teaching hospitals was 52%, and perforation rates were higher in teaching (38%) vs. non-teaching hospitals (35%). Some 91% of patients with perforated appendicitis were treated in urban facilities, and perforation rates were higher in rural hospitals (38%) compared to urban hospitals (36%). Additional information regarding perforation rates by patient- and hospital-level characteristics is available in Table 10. Demographic and perforation rate data by county-level characteristics are reported in Table 11. Values for the low, mid, and high physician density and other category data are reflected, as well as the number and percentage of observations for a given category level. There were no meaningful differences in age-adjusted perforation 70

84 rate by any of the physician categories (primary care physician density, surgeon density, emergency medicine density, or radiologist density). Of interest, the calculated rates were highest for the high density groups in each of the categories analyzed. Similarly, age-adjusted perforation rates by density of hospitals with emergency departments were not measurably different. The high density group for operating rooms, however, had a higher age-adjusted perforation rate when compared with the low and mid categories. County median income age-adjusted perforation rates show the high income group had a lower rate of perforation (34%) when compared with the middle income group (38%) and the low income group (36%). Curiously, the low unemployment rate group experienced higher perforation rates (39%) when compared with the mid and high groups (36%). No noticeable differences in age-adjusted perforation rates are observed by education level achieved, single parent household density, average family size, total county population, or median county household income and patient income by zip code interaction. Logistic regression results are summarized in Table 12. The first hypothesis was tested by analyzing perforation rates for the density of each physician type per county population. Unadjusted model results show a 13% increase in odds of perforation for the high primary care density group when compared with the low primary care density group and no difference in the odds of perforation by surgeon and emergency medicine physician density. Once adjusted for patient, hospital and county characteristics, no significant differences remain among the various physician density types analyzed. Thus, 71

85 study findings do not support the first hypothesis that higher physician densities will translate into lower perforation rates. The second hypothesis was tested by analyzing perforation rates by the density of healthcare resource per county population. Density of hospitals with emergency departments and with operating rooms showed higher odds of perforation for the highest density category in each of the models tested when compared with the low density category. The fully-adjusted model showed a 14% increase in odds of perforation for the high density emergency department category (OR=1.14, 95% CI= ) and a 16% increase in odds of perforation for the high density operating room category (OR=1.16, 95% CI= ). In sum, the results do not support the second hypothesis. In fact, there is evidence that higher density of emergency departments and operating rooms are associated with higher perforation rates when compared with lower density of the same healthcare resources. The third hypothesis tested for in this study was tested by analyzing the interaction of median county income and median household income for patient zip code. The reference group was low county median income and low zip code median income. Unadjusted logistic model results show each of the high zip code income groups had lower odds of perforation when compared with the low zip code, low county income group. Each of the high median county income categories likewise showed lower odds of perforation. After adjusting for patient, hospital and county-level covariates, only the high median county income categories maintained significantly different, in each case 72

86 lower, odds of peroration. Thus, there does not appear to be evidence to support the third hypothesis tested in this study. Secondary analyses were conducted to see if associations between perforation rates and county-level covariates (as listed in Table 12 model 3) exist. Logistic regression was used with perforation as the outcome variable, county level-variables as independent variables, and patient and hospital level variables (as listed in Table 12 models 1 and 2) as control variables. Median county income was also controlled for. Collinearity was tested for using the variance inflation factor (vif) command in Stata. Variables would have been excluded if they were found to possess a vif value above 10; no variables were excluded. Among the various independent variables tested (unemployment, education, family size, single parent density and population density), the only significant results were found for unemployment rate and population density. The odds of perforation were lower for the high (OR=0.84, 95% CI= ) unemployment group when compared with the low unemployment group. Additionally, the odds of perforation were 24% lower for the high population density group when compared with the low population density group (OR=0.76, 95% CI= ). Discussion The results of this study are somewhat comparable with prior work in this field, in particular, Camp et al. s study [92], though differences exist. Unlike Camp et al., for example, the current study found physician density levels and types were not associated with different odds of pediatric perforated appendicitis. Camp et al. reported lower odds 73

87 of perforation for increasing density of pediatricians. After completing the initial analysis, therefore, a post-study was conducted analyzing pediatrician and family practice density in isolation (as compared to as a combined primary care category). The argument for combining the two categories in the first place was that children do not always see pediatricians for primary care; some children see family practice physicians. The results of the post-study reveal results comparable with Camp et al., as the high density pediatrician category had 17% reduced odds of perforation in the final model, adjusting for patient, hospital and county characteristics (OR=0.83; 95% CI= ). Interestingly, high family practice density was associated with a significantly higher odds of perforation (OR=1.24; 95% CI= ) when compared with the low density group. This result may suggest that pediatricians are more effective in triaging pediatric appendicitis and assisting guardians in understanding when to take their child in for care in the event of suspected appendicitis. Another interesting finding in this study is that an overabundance of healthcare facilities does not necessarily correspond with improved outcomes. The fact high density of emergency departments and operating rooms were both associated with higher odds of perforation was an unexpected finding that warrants further research. One reason such a finding might exist is if patients were transferred at a higher rate within counties with a higher density of operating rooms and emergency departments, which might occur within counties with a high density of hospitals. However, post-analyses were run excluding patients who transferred from one facility to another and logistic model results were nearly identical. Such findings do not suggest transfer patients are the reason for elevated 74

88 odds of perforation in areas of high density of emergency departments and operating rooms. However, nearly two thirds of the patients in the study did not have a source of admission reported. Therefore, it would be important in future studies of pediatric appendiceal perforation to include admission source, especially for patients transferred from one facility to another. The results of socioeconomic standing at the patient (zip code) and county level did not support the theory that lower income individuals navigate the healthcare system more effectively in lower income environments. If anything, the results of this study suggests that, in terms of odds of perforated pediatric appendicitis, patients residing in higher income environments will have increased odds of favorable outcomes. While it seems logical that patients would have an easier time networking with similar others, it is obvious from these study results that an environment of higher income produces a greater effect in terms of patient outcomes. This may be linked with greater access to care in more affluent communities. Further, it is possible that social networks in wealthier communities influence more timely response on the guardian s part in responding to the healthcare needs of children, regardless of the socio-economic status of the parent/guardian within the wealthier community. Regarding the secondary analyses, one potential reason for lower perforation rates being associated with increasing unemployment rates could be attributed to increased enrollment on Medicaid (28.6% of the patients in this study were on Medicaid in the low unemployment group as opposed to 34.1% and 46.7% for the mid and high 75

89 unemployment rate groups, respectively). However, the age-adjusted perforation rates for Medicaid are higher than they are for privately insured patients (38.1% vs. 34.8%, see Table 10). Thus, increased Medicaid enrollment is not likely to explain the decrease in perforation rate. An additional explanation for the reduction in perforation rate may simply be due to the fact that an unemployed parent or guardian would have more time to spend with the children he or she is caring for, thus increasing the likelihood for timely response to addressing healthcare needs. Future research into this area is required in order to understand this association more fully. As it relates to population density, the higher population density areas may achieve lower perforation rates due to increased access to healthcare resources. However, analysis of healthcare resource density in the primary study did not find support for the notion that increased density of healthcare resources translate into lower perforation rates; in fact, evidence to the contrary was found. Thus, unanswered questions remain as to how population density and healthcare resource density are associated with perforation rates. Limitations A potential limitation of this study is the use of administrative data. Administrative data consist of billing codes that, it is hoped, are accurately reflective of the initial clinical observations. As Flum et al. note [20], Addiss et al. [27] were the first researchers to use administrative data in the study of pediatric appendectomy outcomes, and their findings are consistent with robust clinical datasets [7, 70]. Limitations specific 76

90 to the Healthcare Costs and Utilization Project, of which the KID databases are a part, have been well documented [139, 140]. Limitations of the use of Area Resource File data include the fact that countylevel data were utilized as patient-level covariates. Such a decision was made in order to preserve the ability to examine potential interactions among patient-level covariates, which would have been lost if patient-level data were aggregated to the county level. As with all ecologic studies, results from this third study are not grounds for drawing causal inference [160] since measures taken at an aggregated level (in this case, county) may not necessarily apply to the individuals residing within those counties. Study results may not be generalizable to non-pediatric patient populations as appendicitis presents differently in children and adults [36]. Additionally, the study population consisted of patients exclusively from the United States. Hence, results may not be generalizable to pediatric populations residing in other parts of the world. 77

91 Chapter 5: Conclusion Objective and Results Summary The overall objective of this dissertation was to examine hospital and community characteristics associated with pediatric appendectomy outcomes. Three studies were performed in order to test specific hypotheses. The first study analyzed negative appendectomy outcomes using data from This study s first hypothesis (children s hospitals will be associated with lower odds of negative appendectomy, especially for very young children, when compared to general hospitals) was supported. The second hypothesis (hospitals participating in health systems will have lower odds of negative appendectomy) was not supported. An exploratory analysis found that children s hospitals have higher odds of negative appendectomy when part of multi-hospital health systems. The third hypothesis (volume of appendectomies will be inversely associated with the odds of negative appendectomy) likewise was not supported. The second study also focused on negative appendectomy outcomes and utilized data from 2000, 2003, 2006 and Study results showed no support for the first hypothesis (children s hospitals will be associated with a greater reduction in odds of negative appendectomy rates over time when compared to general hospitals). In fact, general hospitals were found to have had a greater decrease in the odds of negative appendectomy over the course of the time period studied. The second hypothesis (urban 78

92 hospitals will be associated with a greater reduction in odds of negative appendectomy rates over time when compared to rural hospitals) was supported urban facilities indeed proved to have a greater decrease in the odds of negative appendectomy. The third hypothesis (teaching hospitals will be associated with a greater reduction in odds of negative appendectomy rates over time when compared to non-teaching hospitals) found mixed results. Teaching hospitals appear to have had a greater reduction in odds from 2000 to 2003 and 2003 to 2006, but both teaching and non-teaching hospitals had significant reductions from 2006 to An important caveat for the second and third hypotheses is that only general hospitals were included in the analyses due to the fact that no children s hospitals and too few children s units on general hospitals were classified as urban or teaching. The final study analyzed data from 2009 in a cross-sectional manner and focused on appendiceal perforation. The first hypothesis (patients residing in counties with a higher density of primary care physicians, surgeons, emergency physicians and radiologists will have lower odds of perforated appendicitis) was not supported. However, a post-study analysis revealed that, once primary care physicians were split up into pediatricians and family practice physicians, high density of pediatricians was associated with lower odds of perforated appendicitis. Interestingly, high density of family practice physicians was not associated with decreased odds of perforated appendicitis among pediatric patients. The second hypothesis (the number of hospitals with emergency departments and number of operating rooms in a county (per population) will be inversely associated with the odds of perforation) was likewise not supported. In 79

93 fact, there was evidence the odds of appendiceal perforation were higher in the highest healthcare resource density category when compared with the low density category. The third hypothesis for the final study (patients with lower socioeconomic status residing in counties of higher median incomes levels (as compared to patients of lower socioeconomic status residing in counties of lower income levels) will be more likely to have increased odds of perforated appendix) was not supported. Higher county income was associated with improved outcomes regardless of personal socioeconomic status. In reflecting on the dissertation results in combination, it seems there is a benefit that comes with focusing on pediatric patients. Study one showed that children s hospitals were associated with lower odds of negative appendectomy when compared with general hospitals. Similarly, study three showed high pediatrician density was associated with better odds of appendiceal perforation, while high density of family practice density was not. Pediatric specialization either at an individual physician level (pediatrician vis-à-vis family practitioner) or at a facility level (children s hospital vis-àvis general hospital) certainly appears to have benefits similar to those documented by facilities and practitioners who specialize in any number of fields. Shouldice Hospital in Ontario, Canada is a classic example of a facility that produces superior outcomes as a result of focused efforts (Shouldice focuses exclusively on hernia repair) [161]. Facilities like this have been referred to as focused factories [162]. The question remains, however, why children s units on general hospitals did not perform as well in particular in study one. While the odds of negative appendectomy were not much different for children s units on general hospitals for most age groups, 80

94 they were consistently higher than for children s hospitals (which is to be expected) and general hospitals. One might expect that children s units would see some improvement in outcome over general hospitals. However, that does not appear to be the case. It may be that the perception of focused pediatric services engenders confidence in referring providers, patients, and the providers themselves in utilizing children s units on general hospitals much as one would rely upon a children s hospital. However, as best evidenced by the results for the youngest age group in study one, the results simply are not the same. Further, the exploratory analysis of health system and hospital type in the first study showed that children s hospitals had more favorable negative appendectomy rates when they were stand-alone facilities as opposed to members of a multi-hospital health system. In sum, it appears truly focused factories perform at a higher level for extremely difficult cases (such as correctly diagnosing appendectomy in very young children), while semi-focused factories (i.e. pediatric units on general hospitals) may not produce similar outcomes. Policy Implications In terms of policy implications, the results of the first study are not strong enough to suggest wholesale shifts in the current manner of diagnosis and treatment of appendicitis among pediatric patients in the United States. Though patients treated in children s hospitals appear to consistently experience better outcomes in terms of negative appendectomy rates, it would be misguided to suggest that all suspected pediatric appendectomy cases be treated in children s hospitals; many patients simply do not have access to such facilities. For patients with access to children s hospitals, 81

95 however, especially for younger patients, it would be beneficial for evaluation and treatment of suspected appendicitis to occur at children s hospitals. In other words, it may be worth traveling an extra distance to take a 7 year old child with suspected appendicitis to be evaluated at a children s hospital, while it may not be worth doing so for a 16 year old. Practitioners and healthcare leaders considering the organization and structure of pediatric services may benefit from considering questions raised by this study, such as why children s units at general hospitals have poorer outcomes relative to general hospitals and why children s hospitals that are part of health systems also appear to have poorer performance in terms of negative appendectomy compared to stand alone children s hospitals. As it relates to the second study, it is interesting to note that patients with Medicaid as primary payer had more favorable outcomes when compared with privately insured patients. These study data suggest that Medicaid patients, whether or not the various state Medicaid programs are making explicit efforts to manage pediatric appendicitis, achieved more favorable outcomes over time. To the extent Medicaid programs have made diagnosis of appendicitis a priority, they are to be commended. Clearly, for the number one cause of emergent surgery among pediatric patients [1-3], it would be a prudent decision for health plan administrators to consider policies that would foster the appropriate diagnosis of appendicitis in pediatric populations. The major implication of study three is the finding that, supporting Camp et al. s study results, higher density of pediatricians is associated with decreased odds of 82

96 perforated appendicitis among pediatric patients. While the precise reasons for this are not known, it seems plausible that pediatricians are more knowledgeable of pediatric patient needs than are general family practitioners and that this knowledge may be more easily shared with parents/guardians responsible for initiating care in the event of appendicitis. It is hoped future research would be able to uncover what, if anything, family practitioners could do in order to assist the timely care of pediatric patients experiencing appendicitis, thus reducing perforation rates. Future Research Future research following any of these studies would benefit greatly from data that allow for identification of CT availability at the treating facilities as well as patientspecific income. The third hypothesis of the third study in particular would be much more fairly evaluated considering patient-specific socio-economic data as it stands, the study utilized a zip code measure to proxy patient SES. However, the interaction between county income and patient income would have been more precise it included patient income directly. The observation in study one that children s hospitals appeared to have worse outcomes when participating as members of multi-hospital health systems is an interesting path for future research. What may prove helpful for such research is an initial qualitative approach that would ideally identify potential factors of health system membership that impact children s hospital outcomes (i.e. differences in operating and capital budget allocations, degree of autonomy of medical staff, degree of 83

97 developing/following clinical protocols, degree of centralization, etc.), followed up by a quantitative study measuring those factors previously identified. A finding of null results in study two is reflected in the fact that negative appendectomy outcomes on children s units in general hospitals were no different in 2009 than they were in 2000 (despite improvements in 2003 and 2006). Reasons for this backslide are unknown and warrant further attention. Such future research would do well to focus on actual facilities that reported as children s unit on general hospitals during the time periods in question in order to ascertain what changes may have contributed to the observed increase in negative appendectomy rates. Additionally, future studies could analyze the impact of adding a children s unit at a general hospital or becoming a teaching hospital. Study two in particular, given the longitudinal nature of the study, included only hospitals that reported the same hospital type, teaching status and location over time. Differences in outcomes for facilities undergoing changes in hospital type or teaching status in particular would be interesting and worthwhile to pursue. Lastly, future studies following study three would do well to include a measure of proximity to healthcare resources (see Penfold) as well as potentially examine the association of the interaction of healthcare resource density and population density on perforation rates. Additionally, future studies uncovering what family practice physicians might do to reduce the gap in negative appendectomy outcomes when compared with pediatricians would be beneficial. Conclusion 84

98 As outlined in the introductory chapter, acute appendicitis is the most common cause for emergent surgery among children in the United States. More than 70,000 cases of confirmed appendicitis are diagnosed in the pediatric population in the United States per year, accounting for some $3 billion in hospital charges annually. Potential complications associated with suspected acute appendicitis include misdiagnosed or negative appendectomy (i.e. the surgical removal of a healthy appendix) and perforated appendicitis (i.e. the rupturing of the appendix). The objective of this dissertation was to examine hospital and community characteristics associated with pediatric appendectomy outcomes. The first study examined negative appendectomy in a cross-sectional analysis of pediatric patients, including a secondary analysis involving adult and pediatric appendectomy volumes; the second study focused on the change in negative appendectomy rates over time in a longitudinal study of pediatric patients; and the third study involved perforated appendicitis among pediatric patients, incorporating county-level healthcare resource and socioeconomic variables into the analysis, in a cross-sectional study. The findings in this dissertation add to the growing body of work focused on pediatric appendectomy outcomes. Study results, even when hypotheses were not supported, provide a solid foundation upon which future research in this important field of knowledge can be extended. 85

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108 Appendix: Supplemental Data Figures Figure 1. Structure, Process, Outcome conceptual framework of negative appendectomy outcomes in children TIME Structure Environment in which care is provided/received Process Actual provision/reception of care Outcome Health outcomes (e.g. negative appendectomy) Patient level factors SES, insurance, age, gender, race, geography (rural/urban) Patient level factors Managing symptoms; seeking out care; scheduling/preference constraints; compliance with medical advice; etc. Physician level factors Education, specialty, expertise, volume; referral patterns (primary care) Physician level factors Clinical judgment; diagnostic procedures and tests; diagnosis and treatment decisions Hospital level factors Equipment, facilities, staffing expertise, specialty designation, volume, ownership/governance, system participation Hospital level factors Care protocols and guidelines; diagnostic procedures and tests (staff); accessibility to facilities; etc. 95

109 Figure 2. CONSORT Diagram, Study #1; Hospital Characteristics Associated with Pediatric Negative Appendectomy: A Cross-Sectional Study 2009 HCUP KID Observations: Observations with appendectomies: 70,859 Observations age 0-17: 55,818 Drop if hospital type not reported: 51,086 observations remaining Drop if member of health system not reported: 46,169 observations remaining 2009 HCUP KID Hospitals: Hospitals performing appendectomies: 3,213 Drop if age>17: 2,994 Drop if hospital type not reported: 2,770 remaining Drop if member of health system not reported: 2,486 remaining Final total observations included in study #1: 46,169 Final total hospitals included in study #1: 2,486 96

110 Figure 3. CONSORT Diagram, Study #2; Hospital Characteristics Associated with Pediatric Negative Appendectomy: A Longitudinal Study Total observations HCUP KID 2000: Total appendectomies in patients age 17 or less in KID 2000: 41,667 obs, 2379 hospitals Total observations HCUP KID 2003: Total appendectomies in patients age 17 or less in KID 2003: 49,163 obs, 2836 hospitals Total observations HCUP KID 2006: Total appendectomies in patients age 17 or less in KID 2006: 50,188 obs, 2868 hospitals Total observations HCUP KID 2009: Total appendectomies in patients age 17 or less in KID 2006: 55,818 obs, 2994 hospitals Drop if hospital type missing: Total for KID 2000: 40,916 obs, 2297 hospitals Total for KID 2003: 47,589 obs, 2705 hospitals Total for KID 2006: 48,127 obs, 2681 hospitals Total for KID 2009: 51,086 obs, 2770 hospitals Keep if hospital participated in each of the HCUP KID data collection years ( ) and maintained consistent reporting of hospital type, location and teaching status: Final total observations included in study #2: 74,166 Final total hospitals included in study #2:

111 Figure 4. General socio-economic, cultural and environmental determinants of health Accessed January 24,

112 Figure 5. Determinants of population health Source: Tarlov, Accessed via January 24,