Statewide surveillance of asthma hospitalizations with secondary data

Transcription

1 University of Iowa Iowa Research Online Theses and Dissertations Spring 2002 Statewide surveillance of asthma hospitalizations with secondary data Kirk Tollef Phillips University of Iowa Author granted permission. This thesis is available at Iowa Research Online: Recommended Citation Phillips, Kirk Tollef. "Statewide surveillance of asthma hospitalizations with secondary data." MS (Master of Science) thesis, University of Iowa, Follow this and additional works at: Part of the Epidemiology Commons

2 STATEWIDE SURVEILLANCE OF ASTHMA HOSPITALIZATIONS WITH SECONDARY DATA by Kirk Tollef Phillips A thesis submitted in partial fulfillment of the requirements for the Master of Science degree in Epidemiology in the Graduate College of The University of Iowa May 2002 Thesis supervisor: Professor Laurence Fuortes

3 Graduate College The University of Iowa Iowa City, Iowa CERTIFICATE OF APPROVAL MASTER S THESIS This is to certify that the Master s thesis of Kirk Tollef Phillips has been approved by the Examining Committee for the thesis requirement for the Master of Science degree in Epidemiology at the May 2002 graduation. Thesis committee: --- ; = Thesis supervisor. Member 5<iem^r

4 ABSTRACT The objectives of this study are: 1) to identify and critique alternative data sources for asthma surveillance, 2) to demonstrate a useful method for measuring hospitalized cases of asthma morbidity, and 3) to describe methods applied in the preparation of a reference guide known as the Iowa Asthma Fact Book. The Fact Book characterizes disease prevalence, hospital use, patient demographics and trends, pursuant to policy statements and recommendations for surveillance, noted in the Healthy Iowans 2010 Respiratory Chapter (IDPH 2000). Hospitalization rates and prevalence estimates are included in the Fact Book for use by county and state planners, educators, and others interested in reducing asthma in Iowa. The Fact Book, is based upon secondary data, gathered and evaluated for use in longer term disease surveillance. Use of secondary data can be efficient, by reducing the need to collect original surveys, chart abstracts or other modes of gathering primary data. Certain limitations apply however, in adapting use of secondary data for studies of this kind. Recommendations for longer term surveillance are drawn, accordingly. ii

5 TABLE OF CONTENTS ]Page LIST OF TABLES... LIST OF FIGURES... iv V CHAPTER I INTRODUCTION... 1 Study Objectives... 2 Significance... 2 II LITERATURE REVIEW... 4 A Clinical Definition of Asthma... 4 Disease Surveillance in Public Health... 6 Asthma Surveillance in the United States... 7 Data Sources for Surveillance... 9 Primary Data... 9 Secondary Data III METHODS The Iowa Asthma Fact Book Population in Iowa State Inpatient Data Sets Asthma Case Definitions IV RESULTS V DISCUSSION Use of the Iowa Asthma Fact Book Surveillance Systems Development Operational Issues in the Measurement of Asthma Prevalence.. 46 REFERENCES iii

6 LIST OF TABLES Table Page 1. Number of records (N) and number of missing values for analytical variables in Iowa with state inpatient database by year Frequency of hospitalizations for asthma in state inpatient database by age and year in Iowa with case definition #1 ICD-9-CM code 493 in primary diagnosis Frequency of hospitalizations for asthma in state inpatient database by age and year in Iowa with case definition #2 -- ICD-9-CM code 493 in any diagnosis Frequency of hospitalizations for asthma related diagnoses in state inpatient database by age and year in Iowa with case definition #3 -- asthma related ICD-9-CM codes appearing in primary diagnosis Age specific hospital discharge rates (0 to 4 yrs., 5 to 14 yrs., 15 to 34 yrs.) in Iowa by county with quartiles, case definition #2 -- ICD-9-CM code 493 in any diagnosis, state inpatient database Age specific hospital discharge rates (35 to 64 yrs., 65+ yrs. and all ages) in Iowa by county with quartiles, case definition #2 -- ICD-9-CM code 493 in any diagnosis, state inpatient database Age specific hospital discharge rates (0 to 4 yrs., 5 to 14 yrs., 15 to 34 yrs.) in Iowa by county with quartiles, case definition #2 -- ICD-9-CM code 493 in any diagnosis, state inpatient database Age specific hospital discharge rates (35 to 64 yrs., 65+ yrs. and all ages) in Iowa by county with quartiles, case definition #2 -- ICD-9-CM code 493 in any diagnosis, state inpatient database Crude and age adjusted hospital discharge rates and quartiles for combined years in Iowa by county case definition #2 -- ICD-9-CM code 493 in any diagnosis, state inpatient database 1995 to 1997, 1998 to Age specific hospital discharge rates (0 to 4 yrs., 5 to 14 yrs., 15 to 34 yrs.) in Iowa by population size of counties, case definition #2 -- ICD-9- CM code 493 in any diagnosis, state inpatient database Age specific hospital discharge rates (35 to 64 yrs., 65+ yrs. and all ages) in Iowa by population size of counties, case definition #2 -- ICD-9-CM code 493 in any diagnosis, iv

7 LIST OF FIGURES Figure Page 1. Common asthma triggers Alternative case definitions for Asthma Hospital discharge rate per 100,000 population for asthma, case definition #2 ICD-9-CM Code 493 in any diagnosis, all ages ( ) Steps in planning a surveillance system V

8 1 CHAPTER I INTRODUCTION Surveillance of disease in public health practice has expanded, to include collection, analysis and interpretation of outcome-specific data. Prior to that time, surveillance was mostly restricted to identifying persons with serious communicable disease, so that they could be monitored or quarantined to guide public health measures (Langmuir 1971). Surveillance systems are often managed by state and local public health agencies, in support of epidemiologic studies, aimed at identifying research and service needs. These population based studies help prioritize and design interventions for improving community health. Surveillance systems produce data which are relatively available, and which may be used for a multitude of purposes other than research. Surveillance systems rely heavily upon timely and accurate data, originating from a variety of sources, including hospitals, physicians and clinical laboratories. While these sources may submit primary data by telephone, fax or other direct report, existing records known as secondary data are often used with surveillance systems to reduce costs. The primary function of these secondary data include billing or other administrative purposes, transmitted as hospital discharge data, health insurance claims, and others. Certain efficiencies are achieved in reducing data collection costs, where these secondary data can be used in disease surveillance. There may also be several limitations, many of which are discussed in this study.

9 2 Study Objectives The purpose of the present study is to describe methods of data collection, analysis and reporting asthma information for local health planning purposes. More specific objectives are: 1) to identify and critique alternative data sources for asthma surveillance, 2) to demonstrate a useful method for measuring hospitalized cases of asthma morbidity, and 3) to describe methods applied in the preparation of a reference guide known as the Iowa Asthma Fact Book (IDPH 2002). These study findings should be instructive for the construction of systems supporting state and local surveillance of disease. Significance Asthma affects 14 to 15 million people of all ages and 4.8 million children in the United States, according to findings of a recent National Ambulatory Medical Care Survey (NAMCS) (Nourjah 1999). Physician offices and hospitals are asked to complete this survey annually with patient specific detail (DeLozier 1974). NAMCS results found asthma to be among the five most frequent causes of treatment in emergency rooms and is the third leading cause of preventable hospitalization in the United States (Nourjah 1999). Asthma accounts for about 470,000 hospitalizations and more than 5,000 deaths each year. NAMCS surveys were also analyzed by Mannino, who found an average annual increase of 12% in asthma prevalence during the years of 1980 through 1994; asthma mortality increased at about the same rate during that period. During 1998, there were 16 asthma hospitalizations per 10,000 population in the U.S. and 39 people per 1,000 population had an asthma attack during the previous 12 month period (Mannino, Homa et al. 2002).

10 3 Based upon the National Health Interview Survey during 1988, the Centers for Disease Control (CDC) estimated the prevalence of asthma in Iowa for all ages to be 6.6%, ranking the state to be 26th highest among 50 states surveyed (Anonymous 1998; Rappaport 1998). Wellmark Blue Cross Blue Shield of Iowa estimated the prevalence of pediatric asthma (cases under 17 years) among their membership to be 2.7% in This estimate is based upon insurance claims however, and believed to be an underestimation of asthma prevalence. Such underestimation could be due in part to misclassification of asthma cases. Insurance claims rely upon medical diagnoses originating from a physician which may not be accurate. Physicians seem reluctant to label patients with asthma, preferring diagnostic codes which avoid social or economic consequences to the patient. Asthma is increasing in prevalence worldwide and is more common in English speaking western countries. In a recent international study, surveys were gathered from adults aged 20 to 44 years, finding that asthma in the United States ranked 7th highest among 22 countries reporting. The lowest prevalence rates were from India and Algeria, followed by Italy, France, Belgium and Germany. The highest prevalence rates were predominantly in the British Isles, New Zealand, Australia and the United States (Beasley, Crane et al. 2000). Despite its importance, the CDC noted that no comprehensive surveillance system had been established that measures asthma trends at the state or local level, as of December 1998 (Mannino, Homa et al. 1998). Accordingly, the CDC and other agencies raised asthma surveillance to a high priority for analyzing the etiology, prevalence and development of public health interventions.

11 4 CHAPTER II LITERATURE REVIEW This chapter includes a clinical definition of asthma, as well as definitions of asthma used in surveillance based upon current literature. General methods of chronic disease surveillance are contrasted with methods currently in use for surveilling asthma. More detailed issues of using primary and secondary data sources are provided, as a basis for the design of an asthma surveillance system. A Clinical Definition of Asthma As discussed later in this study, asthma surveillance systems rely heavily upon valid clinical/epidemiologic case definitions and accurate coding of underlying records, usually selected from the International Classification of Diseases (DHHS 2000). These codes should represent the following clinical definition of asthma. Asthma is a chronic inflammatory disease of the airways characterized by episodic, recurrent respiratory symptoms (e.g., wheezing, breathlessness, chest tightness, coughing) and variable airflow obstruction that is reversible either spontaneously or with treatment. The inflammatory process causes the airways to become hyper-responsive to various chemical, biologic or physical stimuli (Bone 1998). Several factors increase the likelihood of triggering or worsening asthma attacks, shown in Figure 1. The mechanism of delivery for each of these exposures is self-evident, yet may be variable with seasonal, occupational or geographical differences. Accordingly, prevention programs are often aimed at reducing outdoor and indoor air

12 5 pollution, community and patient health education, occupational health programs and others. Figure 1. Common Asthma Triggers Inhaled allergens, particularly house dust mites Tobacco smoke Inhaled irritants, particularly sulfur dioxide and formaldehyde fumes Cold air Seasonal pollen or mold Exercise Viral upper respiratory infections Drugs, particularly beta-blockers or analgesics Changes in climate, such as thunderstorms that may increase fungal spores Atmospheric concentrations of SO2, O3, or nitrogen dioxide Food additives, particularly metabisulfite, alcohol, monosodium glutamate and even food coloring (tartrazine) The clinical definition and triggers discussed above are commonly applied in the assessment and treatment of asthma. Symptoms suggesting asthma are relatively straightforward however, they are very non-specific and consistent with a multitude of clinical syndromes. Similarly, spirometry is easy to use in a primary care setting to confirm the presence of airway obstruction however, it is only useful during episodes of bronchospasm and yields a non-specific finding. Clinical judgment is crucial in the final diagnosis of asthma, since these signs and symptoms vary widely from patient to patient as well as within each patient over time. The temporal patterns of symptoms and triggers for asthma discussed above are integral to forming an accurate diagnosis and treatment plan. Case definitions for asthma surveillance should account for these clinical markers directly or indirectly. Thus, surveillance systems based upon secondary data are utilized where these clinical indicators are not accurately available.

13 6 Despite the availability of clinical guidelines for diagnosis and treatment, physicians often lack consensus on the diagnosis and underlying pathogenesis of asthma. The National Institutes of Health (NIH) released a consensus panel report during 1991 which presented emerging evidence of asthma as an inflammatory process (NAEPP 1991). Their more recent consensus panel and review of evidence, characterized asthma as a complex interaction of inflammatory cells, mediators and tissue in the airways. Consequently, asthma is now called a chronic inflammatory disorder of the airways, involving mast cells, eosinophils, T-lymphocytes, neutrophils, smooth muscle and epithelial cells. In more practical terms, this inflammation is manifest as wheezing, breathlessness, chest tightness and cough, particularly at night and early in the morning (Georgitis 1999). These discoveries of asthma pathogenesis over the past decade have refined methods for diagnosis and treatment which are still evolving. The lack of physician consensus is in part, due to an evolving base of evidence as well as historic and regional differences in practice style. Resulting under-treatment and inappropriate therapy have become major foci as possible contributors to preventable morbidity and mortality associated with asthma. The NIH report suggests that hospitalizations due to asthma are preventable when asthma cases are managed properly. Asthma morbidity and mortality can also be reduced with more consistent diagnosis and treatment regimens. Regional variations in the diagnosis of asthma raise concern about misclassification of cases which will be discussed later in this thesis. Disease Surveillance in Public Health Stephen Thacker of the U.S. Centers for Disease Control, offers a definition of surveillance as the ongoing systematic collection, analysis, and interpretation of outcome-specific data for use in planning, implementation, and evaluation of public

14 7 health practice. Thacker further suggests that a surveillance system include the functional capacity for data collection and analysis, as well as the timely dissemination of information... to persons who can undertake effective prevention and control activities (Thacker and Berkelman 1988). Surveillance activities may be active or passive. That is, active surveillance is performed where the investigator initiates procedures to obtain information; passive surveillance is initiated by parties other than the investigator. Certain states for example, require mandatory reporting of occupational or infectious disease and cancer. Employers or clinicians detect cases in these instances, and the submission of their records is passively gathered, from the perspective of the investigator. Surveillance studies often examine populations as a defined group of people, rather than individuals, and are called ecologic or aggregate studies. This is an ecologic study of asthma, describing hospital morbidity of asthma in Iowa counties and various cohorts of age, rurality and others. Ecologic studies are measurements averaged over individuals and the degree of association between exposure and disease need not reflect individual-levels of health status. They are often used to generate hypotheses which may lead to more precise analytic studies using case-control, cohort or other models (Rothman 1998). Asthma Surveillance in the United States In light of increasing asthma morbidity discovered during the mid-1990 s and the absence of coordinated asthma surveillance in state and local health departments, the Council of State and Territorial Epidemiologists and CDC conducted a survey of 54 states and territories, to assess the scope of their asthma surveillance activities (CDC 1996). Of the 51 respondents, 43 reported no asthma surveillance or control programs being conducted on a statewide bases; only Wisconsin maintained a surveillance system

15 8 at that time. State Health departments most commonly attributed a lack of funds and staff shortages, as reasons for not performing asthma control or surveillance. They identified most likely data sources to be hospital discharge records (82%), emergency department visits (31%), and use of public or private health-care services for asthma care (20%), and others. Barriers for using these data include the need to negotiate data release with private entities, legal concerns, or incomplete data systems. The CDC recognized these concerns and soon raised their priority for funding state and local surveillance projects (CDC 1998). In March 1989, the National Asthma Education and Prevention Program was initiated to support grassroots asthma control and surveillance efforts (Schmidt 1999). The program supports a membership of 38 major scientific, professional, governmental and voluntary organizations, with selected surveillance initiatives described below. The Chicago Community Asthma Survey is used in the Chicago area to measure asthma knowledge, attitudes and perceptions. Survey findings are aimed at describing the impact of asthma on individuals, their families and their communities (Grant 1999). Patterns and correlates of asthma hospitalizations and mortality are measured in Chicago with use of hospital discharge data. Descriptive studies were published during the period of 1990 through 1997 to describe hospitalization rates, comorbidities, and outcomes including death (Thomas 1999). The State of New York enumerates prevalence with the Behaviorial Risk Factor Surveillance Study (BRFSS), mortality with death records, and morbidity with hospital discharge records. The state also maintains a registry for occupational lung disease and conducts special studies with Medicaid and managed care insurance administrative data sets. Where possible, reports of pediatric, childhood and adult asthma prevalence and mortality, cost and quality of treatment are produced at the county level. The state of

16 9 New York committed $8.5 million for these activities, supplementing a 5-year agreement with the Centers for Disease Control. During 1998, more than 60 academic and clinical scholars, government agency heads, legislators and other leaders met in California to develop a statewide strategy for asthma. They recognized limited ability to follow changes in prevalence and incidence, mostly relying upon statewide BRFSS, hospital discharge data, and mortality data. The California Department of Health Services presently analyzes asthma deaths by county, race, income and other variables. During the year 2000, the California County Asthma Hospitalization Chart Book was published with county level prevalence tables and charts, based upon discharge data (Hernandez 2000). Their California Asthma Mortality County Chart Book was also released during 2000, including data from their vital statistics data base, for records during 1990 to 1997 (Hernandez 2000). Data Sources for Surveillance Three common methods for obtaining health-related data include 1) subject interviews, 2) direct clinical assessment and 3) review of records or other documentation. The first two approaches will be discussed below as primary data which usually involve collection directly by the investigator or study personnel. The third approach is often called secondary data analysis, because it relies upon existing records which are collected by a different party than the study team, typically not for research purposes. Primary Data Primary data on asthma are generally collected through direct interaction with study subjects or their surrogates. Patients are asked about episodic symptoms of coughing, wheezing, chest tightness and breathing difficulties. Further, they may describe precipitating events and exposures such as exercise, presence of dust and other

17 10 triggers discussed earlier. Their medical history should include patterns of symptoms, family history, medications and several other factors, including related or antecedent respiratory viral infections. Hence, the accuracy of diagnosing asthma, relies heavily upon each patient's ability to report these facts, and is subject to several important factors including: fluency, recall bias, education level, and medical sophistication, as well as access to care and treatment patterns of practitioners. The patient should have sufficient verbal or written language skills to respond reliably to questions and they should be able to understand what is being asked. Surrogates may be used where study subjects are unable to directly participate, and must be familiar enough to the subject to accurately answer on their behalf (Parrish and McDonnell 2000). This is the typical method for study of childhood asthma. Primary data collection often require more resources of time expended by investigators and their subjects. Travel expenses may be higher, particularly where subjects from a large geographical area are under study. Physician diagnosed asthma may also be subject to certain biases discussed in this study, including misclassification and detection bias. Physicians may misdiagnose asthma, based upon unclear symptoms and tests, or they may substitute another respiratory diagnosis, preferring not to label the patient. Detection bias may be problematic, where all true asthmatics do not attend medical care, due to diminished access to treatment or other causes. Both of these sources of bias may result with an underestimation of asthma, when using medical records or most administrative data in studies. Primary data are collected for asthma surveillance with the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance Study (BRFSS). They are described here as primary data collection efforts, even though both survey

18 11 projects make available resulting data sets to investigators for further analysis, after initial tabulations are produced by these national organizations. The NHIS was initiated by the National Center for Health Statistics, and has operated periodically since 1957, with household surveys gathered through face to face interviews conducted by the U.S. Bureau of Census. Survey estimates are published annually with a two to three year lag, for several population subgroups such as race, sex, age, income and geographical regions. Microdata sets are available from the National Center for Health Statistics, yet sampling limitations preclude county level analysis of most survey items. Since 1997, the National Health Interview Survey has contacted a sample of households in the United States, asking about limitations of activity, injuries, health insurance, and access to health care, health care utilization, health conditions, behaviors and immunizations. Separate instruments and methods are applied with adults and children in the household under 18 years of age. Asthma cases are defined with two questions - Has a doctor or other health professional ever told you that your child has asthma? Persons answering yes, are further asked During the past 12 months, has your child had an episode of asthma or an asthma attack? Asthma prevalence and attack rates are reported with these self reported survey items (Botman SL 2000). The Behavioral Risk Factor Surveillance System (BRFSS) was developed to address one major weakness of the NHIS, that state-specific estimates of behavioral risk and disease prevalence were not available. The BRFSS then, was implemented through telephone surveys within 29 states during the early 1980 s; since 1994, it has gained participation by all 50 states, the District of Columbia, and three U.S. territories. Standard core questionnaire items for selected disease case-mixes were developed by the Centers for Disease Control, for use by state health departments. While the BRFSS was

19 12 originally designed as a state-wide surveillance method, a number of states have applied the instrument with stratification and oversampling, to support regional comparisons within (CDC 2000). Prior to the year 2000, state-wide asthma prevalence was estimated with the NHIS and as noted, more accurate state-wide estimates are now available through the BRFSS. While sampling methods with the BRFSS are used to estimate prevalence at state-wide and smaller geographic areas, the CDC has noted three limitations with their initial findings. The median response rate to BRFSS was only 51.3%. Certain residents such as lower income families, may not have telephones and be available for surveys. As described earlier, case definitions of asthma rely upon patient recall of their physician diagnosing asthma and their recall may be limited (CDC 2001). Secondary Data Due to certain efficiencies of cost, availability, convenience and relevance, existing data sets are often considered for use in surveillance studies. As noted earlier, data sets may be obtained from the above NHIS and BRFSS studies for further analysis, and would be considered secondary data since they were already gathered and archived for use by other investigators. Secondary data may include vital records, administrative data such as hospital discharge records and billing claims, and health-interview surveys (Teutsch 2000). In addition to the apparent cost savings, secondary data are commonly used in descriptive studies which generate hypotheses for more detailed studies. In public health studies, secondary data are often used to compare patient age, gender, diagnosis and geographic cohorts, as well as trends in phenomena such as risk factors, demographics and health outcomes. Steward points out that population data sets, such as Bureau of the Census data, may be used to compare samples to general population

20 13 characteristics in order to examine the representative-ness of the study sample (Steward 1993). On the other hand, use of secondary data often imposes limitations upon research methods and data utility. Certain sampling bias or size limitations may not suit the ensuing needs of secondary data studies. Operational definitions of cases or study populations may not be suitable. These concerns warrant careful examination of the original data collection methods to determine how they may affect study goals or prevalence estimates.

21 14 CHAPTER III METHODS This chapter includes a description of methods employed in writing the Iowa Asthma Fact Book, considering strengths and limitations of secondary data. The impetus for developing the Fact Book is described immediately below, with detailed steps of data collection and analysis of population data sets, state inpatient data sets, health insurance administrative data, and selection of asthma case definitions. The Iowa Asthma Fact Book Given the lack of surveillance systems assessing asthma within states, and a recent charge by the Respiratory Chapter of Healthy Iowans 2010, a small grant proposal was prepared by the author of this thesis and faculty supervisors, to prepare an initial asthma surveillance report based upon secondary, available data. The grant was successfully funded by the Centers for Disease Control for work to be completed during the year prior to publication of this thesis. The resulting Iowa Asthma Fact Book, is based upon several secondary data sets which were gathered and evaluated for use in longer term ongoing disease surveillance. The data sets include the State Inpatient Database (SID) and insurance claims from two payers. Methods and tables discussed in this thesis are drawn primarily from the SID portion of the Fact Book. The Asthma Fact Book characterizes disease (asthma) prevalence, hospital use, patient demographics and trends, pursuant to policy statements and recommendations for surveillance, noted in the Healthy Iowans 2010 Respiratory Chapter (IDPH 2000). Hospitalization rates and prevalence estimates are tabulated in the Fact Book for use by

22 15 county and state planners, educators, and others interested in understanding and reducing the impact of asthma among Iowans. Tables and figures are shown for specific age, gender and county populations, in a manner aimed at identifying and focusing interventions toward populations suffering higher rates of asthma. Maps are used to display geographic patterns; bar charts are quartile scored to help identify counties with higher rates, and; statistical tables are designed to provide underlying details of prevalence estimates. Population in Iowa Population counts for Iowa counties were obtained from the Population Estimates Program, Population Division, U.S. Census Bureau, Washington, DC. These estimates were reported for each year of July 1, 1990 through July 1, 1999, in an on-line file (CO ). Population counts for the year 2000 were projected from this file, based upon linear trends among the period of 1997 through State Inpatient Data Sets Data for discharges from Iowa hospitals during 1994 through 2000 were provided by the Iowa Department of Public Health. This statewide inpatient database is maintained by the Iowa Hospital Association, based upon patient level records for all acute admissions originating from Iowa hospitals. Individual patient identifiers were not available which precluded the identification of multiple admissions for a given patient or asthma incidence. Electronic files were requested for all hospital discharges in Iowa during 1994 through 2000, containing diagnoses of asthma (ICD ) or other respiratory diagnoses consistent with asthma (ICD 466, , , , 504 and 506) in any of ten possible diagnosis fields.

23 16 Each discharge record included age, race, gender, payment source, site of hospital, zip code of patient residence, diagnoses and procedures, DRG, length and cost of hospitalization and disposition. As shown in Table 1, selected variables were examined for missing data to ensure proper calculation of rates. For example, race was not coded in nearly 20% of those records submitted. Hence, the Fact Book could not include this variable in producing incidence rates by race. Table 1. Number of records (N) and number of missing values for analytical variables in Iowa with state inpatient database by year Variable (N=55,099) (N=58,191) (N=58,450) ADMTYPE 1,209 1, AGE BDATE COUNTY 3,487 3,401 3,732 DIAG RACE 11,337 12,355 11,511 SEX SRCPAY TOTCHRG ZIP Several illustrations in the Fact Book estimate asthma hospitalization rates for county residents in Iowa. These rates indicate a level of morbidity during three year periods of 1995 through 1997 and 1998 through Rates were calculated by summing the number of hospitalizations for each three-year period, dividing by the sum of annual population counts for the period, and multiplying the result by 100,000. The result can be interpreted as an average per capita rate of hospitalization, or a period

24 17 prevalence rate of hospitalization. Prevalence rates can be expressed in three different ways, depending upon whether they refer to people who have a condition or event at a point in time (point prevalence) or at any time during a period of time (period prevalence), or at any time in their lifetime (lifetime prevalence). In this case, period prevalence rates were used to improve the statistical reliability of certain age, sex and county cohorts. Since hospitalized asthma cases are relatively rare, the combination of three year periods permitted comparisons of persons within age groups and counties, as shown in tables 5 through 11. Similar methods were applied in a recent chart book of hospitalized asthma cases in California, where the authors noted that rates were calculated for three years combined to provide a more stable estimate for the more sparsely populated counties. (Hernandez 2000) While these rates are more stable, it should be noted however, that they are less sensitive to annual changes and hence, their utility for longitudinal surveillance is diminished. Where the number of cases within an age or sex cohort in a sparsely populated county was 5 or fewer, the rates were assigned a value of 0 to further improve the stability of rates shown in this study. These methods of suppressing the calculation of rates for cohorts with low numerator counts and combining three years of hospitalization for each report period, are believed to improve the stability of rates shown in this report however, more sophisticated power calculations are needed to understand the extent of variation in reported rates which may still be due to small numerators or random noise. Hospitalization rates shown in this study are based upon patient residence within counties, instead of counties in which they were treated. If for example, a patient living in Johnson county attended hospital treatment in Linn county, their hospitalization would be counted in the Johnson county statistics. This patient origin/destination distinction is

25 18 often used in population based studies which assess exposure and other characteristics of the patient s residence, which may influence asthma. The State Inpatient Data (SID) used in this study were generated by hospitals in Iowa. Consequently, additional adjustments for Iowans treated for asthma outside of Iowa were calculated with an additional data set acquired from the Iowa Hospital Association. Asthma hospitalizations for persons living in certain counties were inflated, where their patient origin/destination patterns reflected a migration of hospitalizations out of state, in an additional SID data set representing Iowans hospitalized for all causes in surrounding states during Adjustment factors for residents of each Iowa county traveling out of state were calculated and applied to all county specific rates in this report. Asthma Case Definitions Three case definitions of asthma and related diagnoses were considered in preparing the Fact Book, as defined by diagnostic codes appearing on each record of hospitalization; they were formed with ICD-9-CM codes shown in Figure 2. Case definitions #2 and #3 were developed by the Council of State and Territorial Epidemiologists (CSTE 2000); case-definition #3 was crafted by a consensus panel of faculty advisors, including two physicians and an epidemiologist.

26 19 Figure 2. Alternative Case Definitions for Asthma Case Definition #1 - ICD-9-CM Code 493 reported in the primary diagnosis field. Case Definition #2 - ICD-9-CM Code 493 reported in any diagnosis field. Case Definition #3 - Asthma related diagnostic codes reported in the primary diagnosis field, from the following list: 466 Acute bronchitis and bronchiolitis Laryngeal spasm 480 Viral pneumonia 481 Pneumococcal pneumonia 482 Other bacterial pneumonia 483 Pneumonia due to other specified organism 484 Pneumonia in infectious disease classified elsewhere 485 Bronchopneumonia, organism unspecified 486 Pneumonia, organism unspecified 490 Bronchitis, not specified as acute or chronic 491 Chronic bronchitis 492 Emphysema 494 Bronchiectasis 495 Extrinsic allergic alveolitis 496 Chronic airway obstruction, not elsewhere classified 504 Pneumonopatny due to inhalation of other dust 506 Respiratory condition Tables 2, 3 and 4 below, include case counts by year and age cohort for these alternative definitions of asthma. Table 2 applies the most restrictive definition, with a single asthma code (ICD-9-CM 493) appearing only in the primary diagnosis field. In describing hospitalized cases of asthma, this definition would offer higher specificity, in that patients without asthma are more likely removed from consideration. Specificity then, is the fraction of patients without asthma that are correctly identified as negative by the definition. Table 3 allows asthma cases to include an ICD-9-CM of 493 in any diagnostic field. That is, asthma may be co-morbid with other diseases which are considered the primary cause of hospitalization. Accordingly, this definition may be less sensitive. Sensitivity in this case, is the fraction of patients with asthma that are correctly identified as positive by the definition. Table 4 provides for the broadest definition of asthma, as it is defined by a list of asthma related diagnoses shown in Figure 2. Clearly,

27 20 this definition would diminish specificity and sensitivity, from earlier definitions discussed. However, the broader definition may be useful for certain studies, where data sets may include diagnoses which are frequently substituted for ICD-9-CM code 493. Table 2. Frequency of hospitalizations for asthma in state inpatient database by age and year in Iowa with case definition #1 ICD-9-CM code 493 in primary diagnosis Year 0-4 yrs 5-19 yrs yrs yrs 65+ yrs Total , , , , , ,750

28 21 Table 3. Frequency of hospitalizations for asthma in state inpatient database by age and year in Iowa with case definition #2 ICD-9-CM code 493 in any diagnosis Year 0-4 yrs 5-19 yrs yrs yrs 65+ yrs Total ,281 1,416 2,690 2,124 3,456 10, ,492 1,584 2,942 2,260 3,541 11, ,321 1,492 3,026 2,344 3,755 11, ,205 1,425 2,990 2,438 3,790 11, ,123 1,017 1,387 4, ,184 1,146 1,525 5,393 Table 4. Frequency of hospitalizations for asthma related diagnoses in state inpatient database by age and year in Iowa with case definition #3 asthma related ICD-9-CM codes appearing in primary diagnosis Year 0-4 yrs 5-19 yrs yrs yrs 65+ yrs Total , ,331 3,087 12,098 20, , ,541 3,271 12,732 22, , ,360 3,285 12,709 21, , ,296 3,280 13,203 21, , ,630 3,746 14,720 24, , ,562 4,350 16,043 25,699 Tests for the best definition of asthma can be performed with patient records which may have a gold standard definition of asthma, such as clinical examination and confirmed diagnoses for each patient. A model commonly used for measuring the performance of screening tests for disease can be applied, which would compare case definitions from ICD9-CM codes with clinically defined cases. True positive and true

29 22 negative cases would be identified, along with false positive and false negative cases. Specificity, sensitivity and predictive values would also be calculated to identify the strength of performance of the diagnostic codes selected. The positive predictive value would describe the fraction of people with positive asthma according to the diagnostic codes, who actually have asthma according to the clinical examination. Tables, figures and maps in the Fact Book apply the case definition #2, with ICD9-CM code 493 appearing in any diagnostic field; this definition was recommended by the Council of State and Territorial Epidemiologists (CSTE 2000).

30 23 CHAPTER IV RESULTS Tables 5 and 6 display age specific hospital discharge rates during 1995 through 1997 for specific age cohorts. Tables 7 and 8 include the same rates for the period 1998 through During the period of 1995 through 1998, wide variations in hospital discharge rates were found across counties for all ages, ranging from 93 discharges per 100,000 in Lyon County to Hamilton County s rate of 1,134. Lyon County again held the lowest rate of 115 discharges per 100,000 during the period of 1998 through 2000, and Hamilton was highest among 99 Iowa counties, with a rate of 973. The overall rate of asthma hospitalizations increased from 399 during the period of 1995 through 1997 to the rate of 412 during the period of 1998 through 2000, representing a 1% annual increase. As shown in tables 5 and 6, wide differences were also found in hospital use by age cohorts, with the lowest rate of 25 discharges per 100,000 among Iowans aged 5 to 15 years, and the highest rate of 945 discharges per 100,000 among Iowans 65 years or older. Tables 7 and 8 reflect a bimodal distribution of age, with young children aged 0 to 5 years and Iowans 65 years and older experiencing higher rates of treatment than middle age ranges. Age cohorts in the Fact Book were designed to be biologically meaningful, and consistent with other public health planning efforts. These tables are designed for reference by county health planners, as they may pursue studies of particular aged sub-populations, or assessing their counties relatively higher or lower hospitalization rates, when compared with other counties in Iowa. Traditional studies of geographic variation in hospitalization rates, suggest that counties

31 24 with higher or lower rates might be targeted for further study and possible intervention which could lead to more consistent treatment of asthma across the state. Interventions may also be designed to reduce hospitalization of asthma patients. That is, most exacerbations of asthma are treated at home and consequently, hospital discharge rates depict patients with higher severity of illness or those without adequate treatment, maintenance or preventive services.

32 25 Table 5. Age specific hospital discharge rates (0 to 4 yrs., 5 to 14 yrs., 15 to 34 yrs.) in Iowa by county with quartiles, case definition #2 -- ICD-9-CM code 493 in any diagnosis, state inpatient database to 4 years 5 to 14 years 15 to 34 years County Cases Rate Quartile Cases Rate Quartile Cases Rate Quartile All Iowa 3, , , Adair Low Adams High Allamakee Low Low Appanoose High Audubon High Benton Low 8 69 Low Black Hawk High High High Boone Low Bremer Low Buchanan Low Low Buena Vista Low Low Butler Low Calhoun 21 1,082 High High High Carroll Low 6 55 Low Low Cass Low Cedar Cerro Gordo High Cherokee Low Chickasaw 26 1,015 High Low Clarke Clay Low Clayton Low Clinton 481 4,857 High High High Crawford Low Dallas Low Davis Decatur Delaware Low Low Des Moines High High High

33 26 Table 5. - continued 0 to 4 years 5 to 14 years 15 to 34 years County Cases Rate Quartile Cases Rate Quartile Cases Rate Quartile Dickinson High Dubuque Low Low Low Emmet High Fayette Floyd Hamilton 62 2,077 High High High Hancock Low Hardin Harrison High Henry Howard Low Humboldt 28 1,515 High Low Ida Iowa Jackson 41 1,034 High Low Jasper High Low Jefferson Johnson High Jones Low Keokuk High High Kossuth Low Low Lee 86 1,175 High High High Linn High High Louisa Lucas Lyon Low Madison Low Mahaska Marion High Marshall Mills Low Mitchell Low Monona 17 1,038 High High Monroe

34 27 Table 5. - continued 0 to 4 years 5 to 14 years 15 to 34 years County Cases Rate Quartile Cases Rate Quartile Cases Rate Quartile Montgomery High Low Muscatine Low O'Brien Low Osceola Page Low Low High Palo Alto Plymouth Low Low Low Pocahontas High Low Polk High High 1, High Pottawatt Poweshiek High High Ringgold High Sac Scott High High Shelby Low Sioux Low 9 59 Low Low Story Low Low Tama Low Low Taylor Union Van Buren High Wapello High High High Warren Washington Low High High Wayne High High Webster 155 1,946 High High High Winnebago Winneshiek Low 5 63 Low Woodbury Worth Wright High

35 28 Table 6. Age specific hospital discharge rates (35 to 64 yrs., 65+ yrs. and all ages) in Iowa by county with quartiles, case definition #2 ICD-9-CM code 493 in any diagnosis, state inpatient database to 64 years 65+ years All ages County Count Rate Quartile Count Rate Quartile Count Rate Quartile Iowa 10, , , Adair ,013 High High Adams Low Low Allamakee Low Low Low Appanoose Low Audubon Benton High Black Hawk High 1, High Boone Bremer Low 125 1,108 High Buchanan High Buena Vista Low 122 1,117 High Butler High Calhoun High 100 1,287 High High Carroll Low Low Cass Low Cedar C. Gordo Cherokee Low Low Low Chickasaw Clarke High Clay Clayton Low Low Low Clinton High High 1, High Crawford 9 53 Low Low Low Dallas Davis Low Low Low Decatur Low Low Low Delaware Low Low Low Des Moines High High Dickinson Dubuque Low Low Emmet Fayette Low Floyd Low

36 29 Table 6. - continued 35 to 64 years 65+ years All ages County Count Rate Quartile Count Rate Quartile Count Rate Quartile Franklin Low Fremont Low Low Greene High Grundy Guthrie ,341 High High Hamilton High 252 2,814 High 554 1,134 High Hancock ,040 High Hardin Harrison High High Henry Howard Low Low Low Humboldt High Ida Low Low Low Iowa ,139 High High Jackson Low Jasper High High Jefferson Johnson , Jones Low Low Keokuk High High Kossuth High Lee High 238 1,240 High High Linn High High 2, High Louisa Lucas High Low Lyon 7 53 Low Low Low Madison ,092 High Mahaska Marion High Marshall Mills Mitchell Low Low Low Monona Monroe Montgom High Muscatine Low O'Brien Low Osceola Low Low Page High 183 1,766 High High Palo Alto Low Low Low Plymouth Low Low

37 30 Table 6. - continued 35 to 64 years 65+ years All ages County Count Rate Quartile Count Rate Quartile Count Rate Quartile Pocahontas High 106 1,778 High High Polk 1, High 1, High 5, High Poweshiek High 151 1,519 High High Ringgold Low Sac High Scott High , High Shelby ,141 High Sioux Low Low Story Low Tama Taylor Low Low Low Union Van Buren High High High Wapello High 254 1,309 High High Warren Washington Wayne High 58 1,140 High High Webster High High Winnebago Low Winneshiek Low Low Low Woodbury Low Worth Low Low Wright High High

38 31 Table 7. Age specific hospital discharge rates (0 to 4 yrs., 5 to 14 yrs., 15 to 34 yrs.) in Iowa by county with quartiles, case definition #2 ICD-9-CM code 493 in any diagnosis, state inpatient database to 4 years 5 to 14 years 15 to 34 years County Count Rate Quartile Count Rate Quartile Count Rate Quartile Iowa 2, , , Adair Adams Low Allamakee Low Appanoose Audubon Low Benton Black Hawk High High Boone Bremer Low Low Buchanan High Buena Vista Low Low Butler High Calhoun High High Carroll Low 9 85 Low Cass Low High Cedar High Cerro Gordo High Cherokee Chickasaw High High Clarke High High Clay Low Clayton Low Low Clinton 383 4,012 High High High Crawford Low Low Dallas Low Davis Decatur Low Delaware Low Low Des Moines High High High Dickinson Low High

39 32 Table 7. - continued 0 to 4 years 5 to 14 years 15 to 34 years County Count Rate Quartile Count Rate Quartile Count Rate Quartile Dubuque Low Emmet High Fayette Low Greene High High Grundy Guthrie Low Hamilton High High Hancock Hardin Harrison Low Henry Low High Howard Humboldt 30 1,647 High Ida Iowa Jackson Low Low Jasper High High Jefferson High High Johnson Low High High Jones Low High Keokuk High Kossuth Low Lee 75 1,046 High High High Linn High Louisa Low Lucas High High Lyon Madison Mahaska Low Marion Marshall High Mills Low Low Mitchell Low Monona High High Monroe Low

40 33 Table 7. - continued 0 to 4 years 5 to 14 years 15 to 34 years County Count Rate Quartile Count Rate Quartile Count Rate Quartile Montgom Low Muscatine Low High O Brien Osceola Page Palo Alto Low Plymouth Low Pocahontas 18 1,096 High High Low Polk High Pottawatt Low Poweshiek High High High Ringgold 3 0 Low 2 0 Low Sac Low Scott High High High Shelby 5 0 Low Sioux Low 7 45 Low Low Story Low Low Tama Taylor 1 0 Low 2 0 Low Union Van Buren 5 0 Low 4 0 Low Wapello High High Warren Washington High High Wayne 3 0 Low 0 0 Low High Webster 120 1,548 High High High Winnebago Low Low Winneshiek Low Woodbury Worth 5 0 Low 4 0 Low Low Wright High 0 0 Low

41 34 Table 8. Age specific hospital discharge rates (35 to 64 yrs., 65+ yrs. and all ages) in Iowa by county with quartiles, case definition #2 ICD-9-CM code 493 in any diagnosis, state inpatient database to 64 years 65+ years All ages County Count Rate Quartile Count Rate Quartile Count Rate Quartile Iowa 12, , , Adair High 127 2,387 High High Adams Low Allamakee Low Low Low Appanoose High 90 1,121 High High Audubon Low Low Benton , Black Hawk High 596 1,157 High 1, High Boone Bremer Buchanan Buena Vista Low Low Butler , Calhoun ,230 High High Carroll Low Low Low Cass Cedar Cerro Gordo High Cherokee Chickasaw , Clarke ,340 High High Clay ,287 High Clayton Low Low Clinton High 256 1,068 1, High Crawford Low Low Dallas ,226 High Davis Low Low Low Decatur Low Low Delaware Low Low Des Moines High High Dickinson

42 35 Table 8. - continued 35 to 64 years 65+ years All ages County Count Rate Quartile Count Rate Quartile Count Rate Quartile Dubuque Low Emmet High 76 1,166 High High Franklin Low Low Fremont Low Low Low Greene High High Grundy Low Guthrie ,626 High High Hamilton High 214 2,460 High High Hancock , Hardin ,174 High Harrison Low Low Henry ,152 High Howard Low Low Low Humboldt Low Low Ida Low Low Low Iowa ,267 High Jackson Jasper High 217 1,240 High High Jefferson Johnson High 284 1,143 High 1, Jones High Low Keokuk High High Kossuth Low 112 1,117 High Lee High 252 1,344 High High Linn High 696 1,002 2, Louisa Low Low Lucas Low Lyon Low Low Low Madison Mahaska Marion High 152 1, Marshall Mills Low Low Mitchell Low Low Low Monona ,399 High High Monroe ,211 High

43 36 Table 8. - continued 35 to 64 years 65+ years All ages County Count Rate Quartile Count Rate Quartile Count Rate Quartile Montgom High Muscatine O'Brien Osceola Low Low Low Page , Palo Alto Plymouth Low Pocahontas High Polk 2, High 1,340 1,080 5, Pottawatt Low Poweshiek High 195 1,985 High High Ringgold Low Sac High 86 1,119 High High Scott , Shelby , Sioux Low Low Low Story Low Tama , Taylor Low Union Van Buren High 54 1,171 High High Wapello High 332 1,737 High High Warren Washington High Wayne High 58 1,224 High High Webster Low High Winnebago Low Low Low Winneshiek Low Low Low Woodbury Low 1, Worth Low Wright High 92 1, High

44 37 Differences between county level hospital discharge rates for all ages were compared for the two time periods of and , to determine if there was a significant increase or decrease in rates. A paired T-test was selected, after testing for certain underlying assumptions of the data. Since the rates for each period are associated with each respective county, the t-test assumes that rates are not independent. The t-test also assumes that the distribution of rates across counties is normal; tests of skewness and kurtosis did not reject normality hence, the t-test was considered to be appropriate. A two-tailed hypothesis was drawn, believing that rates could increase or decrease over time. Findings of the t-test, suggest that the rates significantly increased with a probability level of.004. Table 9 includes crude and age adjusted hospital discharge rates of hospitalization as single-summary rates of all residents for each county. As noted earlier, a direct method of age adjustment was applied, supporting the comparison of counties between each other and with the state, after removing the influence of different age distributions among the general populations of these areas. Counties were rank ordered by discharge rates, to develop quartile ranges noted in tables 5 through 9 with to delineate low (quartile I), medium low (quartile II), medium high (quartile III), and high (quartile IV) ranges. Age cohorts are described separately in each table.

45 38 Table 9. Crude and age adjusted hospital discharge rates and quartiles for combined years in Iowa by county (case definition #2 ICD-9-CM code 493 in any diagnosis, state inpatient database 1995 to 1997, 1998 to 2000) 1995 to to 2000 Crude Adjusted Crude Adjusted County Cases Rate Rate Quartile Cases Rate Rate Quartile Iowa 34, , Adair High High Adams Allamakee Low Low Appanoose High Audubon Low Benton Black Hawk 1, High 1, High Boone Bremer Buchanan Buena Vista Low Butler Calhoun High High Carroll Low Low Cass Cedar Cerro Gordo Cherokee Low Chickasaw Clarke High Clay Clayton Low Low Clinton 1, High 1, High Crawford Low Low Dallas Davis Low Low Decatur Low Low Delaware Low Low

46 39 Table 9. - continued 1995 to to 2000 Crude Adjusted Crude Adjusted County Cases Rate Rate Quartile Cases Rate Rate Quartile Delaware Low Low Des Moines High High Dickinson Dubuque Low Fremont Low Low Greene High Grundy Low Guthrie High Hamilton 554 1,134 1,019 High High Hancock Hardin Harrison High Low Henry Howard Low Low Humboldt Ida Low Low Iowa Jackson Jasper High High Jefferson Johnson 1, , High Jones Low Keokuk High High Kossuth Lee High High Linn 2, High 2, High Louisa Lucas Lyon Low Low Madison High Mahaska Marion High

47 40 Table 9. - continued 1995 to to 2000 Crude Adjusted Crude Adjusted County Cases Rate Rate Quartile Cases Rate Rate Quartile Marshall Mills Low Monroe Montgom Muscatine O'Brien Low Osceola Low Low Page High Palo Alto Low Plymouth Low Pocahontas High Polk 5, High 5, High Pottawatt Poweshiek High High Ringgold Sac Scott 2, High 2, High Shelby Sioux Low Low Story Low Tama Taylor Low Low Union Van Buren High High Wapello High High Warren Washington High High Wayne High High Webster High High Winnebago Low Low Winneshiek Low Low Woodbury , Worth Low Wright High High

48 41 Hospitalization rates are shown by size of community in table 10 below. Accordingly, larger communities including metropolitan statistical areas (MSA s) experience higher rates of hospitalization for asthma. Table 10. Age specific hospital discharge rates (0 to 4 yrs., 5 to 14 yrs., 15 to 34 yrs.) in Iowa by population size of counties, case definition #2 ICD-9- CM code 493 in any diagnosis, state inpatient database to 4 yrs 5 to 14 yrs 15 to 34 yrs County Size Count Rate Count Rate Count Rate All Iowa 2, , , ,000 TO 19,000 20,000 TO MSA , , , MSA 1, , Table 11. Age specific hospital discharge rates (35 to 64 yrs., 65+ yrs. and all ages) in Iowa by population size of counties, case definition #2 -- ICD-9-CM code 493 in any diagnosis, to 64 yrs 65+ Yrs All Ages County Size Count Rate Count Rate Count Rate All Iowa 12, , , ,000 TO 19,000 20,000 TO MSA , , , MSA 6, , ,

49 42 Maps similar to Figure 3 are used in the Fact book, shown by age cohort, to illustrate geographical patterns of hospital use. For example, contiguous counties oftentimes demonstrate consistently higher or lower rates in these maps. Figure 3. Hospital discharge rate per 100,000 population for asthma, case definition #2 -- ICD-9-CM Code 493 in any diagnosis, all ages ( )

50 43 CHAPTER V DISCUSSION Use of the Iowa Asthma Fact Book Tables and figures in the Iowa Asthma Fact Book may be useful for the identification of risk groups, leading to the design of intervention strategies. Rates are shown for counties, rurality (metro versus non-metro counties), gender, and ages. Populations with higher or lower rates may be candidates for further study or action. Prevalence and hospitalization rates are used to illustrate the presence of asthma, and service utilization respectively. County planners are urged to identify higher or lower rates of treatment for age or gender cohorts. Aberrant rates may be due to over utilization or limited access to care and interventions can be designed, accordingly. Descriptive studies of this kind tend to raise more questions than answers. That is, variations in utilization rates are described here without a discussion of likely causes, such as environmental exposure, access to treatment, and others. Variations in hospitalization rates have been extensively studied with a method known as small area analysis. These studies typically produce per capita rates of treatment for residents of counties, health market areas and other geographical areas in which patients reside. Wide differences or variability between these geographical areas is cited for certain diseases and less variability is cited for others. Underlying statistical methods for comparing variability across different diagnosis-related groups (DRG) have been challenged by Diehr, et al. She suggests that small area analysis studies do not adjust for the prevalence of each DRG, nor do they distinguish between variation among or within areas. Further, small area analysis studies typically do not account for multiple

51 44 hospitalizations which may have been incurred by the same patient (Diehr 1993). Without more definitive tests of statistical power and other concerns raised by Diehr, many variations in hospitalization rates across counties may be due to chance alone. All rates in the Iowa Fact Book were based upon secondary data, i.e. data collected and available from other parties including the U.S. census, Iowa Hospital Association and participating insurers. Use of secondary data can be efficient, by reducing the need to collect original surveys, chart abstracts or other modes of gathering primary data. Certain limitations apply however, in adapting use of secondary data for studies of this kind. For example, insurance claims, originally used for financial reporting of hospital or other services, may not have perfect clinical accuracy in diagnostic coding. Asthma prevalence rates cited in the Fact Book, are based upon insurance claims and administrative data, and are lower than prevalence rates in the literature, based upon survey or clinical chart abstraction. These differences may be due to misclassification bias of coding administrative data. Anecdotal evidence has suggested that providers are hesitant to label or stigmatize patients with asthma, when filing insurance claims. Sufficient data were not available to calculate county specific rates on an annual basis. That is, the lower number of asthma cases found in certain rural counties, did not support the calculation of reliable rates of annual treatment. Consequently, three years of data were combined for county specific rates shown in this report. While these rates are more reliable, they are less sensitive to change over time than annual rates, and may not support longitudinal studies aimed at evaluating community interventions. In light of concerns raised by Diehr et.al., more powerful tests of statistical reliability should be performed, before rigorous findings are drawn about differences in these hospitalization rates or underlying causes.

52 45 Surveillance Systems Development The context for disease surveillance has been driven in part by the establishment of national health targets for health promotion and disease prevention, first established in publication of Healthy People by the U.S. Surgeon General (U.S. Department of Health 1979). Since the publication of this first report, health policy has adopted a management by objectives process for setting preferred levels of disease. Local, state and national surveillance programs have aimed to measure the progress of these objectives. Indeed, the Iowa Asthma Fact Book was written in response to surveillance objectives drawn by the Respiratory Chapter of Healthy Iowans 2010, a statewide policy group operating under similar principles of the national Healthy People initiatives. Objectives in the Fact Book addressed community education, reduction of environmental hazards and development of surveillance tools (IDPH 2002). Developing health policy with a management by objective model however, is subject to a certain chicken and egg principal where data must be found to craft empirical baseline and longer term objectives. This principal was advanced by Deborah Maiese who asked which came first: the Healthy People objective or the data source to track the objective? (Maiese 1998). While Maiese cites considerable progress in our ability to track the Healthy People 2000 objectives as compared to 1990 objectives, she notes that significant progress is still needed. In the case of Healthy Iowans, volunteers were aware of worsening national trends of asthma epidemiology however, they lacked state or local data for validation of those trends or for planning local intervention programs.

53 46 Operational Issues in the Measurement of Asthma Prevalence Case definitions of asthma are operationalized in three ways, (1) by asking the patient directly if they have asthma symptoms, (2) asking the patient if they have been diagnosed with asthma by a physician, or (3) with use of diagnosis codes recorded by a physician while using existing data. Reliance upon the patient s history alone, lacks clinical validation and as self-reported data, is subject to patient recall bias. The conduct of a medical history, physical, and tests such as spirometry is rarely applied in a population based manner. Reliance upon a medical diagnosis using existing data, may be subject to detection bias, where all patients with asthma are not attending treatment and thus, are not included in resulting studies. Physicians may also be reluctant to label a patient with asthma, fearing social or economic consequences for the patient. All of these approaches are subject to poor consensus regarding diagnostic methods useful for developing prevalence estimates. Strengths and limitations of these operational definitions are associated with primary and secondary data collection methods discussed earlier in this thesis. Several case definitions were evaluated with secondary data collected for use in the Iowa Asthma Factbook, choosing between up to nine diagnostic codes which appear on a single patient s hospital discharge record. Considerations included the validity of codes as they may accurately represent a clinical definition of asthma or as certain codes may be used in substitution of asthma codes by health care providers, given certain biases discussed earlier. For example, misclassification bias may be incurred where physicians are reluctant to label patients with asthma. Operational case definitions of asthma are tailored to the study design to be used in epidemiological studies. Incidence studies estimate the number of new asthma cases, and require analyses of onset of new respiratory symptoms of a defined population over

54 47 time. Due to the chronic nature of asthma, incidence studies often involve lengthy periods of follow-up and large resources (Pearce 1998). While incidence studies are costly, they are preferred in studies of etiology since they collect and analyze all relevant information about the source population. Prevalence studies use a cross-sectional approach, counting the number of cases at a single point in time yielding point prevalence estimates, or over a defined period of time yielding period prevalence estimates. Prevalence studies are more commonly used in asthma surveillance, due to their lower cost and greater value in addressing the public health impact of asthma. Unfortunately, patient identifiers were not available with the discharge abstracts collected for the Fact Book hence, longitudinal studies of individual patients were not possible. Further, this study could not account for multiple hospitalizations of a single subject and is subject to limitations cited by Diehr, et al. As noted earlier however, ecologic models were used to characterize groups of patients during certain time periods and within age and other cohorts. Disease registries which are able to track the changing diagnoses, treatments and health status of patients over time are preferred over ecologic studies of this kind. Registries operate with much more precision about individual patients, which can support scientific experiments and studies that reduce variation of extraneous factors (Rothman 1998). Where surveillance studies ideally involve a general population, the Iowa Asthma Fact Book measured asthma morbidity based on hospitalization episodes. These cases of asthma include patients with a high severity of illness and are a small subset of asthmatics in the general population. This study of hospital morbidity then, falls significantly short of assessing asthma prevalence in the general population. Objectives for reducing high severity asthma cases, or resources consumed in their treatment can be drawn accordingly. Alternatively, rates of asthma exacerbation may be more completely

55 48 measured with surveys noted earlier, or administrative data reflecting emergency room visits, or office treatments. Hospitalization for asthma is relatively rare, when compared with outpatient or emergency room treatments. As shown in tables 5 through 9, sufficient data were not available to calculate county specific rates on an annual basis. That is, the lower number of asthma cases found in certain rural counties, did not support the calculation of reliable rates of annual treatment. Consequently, three years of data were combined for county specific rates shown in these tables. While these rates are more reliable, they are less sensitive to change over time than annual rates, and may not support longitudinal studies aimed at evaluating community interventions. Community based surveillance systems for asthma and other diseases should be developed, considering planning steps shown below, in Figure 4. Teutsch (2000) offers a carefully organized approach to developing surveillance systems with several steps, including a clear statement of surveillance objectives and case definitions. Figure 4. Steps in Planning a Surveillance System Establish objectives Develop case definitions Determine data source or data-collection mechanism (type of system) Develop data-collection instruments Field-test methods Develop and test analytic approach Develop dissemination mechanism Surveillance systems should be formed within a framework of health policy which is accounting for the interests of individual patients and communities. Inventories of existing data and surveillance systems addressing all diseases should be performed carefully to document their validity, reliability and suitability for use in population based

56 49 studies. These systems may serve as additional precedents for the refinement of asthma surveillance. Finally, case definitions of asthma should continue to evolve with the improvement of diagnostic and reporting procedures. The Iowa Asthma Fact Book is offered as a reference document for community health planners and a precedent for continued surveillance of hospital treatment of asthma, subject to several limitations discussed in this thesis. Hospitalized cases of asthma were described in this study as higher severity cases. Variability in county-based rates of hospital asthma may be due in part to random variability, but also may be attributed to other causes which give rise to community based intervention programs. Physicians are developing more consistent methods for diagnosing and treating these patients, as noted by the National Heart Lung Blood Institute. Medical education is underway, to encourage home and outpatient modalities of treatment; hospitalization of asthma patients is considered by some, to be a treatment of last resort, where home or outpatient modalities have failed. Several other intervention programs are described in the Healthy Iowans 2010 policy document and other community based asthma programs cited in this thesis. They include educational programs, patient self-management of asthma exacerbations, and local initiatives aimed at reducing the release of airborne contaminants which trigger asthma attacks, such as tobacco, allergens, hygiene, wood heat and toxins released by local industries.

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