Los hijos de la resurrección (13-I-82/17-I-82)

This research utilizes encrypted individual-identifier level empirical data, obtained from the Centers for Medicare and Medicaid Services, which has not been considered in prior

published studies pertaining to the differential recovery of individuals in disaster affected rural communities. Access to this information was approved by the U.S. Centers for Medicare and Medicaid Services (CMS) after peer-review of a request for data for all Medicare recipients in the 17 declared rural counties, the 15 denied rural counties, and 12 control rural counties that did not request an Individual Assistance Stafford Act disaster declaration in any year from 2004- 2009 (See Table 12).

167 Table 12: 2008 Illinois case study counties

DENIED COUNTIES DECLARED COUNTIES CONTROL COUNTIES

Alexander Adams Bond

Franklin Calhoun Cass

Gallatin Clark Christian

Jackson Coles Dewitt

Jefferson Crawford Fulton

Johnson Cumberland Marshall

Marion Douglas Mason

Massac Edgar Montgomery

Perry Hancock Morgan

Pulaski Henderson Putnam

Randolph Iroquois Scott

Saline Jasper Stark

Union Jersey

White Lawrence

Williamson Livingston

Mercer Whiteside

Health Insurance Portability and Accountability Act (HIPAA) requirements have been met by utilizing encrypted identifiers and protected data bases. A stratified random sample of 12,000 individual Medicare recipients who maintained community-based residence in the same county of the study area from 2007-2009 are included in the CMS datasets. Diagnostic groupings of 78 stress-related disease and control indicators were considered in a manner consistent with prior studies (Burton et al. 2009; Holman and Silver 2011)(See Table 13) and with the extensive list of references to psychological and physiological stress-related disorders that are provided in Chapter 1: Introduction and in Chapter 2.7 Health, Well-being, and the Mechanism for Post Disaster Stress- Related Illness. The diagnostic grouping scheme and the selection of comorbid disorders in the research design is consistent with the peer-reviewed literature pertaining to stress-related disease, the peer-reviewed federally approved research study protocols from the

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Centers for Medicare and Medicaid Services that were, as previously stated, required to obtain the encrypted individually identified files used in the analysis, and the peer-reviewed National Science Foundation SBE Doctoral Dissertation Research Improvement Grant (SBE DDRIG #1233352) approved research study protocols. Each of the ICD-9CM codes selected is associated with a stress-related disorder that is recognized in the medical literature as a

component of the diagnostic grouping category to which it is assigned (Costa et al. 1982; Cohen and Ginsburg 1990; Colantonio et al. 1992; Sartorius et al. 1996;Korszun et al. 1998; Kroenke et al. 1998; Beekman et al. 2000; Mayer 2000; Linton 2000; Noyes 2001; Cenac et al. 2002; Gur 2004; Neugebauer 2004; Furman et al. 2005; Schoevers 2005; Bruce et al. 2005; Suls and Bunde 2005; Best et al. 2006; Nicholson et al. 2007; Seignourel 2008; Mizyed, Fass and Fass 2009; Saczynski 2010; Byers and Yaffe 2011; Wilson et al. 2011; Lambiase, Kubzansky and Thurston 2014). The use of diagnostic grouping methods for disease specific studies and categorization schemes is well-established in the medical literature (Robinson 2007). Variations of this approach have been applied by private and federal entities and include the methodology of the United States Department of Health and Human Services (HHS) Agency for Healthcare

Research and Quality (AHRQ) in the Healthcare Cost and Utilization Project (HCUP)-Clinical Classifications Software (CCS) for ICD-9-CM (HCUP CCS 2014), the Johns Hopkins University Health Services Research and Development Center -Adjusted Clinical Groups (ACGs) (Health Services Research and Development Center 2011) , the Diagnostic Cost Groups/Hierarchical Condition Categories (DCG/HCCs) (Ash et al. 2000), and the 3M-Clinical Risk Groups (CRGs) (Hughes et al. 2004). These grouping mechanisms can be utilized to analyze the influence of a variety of environmental and societal factors on categories of disease with similar characteristics, comorbidities, and precipitating factors. These techniques obviate the problems associated with

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fragmented analyses of individual ICD-9-CM codes that may suppress significant findings when the individual codes are infrequently observed and/or not appropriately grouped. Additionally, studies of physiological aspects of stress related disease may be subject to ill-defined clinical conditions that may be associated with zealous use of ICD-9CM codes by practitioners, pending a more conclusive diagnosis (i.e. fibromyalgia, chronic fatigue syndrome). The problems

associated with overlap of clinical conditions/presentations (Aaron and Buchwald 2001) is addressed in this analysis by utilizing a single count of visits within diagnostic grouping, regardless of the number of ICD-9-CM codes that were assigned to a patient for that visit. It is acknowledged that alternative approaches to the analysis of frequency of service for disease exist but the use of diagnostic groupings with the assignment of one visit or equivalent, independent of the number of symptom/diagnosis/medication findings in each group at the time of visit, is well-established in the peer reviewed medical literature (Herrmann et al. 1998; Maynard and Cox 1998; Mechanic, McAlpine and Olfson 1998; Bao and Sturm 2001; Pottick, McAlpine and Andelman 2000; Duffy 2004; Helgason, Tomasson. and Zoega 2004; Lau et al. 2005; Sayers et al. 2007; Cohen et al. 2010). Additionally, none of the aforementioned references provide itemized frequency counts for the subordinate level diagnosis/symptom/medication in the

respective diagnostic grouping but this information is defined in Table 14 to support the analysis. The study is restricted to Medicare recipients who were 65 years old or greater at the time of the disaster event. The Medicare eligible age group was selected due to the specified emphasis on elderly populations in the Stafford Act determination criteria and prior research findings associated with stress-related vulnerabilities and resiliencies in this age strata (Kilijanek and Drabek 1979; Krause 1987; Ticehurst et al. 1996). This data includes all individual

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files, for Diagnostic and Statistical Manual of Mental Disorder (DSM) anxiety and depressive disorders that are related to psychological stressors such as disasters and the associated International Statistical Classification of Diseases and Related Health Problems (ICD 9 CM) diagnostic codes for these disorders and stress-related physiological conditions.

The names and definitions of all disorders considered in the analysis and their respective grouping categories are summarized in Table 13.

Table 13: Stress-related disorders and control indicators by grouping variable

Anxiety Depression

Acute

Vascular Dementia Gastrointestinal Somatic Control

Anxiety Acute Cerebrovascular Disease Alzheimer’s Gastroesophageal Reflux Disease Back/Neck/Chronic Pain Cholecystitis Depression Acute Myocardial Infarction Senile Dementia Gastritis Chronic Fatigue Syndrome/Fatigue Otitis externa Generalized Anxiety Disorder Cerebral Infarction Senility w/o Psychosis Irritable Bowel

Syndrome Fibromyalgia Otitis media Panic

Disorder Angina Ulcer Generalized pain

PTSD Headache- Migraine/Tension Irritable Bowel Syndrome Polymyalgia rheumatica Sleep Disorder Temporomandibular Joint Syndrome Vertigo Unspecified

Individual level socio-demographic information pertaining to each Medicare

beneficiary’s race, gender, date of birth, and dual-eligible Medicaid status (Low Income, < 135 percent of federal poverty level) was also obtained from the encrypted CMS database. This information will be utilized to control for socio-demographic variations in utilization of

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Medicare services and to assess the relationships between these characteristics and the incidence of stress-related disease visits in the study area. Prior research has documented differences in the incidence of stress-related disorders associated with the demographic variables under analysis (Boman 1979; Norris, Friedman, and Watson 2002; Cutter, Boruff and Shirley; Cutter 2006; Barr 2008; Burton, Skinner, Uscher-Pines, et al. 2009; Holman and Silver 2011). The

consideration of the potential for the combined effects of poverty, age, race, gender, and health service access to exacerbate post disaster stress-related illness is critical to an evaluation of equity in the distribution of federal disaster relief.

The Robert Wood Johnson Foundation (RWJF) (2013) has developed a reverse coded Z - scoring system to rank counties based on access to health care. The Lack of Access metric considers several factors including “the percentage of the population that could (or could not) get medical care when needed; the number of patients served by a federally qualified health center (FQHC); and the availability of primary care providers in a community.” Lack of access to health care is a valid consideration in the determination of different patterns of utilization for medical services and the RWJF scoring system will be applied in this analysis. County level data pertaining to all federal grant dollars distributed for fiscal years 2008 and 2009 was obtained from the U.S. Census Bureau (2009a, 2010). This information will be utilized as a control variable for the consideration of Stafford Act and non-Stafford Act related county-level federal financial support in the study area for FY 2008 and 2009.

The consideration of social capital as a measure of community resilience and as a valued asset in disaster recovery has been acknowledged in the academic literature (Nakagawa and Shaw 2004; Norris et al. 2008). This is of particular importance in communities where denials have been issued for disaster declaration requests. The Stafford Act acknowledges the

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importance of social capital by indicating that Individual Assistance declarations may not be issued in communities where adequate volunteer support is available to address the needs of disaster survivors. The Northeast Regional Center for Rural Development (NERCRD) has created a Social Capital Index from 14 county level demographic variables, utilizing Principal Component Analysis (Rupasingha and Goetz 2008). The scored variables included in the NERCRD Social Capital Index are based on the following county level attributes: number of religious organizations, civic and social associations, business associations, political

organizations, professional organizations, labor organizations, bowling centers, physical fitness facilities, public golf courses, sport clubs, managers, and promoters, population, voter turnout, survey response rate, and number of non-profit organizations without including those with an international approach. This index will be applied in the analysis of disaster recovery.

Estimated Individual Assistance related property damages for the respective disaster declared and denied counties were obtained from the Illinois Emergency Management

Association, the FEMA Preliminary Damage Analysis, and the U.S. Small Business Association. This information is considered to control for the relative effect of property losses on emotional stress and the potential need for health services associated with office/outpatient visits.