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<article xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" article-type="research-article"><?properties open_access?><?properties manuscript?><front><journal-meta><journal-id journal-id-type="nlm-journal-id">101587185</journal-id><journal-id journal-id-type="pubmed-jr-id">40726</journal-id><journal-id journal-id-type="nlm-ta">Amyotroph Lateral Scler Frontotemporal Degener</journal-id><journal-id journal-id-type="iso-abbrev">Amyotroph Lateral Scler Frontotemporal Degener</journal-id><journal-title-group><journal-title>Amyotrophic lateral sclerosis &#x00026; frontotemporal degeneration</journal-title></journal-title-group><issn pub-type="ppub">2167-8421</issn><issn pub-type="epub">2167-9223</issn></journal-meta><article-meta><article-id pub-id-type="pmid">29020837</article-id><article-id pub-id-type="pmc">5815913</article-id><article-id pub-id-type="doi">10.1080/21678421.2017.1384021</article-id><article-id pub-id-type="manuscript">HHSPA917484</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Evaluating the completeness of the national ALS registry, United
States</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>KAYE</surname><given-names>WENDY E.</given-names></name><xref ref-type="aff" rid="A1">1</xref></contrib><contrib contrib-type="author"><name><surname>WAGNER</surname><given-names>LAURIE</given-names></name><xref ref-type="aff" rid="A1">1</xref></contrib><contrib contrib-type="author"><name><surname>WU</surname><given-names>RUOMING</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>MEHTA</surname><given-names>PAUL</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib></contrib-group><aff id="A1">
<label>1</label>McKing Consulting Corporation, Atlanta, GA, USA</aff><aff id="A2">
<label>2</label>Agency for Toxic Substances and Disease Registry, Atlanta, GA,
USA</aff><author-notes><corresp id="FN1">Correspondence: Wendy Kaye, PhD, McKing Consulting Corporation,
2900 Chamblee Tucker Road, Building 10, Suite 100, Atlanta, GA 30341, USA. Tel.
770-220-0608. <email>wek1@cdc.gov</email></corresp></author-notes><pub-date pub-type="nihms-submitted"><day>3</day><month>11</month><year>2017</year></pub-date><pub-date pub-type="epub"><day>11</day><month>10</month><year>2017</year></pub-date><pub-date pub-type="ppub"><month>2</month><year>2018</year></pub-date><pub-date pub-type="pmc-release"><day>17</day><month>2</month><year>2018</year></pub-date><volume>19</volume><issue>1-2</issue><fpage>112</fpage><lpage>117</lpage><!--elocation-id from pubmed: 10.1080/21678421.2017.1384021--><permissions><license license-type="open-access"><license-p>This is an Open Access article distributed under the terms of the
Creative Commons Attribution-NonCommercial-NoDerivatives License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc-nd/4.0/">http://creativecommons.org/licenses/by-nc-nd/4.0/</ext-link>), which
permits non-commercial re-use, distribution, and reproduction in any medium,
provided the original work is properly cited, and is not altered,
transformed, or built upon in any way.</license-p></license></permissions><abstract><p id="P1">Our objective was to evaluate the completeness of the United States
National ALS Registry (Registry). We compared persons with ALS who were
passively identified by the Registry with those actively identified in the State
and Metropolitan Area ALS Surveillance project. Cases in the two projects were
matched using a combination of identifiers, including, partial social security
number, name, date of birth, and sex. The distributions of cases from the two
projects that matched/did not match were compared and Chi-square tests conducted
to determine statistical significance. There were 5883 ALS cases identified by
the surveillance project. Of these, 1116 died before the Registry started,
leaving 4767 cases. We matched 2720 cases from the surveillance project to those
in the Registry. The cases identified by the surveillance project that did not
match cases in the Registry were more likely to be non-white, Hispanic, less
than 65 years of age, and from western states. The methods used by the Registry
to identify ALS cases, i.e. national administrative data and self-registration,
worked well but missed cases. These findings suggest that developing strategies
to identify and promote the Registry to those who were more likely to be
missing, e.g. non-white and Hispanic, could be beneficial to improving the
completeness of the Registry.</p></abstract><kwd-group><kwd>Amyotrophic lateral sclerosis (ALS)</kwd><kwd>National ALS Registry</kwd><kwd>ALS surveillance</kwd><kwd>Registry data</kwd></kwd-group></article-meta></front><body><sec sec-type="intro" id="S1"><title>Introduction</title><p id="P2">Amyotrophic lateral sclerosis (ALS) is a rare progressive neurodegenerative
disease that is diagnosed through a combination of signs and symptoms. A recent
review of longstanding European registries estimates incidence at 2.6 per 100,000
person years and prevalence rates of 7&#x02013;9 per 100,000 persons (<xref rid="R1" ref-type="bibr">1</xref>). Some studies suggest that ALS rates are higher among
non-Hispanic Caucasians (whites) in western countries compared with those of
African, Asian, and Hispanic descent (minorities) (<xref rid="R2" ref-type="bibr">2</xref>&#x02013;<xref rid="R4" ref-type="bibr">4</xref>). There are limited
data regarding the population-based epidemiology of ALS in the United States (US)
with most studies having been conducted in limited geographic areas (<xref rid="R5" ref-type="bibr">5</xref>&#x02013;<xref rid="R7" ref-type="bibr">7</xref>).
Recent data from the National ALS Registry estimated prevalence of ALS in the United
States at 5.0 per 100,000 population in 2013 (<xref rid="R8" ref-type="bibr">8</xref>).</p><p id="P3">In October 2010, the Agency for Toxic Substances and Disease Registry (ATSDR)
launched the National Amyotrophic Lateral Sclerosis (ALS) Registry (Registry) to
implement Public Law No: 110-373 which signed into law a bill to amend the Public
Health Service Act to provide for the establishment of an Amyotrophic Lateral
Sclerosis Registry. Because of the challenges experienced by ATSDR and other
researchers trying to obtain information directly from medical care providers, the
National ALS Registry uses nontraditional methods to identify persons with ALS
including using administrative data from the Centers for Medicaid and Medicare
Services (CMS), the Veterans Health Administration (VHA), and the Veterans Benefits
Administration (VBA), and a self-registration component (<xref rid="R9" ref-type="bibr">9</xref>,<xref rid="R10" ref-type="bibr">10</xref>). In order to
evaluate the completeness of the National ALS Registry, ATSDR conducted a separate
ALS surveillance project using active case-finding methods in three states (Florida,
New Jersey, and Texas) and eight metropolitan areas (Atlanta, Baltimore, Chicago,
Detroit, Las Vegas, Los Angeles, Philadelphia, and San Francisco) (<xref rid="R11" ref-type="bibr">11</xref>) and calculated the concordance between the two
sources. A bill to amend the Public Health Service Act to provide for the
establishment of an Amyotrophic Lateral Sclerosis Registry, S. 1382: ALS Registry
Act, was signed into law on October 10, 2008 by President George W. Bush and became
Public Law No: 110-373. The purpose of the Registry as described in the bill, is to
(1) better describe the incidence and prevalence of ALS in the United States; (2)
examine appropriate factors, such as environmental and occupational, that might be
associated with the disease; (3) better outline key demographic factors (such as
age, race or ethnicity, gender, and family history of individuals who are diagnosed
with the disease) associated with the disease; and (4) better examine the connection
between ALS and other motor neuron disorders that can be confused with ALS,
misdiagnosed as ALS, and in some cases progress to ALS (<xref rid="R12" ref-type="bibr">12</xref>). The Registry collects personal health information
that may provide a basis for further scientific studies of potential risks for
developing ALS.</p><p id="P4">ATSDR developed and tested case-finding methodology for the National ALS
Registry that identified ALS cases using administrative data from CMS, VHA, and VBA.
The Registry pilot study developed an algorithm using variables from the
administrative data that identified true cases of ALS. The best algorithm had a
sensitivity of 87% and a specificity of 85%. The pilot study sites
identified &#x0003c;1&#x02013;22% of the cases in only the clinical
databases and therefore missed by administrative data alone. The study results
showed that administrative data could be used to create an ALS surveillance system
although it would be necessary to identify other sources of data or methods to
capture those ALS cases not covered by the administrative databases (<xref rid="R13" ref-type="bibr">13</xref>). A self-registration component was initiated because
of previous experience trying to obtain information directly from medical care
providers. In October 2010, ATSDR launched a web-portal where persons with ALS could
enroll in the National ALS Registry. To enter the National ALS Registry through its
web portal, patients must answer a series of validation (screening) questions. These
validation questions were obtained from the Veterans Administration&#x02019;s ALS
Registry (which is no longer enrolling persons with ALS) and were found to be
effective; 93.4% of those who passed the screening questions were determined
by a neurologist to have ALS/motor neuron disease (<xref rid="R14" ref-type="bibr">14</xref>). This two-pronged approach tries to identify all cases of ALS within
the United States (<xref rid="R8" ref-type="bibr">8</xref>&#x02013;<xref rid="R10" ref-type="bibr">10</xref>).</p><p id="P5">Traditionally in the United States, surveillance systems have relied on
physicians and other health care providers to report notifiable health information
to state or local health departments and when appropriate this information is
reported to federal health authorities. In the United States, the designation of a
&#x0201c;reportable&#x0201d; health condition is conferred by the Council of State
and Territorial Epidemiologists (CSTE) and this designation is usually reserved for
infectious diseases. Because ALS is not an infectious disease, it is not considered
a &#x0201c;reportable/notifiable&#x0201d; disease. In this assessment, we evaluated
the completeness of the Registry by comparing ALS cases identified in the selected
state and metropolitan areas which used traditional case ascertainment methods to
those ALS cases identified by the Registry which used non-traditional surveillance
case ascertainment methods.</p></sec><sec sec-type="materials|methods" id="S2"><title>Materials and methods</title><p id="P6">We compared the cases of ALS identified by the two different case
ascertainment methods, the National ALS Registry and the state and metropolitan
surveillance project. In brief, the National ALS Registry uses a two-pronged
approach, i.e. administrative databases and self-registration. The state and
metropolitan area surveillance projects used active case ascertainment, i.e.
physician reporting, medical records abstraction, and death certificates. In the
state and metropolitan area surveillance project, areas were selected to
over-represent racial and ethnic minorities so that the number of cases in these
groups would be sufficient to estimate prevalence by race and ethnicity. A detailed
description of this methodology can be found in the paper by Wagner et al. (<xref rid="R11" ref-type="bibr">11</xref>).</p><p id="P7">The state and metropolitan surveillance project collected prevalent cases of
ALS for January 1, 2009 through December 31, 2011 while the National ALS Registry
began to identify cases on October 19, 2010. Therefore, we submitted the cases
identified by the state and metropolitan surveillance project to the National Death
Index to obtain date of death when available. All cases identified in the state and
metropolitan area surveillance project who died before the start of the Registry,
October 19, 2010, were removed from further analysis. This adjustment made the
time-period for both projects&#x02019; case ascertainments the same, October 19,
2010 through December 31, 2011.</p><p id="P8">Because of state privacy and human subjects protection regulations, ALS cases
identified in the state and metropolitan projects could not be added to the National
ALS Registry and could only be used for comparison purposes. Cases in the two
projects were matched using a combination of information identifying including
partial SSN, name, date of birth, and sex. The distribution of cases from the state
and metropolitan surveillance system that matched or did not match the National ALS
Registry were compared. Chi-square tests were conducted to determine whether there
was a statistically significant difference in the characteristics of cases
identified in the state and metropolitan area surveillance project being/or not
being identified by the Registry. An alpha level of <italic>p</italic> =
0.05 was used to determine statistical significance.</p><p id="P9">Multivariable regression analyses were used to examine whether cases matched
between the two data sources differed, as well as to examine interaction between
case characteristics. When examining interactions, we included the main effect
variables along with the corresponding interaction terms. The interaction terms
examined included: age group and Medicare, race and state, age group and El Escorial
criteria, and race and El Escorial criteria. Stepwise model selection method was
applied with an inclusion criterion of <italic>p</italic> &#x02264; 0.10. All data
analysis was performed using SAS<sup>&#x000a9;</sup> (<xref rid="R15" ref-type="bibr">15</xref>).</p></sec><sec sec-type="results" id="S3"><title>Results</title><p id="P10">The state and metropolitan surveillance project identified 5883 cases of
ALS. Of these, 1116 died before the National ALS Registry started, leaving us 4767
cases. Of the 4767 cases identified by the state and metropolitan surveillance
project and alive during the eligibility period (October 19, 2010 through December
31, 2011), 2720 (57%) matched a case identified by the National ALS
Registry. The cases identified by the state and metropolitan area surveillance
project that did not match cases in the Registry were more likely to be non-white,
Hispanic, less than 65 years of age, and from western states (<xref rid="T1" ref-type="table">Table 1</xref>). In addition to demographic differences between
those identified by the state and metropolitan surveillance project who matched and
those who did not match cases identified by the Registry, there were significant
differences in the stage of ALS at the time of the report and the types of insurance
used, i.e. those who were diagnosed with possible or unclassified ALS and those who
did not use Medicare were less likely to be identified by the National ALS Registry
(<xref rid="T2" ref-type="table">Table 2</xref>).</p><p id="P11">The best model for the multiple logistic regression analysis showed an ALS
case identified in the state and metropolitan surveillance projects was less likely
to be in the National ALS Registry if nonwhite, Hispanic, living in the western part
of the US, and not having Medicare for insurance (<xref rid="T3" ref-type="table">Table 3</xref>). According to the stepwise methods, no interactions were
significant (data not shown).</p></sec><sec sec-type="discussion" id="S4"><title>Discussion</title><p id="P12">There have been a number of smaller efforts to establish surveillance
systems for neurodegenerative diseases in the US such as ALS, Parkinson&#x02019;s
disease, and Alzheimer&#x02019;s disease/dementia (<xref rid="R7" ref-type="bibr">7</xref>,<xref rid="R16" ref-type="bibr">16</xref>&#x02013;<xref rid="R19" ref-type="bibr">19</xref>), the National ALS Registry however, is the first
attempt in the US to create a national surveillance system for any neurodegenerative
disease. These smaller efforts used a variety of case ascertainment methods
including physician reporting, medical claims data, prescription data for disease
specific medications, advocacy group reporting, and self-reporting making them
difficult to compare. ALS registries in Europe have been operating for about two
decades and provide insights in how to operate a successful registry along with
limitations (<xref rid="R1" ref-type="bibr">1</xref>,<xref rid="R20" ref-type="bibr">20</xref>). However, because of the health care delivery system along with the
ethnic/racial diversity and population size of the US (more than 300 million
compared with the approximately 26 million in the six population registries used to
estimate ALS incidence in Europe) (<xref rid="R21" ref-type="bibr">21</xref>), it is
not possible to create a national registry in the US using the same methodology as
used in Europe. What is clear is that it is important to obtain information from
multiple sources such as medical claims data, prescription data, physician
reporting, when feasible, and self-identification in order to have the most complete
ascertainment possible.</p><p id="P13">Therefore, because of the fractured health care system in the United States,
the National ALS Registry has used a unique two-pronged approach to identify persons
with ALS, i.e. cases identified via an algorithm in federal administrative datasets
and self-registration (<xref rid="R8" ref-type="bibr">8</xref>,<xref rid="R10" ref-type="bibr">10</xref>,<xref rid="R13" ref-type="bibr">13</xref>). Although
the Registry identified 13,282 for a revised prevalence of 4.3 in 2011 (<xref rid="R8" ref-type="bibr">8</xref>), it was anticipated that individuals with ALS
could be missing. The comparison of a cohort of ALS cases actively collected from
neurologists in a geographically and racially/ethnically diverse population allowed
for the identification of subgroups of the population that are more likely to be
missing from the Registry. This comparison showed that a person with ALS was more
likely to be missing from the Registry if they were non-white, Hispanic, living in
the western part of the United States, and were not using Medicare for insurance.
This could be because those who are non-white or Hispanic are less likely to be seen
in ALS specialty clinics and therefore less likely to learn about the Registry. It
could also be because they have less access to computers which are needed for
self-registration. Racial difference could also explain the difference seen by
region of the country as the West Census Region is 47% minority according to
the 2010 census (<xref rid="R22" ref-type="bibr">22</xref>). Although those with ALS
are eligible for Medicare once receiving Social Security Disability Insurance (SSDI)
(<xref rid="R23" ref-type="bibr">23</xref>), this group may be less likely to be
eligible for SSDI because of the jobs held, length of employment, or knowledge about
this benefit. In addition, those with a less definitive diagnosis of ALS based on
the El Escorial criteria reported in the state and metropolitan surveillance project
were also more likely to be missing. This could be because those being told that
they &#x0201c;possibly have ALS&#x0201d; are less likely to seek benefits for ALS or
self-register even though studies have shown that those given a diagnosis of
suspected or possible ALS El Escorial category do have ALS (<xref rid="R24" ref-type="bibr">24</xref>). An El Escorial criteria category is not available in
the Registry because it is not available in administrative claims data and not
something that is self-reported in the registration portal.</p><p id="P14">Age was not significant in the logistic regression analysis; however, those
identified in the state and metropolitan surveillance project who were not in the
Registry were more likely to be less than 65 years of age. Like race, this could be
related to those less than 65 years of age only being eligible for Medicare after
receiving SSDI. This could delay identifying them in administrative data and making
their identification more reliant on self-enrollment in the Registry.</p><p id="P15">Although the incidence rate of ALS among nonwhites is lower than that for
whites (<xref rid="R2" ref-type="bibr">2</xref>,<xref rid="R3" ref-type="bibr">3</xref>), this should not impact who is identified by the Registry, e.g. the
percentage of people identified by race and ethnicity should be the same. The
National ALS Registry has partnered with national and regional ALS patient advocacy
groups such as the ALS Association (ALSA), Muscular Dystrophy Association (MDA), and
the Les Turner ALS Foundation to educate and increase awareness. These groups
directly work with ALS patients and their caregivers providing assistance and
coordinating care. In general, the patient population served by these groups tends
to be white with a mixture of chapter and clinic locations where a larger percentage
of minority groups are represented, such as Los Angeles, San Francisco, Atlanta,
Chicago, and Miami. ATSDR will work with these groups to target outreach to minority
populations. However, more targeted outreach will likely be needed because minority
populations tend to be under-represented in support groups (<xref rid="R25" ref-type="bibr">25</xref>,<xref rid="R26" ref-type="bibr">26</xref>).</p><p id="P16">In 2017, in order to reach more Hispanics with ALS, ATSDR will launch a site
in Spanish where individuals can learn more about ALS, register, and take risk
factor surveys. Once launched, a Spanish public service announcement will be
disseminated via social media such as Facebook and Twitter and through our partner
groups to educate individuals about ALS and facets of the Registry. In addition,
ATSDR is exploring opportunities to work with national Spanish groups such as the
National Hispanic Medical Association, National Council of La Raza, and others.</p><p id="P17">To better reach African Americans with ALS, the Registry plans to reach out
to organizations such as the National Medical Association, large churches located in
metropolitan areas serving the African American community, as well as the National
Association for the Advancement of Colored People. This targeted approach should
allow the Registry to reach underserved and under-represented populations.</p><p id="P18">The Registry is also exploring ways to work with neurologists not practicing
at major academic facilities or ALS specialty clinics to make them aware of the
Registry so that they can inform their patients. However, this is particularly
challenging because this group of neurologists changes frequently due to
relocations, retirements, and new graduates entering the specialty. In addition,
this group of neurologists may only occasionally see an ALS patient (<xref rid="R11" ref-type="bibr">11</xref>). It will be important to develop messaging specific
for this group that can be used on a regular basis so that the neurologists
remembers the Registry and can provide materials to an ALS patient when one is
seen.</p></sec><sec sec-type="conclusions" id="S5"><title>Conclusions</title><p id="P19">Although the comparison of a cohort of ALS cases actively collected from
neurologists in a geographically and racially/ethnically diverse population compared
with those identified by the National ALS Registry identified subgroups of the
population that are more likely to be missing from the Registry, the Registry has
been able to increase the number of cases detected since the first report (<xref rid="R6" ref-type="bibr">6</xref>) covering October 19, 2010 through December
31, 2011. In addition, the Registry can use this valuable information to target
under-represented groups and has developed a plan for outreach to these populations.
The National ALS Registry is committed to increasing racial and ethnic minority
enrollment on a local and national scale in order to have more complete
epidemiological data on ALS in the United States.</p></sec></body><back><ack id="S6"><p>The authors thank the state and local health departments and other organizations that
assisted with providing data for the state and metropolitan area surveillance
projects.</p><p><bold>Funding</bold></p><p>The state and metropolitan area surveillance projects were funded by the Agency for
Toxic Substances and Disease Registry.</p></ack><fn-group><fn fn-type="COI-statement" id="FN2"><p><bold>Declaration of interest</bold></p><p>W. Kaye L Wagner, R Wu and P. Mehta report no disclosures.</p></fn><fn id="FN3"><p><bold>Disclaimer</bold></p><p>The findings and conclusions in this presentation have not been formally
disseminated by the Agency for Toxic Substances and Disease Registry and should
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0.00001</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;&#x0003c;65</td><td valign="top" align="right" rowspan="1" colspan="1">1321</td><td valign="top" align="right" rowspan="1" colspan="1">64.5%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;&#x0003c;65</td><td valign="top" align="right" rowspan="1" colspan="1">1500</td><td valign="top" align="right" rowspan="1" colspan="1">55.1%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;&#x02265;65</td><td valign="top" align="right" rowspan="1" colspan="1">705</td><td valign="top" align="right" rowspan="1" colspan="1">34.4%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;&#x02265;65</td><td valign="top" align="right" rowspan="1" colspan="1">1195</td><td valign="top" align="right" rowspan="1" colspan="1">43.9%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="right" rowspan="1" colspan="1">21</td><td valign="top" align="right" rowspan="1" colspan="1">1.0%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="right" rowspan="1" colspan="1">25</td><td valign="top" align="right" rowspan="1" colspan="1">0.9%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2047</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2720</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Race</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">Race</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">34.01, <italic>p</italic>&#x0003c;
0.00001</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;White</td><td valign="top" align="right" rowspan="1" colspan="1">1409</td><td valign="top" align="right" rowspan="1" colspan="1">68.8%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;White</td><td valign="top" align="right" rowspan="1" colspan="1">2071</td><td valign="top" align="right" rowspan="1" colspan="1">76.1%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Black</td><td valign="top" align="right" rowspan="1" colspan="1">213</td><td valign="top" align="right" rowspan="1" colspan="1">10.4%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Black</td><td valign="top" align="right" rowspan="1" colspan="1">237</td><td valign="top" align="right" rowspan="1" colspan="1">8.7%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Asian</td><td valign="top" align="right" rowspan="1" colspan="1">98</td><td valign="top" align="right" rowspan="1" colspan="1">4.8%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Asian</td><td valign="top" align="right" rowspan="1" colspan="1">84</td><td valign="top" align="right" rowspan="1" colspan="1">3.1%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Other</td><td valign="top" align="right" rowspan="1" colspan="1">9</td><td valign="top" align="right" rowspan="1" colspan="1">0.4%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Other</td><td valign="top" align="right" rowspan="1" colspan="1">10</td><td valign="top" align="right" rowspan="1" colspan="1">0.4%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="right" rowspan="1" colspan="1">318</td><td valign="top" align="right" rowspan="1" colspan="1">15.5%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="right" rowspan="1" colspan="1">318</td><td valign="top" align="right" rowspan="1" colspan="1">11.7%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2047</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2720</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Sex</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">Sex</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">1.78, <italic>p</italic> = 0.18</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Male</td><td valign="top" align="right" rowspan="1" colspan="1">1148</td><td valign="top" align="right" rowspan="1" colspan="1">56.1%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Male</td><td valign="top" align="right" rowspan="1" colspan="1">1578</td><td valign="top" align="right" rowspan="1" colspan="1">58.0%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Female</td><td valign="top" align="right" rowspan="1" colspan="1">899</td><td valign="top" align="right" rowspan="1" colspan="1">43.9%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Female</td><td valign="top" align="right" rowspan="1" colspan="1">1142</td><td valign="top" align="right" rowspan="1" colspan="1">42.0%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2047</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2720</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Hispanic</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">Hispanic</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">37.60, <italic>p</italic>&#x0003c;
0.00001</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Yes</td><td valign="top" align="right" rowspan="1" colspan="1">300</td><td valign="top" align="right" rowspan="1" colspan="1">14.7%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Yes</td><td valign="top" align="right" rowspan="1" colspan="1">270</td><td valign="top" align="right" rowspan="1" colspan="1">9.9%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;No</td><td valign="top" align="right" rowspan="1" colspan="1">1454</td><td valign="top" align="right" rowspan="1" colspan="1">71.0%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;No</td><td valign="top" align="right" rowspan="1" colspan="1">2136</td><td valign="top" align="right" rowspan="1" colspan="1">78.5%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="right" rowspan="1" colspan="1">293</td><td valign="top" align="right" rowspan="1" colspan="1">14.3%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="right" rowspan="1" colspan="1">314</td><td valign="top" align="right" rowspan="1" colspan="1">11.5%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2047</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2720</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Area<xref rid="TFN1" ref-type="table-fn">*</xref></td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">Area<xref rid="TFN1" ref-type="table-fn">*</xref></td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">84.05, <italic>p</italic>&#x0003c;
0.00001</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;TX, FL, AT</td><td valign="top" align="right" rowspan="1" colspan="1">997</td><td valign="top" align="right" rowspan="1" colspan="1">48.7%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;TX, FL, AT</td><td valign="top" align="right" rowspan="1" colspan="1">1606</td><td valign="top" align="right" rowspan="1" colspan="1">59.0%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;LA, SF, LV</td><td valign="top" align="right" rowspan="1" colspan="1">544</td><td valign="top" align="right" rowspan="1" colspan="1">26.6%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;LA, SF, LV</td><td valign="top" align="right" rowspan="1" colspan="1">440</td><td valign="top" align="right" rowspan="1" colspan="1">16.2%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;NJ, BA, PH, DT, CI</td><td valign="top" align="right" rowspan="1" colspan="1">506</td><td valign="top" align="right" rowspan="1" colspan="1">24.7%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;NJ, BA, PH, DT, CI</td><td valign="top" align="right" rowspan="1" colspan="1">674</td><td valign="top" align="right" rowspan="1" colspan="1">24.8%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2047</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2720</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr></tbody></table><table-wrap-foot><fn id="TFN1"><label>*</label><p>TX: Texas; FL: Florida; AT: Atlanta; LA: Los Angeles; SF: San Francisco; LV:
Las Vegas; NJ: New Jersey; BA: Baltimore; PH: Philadelphia; DT: Detroit; CI:
Chicago.</p></fn></table-wrap-foot></table-wrap><table-wrap id="T2" position="float" orientation="landscape"><label>Table 2</label><caption><p>Comparison of ALS diagnosis criteria and insurance for matched and unmatched
cases.</p></caption><table frame="hsides" rules="none"><thead><tr><th colspan="3" valign="bottom" align="center" rowspan="1">Unmatched <hr/></th><th colspan="3" valign="bottom" align="center" rowspan="1">Matched <hr/></th><th valign="bottom" align="center" rowspan="1" colspan="1">Chi-square <hr/></th></tr></thead><tbody><tr><td valign="top" align="left" rowspan="1" colspan="1">El Escorial criteria</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">65.79, <italic>p</italic>&#x0003c;0.00001</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Definite</td><td valign="top" align="right" rowspan="1" colspan="1">961</td><td valign="top" align="right" rowspan="1" colspan="1">46.9%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Definite</td><td valign="top" align="right" rowspan="1" colspan="1">1460</td><td valign="top" align="right" rowspan="1" colspan="1">53.7%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Probable</td><td valign="top" align="right" rowspan="1" colspan="1">430</td><td valign="top" align="right" rowspan="1" colspan="1">21.0%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Probable</td><td valign="top" align="right" rowspan="1" colspan="1">603</td><td valign="top" align="right" rowspan="1" colspan="1">22.2%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Probable + Lab</td><td valign="top" align="right" rowspan="1" colspan="1">165</td><td valign="top" align="right" rowspan="1" colspan="1">8.1%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Probable + Lab</td><td valign="top" align="right" rowspan="1" colspan="1">254</td><td valign="top" align="right" rowspan="1" colspan="1">9.3%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Possible</td><td valign="top" align="right" rowspan="1" colspan="1">358</td><td valign="top" align="right" rowspan="1" colspan="1">17.5%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Possible</td><td valign="top" align="right" rowspan="1" colspan="1">290</td><td valign="top" align="right" rowspan="1" colspan="1">10.7%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unclassified</td><td valign="top" align="right" rowspan="1" colspan="1">133</td><td valign="top" align="right" rowspan="1" colspan="1">6.5%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unclassified</td><td valign="top" align="right" rowspan="1" colspan="1">113</td><td valign="top" align="right" rowspan="1" colspan="1">4.2%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2047</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2720</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Combined El Escorial criteria</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">47.63, <italic>p</italic>&#x0003c;0.00001</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Definite + Probable</td><td valign="top" align="right" rowspan="1" colspan="1">1556</td><td valign="top" align="right" rowspan="1" colspan="1">76.0%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Definite + Probable</td><td valign="top" align="right" rowspan="1" colspan="1">2317</td><td valign="top" align="right" rowspan="1" colspan="1">85.2%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Possible + Unclassified</td><td valign="top" align="right" rowspan="1" colspan="1">491</td><td valign="top" align="right" rowspan="1" colspan="1">24.0%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Possible + Unclassified</td><td valign="top" align="right" rowspan="1" colspan="1">403</td><td valign="top" align="right" rowspan="1" colspan="1">14.8%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2047</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2720</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Medicare</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">359.82,
<italic>p</italic>&#x0003c;0.00001</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Yes</td><td valign="top" align="right" rowspan="1" colspan="1">788</td><td valign="top" align="right" rowspan="1" colspan="1">38.5%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Yes</td><td valign="top" align="right" rowspan="1" colspan="1">1799</td><td valign="top" align="right" rowspan="1" colspan="1">66.1%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;No</td><td valign="top" align="right" rowspan="1" colspan="1">1236</td><td valign="top" align="right" rowspan="1" colspan="1">60.4%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;No</td><td valign="top" align="right" rowspan="1" colspan="1">902</td><td valign="top" align="right" rowspan="1" colspan="1">33.2%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="right" rowspan="1" colspan="1">23</td><td valign="top" align="right" rowspan="1" colspan="1">1.1%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="right" rowspan="1" colspan="1">19</td><td valign="top" align="right" rowspan="1" colspan="1">0.7%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2047</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2720</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Veterans Administration (VA)</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">3.54, <italic>p</italic> = 0.17</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Yes</td><td valign="top" align="right" rowspan="1" colspan="1">116</td><td valign="top" align="right" rowspan="1" colspan="1">5.7%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Yes</td><td valign="top" align="right" rowspan="1" colspan="1">175</td><td valign="top" align="right" rowspan="1" colspan="1">6.4%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;No</td><td valign="top" align="right" rowspan="1" colspan="1">1908</td><td valign="top" align="right" rowspan="1" colspan="1">93.2%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;No</td><td valign="top" align="right" rowspan="1" colspan="1">2526</td><td valign="top" align="right" rowspan="1" colspan="1">92.9%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="right" rowspan="1" colspan="1">23</td><td valign="top" align="right" rowspan="1" colspan="1">1.1%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="right" rowspan="1" colspan="1">19</td><td valign="top" align="right" rowspan="1" colspan="1">0.7%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2047</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2720</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Private Insurance</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">3.14, <italic>p</italic> = 0.21</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Yes</td><td valign="top" align="right" rowspan="1" colspan="1">898</td><td valign="top" align="right" rowspan="1" colspan="1">43.9%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Yes</td><td valign="top" align="right" rowspan="1" colspan="1">1232</td><td valign="top" align="right" rowspan="1" colspan="1">45.3%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;No</td><td valign="top" align="right" rowspan="1" colspan="1">1126</td><td valign="top" align="right" rowspan="1" colspan="1">55.0%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;No</td><td valign="top" align="right" rowspan="1" colspan="1">1469</td><td valign="top" align="right" rowspan="1" colspan="1">54.0%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="right" rowspan="1" colspan="1">23</td><td valign="top" align="right" rowspan="1" colspan="1">1.1%</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="right" rowspan="1" colspan="1">19</td><td valign="top" align="right" rowspan="1" colspan="1">0.7%</td><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2047</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Total</td><td valign="top" align="right" rowspan="1" colspan="1">2720</td><td valign="top" align="right" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr></tbody></table></table-wrap><table-wrap id="T3" position="float" orientation="landscape"><label>Table 3</label><caption><p>Regression on significant univariate variables, age group and the two
interactions.</p></caption><table frame="hsides" rules="groups"><thead><tr><th valign="bottom" rowspan="2" align="left" colspan="1">Effect</th><th valign="bottom" rowspan="2" align="center" colspan="1">DF</th><th valign="bottom" rowspan="2" align="left" colspan="1">Parameter Estimates</th><th colspan="2" valign="bottom" align="center" rowspan="1">95% Confidence Limits
<hr/></th><th valign="bottom" rowspan="2" align="left" colspan="1">Wald Chi-Square</th><th valign="bottom" rowspan="2" align="left" colspan="1"><italic>p</italic> Value</th></tr><tr><th valign="bottom" align="left" rowspan="1" colspan="1">Lower</th><th valign="bottom" align="left" rowspan="1" colspan="1">Upper</th></tr></thead><tbody><tr><td valign="top" align="left" rowspan="1" colspan="1">Intercept</td><td valign="top" align="center" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;0.0353</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.0776</td><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;0.1482</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Medicare</td><td valign="top" align="center" rowspan="1" colspan="1">2</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">323.92</td><td valign="top" align="left" rowspan="1" colspan="1">&#x0003c;0.0001</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Yes</td><td valign="top" align="center" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;1.1173</td><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;0.9954</td><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;1.2391</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="center" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;0.2940</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.3292</td><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;0.9172</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">State Combined</td><td valign="top" align="center" rowspan="1" colspan="1">2</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;65.13</td><td valign="top" align="left" rowspan="1" colspan="1">&#x0003c;0.0001</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;LA, SF, LV</td><td valign="top" align="center" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.6398</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.7982</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.4813</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;NJ, BA, PH, DT, CI</td><td valign="top" align="center" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.2967</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.4448</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.1486</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Ethnicity</td><td valign="top" align="center" rowspan="1" colspan="1">2</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;12.40</td><td valign="top" align="left" rowspan="1" colspan="1">&#x0003c;0.0020</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Hispanic</td><td valign="top" align="center" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.3590</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.5614</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.1566</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="center" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.1509</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.3498</td><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;0.0481</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">Race</td><td valign="top" align="center" rowspan="1" colspan="1">4</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;12.10</td><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;0.0167</td></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;African American</td><td valign="top" align="center" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.2339</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.4422</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.0256</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Asian</td><td valign="top" align="center" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.2692</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.5884</td><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;0.0501</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Other</td><td valign="top" align="center" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.4302</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;1.3832</td><td valign="top" align="left" rowspan="1" colspan="1">&#x000a0;&#x000a0;0.5228</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr><tr><td valign="top" align="left" rowspan="1" colspan="1">&#x02003;Unknown</td><td valign="top" align="center" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.2486</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.4511</td><td valign="top" align="left" rowspan="1" colspan="1">&#x02212;0.0462</td><td valign="top" align="left" rowspan="1" colspan="1"/><td valign="top" align="left" rowspan="1" colspan="1"/></tr></tbody></table><table-wrap-foot><fn id="TFN2"><p>Goodness of Fit <italic>p</italic>-value = 0.082.</p></fn></table-wrap-foot></table-wrap></floats-group></article>