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1<?xml version="1.0" encoding="iso-8859-1"?>
2
3<HTML_CONTENT xmlns:ibis="http://www.ibisph.org">
4
5        <TITLE>Tracking Methods</TITLE>
6
7        <CONTENT>
8                <h2>Data Processing and Analysis in the NM EPHT Network</h2>
9                <br/>
10                <ul class="Indent">
11                        <li>
12                                <a ibis:hash="geocoding">Geocoding</a>
13                        </li>
14                        <li>
15                                <a ibis:hash="age-adjust">Age Adjustment in Analysis</a>
16                        </li>
17                        <li>
18                                <a ibis:hash="time-series">Time Series Data and Analysis</a>
19                        </li>
20                        <li>
21                                <a ibis:hash="linking">Linking Health and Environmental Data</a>
22                        </li>
23                        <li>
24                                <a ibis:hash="metadata">Metadata</a>
25                        </li>
26                        <li>
27                                <a ibis:hash="satellite">Satellite Data</a>
28                        </li>
29                </ul>
30
31               
32                        <a name="geocoding"/>
33               
34                <br/><br/>
35                <h4>Geocoding</h4>
36                <br/>
37                The geocoding of health outcome and environmental data is a way to assign geographic
38                locators to the data, thus enabling linkage of multiple types of data. Much of our
39                data are geocoded with latitude-longitude coordinates assigned to a street address.
40                Street address, city, county, zip code, and Census tract are the fields typically
41                used to geocode Tracking data.
42
43                <br/><br/>
44                This <a href="contentfile/pdf/Geocoding_WAthas_Dec05.pdf" title="PDF file with EPHT geocoding process information">
45                PDF report</a> describes some of the details of geocoding cancer data and some of the
46                issues associated with these efforts.
47
48               
49                        <a ibis:hash="backtotop">back to top</a>
50               
51
52               
53                        <a name="age-adjust"/>
54               
55                <br/><br/>
56                <h4>Age Adjustment in Analysis</h4>
57                <br/><br/>
58                Rates might require adjusting due to factors such as age. If the groups differ with respect
59                to this factor, rates cannot be correctly compared unless the factor is adjusted for. For age,
60                the general practice is to adjust rates to a standard population (e.g., the United States
61                population from the 2000 Census). Using this practice, the mortality rate for Florida and
62                the mortality rate for Alaska would both be age-adjusted to the U.S. standard population.
63                The two mortality rates could then be compared.
64
65                <br/><br/>
66                For mortality, age adjustment is calculated as follows:
67                <ol class="Indent">
68                        <li>Choose a standard population with a known distribution (U.S. Census 2000 Population).</li>
69                        <li>Calculate the age-specific death rates for the two populations (Alaska and Florida
70                        in this case).</li>
71                        <li>Calculate the age-specific expected number of deaths based on the standard population
72                        (multiply the age-specific death rates for the two states by the number of people in the
73                        respective age class in the standard population).</li>
74                        <li>For each state, add the expected numbers of deaths over all age classes. Divide the
75                        resulting total number of expected deaths by the total number of people in the standard
76                        population. These are the age-adjusted death rates.</li>
77                </ol>
78
79                <br/><br/>
80                Age-adjusted rates are used for many of the diseases tracked on the NM EPHT site but are not
81                always the preferred method for comparison.
82
83               
84                        <a ibis:hash="backtotop">back to top</a>
85
86                        <a name="time-series"/>
87               
88                <br/><br/>
89               
90                <h4>Time Series Data and Analysis</h4>
91                <br/>
92                A <span class="Bold">time series</span> is a sequence of data points, typically measured at
93                specified time intervals in order to understand the trends of the data over time. These
94                trends can sometimes be used to forecast future events based on known past events, that
95                is to predict future data points before they are measured. The term time series analysis
96                is used to distinguish trend analysis from an analysis in which there is no natural ordering
97                of the individual observations, and also from the spatial data analyses we use to relate
98                our data to geographic locations. A time series model will generally reflect the fact that
99                observations close together in time will be more closely related to each other than observations
100                further apart in time. In addition, time series models will often make use of the natural
101                one-way ordering of time so that values in a series for a given time will be expressed as
102                deriving in some way from past values, rather than from future values.
103
104                <br/><br/>
105                The time series data presented on this site typically relate a health outcome, such as thyroid
106                cancer, with time through representation of the health and time data on a graph. In the case of
107                thyroid cancer, rates are increasing over time, whereas myocardial infarction rates are decreasing
108                over time. In the case of arsenic concentrations in drinking water, rates are generally constant
109                over time. All of these time series can be evaluated to determine the statistical significance
110                of the changes and the magnitude of the rate of changes.
111
112               
113                        <a ibis:hash="backtotop">back to top</a>
114               
115
116               
117                        <a name="linking"/>
118               
119                <br/><br/>
120                <h4>Linking Health and Environmental Data</h4>
121                <br/>
122                Linkage studies refer to investigations that connect environmental and health outcome data in
123                time and place within a population. The primary goal of linkage studies is to facilitate
124                understanding of the relationship between diseases and the environment. A good example of the
125                impact such studies may have is the removal of lead from gasoline in the 1970's, following a
126                series of national linkage studies showing that blood lead levels decreased in direct relation
127                to declining lead use in gasoline. In this instance, a clear cause-effect relationship existed
128                between environmental hazard and health effect, and measuring the declining lead content of
129                gasoline was relatively easy to do.
130
131                <br/><br/>
132                Unfortunately, for many diseases the cause-effect relationship is not clear, and environmental
133                hazards cannot be measured as easily. Furthermore, many adverse health outcomes may result from
134                exposures to multiple different hazards, some received in the short term and others received
135                over a longer, more protracted time period. For these reasons, it is important that linkage be
136                approached in a scientifically rigorous manner. However, even here, caution is needed in the
137                interpretation of results since the environmental data used in linkage analysis ordinarily is
138                not collected on an individual basis; rather, data are collected across broad populations,
139                such as a county or particular region within a state. Consequently, a study that does find a
140                relationship between the level of an environmental hazard and the occurrence of a health outcome
141                cannot be used to conclude that the hazard actually caused the health outcome, since it is not
142                known who among the population actually received exposure at the levels of interest. Rather,
143                the results of such studies can be used to generate hypotheses on causation, which can then be
144                tested in more formal studies involving recruitment of study subjects and collection of data
145                n an individual level.
146
147                <br/><br/>
148                Detailed studies on health and environmental data linkages are presented at NM EPHT Health
149                Effects: <a href="health_effects/linkage-studies.html">Health and Environment Linkage Studies</a>.
150
151               
152                        <a ibis:hash="backtotop">back to top</a>
153               
154
155               
156                        <a name="metadata"/>
157               
158                <br/><br/>
159                <h4>Metadata</h4>
160                <br/>
161                Metadata are "data about data." There are several types of metadata, and these can be broadly
162                defined under the categories of Descriptive, Structural, and Administrative. (These are taken
163                from the CDC EPHT <a href="contentfile/pdf/Metadata_FAQs.pdf" title="PDF: Metadata Frequently Asked Questions">
164                Metadata Workgroup's FAQ</a> document.)
165                <ul class="Indent">
166                        <li>Descriptive Metadata: Information that describes the content, quality, and context
167                        of a data resource for the purpose of facilitating identification and discovery. It may
168                        reference additional information like quality assurance documents and data dictionaries.
169                        Through descriptive metadata a user can learn the what, why, when, who, where, and how
170                        for a data resource.</li>
171                        <li>Structural Metadata: Information about how the item is put together or arranged such
172                        as the table of contents page, individual page numbers, or illustration. It basically
173                        describes the structure of an item, such as a book, so that all of the pages of that
174                        item can be displayed in the correct order. In the electronic world it facilitates
175                        navigation and presentation of electronic resources.</li>
176                        <li>Administrative Metadata: Includes information about resolution, bit depth, type
177                        of equipment used to produce the file, storage format, and file name and location.
178                        It can also include basic facts on ownership, rights, and reproduction information.</li>
179                </ul>
180                <br/><br/>
181                The Environmental Public Health Tracking Network makes extensive use of Descriptive Metadata.
182                Metadata are considered the backbone of the EPHT Network.
183                Metadata Benefits: As more data are created and stored, there is a need to document data
184                resources for future use and to improve accessibility. Creating Descriptive Metadata:
185                <ul class="Indent">
186                        <li>Helps an organization arrange and maintain its data assets.</li>
187                        <li>Limits duplication of effort by ensuring that others in the organization are aware
188                        of the existence of data resources.</li>
189                        <li>Assists in both determining and improving the quality of data resources.</li>
190                        <li>Improves an organization's ability to comply with rules, regulations, and policies
191                        related to data access.</li>
192                        <li>Reduces the loss of institutional memory for data resources when key staff move on.</li>
193                        <li>Provides information about an organization's data holdings so that users can locate
194                        available resources relevant to an area of interest or study.</li>
195                        <li>Provides the ability to advertise and promote the availability of data resources via
196                        online services.</li>
197                        <li>Supplies the means to document limitations about the data resource or disclaimers
198                        that are important for potential users to be aware of.</li>
199                </ul>
200
201                <br/><br/>
202                The Centers for Disease Control and Prevention (CDC), through EPHT Grantee efforts on the
203                Metadata Workgroup and a contract with Northrop Grumman, developed the EPHT Metadata Creation
204                Tool (MCT). The EPHT MCT generates customized, FGDC-compliant metadata files from information
205                entered into Web forms. A New Mexico version of this tool is provided on the NM EPHT Web server;
206                those who wish to use the MCT to create metadata must be assigned a username and password. (FGDC:
207                Federal Geographic Data Committee)
208                <br/><br/>
209                During this initial stage of New Mexico EPHT implementation access to the MCT will be restricted
210                to NM Tracking Team members, who may request an EPHT MCT username and password from the NM EPHT
211                Webmaster, doh-eheb AT state DOT nm DOT us.
212                <ul class="Indent">
213                        <li>
214                                <a href="contentfile/pdf/Metadata_Content_Guidance_Version_1-0.pdf" 
215                                title="PDF: EPHT Metadata Content Guidance Document">Metadata Content Guidance
216                                Document, Version 1.0</a> (PDF)  View or save this PDF Metadata Content Guidance
217                                Document that the EPHT Metadata Workgroup developed to explain metadata, show examples
218                                of environmental and health metadata records, and describe the Metadata Creation Tool.</li>
219                        <li>
220                                <a class="blank-target" href="http://epht-mct.unm.edu/" title="NM Metadata Creation Tool, requires password, external site opens in new tab or window">
221                                New Mexico EPHT Metadata Creation Tool</a> (external Web site)  Once you have received
222                                log-in information (username and password) from the NM EPHT Program, you can go to the
223                                Web site (http://epht-mct.unm.edu/), log in, and create your metadata file. Request an
224                                NM EPHT Metadata Creation Tool username and password New Mexico Tracking Team only:
225                                doh-eheb AT state DOT nm DOT us.</li>
226                </ul>
227
228               
229                        <a ibis:hash="backtotop">back to top</a>
230               
231
232               
233                        <a name="satellite"/>
234               
235                <br/><br/>
236                <h4>Satellite Data</h4>
237                <br/>
238                Satellite data provide spectral information for the Earth's surface, geology, waters, and
239                atmosphere on a regular basis. Researchers and applied scientists use satellite data and
240                derived data products for synoptic, repeated analysis of specific issues and questions.
241                For example, data from the Visible Red (Red) and Near Infrared (NIR) spectral ranges can
242                be calculated to provide an index for vegetation greenness, the Normalized Difference Vegetation
243                Index NDVI. NDVI = (NIR - Red)/(NIR + Red)
244                <br/><br/>
245                Vegetation greenness provides an indication of water content in plants (as with stages
246                of agricultural crop growth), indicates areas of no vegetation (such as areas of rock or
247                snow cover), and shows changes in land cover and plant health over time (for example, plant
248                cover before a wildfire compared with, later, extent of the fire through lost vegetation).
249                The land cover classification process (which categorizes type and extent of land cover) uses
250                the vegetation index as a layer of information, in addition to the spectral satellite data
251                and other identifying data. A land cover classification is useful as a layer of information
252                in many analyses; for example, it provides a method for assigning values to plant types for
253                how well they hold dirt particles on the ground, and is used in erosion and dust forecast models.
254
255                <br/><br/>
256                Much of the satellite data used in the NM EPHT Program comes from National Aeronautics and
257                Space Administration (NASA) sensors. Learn more about <a class="blank-target" href="http://www.nasa.gov/topics/earth.html" 
258                title="NASA Earth Topics page, external site opens in new tab or window">NASA Earth programs</a> or
259                browse <a class="blank-target" href="http://www.nasa.gov/topics/earth/earth_images_archive_1.html" 
260                title="NASA Earth Image Archives page, external site opens in new tab or window">NASA image galleries</a> 
261                (both are external sites). Landsat and MODIS data are commonly used in image analysis; browse the
262                <a class="blank-target" href="http://landsat.gsfc.nasa.gov/images/" title="Landsat Image Gallery,
263                external site opens in new tab or window">Landsat image gallery</a> and the <a class="blank-target" 
264                href="http://modis.gsfc.nasa.gov/gallery/" title="MODIS Image Gallery, external site opens in new
265                tab or window">MODIS image gallery</a> (both are external sites). There are numerous commercial
266                satellites in orbit; the company GeoEye operates IKONOS and GeoEye-1. Browse the <a class="blank-target" 
267                href="http://www.geoeye.com/CorpSite/gallery/default.aspx" title="GeoEye Image Gallery, external site
268                opens in new tab or window">GeoEye image gallery</a> (external site). This PDF file, <a href="contentfile/pdf/TBudge_remotesensing.pdf">
269                TBudge_remotesensing.pdf</a>, was created from a PowerPoint presentation developed by Tom Budge,
270                Remote Sensing Manager at The University of New Mexico Earth Data Analysis Center. It describes
271                many of the satellite sensors and their image products, the significance of pixel and temporal
272                resolutions, and differences in information between 8-bit and 11-bit data. Note: The PDF file is
273                13.6 MB and will take a while to download or load into the browser, depending upon your Internet connection.
274
275               
276                        <a ibis:hash="backtotop">back to top</a>
277               
278
279                <br/><br/>
280                The four images below illustrate slides from an EPHT PowerPoint presentation on using NASA satellite data products in the NM EPHT Program.
281                <br/><br/>
282                <img class="imgItem" src="contentfile/image/satellitedata_slide1.jpg" alt="Satellite Data slide 1" height="488" width="650" />
283                <a ibis:hash="backtotop">back to top</a>
284               
285                <br/><br/>
286                <img src="contentfile/image/satellitedata_slide2.jpg" alt="Satellite Data slide 2" height="488" width="650" />
287                <a ibis:hash="backtotop">back to top</a>
288               
289                <br/><br/>
290                <img src="contentfile/image/satellitedata_slide3.jpg" alt="Satellite Data slide 3" height="488" width="650" />
291                <a ibis:hash="backtotop">back to top</a>
292               
293                <br/><br/>
294                <img src="contentfile/image/satellitedata_slide4.jpg" alt="Satellite Data slide 4" height="488" width="650" />
295                <a ibis:hash="backtotop">back to top</a>
296               
297        </CONTENT>
298</HTML_CONTENT>
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