1. How Does This Data Model Work?
   This real-time data model is unique in terms Asthma Admissions data that it interprets, but the mechanics that drive it are in the same genre as other "online clocks," "internet clocks," "counters," and "countdowns" that you may have come across on other websites. The bulk of the data analysis was done with desktop statistical programs, and then the raw values calculated for the year were incorporated into the time-sensitive engine that was programmed with a web-friendly programming language, and a static HTML front page was designed to display the results in a manner that would easily facilitate printing. Some other interesting and fun "Online Clocks" Include:
   • The World Game Institute's World-o-Meters™
   • U.S. Census Bureau's Population Clocks
   • The Stop HIV Infections Clock
   • Bill Gate's Net Worth Page
   • U.S. National Debt Clock
   • Drug War Clock
   
   
   
2. Are The Numbers Generate By This Model For Real?
   In short, not exactly, but they were calculated given real numbers for the last two most rescent years (2000 and 2001).
   
   
   
3. How Did You Calculate The Numbers Used To Drive This Model?
   The last two years of available data on New York City Zone Improvement Plan (ZIP) Code-level asthma case admissions was obtained to generate this model. A simple linear algorithm was used to determine the slope of the trend between the years 2000 and 2001, then extended further to predict the values for 2002 and 2003. Using the predicted number of cases for 2003, average lapse times between admissions over the course of the year were calculated for each individual ZIP Code. These values were then plugged into the time-relevant element of the model to be calculated in real-time given the amount of time that has passed since the beginning of the year and the moment the page was loaded.
   
   
   
   In the example above, there were an additional 643 cases of asthma hospital admissions between the years 2000 and 2001. Given this slope, the value for 2003 is figured to be 27, 575 cases, or 1,286 additional cases since 2001. Over the course of the year, that would be one new or repeat hospital admission for asthma every 19 minutes, 3.64 seconds. Thus, every 19.06 minutes since midnight January 1, 2003 the counter would be incremented by one case in order to reach the predicted value of 27,575 by midnight on December 31, 2003.
   
   
   
4. Why Did You Use The "Admissions" Data Instead Of The "Per Person Discharge" Data?
   What we wanted to accomplish was to display all possible new and returning asthma hospital admissions cases for the year. This creates a more realistic picture of the epidemic. (See also FAQ #7 below)
   
   The "Per Person Discharge" data gives the number of different individuals admitted for asthma. If an individual is admitted for a different diagnosis, than that person will be counted each time. If that individual is admitted more than once for the same diagnosis than that person will be counted only once.
   
   Analyzing the "Per Person Discharge" data between years will determine first time admissions for individuals with asthma since the earlier year.
   
   The "Admissions" data counts all inpatient admissions, even when the same person is admitted repeatedly for the same diagnosis. The "Admissions" data tallies all new and repeat visits for the particular diagnosis.
   
   Analyzing the difference between the "Admissions" and the "Per Person Discharge" data will determine the number of return admissions by an individual for the same diagnosis.
   
   
   
5. What's The Difference Between "Cases" and "Rates"?
   This model calculates the number of cases for asthma hospital admissions, which is, in a sense, the raw data. For example, in the year 2000, Bushwick (Z-11237) in Brooklyn had a total of 500 cases of asthma while Mott Haven/Port Morris (Z-10454) in the Bronx had a total of 399 cases. It would appear that the cases in Bushwick are higher than in Mott Haven/Port Morris, this is true. Now, normalizing the data by the respective neighborhood population will generate the asthma rate. Thus, the rates for asthma are actually higher in Mott Haven/Port Morris (114 per 10,000) versus Bushwick (102 per 10,000), considering the ZIP Code population of 34,976 and 48,910 respectively for the 2000 Census.
   
   
   
6. Where Did You Get The Data for this model?
   The data was obtained from the Infoshare Community Data System, an online archive of health and demographic data for New York City developed by Community Studies of New York at the Department of Urban Studies at Queens College. Access was obtained through the Data Statistics and Management Core of the Columbia Center for Children's Environmental Health at Columbia University's Mailman School of Public Health.
   
   
   
7. Do You Plan To Improve This Model?
   Given the current design of the model there is one particular change that can be implemented into a future redesign: a table that makes a distinction between new cases and repeat cases in addition to the total admissions currently displayed.
   
   An improved version will incorporate the previous implementation explained above, but use a more extensive prediction algorithm that examines all available data for the years 1994 to 2001.
   
   
   
8. Who Do I Submit My Questions To About This Project?
   Send all questions, comments, or suggestions to the Website Manager, Carlos M. Jusino. His e-mail address is Carlos@weact.org.

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