Complexity in Teen Births in Texas: Measurement and Association with Demographic Characteristics and Policy Changes

2.50
Hdl Handle:
http://hdl.handle.net/10755/152882
Type:
Presentation
Title:
Complexity in Teen Births in Texas: Measurement and Association with Demographic Characteristics and Policy Changes
Abstract:
Complexity in Teen Births in Texas: Measurement and Association with Demographic Characteristics and Policy Changes
Conference Sponsor:Sigma Theta Tau International
Conference Year:2002
Conference Date:July, 2002
Author:Hamilton, Patricia, PhD
P.I. Institution Name:Texas Woman's University
Title:Professor
Objectives The first objective of the study was to quantify the amount of complexity in the time series of births to teens in Texas. The measure of complexity took the form of a fractal scaling parameter. The second objective was to determine the association between the fractal scaling parameter and key demographic characteristics as well as with changes in social policy intended to alter teen reproductive behavior Design The study was a time series design using nonlinear dynamical analysis and correlational procedures. The approach is based on chaos theory and the time series analysis is similar to that currently used in physics and mathematics to understand a new property of phenomena known as fractal scaling. Population, Sample, Setting, Years The population studied was every birth from 1990 through 2000 to a young woman under that age of 20 who resided in Texas. Analysis was conducted on the county level. There are 254 counties in Texas. Concepts or Variables Studied Fractal scaling is the primary variable. This variable is thought to encode information about the complexity of the processes that generate events that can be observed, such as teen births. Demographic variables such as population density, degree of rurality/urbanity as well as educational and income levels of counties were associated with the derived fractal scaling parameter. In addition, key policies including the Welfare Reform Act of 1996 were studied in relation changes in the fractal scaling of the time series of each county. Methods The data were extracted from non-identifiable birth certificate data obtained from the Texas Department of Health. Births were aggregated to form daily counts. These counts were then concatenated into a time series of 3652 days for each county in the state. Nonlinear dynamical analysis was performed to quantify the degree of complexity through detection of the fractal scaling of each time series. Logistic regression, linear regression and correlation were used to determine associations among study variables. Findings The study hypothesis related to longer memory being associated with demographic characteristics was supported. The correlation between length of memory and county demographic profile is moderate and statistically significant (r=.345, p=000). The memory within the time series of particular sub-groups varies in the way we predicted. The weekly and annual periodicities in teen births are greater for unmarried than married teens, higher for whites than for minorities (regardless of marital status) and are stronger in urban than in rural areas of the state. We feel we can say with confidence that like demographics are associated with fractal scaling properties among different time series. The study hypothesis that the teen birth data will exhibit characteristics of both non-stationarity and determinism also was supported for larger counties but not for smaller ones. Fractal scaling was observed for the state as a whole and for counties with no more than 50% of days without a teen birth occurring. Again, this lends support for our hypothesis that demographic characteristics are associated with dynamics in the time series of births to teens in Texas. Only 17 of Texas' 254 counties demonstrated a change in the number of births to teens following the Welfare Reform Act of 1996. However, more counties showed a change in fractal scaling properties. Conclusions The processes that generate teen births are linked in such a way as to result in a complex observable time series. The complexity of the time series can be quantified and correlated with demographic variables as well as with changes in social policy. The effects of social policy may be detectable in changed dynamics before they are detectable in changing numbers of births. The effects of social policy may differ among counties of differing demographic characteristics. Implications Florence Nightingale first called for the evaluation of the effects of social policy over 150 years ago. Nurses are in a unique position to observe complex human behaviors as well as their antecedents and their consequences. By using the most advanced analysis techniques currently available nurses can identify and measure complexity in more detail than ever before. With this new information we will be better able to target, plan and evaluate public health interventions and policies. Chaos theory is a useful framework from which to better understand public health problems such as teen births.

Repository Posting Date:
26-Oct-2011
Date of Publication:
Jul-2002
Sponsors:
Sigma Theta Tau International

Full metadata record

DC FieldValue Language
dc.typePresentationen_GB
dc.titleComplexity in Teen Births in Texas: Measurement and Association with Demographic Characteristics and Policy Changesen_GB
dc.identifier.urihttp://hdl.handle.net/10755/152882-
dc.description.abstract<table><tr><td colspan="2" class="item-title">Complexity in Teen Births in Texas: Measurement and Association with Demographic Characteristics and Policy Changes</td></tr><tr class="item-sponsor"><td class="label">Conference Sponsor:</td><td class="value">Sigma Theta Tau International</td></tr><tr class="item-year"><td class="label">Conference Year:</td><td class="value">2002</td></tr><tr class="item-conference-date"><td class="label">Conference Date:</td><td class="value">July, 2002</td></tr><tr class="item-author"><td class="label">Author:</td><td class="value">Hamilton, Patricia, PhD</td></tr><tr class="item-institute"><td class="label">P.I. Institution Name:</td><td class="value">Texas Woman's University</td></tr><tr class="item-author-title"><td class="label">Title:</td><td class="value">Professor</td></tr><tr class="item-email"><td class="label">Email:</td><td class="value">phamilton@twu.edu</td></tr><tr><td colspan="2" class="item-abstract">Objectives The first objective of the study was to quantify the amount of complexity in the time series of births to teens in Texas. The measure of complexity took the form of a fractal scaling parameter. The second objective was to determine the association between the fractal scaling parameter and key demographic characteristics as well as with changes in social policy intended to alter teen reproductive behavior Design The study was a time series design using nonlinear dynamical analysis and correlational procedures. The approach is based on chaos theory and the time series analysis is similar to that currently used in physics and mathematics to understand a new property of phenomena known as fractal scaling. Population, Sample, Setting, Years The population studied was every birth from 1990 through 2000 to a young woman under that age of 20 who resided in Texas. Analysis was conducted on the county level. There are 254 counties in Texas. Concepts or Variables Studied Fractal scaling is the primary variable. This variable is thought to encode information about the complexity of the processes that generate events that can be observed, such as teen births. Demographic variables such as population density, degree of rurality/urbanity as well as educational and income levels of counties were associated with the derived fractal scaling parameter. In addition, key policies including the Welfare Reform Act of 1996 were studied in relation changes in the fractal scaling of the time series of each county. Methods The data were extracted from non-identifiable birth certificate data obtained from the Texas Department of Health. Births were aggregated to form daily counts. These counts were then concatenated into a time series of 3652 days for each county in the state. Nonlinear dynamical analysis was performed to quantify the degree of complexity through detection of the fractal scaling of each time series. Logistic regression, linear regression and correlation were used to determine associations among study variables. Findings The study hypothesis related to longer memory being associated with demographic characteristics was supported. The correlation between length of memory and county demographic profile is moderate and statistically significant (r=.345, p=000). The memory within the time series of particular sub-groups varies in the way we predicted. The weekly and annual periodicities in teen births are greater for unmarried than married teens, higher for whites than for minorities (regardless of marital status) and are stronger in urban than in rural areas of the state. We feel we can say with confidence that like demographics are associated with fractal scaling properties among different time series. The study hypothesis that the teen birth data will exhibit characteristics of both non-stationarity and determinism also was supported for larger counties but not for smaller ones. Fractal scaling was observed for the state as a whole and for counties with no more than 50% of days without a teen birth occurring. Again, this lends support for our hypothesis that demographic characteristics are associated with dynamics in the time series of births to teens in Texas. Only 17 of Texas' 254 counties demonstrated a change in the number of births to teens following the Welfare Reform Act of 1996. However, more counties showed a change in fractal scaling properties. Conclusions The processes that generate teen births are linked in such a way as to result in a complex observable time series. The complexity of the time series can be quantified and correlated with demographic variables as well as with changes in social policy. The effects of social policy may be detectable in changed dynamics before they are detectable in changing numbers of births. The effects of social policy may differ among counties of differing demographic characteristics. Implications Florence Nightingale first called for the evaluation of the effects of social policy over 150 years ago. Nurses are in a unique position to observe complex human behaviors as well as their antecedents and their consequences. By using the most advanced analysis techniques currently available nurses can identify and measure complexity in more detail than ever before. With this new information we will be better able to target, plan and evaluate public health interventions and policies. Chaos theory is a useful framework from which to better understand public health problems such as teen births.<br/><br/></td></tr></table>en_GB
dc.date.available2011-10-26T11:53:38Z-
dc.date.issued2002-07en_GB
dc.date.accessioned2011-10-26T11:53:38Z-
dc.description.sponsorshipSigma Theta Tau Internationalen_GB
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