Details:

Alternative Title:
Prev Chronic Dis
Publisher's site:
http://dx.doi.org/10.5888/pcd15.180177
Personal Author:
Holzhauser, Calla ; Da Rosa, Patricia ; Michael, Semhar
Holzhauser, Calla ; Da Rosa, Patricia ; Michael, Semhar Less -
Description:
Introduction

The All Women Count! (AWC!) program is a no-cost breast and cervical cancer screening program for qualifying women in South Dakota. Our study aimed to identify counties with similar socioeconomic characteristics and to estimate the number of women who will use the program for the next 5 years.

Methods

We used AWC! data and sociodemographic predictor variables (eg, poverty level [percentage of the population with an annual income at or below 200% of the Federal Poverty Level], median income) and a mixture of Gaussian regression time series models to perform clustering and forecasting simultaneously. Model selection was performed by using Bayesian information criterion (BIC). Forecasting of the predictor variables was done by using an autoregressive integrated moving average model.

Results

By using BIC, we identified 5 clusters showing the groups of South Dakota counties with similar characteristics in terms of predictor variables and the number of participants. The mixture model identified groups of counties with increasing or decreasing trends in participation and forecast averages per cluster.

Conclusion

The mixture of regression time series model used in this study allowed for the identification of similar counties and provided a forecasting model for future years. Although several predictors contributed to program participation, we believe our forecasting analysis by county may provide useful information to improve the implementation of the AWC! program by informing program managers on the expected number of participants in the next 5 years. This, in turn, will help in data-driven resource allocation.


Subject:
[+]
Bayes Theorem
Breast Neoplasms
Early Detection Of Cancer
Female
Forecasting
Health Promotion
Humans
Mammography
Mass Screening
Original Research
Papanicolaou Test
Poverty
Program Evaluation
Uterine Cervical Neoplasms

Source:
Prev Chronic Dis. 15
Pubmed ID:
30367718
Pubmed Central ID:
PMC6219850
Document Type:
Journal Article
Funding:
U01 DP006293/DP/NCCDPHP CDC HHS/United States ; U01DP006293/ACL HHS/ACL HHS/United States
U01 DP006293/DP/NCCDPHP CDC HHS/United States ; U01DP006293/ACL HHS/ACL HHS/United States Less -
Place as Subject:
South Dakota
Main Document Checksum:
[+]
urn:sha256:dbb47811da144a1f3137dc37b875be04db7461fffa54299d9574894d3bd36d53
File Type:

Supporting Files

• 	PCD-15-E130.nxml	xml
  	PCD-15-E130s01.gif	gif
  	PCD-15-E130s01.jpg	jpeg
  	PCD-15-E130s02.gif	gif
  	PCD-15-E130s02.jpg	jpeg

More +