Limits...
Assessing community variation and randomness in public health indicators.

Arndt S, Acion L, Caspers K, Diallo O - Popul Health Metr (2011)

Bottom Line: Simulations of populations and an example using real data are provided.The measure based on the simple chi-square statistic seems superior, offering better protection against Type I errors and providing more accurate estimates of the true community variance.Methods for improving poor indices are discussed.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242 USA. stephan-arndt@uiowa.edu.

ABSTRACT

Background: Evidence-based health indicators are vital to needs-based programming and epidemiological planning. Agencies frequently make programming funds available to local jurisdictions based on need. The use of objective indicators to determine need is attractive but assumes that selection of communities with the highest indicators reflects something other than random variability from sampling error.

Methods: The authors compare the statistical performance of two heterogeneity measures applied to community differences that provide tests for randomness and measures of the percentage of true community variation, as well as estimates of the true variation. One measure comes from the meta-analysis literature and the other from the simple Pearson chi-square statistic. Simulations of populations and an example using real data are provided.

Results: The measure based on the simple chi-square statistic seems superior, offering better protection against Type I errors and providing more accurate estimates of the true community variance.

Conclusions: The heterogeneity measure based on Pearson's χ2 should be used to assess indices. Methods for improving poor indices are discussed.

No MeSH data available.


Plot of intraclass correlations and  (blue +) and  (green o) values against 100% agreement (red line).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3045330&req=5

Figure 1: Plot of intraclass correlations and (blue +) and (green o) values against 100% agreement (red line).

Mentions: In the second simulation, the spread among communities was varied randomly from 0 to 0.8, centered at 0.5, which corresponds to between-community variances of 0 to 0.0533. Two samples were taken from each of 20,000 randomly sampled populations in order to calculate interclass correlations estimating the proportion of between-community variation to total variation (between and within). Figure 1 shows the plot of the intraclass correlations versus (blue +) and (green o). The Pearson correlations between the intraclass correlation and both and were all greater than 0.98. Similarly, the correlations between and was 0.97. The Spearman correlations were all greater than 0.99.


Assessing community variation and randomness in public health indicators.

Arndt S, Acion L, Caspers K, Diallo O - Popul Health Metr (2011)

Plot of intraclass correlations and  (blue +) and  (green o) values against 100% agreement (red line).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3045330&req=5

Figure 1: Plot of intraclass correlations and (blue +) and (green o) values against 100% agreement (red line).
Mentions: In the second simulation, the spread among communities was varied randomly from 0 to 0.8, centered at 0.5, which corresponds to between-community variances of 0 to 0.0533. Two samples were taken from each of 20,000 randomly sampled populations in order to calculate interclass correlations estimating the proportion of between-community variation to total variation (between and within). Figure 1 shows the plot of the intraclass correlations versus (blue +) and (green o). The Pearson correlations between the intraclass correlation and both and were all greater than 0.98. Similarly, the correlations between and was 0.97. The Spearman correlations were all greater than 0.99.

Bottom Line: Simulations of populations and an example using real data are provided.The measure based on the simple chi-square statistic seems superior, offering better protection against Type I errors and providing more accurate estimates of the true community variance.Methods for improving poor indices are discussed.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa 52242 USA. stephan-arndt@uiowa.edu.

ABSTRACT

Background: Evidence-based health indicators are vital to needs-based programming and epidemiological planning. Agencies frequently make programming funds available to local jurisdictions based on need. The use of objective indicators to determine need is attractive but assumes that selection of communities with the highest indicators reflects something other than random variability from sampling error.

Methods: The authors compare the statistical performance of two heterogeneity measures applied to community differences that provide tests for randomness and measures of the percentage of true community variation, as well as estimates of the true variation. One measure comes from the meta-analysis literature and the other from the simple Pearson chi-square statistic. Simulations of populations and an example using real data are provided.

Results: The measure based on the simple chi-square statistic seems superior, offering better protection against Type I errors and providing more accurate estimates of the true community variance.

Conclusions: The heterogeneity measure based on Pearson's χ2 should be used to assess indices. Methods for improving poor indices are discussed.

No MeSH data available.