The Health Status of Southern Children: A Neglected Regional Disparity

Purpose: Great variations exist in child health outcomes among states in the United States, with southern states consistently ranked among the lowest in the country. Investigation of the geographical distribution of children’s health status and the regional factors contributing to these outcomes has been neglected. We attempted to identify the degree to which region of residence may be linked to health outcomes for children with the specific aim of determining whether living in the southern region of the United States is adversely associated with children’s health status. Methods: A child health index (CHI) that ranked each state in the United States was computed by using statespecific composite scores generated from outcome measures for a number of indicators of child health. Five indicators for physical health were chosen (percent low birth weight infants, infant mortality rate, child death rate, teen death rate, and teen birth rates) based on their historic and routine use to define health outcomes in children. Indicators were calculated as rates or percentages. Standard scores were calculated for each state for each health indicator by subtracting the mean of the measures for all states from the observed measure for each state. Indicators related to social and economic status were considered to be variables that impact physical health, as opposed to indicators of physical health, and therefore were not used to generate the composite child health score. These variables were subsequently examined in this study as potential confounding variables. Mapping was used to redefine regional groupings of states, and parametric tests (2-sample t test, analysis of means, and analysis-of-variance F tests) were used to compare the means of the CHI scores for the regional groupings and test for statistical significance. Multiple regression analysis computed the relationship of region, social and economic indicators, and race to the CHI. Simple linear-regression analyses were used to assess the individual effect of each indicator. Results: A geographic region of contiguous states, characterized by their poor child health outcomes relative to other states and regions of the United States, exists within the “Deep South” (Mississippi, Louisiana, Arkansas, Tennessee, Alabama, Georgia, North Carolina, South Carolina, and Florida). This Deep-South region is statistically different in CHI scores from the US Census Bureau– defined grouping of states in the South. The mean of CHI scores for the Deep-South region was \u3e1 SD below the mean of CHI scores for all states. In contrast, the CHI score means for each of the other 3 regions were all above the overall mean of CHI scores for all states. Regression analysis showed that living in the Deep- South region is a stronger predictor of poor child health outcomes than other consistently collected and reported variables commonly used to predict children’s health. Conclusions: The findings of this study indicate that region of residence in the United States is statistically related to important measures of children’s health and may be among the most powerful predictors of child health outcomes and disparities. This clarification of the poorer health status of children living in the Deep South through spatial analysis is an essential first step for developing a better understanding of variations in the health of children. Similar to early epidemiology work linking geographic boundaries to disease, discovering the mechanisms/pathways/causes by which region influences health outcomes is a critical step in addressing disparities and inequities in child health and one that is an important and fertile area for future research. The reasons for these disparities may be complex and synergistically related to various economic, political, social, cultural, and perhaps even environmental (physical) factors in the region. This research will require the use and development of new approaches and applications of spatial analysis to develop insights into the societal, environmental, and historical determinants of child health that have been neglected in previous child health outcomes and policy research. The public policy implications of the findings in this study are substantial. Few, if any, policies identify these children as a high-risk group on the basis of their region of residence. A better understanding of the depth and breadth of disparities in health, education, and other social outcomes among and within regions of the United States is necessary for the generation of policies that enable policy makers to address and mitigate the factors that influence these disparities. Defining and clarifying the regional boundaries is also necessary to better inform public policy decisions related to resource allocation and the prevention and/or mitigation of the effects of region on child health. The identification of the Deep South as a clearly defined sub-region of the Census Bureau’s regional definition of the South suggests the need to use more culturally and socially relevant boundaries than the Census Bureau regions when analyzing regional data for policy development

Using ANOM slicing for multi-way models with significant interaction

Multi-way (multifactor) models with significant interaction can be analyzed using simple e?ect comparisons. These F-tests are multiple comparisons, which are referred to as slice tests (e.g., in a two-factor study one slices by factor A by comparing the levels of factor B for each level of A). Slicing uses the full model degrees of freedom and mean squared error (MSE). This paper shows how to use analysis of means (ANOM) methods analogous to ANOVA F-test slicing to perform multiple comparisons. This approach results in a set of powerful decision charts that can be used to assess both statistical and practical significance

Two non-parametric, analysis-of-means-type tests for homogeneity of variances

After a brief review of the literature, two non-parametric tests for homogeneity of variances are presented. The first test is based on the analysis of means for ranks, which is a non-parametric version of the analysis of means (ANOM) that uses ranks as input for an ANOM test. The second test uses inverse normal scores of the ranks of scale transformations of the observations as input to the ANOM. Both homogeneity of variances tests can be presented in a graphical form, which makes it easy for practitioners to assess the practical and the statistical significance. A Monte Carlo study is used to show that these tests have power comparable with that of well-known robust tests for homogeneity of variances.

How well do functional assessments of mobility and balance discriminate fallers and recurrent fallers from non-fallers among ambulatory older adults in the community?

Purpose: 1) To compare the ability of functional mobility and balance assessments in discriminating fallers from non-fallers and recurrent fallers from those with fewer or no falls. 2) To compare the discriminatory accuracy of cut-off scores specific to this study sample with that of cut-off scores proposed in the literature for community-dwelling older adults. Methods: In a sample of 39 ambulatory older adults living independently in the community, fallers were identified on the basis of number of falls in the past year. Seven functional tests of mobility and balance were used to identify fallers and recurrent fallers on the basis of their fall history. Results: Discrimination of fallers from non-fallers was poor: Only a high-level balance assessment significantly discriminated these groups (p = 0.0498, area under the curve [AUC] = 0.68). Four assessments significantly discriminated recurrent fallers from those with fewer or no falls (ps = 0.006–0.009), but their discriminatory powers were not significantly different from one another (AUCs = 0.77–0.80, p \u3e 0.05). For two assessments, cutoff scores based on the study sample enhanced discriminatory accuracy relative to the literature-based cutoff scores. Conclusions: To improve fall prediction for ambulatory community-dwelling older adults, future prospective studies should consider including high-level mobility and balance assessments and targeting cutoff scores to the level of function of this relatively high-functioning population

Health beliefs regarding latent tuberculosis among ethnic groups in Northeast Florida

To compare health beliefs regarding latent tuberculosis infection (LTBI) in the United States-born (USB) versus Foreign-born (FB) population. Methods. Families of children seeking LTBI treatment at our clinics completed a questionnaire. 40 USB and 48 FB respondents participated. Results were organized according to the Health Belief Model. Belief in the threat/existence of the disease: FB were less likely to believe in LTBI but more likely to believe in a TB cure, more likely to have heard of BCG, to believe it protects from TB and causes a positive skin test. Belief in the effectiveness of the treatment: USB were more likely to understand reasons for lengthy treatment and resistance risks with partial treatment. Factors that may hinder or support compliance: FB were more likely to favor a joint family decision to take medication. FB in NE Florida have significantly different health beliefs. Addressing these may improve adherence

How Well Do Functional Assessments Of Mobility And Balance Discriminate Fallers And Recurrent Fallers From Non-Fallers Among Ambulatory Older Adults In The Community?

Purpose: 1) To compare the ability of functional mobility and balance assessments in discriminating fallers from non-fallers and recurrent fallers from those with fewer or no falls. 2) To compare the discriminatory accuracy of cut-off scores specific to this study sample with that of cut-off scores proposed in the literature for community-dwelling older adults. Methods: In a sample of 39 ambulatory older adults living independently in the community, fallers were identified on the basis of number of falls in the past year. Seven functional tests of mobility and balance were used to identify fallers and recurrent fallers on the basis of their fall history. Results: Discrimination of fallers from non-fallers was poor: Only a high-level balance assessment significantly discriminated these groups (p = 0.0498, area under the curve [AUC] = 0.68). Four assessments significantly discriminated recurrent fallers from those with fewer or no falls (ps = 0.006–0.009), but their discriminatory powers were not significantly different from one another (AUCs = 0.77–0.80, p \u3e 0.05). For two assessments, cutoff scores based on the study sample enhanced discriminatory accuracy relative to the literature-based cutoff scores. Conclusions: To improve fall prediction for ambulatory community-dwelling older adults, future prospective studies should consider including high-level mobility and balance assessments and targeting cutoff scores to the level of function of this relatively high-functioning population