Racial and Ethnic Disparities in Post-neonatal Mortality in Florida

This policy-related study examines primary care delivery methods in reducing population health disparities. We use post- neonatal mortality as an indicator ofpopulation health within counties to study the effects ofusing contracted service providers compared to direct provision of primary care by county health departments in improving health equity. We analyzed post- neonatal mortality data collected annually between 1997 and 2006 from ten ofFlorida’s most populous counties (\u3e500,000). Using Poisson regression analyses with generalized estimating equations (GEE), we examined differences in post-neonatal mortality among racial and ethnic groups; and among counties and groups over time. The results show significant differences in post-neonatal mortality between Black and White groups in both counties that outsource county health department primary care services and also counties that do not outsource these services. After adjusting for low birth weight and age ofthe mother (\u3c 20 years), the post-neonatal mortality rate for black infants remains higher in outsourced counties but not in non- outsourced counties. The increase in disparity in post-neonatal mortality rates between black and white infants in outsourced counties compared to non-outsourced counties is also significant. Contracted service providers are being used with greater frequency to expand access to health services with the idea that they can improve health outcomes; however, these data show that all groups may not benefit equally under this mechanism ofservice delivery

Measures of Highly Functioning Health Coalitions: Corollaries for an Effective Public Health System

In Tennessee, health coalitions provide guidance in conducting community assessments, health improvement plans and policies and delivering of health and human services, which are considered core functions of public health. In fact, it has been postulated that these coalitions may serve as the organizational embodiment of the local public health system (LPHS). This study identifies functional characteristics of 63 Tennessee County Health Councils (CHCs), advisory councils to local and regional governmental public health agencies on broad issues of health, that contribute to its ability to operate as the primary advising entity of the LPHS. Exploratory factor analysis was conducted on 20 questions serving as proxy measures of functional characteristics. Eight functional characteristics related to structure, operations and leadership were identified. These characteristics are essential in further developing and tracking capacity and performance of health coalitions serving as an advisory and possibly decision making entity of the LPHS. This study also lays the groundwork to explore how to link coalition function with performance in order to determine characteristics that are most strongly associated with optimal performance and population health

Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability

Background: Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models.Methods: We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants.Results: We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures.Conclusions: The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC

Most Published Research Findings Are False—But a Little Replication Goes a Long Way

While the authors agree with John Ioannidis that "most research findings are false," here they show that replication of research findings enhances the positive predictive value of research findings being true

The effects on population health status of using dedicated property taxes to fund local public health agencies

<p>Abstract</p> <p>Background</p> <p>In the United States, a dedicated property tax describes the legal authority given to a local jurisdiction to levy and collect a tax for a specific purpose. We investigated for an association of locally dedicated property taxes to fund local public health agencies and improved health status in the eight states designated as the Mississippi Delta Region.</p> <p>Methods</p> <p>We analyzed the difference in health outcomes of counties with and without a dedicated public health tax after adjusting for a set of control variables using regression models for county level data from 720 counties of the Mississippi Delta Region.</p> <p>Results</p> <p>Levying a dedicated public health tax for counties with per capita income above $28,000 is associated with improved health outcomes of those counties when compared to counties without a dedicated property tax for public health. Alternatively, levying a dedicated property tax in counties with lower per capita income is associated with poor health outcomes.</p> <p>Conclusions</p> <p>There are both positive and negative consequences of using dedicated property taxes to fund public health. Policymakers should carefully examine both the positive association of improved health outcomes and negative impact of taxation on poor populations before authorizing the use of dedicated local property tax levies to fund public health agencies.</p

Sample size requirements to detect the effect of a group of genetic variants in case-control studies

<p>Abstract</p> <p>Background</p> <p>Because common diseases are caused by complex interactions among many genetic variants along with environmental risk factors, very large sample sizes are usually needed to detect such effects in case-control studies. Nevertheless, many genetic variants act in well defined biologic systems or metabolic pathways. Therefore, a reasonable first step may be to detect the effect of a group of genetic variants before assessing specific variants.</p> <p>Methods</p> <p>We present a simple method for determining approximate sample sizes required to detect the average joint effect of a group of genetic variants in a case-control study for multiplicative models.</p> <p>Results</p> <p>For a range of reasonable numbers of genetic variants, the sample size requirements for the test statistic proposed here are generally not larger than those needed for assessing marginal effects of individual variants and actually decline with increasing number of genetic variants in many situations considered in the group.</p> <p>Conclusion</p> <p>When a significant effect of the group of genetic variants is detected, subsequent multiple tests could be conducted to detect which individual genetic variants and their combinations are associated with disease risk. When testing for an effect size in a group of genetic variants, one can use our global test described in this paper, because the sample size required to detect an effect size in the group is comparatively small. Our method could be viewed as a screening tool for assessing groups of genetic variants involved in pathogenesis and etiology of common complex human diseases.</p

Inflammation gene variants and susceptibility to albuminuria in the U.S. population: analysis in the Third National Health and Nutrition Examination Survey (NHANES III), 1991-1994

<p>Abstract</p> <p>Background</p> <p>Albuminuria, a common marker of kidney damage, serves as an important predictive factor for the progression of kidney disease and for the development of cardiovascular disease. While the underlying etiology is unclear, chronic, low-grade inflammation is a suspected key factor. Genetic variants within genes involved in inflammatory processes may, therefore, contribute to the development of albuminuria.</p> <p>Methods</p> <p>We evaluated 60 polymorphisms within 27 inflammatory response genes in participants from the second phase (1991-1994) of the Third National Health and Nutrition Examination Survey (NHANES III), a population-based and nationally representative survey of the United States. Albuminuria was evaluated as logarithm-transformed albumin-to-creatinine ratio (ACR), as ACR ≥ 30 mg/g, and as ACR above sex-specific thresholds. Multivariable linear regression and haplotype trend analyses were conducted to test for genetic associations in 5321 participants aged 20 years or older. Differences in allele and genotype distributions among non-Hispanic whites, non-Hispanic blacks, and Mexican Americans were tested in additive and codominant genetic models.</p> <p>Results</p> <p>Variants in several genes were found to be marginally associated (uncorrected P value < 0.05) with log(ACR) in at least one race/ethnic group, but none remained significant in crude or fully-adjusted models when correcting for the false-discovery rate (FDR). In analyses of sex-specific albuminuria, <it>IL1B </it>(rs1143623) among Mexican Americans remained significantly associated with increased odds, while <it>IL1B </it>(rs1143623), <it>CRP </it>(rs1800947) and <it>NOS3 </it>(rs2070744) were significantly associated with ACR ≥ 30 mg/g in this population (additive models, FDR-P < 0.05). In contrast, no variants were found to be associated with albuminuria among non-Hispanic blacks after adjustment for multiple testing. The only variant among non-Hispanic whites significantly associated with any outcome was <it>TNF </it>rs1800750, which failed the test for Hardy-Weinberg proportions in this population. Haplotypes within <it>MBL2</it>, <it>CRP</it>, <it>ADRB2, IL4R</it>, <it>NOS3</it>, and <it>VDR </it>were significantly associated (FDR-P < 0.05) with log(ACR) or albuminuria in at least one race/ethnic group.</p> <p>Conclusions</p> <p>Our findings suggest a small role for genetic variation within inflammation-related genes to the susceptibility to albuminuria. Additional studies are needed to further assess whether genetic variation in these, and untested, inflammation genes alter the susceptibility to kidney damage.</p

Linear and Non-Linear Associations of Gonorrhea Diagnosis Rates with Social Determinants of Health

Identifying how social determinants of health (SDH) influence the burden of disease in communities and populations is critically important to determine how to target public health interventions and move toward health equity. A holistic approach to disease prevention involves understanding the combined effects of individual, social, health system, and environmental determinants on geographic area-based disease burden. Using 2006–2008 gonorrhea surveillance data from the National Notifiable Sexually Transmitted Disease Surveillance and SDH variables from the American Community Survey, we calculated the diagnosis rate for each geographic area and analyzed the associations between those rates and the SDH and demographic variables. The estimated product moment correlation (PMC) between gonorrhea rate and SDH variables ranged from 0.11 to 0.83. Proportions of the population that were black, of minority race/ethnicity, and unmarried, were each strongly correlated with gonorrhea diagnosis rates. The population density, female proportion, and proportion below the poverty level were moderately correlated with gonorrhea diagnosis rate. To better understand relationships among SDH, demographic variables, and gonorrhea diagnosis rates, more geographic area-based estimates of additional variables are required. With the availability of more SDH variables and methods that distinguish linear from non-linear associations, geographic area-based analysis of disease incidence and SDH can add value to public health prevention and control programs

Testing homogeneity with an ordered alternative in a two-factor layout by combiningp-values

Bivariate trends, combiningp-values, Fisher's method, likelihood ratio tests, matrix ordering, order restricted tests, two-moment approximations,