Vital signs: leading causes of death, prevalence of diseases and risk factors, and use of health services among Hispanics in the United States - 2009-2013

BACKGROUND

Hispanics and Latinos (Hispanics) are estimated to represent 17.7% of the U.S. population. Published national health estimates stratified by Hispanic origin and nativity are lacking.

METHODS

Four national data sets were analyzed to compare Hispanics overall, non-Hispanic whites (whites), and Hispanic country/region of origin subgroups (Hispanic origin subgroups) for leading causes of death, prevalence of diseases and associated risk factors, and use of health services. Analyses were generally restricted to ages 18-64 years and were further stratified when possible by sex and nativity.

RESULTS

Hispanics were on average nearly 15 years younger than whites; they were more likely to live below the poverty line and not to have completed high school. Hispanics showed a 24% lower all-cause death rate and lower death rates for nine of the 15 leading causes of death, but higher death rates from diabetes (51% higher), chronic liver disease and cirrhosis (48%), essential hypertension and hypertensive renal disease (8%), and homicide (96%) and higher prevalence of diabetes (133%) and obesity (23%) compared with whites. In all, 41.5% of Hispanics lacked health insurance (15.1% of whites), and 15.5% of Hispanics reported delay or nonreceipt of needed medical care because of cost concerns (13.6% of whites). Among Hispanics, self-reported smoking prevalences varied by Hispanic origin and by sex. U.S.-born Hispanics had higher prevalences of obesity, hypertension, smoking, heart disease, and cancer than foreign-born Hispanics: 30% higher, 40%, 72%, 89%, and 93%, respectively.

CONCLUSION

Hispanics had better health outcomes than whites for most analyzed health factors, despite facing worse socioeconomic barriers, but they had much higher death rates from diabetes, chronic liver disease/cirrhosis, and homicide, and a higher prevalence of obesity. There were substantial differences among Hispanics by origin, nativity, and sex.

IMPLICATIONS FOR PUBLIC HEALTH

Differences by origin, nativity, and sex are important considerations when targeting health programs to specific audiences. Increasing the proportions of Hispanics with health insurance and a medical home (patientcentered, team-based, comprehensive, coordinated health care with enhanced access) is critical. A feasible and systematic data collection strategy is needed to reflect health diversity among Hispanic origin subgroups, including by nativity.

Classification tree analysis of race-specific subgroups at risk for a central venous catheter-related bloodstream infection

BACKGROUND

Studies of racial disparities in patient safety events often do not use race-specific risk adjustment and do not account for reciprocal covariate interactions. These limitations were addressed by using classification tree analysis separately for black patients and white patients to identify characteristics that segment patients who have increased risks for a venous catheter-related bloodstream infection.

METHODS

A retrospective, cross-sectional analysis of 5,236,045 discharges from 103 Florida acute hospitals in 2005-2009 was conducted. Hospitals were rank ordered on the basis of the black/white Patient Safety Indicator (PSI) 7 rate ratio as follows: Group 1 (white rate higher), Group 2, (equivalent rates), Group 3, (black rate higher), and Group 4, (black rate highest). Predictor variables included 26 comorbidities (Elixhauser Comorbidity Index) and demographic characteristics. Four separate classification tree analyses were completed for each race/hospital group.

RESULTS

Individual characteristics and groups of characteristics associated with increased PSI 7 risk differed for black and white patients. The average age for both races was different across the hospital groups (p < .01). Weight loss was the strongest single delineator and common to both races. The black subgroups with the highest PSI 7 risk were Medicare beneficiaries who were either < or = 25.5 years without hypertension or < or = 39.5 years without hypertension but with an emergency or trauma admission. The white subgroup with the highest PSI 7 risk consisted of patients < or = 45.5 years who had congestive heart failure but did not have either hypertension or weight loss.

DISCUSSION

Identifying subgroups of patients at risk for a rare safety event such as PSI 7 should aid effective clinical decisions and efficient use of resources and help to guide patient safety interventions.

Health insurance coverage - United States, 2008 and 2010

One out of four adults aged 19-64 years reported not having health insurance at some time during 2011, with a majority remaining uninsured for ≥1 year. In the first quarter of 2010, an estimated 59.1 million persons had no health insurance for at least part of the year, an increase from 58.7 million in 2009 and 56.4 million in 2008. The unemployment rate increased from 5.8% to 9.3% from 2008 to 2009, the largest 1-year increase on record. Losing or changing jobs was the primary reason persons experienced a gap in health insurance. Employment-based coverage for persons aged <65 years continued to erode for the ninth year in a row, falling 3.0 percentage points from 61.9% in 2008 to 58.9% in 2009. Persons aged 18-64 years with no health insurance during the preceding year were seven times as likely as those continuously insured to forgo needed health care because of cost.

Disparity in race-specific comorbidities associated with central venous catheter-related bloodstream infection (AHRQ-PSI7)

Studies of racial disparities in hospital-level patient safety outcomes typically apply a race-common approach to risk adjustment. Risk factors specific to a minority population may not be identified in a race-common analysis if they represent only a small percentage of total cases. This study identified patient comorbidities and characteristics associated with the likelihood of a venous catheter-related bloodstream infection (Agency for Healthcare Research and Quality Patient Safety Indicator 7 [PSI7]) separately for blacks and whites using race-specific logistic regression models. Hospitals were ranked by the racial disparity in PSI7 and segmented into 4 groups. The analysis identified both black- and white-specific risk factors associated with PSI7. Age showed race-specific reverse association, with younger blacks and older whites more likely to have a PSI7 event. These findings suggest the need for race-specific covariate adjustments in patient outcomes and provide a new context for examining racial disparities.

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.

Estimating the contribution of genetic variants to difference in incidence of disease between population groups

Genome-wide association studies have identified multiple genetic susceptibility variants to several complex human diseases. However, risk-genotype frequency at loci showing robust associations might differ substantially among different populations. In this paper, we present methods to assess the contribution of genetic variants to the difference in the incidence of disease between different population groups for different scenarios. We derive expressions for the contribution of a single genetic variant, multiple genetic variants, and the contribution of the joint effect of a genetic variant and an environmental factor to the difference in the incidence of disease. The contribution of genetic variants to the difference in incidence increases with increasing difference in risk-genotype frequency, but declines with increasing difference in incidence between the two populations. The contribution of genetic variants also increases with increasing relative risk and the contribution of joint effect of genetic and environmental factors increases with increasing relative risk of the gene-environmental interaction. The contribution of genetic variants to the difference in incidence between two populations can be expressed as a function of the population attributable risks of the genetic variants in the two populations. The contribution of a group of genetic variants to the disparity in incidence of disease could change considerably by adding one more genetic variant to the group. Any estimate of genetic contribution to the disparity in incidence of disease between two populations at this stage seems to be an elusive goal.

The ACE I/D polymorphism in US adults: limited evidence of association with hypertension-related traits and sex-specific effects by race/ethnicity

BACKGROUND

The insertion/deletion (I/D) variant (rs4646994) of the angiotensin I-converting enzyme (ACE) gene is one of the most studied polymorphisms in relation to blood pressure and essential hypertension in humans. The evidence to date, however, on an association of this variant with blood pressure-related outcomes has been inconclusive.

METHODS

We examined 5,561 participants of the Third National Health and Nutrition Examination Survey (NHANES III), a population-based and nationally representative survey of the United States, who were ≥20 years of age and who self-identified as non-Hispanic white, non-Hispanic black, or Mexican American. Within each race/ethnicity, we assessed genetic associations of the I/D variant with systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension, as well as genotype-sex interactions, in four genetic models (additive, dominant, recessive, and codominant).

RESULTS

The frequency of the I/D variant differed significantly by race/ethnicity (P = 0.001). Among non-Hispanic blacks, the D allele was significantly associated (P < 0.05) with increased SBP in additive and dominant covariate-adjusted models and was also associated with increased DBP in dominant models when participants taking ACE inhibitors were excluded from the analyses. No other significant associations were observed in any race/ethnic group. Significant genotype-sex interactions were detected among Mexican Americans, for whom positive associations with SBP and hypertension were seen among females, but not males.

CONCLUSIONS

This study gives limited support for association of the ACE I/D variant with blood pressure and for sex-specific effects among particular race/ethnic groups, though we cannot rule out the role of genetic or environmental interactions.

Short report: Race and rickettsiae: a United States perspective

US surveillance programs for Rocky Mountain spotted fever (RMSF), ehrlichiosis, and anaplasmosis collect demographic data on patients, including race and ethnicity. Reporting of these diseases among race groups is not uniform across the United States. Because a laboratory confirmation is required to meet the national surveillance case definition, reporting may be influenced by a patient's access to healthcare. Determining the association between race and ethnicity with incidence of rickettsial infections requires targeted, active surveillance.

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.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Rationale for regular reporting on health disparities and inequalities - United States

Most U.S. residents want a society in which all persons live long, healthy lives; however, that vision is yet to be realized fully. As two of its primary goals, CDC aims to reduce preventable morbidity and mortality and to eliminate disparities in health between segments of the U.S. population. The first of its kind, this 2011 CDC Health Disparities and Inequalities Report (2011 CHDIR) represents a milestone in CDC's long history of working to eliminate disparities. Health disparities are differences in health outcomes and their determinants between segments of the population, as defined by social, demographic, environmental, and geographic attributes. Health inequalities, which is sometimes used interchangeably with the term health disparities, is more often used in the scientific and economic literature to refer to summary measures of population health associated with individual- or group-specific attributes (e.g., income, education, or race/ethnicity). Health inequities are a subset of health inequalities that are modifiable, associated with social disadvantage, and considered ethically unfair. Health disparities, inequalities, and inequities are important indicators of community health and provide information for decision making and intervention implementation to reduce preventable morbidity and mortality. Except in the next section of this report that describes selected health inequalities, this report uses the term health disparities as it is defined in U.S. federal laws and commonly used in the U.S. public health literature to refer to gaps in health between segments of the population.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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, IL1B (rs1143623) among Mexican Americans remained significantly associated with increased odds, while IL1B (rs1143623), CRP (rs1800947) and NOS3 (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 TNF rs1800750, which failed the test for Hardy-Weinberg proportions in this population. Haplotypes within MBL2, CRP, ADRB2, IL4R, NOS3, and VDR were significantly associated (FDR-P < 0.05) with log(ACR) or albuminuria in at least one race/ethnic group.

CONCLUSIONS

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.

Improvements in ability to detect undiagnosed diabetes by using information on family history among adults in the United States

Family history is an independent risk factor for diabetes, but it is not clear how much adding family history to other known risk factors would improve detection of undiagnosed diabetes in a population. Using the National Health and Nutrition Examination Survey for 1999-2004, the authors compared logistic regression models with established risk factors (model 1) with a model (model 2) that also included familial risk of diabetes (average, moderate, and high). Adjusted odds ratios for undiagnosed diabetes, using average familial risk as referent, were 1.7 (95% confidence interval (CI): 1.2, 2.5) and 3.8 (95% CI: 2.2, 6.3) for those with moderate and high familial risk, respectively. Model 2 was superior to model 1 in detecting undiagnosed diabetes, as reflected by several significant improvements, including weighted C statistics of 0.826 versus 0.842 (bootstrap P = 0.001) and integrated discrimination improvement of 0.012 (95% CI: 0.004, 0.030). With a risk threshold of 7.3% (sensitivity of 40% based on model 1), adding family history would identify an additional 620,000 (95% CI: 221,100, 1,020,000) cases without a significant change in false-positive fraction. Study findings suggest that adding family history of diabetes can provide significant improvements in detecting undiagnosed diabetes in the US population. Further research is needed to validate the authors' findings.

A Hardy-Weinberg equilibrium test for analyzing population genetic surveys with complex sample designs

Testing for deviations from Hardy-Weinberg equilibrium is a widely recommended practice for population-based genetic association studies. However, current methods for this test assume a simple random sample and may not be appropriate for sample surveys with complex survey designs. In this paper, the authors present a test for Hardy-Weinberg equilibrium that adjusts for the sample weights and correlation of data collected in complex surveys. The authors perform this test by using a simple adjustment to procedures developed to analyze data from complex survey designs available within the SAS statistical software package (SAS Institute, Inc., Cary, North Carolina). Using 90 genetic markers from the Third National Health and Nutrition Examination Survey, the authors found that survey-adjusted and -unadjusted estimates of the disequilibrium coefficient were generally similar within self-reported races/ethnicities. However, estimates of the variance of the disequilibrium coefficient were significantly different between the 2 methods. Because the results of the survey-adjusted tests account for correlation among participants sampled within the same cluster, and the possibility of having related individuals sampled from the same household, the authors recommend use of this test when analyzing genetic data originating from sample surveys with complex survey designs to assess deviations from Hardy-Weinberg equilibrium.

Using lifetime risk estimates in personal genomic profiles: estimation of uncertainty

Personal genome tests are now offered direct-to-consumer (DTC) via genetic variants identified by genome-wide association studies (GWAS) for common diseases. Tests report risk estimates (age-specific and lifetime) for various diseases based on genotypes at multiple loci. However, uncertainty surrounding such risk estimates has not been systematically investigated. With breast cancer as an example, we examined the combined effect of uncertainties in population incidence rates, genotype frequency, effect sizes, and models of joint effects among genetic variants on lifetime risk estimates. We performed simulations to estimate lifetime breast cancer risk for carriers and noncarriers of genetic variants. We derived population-based cancer incidence rates from Surveillance, Epidemiology, and End Results (SEER) Program and comparative international data. We used data for non-Hispanic white women from 2003 to 2005. We derived genotype frequencies and effect sizes from published GWAS and meta-analyses. For a single genetic variant in FGFR2 gene (rs2981582), combination of uncertainty in these parameters produced risk estimates where upper and lower 95% simulation intervals differed by more than 3-fold. Difference in population incidence rates was the largest contributor to variation in risk estimates. For a panel of five genetic variants, estimated lifetime risk of developing breast cancer before age 80 for a woman that carried all risk variants ranged from 6.1% to 21%, depending on assumptions of additive or multiplicative joint effects and breast cancer incidence rates. Epidemiologic parameters involved in computation of disease risk have substantial uncertainty, and cumulative uncertainty should be properly recognized. Reliance on point estimates alone could be seriously misleading.

Evaluation of the discriminative accuracy of genomic profiling in the prediction of common complex diseases

Genetic testing for susceptibility to common diseases based on a combination of genetic markers may be needed because the effect size associated with each genetic marker is small. Whether or not a genome profile based on a combination of markers could yield a useful test can be evaluated by assessing the discriminative accuracy. The authors present a simple method to calculate the clinical discriminative accuracy of a genomic profile when the relative risk and genotype frequency of each genotype are known. In addition, the clinical discriminative accuracy of a genetic test is presented for given values of the heritability and prevalence of the disease and for the population-attributable fraction of the combined genetic markers. For given values of relative risk and genotype frequency, the discriminative accuracy increases with increasing heritability but declines with increasing prevalence of the disease. For a given value of population-attributable fraction, the discriminative accuracy increases with increasing relative risks, but declines with increasing genotype frequency. On the basis of population-attributable fraction and estimates of heritability of disease, the number of risk genotypes required to have a reasonable clinical discriminative accuracy is much higher than the genome profiles available at present.

The impact of genotype frequencies on the clinical validity of genomic profiling for predicting common chronic diseases

PURPOSE

Single genetic variants in multifactorial disorders typically have small effects, so major increases in disease risk are expected only from the simultaneous exposure to multiple risk genotypes. We investigated the impact of genotype frequencies on the clinical discriminative accuracy for the simultaneous testing of 40 independent susceptibility genetic variants.

METHODS

In separate simulation scenarios, we varied the genotype frequency from 1% to 50% and the odds ratio for each genetic variant from 1.1 to 2.0. Population size was 1 million and the population disease risk was 10%. Discriminative accuracy was quantified as the area under the receiver-operating characteristic curve. Using an example of genomic profiling for type 2 diabetes, we evaluated the area under the receiver-operating characteristic curve when the odds ratios and genotype frequencies varied between five postulated genetic variants.

RESULTS

When the genotype frequency was 1%, none of the subjects carried more than six of 40 risk genotypes, and when risk genotypes were frequent (> or =30%), all carried at least six. The area under the receiver-operating characteristic curve did not increase above 0.70 when the odds ratios were modest (1.1 or 1.25), but higher genotype frequency increased the area under the receiver-operating characteristic curve from 0.57 to 0.82 and from 0.63 to 0.93 when odds ratios were 1.5 or 2.0. The example of type 2 diabetes showed that the area under the receiver-operating characteristic curve did not change when differences in the odds ratios were ignored.

CONCLUSIONS

Given that the effects of susceptibility genes in complex diseases are small, the feasibility of future genomic profiling for predicting common diseases will depend substantially on the frequencies of the risk genotypes.

Prevalence in the United States of selected candidate gene variants: Third National Health and Nutrition Examination Survey, 1991-1994

Population-based allele frequencies and genotype prevalence are important for measuring the contribution of genetic variation to human disease susceptibility, progression, and outcomes. Population-based prevalence estimates also provide the basis for epidemiologic studies of gene-disease associations, for estimating population attributable risk, and for informing health policy and clinical and public health practice. However, such prevalence estimates for genotypes important to public health remain undetermined for the major racial and ethnic groups in the US population. DNA was collected from 7,159 participants aged 12 years or older in Phase 2 (1991-1994) of the Third National Health and Nutrition Examination Survey (NHANES III). Certain age and minority groups were oversampled in this weighted, population-based US survey. Estimates of allele frequency and genotype prevalence for 90 variants in 50 genes chosen for their potential public health significance were calculated by age, sex, and race/ethnicity among non-Hispanic whites, non-Hispanic blacks, and Mexican Americans. These nationally representative data on allele frequency and genotype prevalence provide a valuable resource for future epidemiologic studies in public health in the United States.

Genomic medicine and racial/ethnic health disparities: promises, perils, and the challenges for health care and public health policy

Scientific and policy debates following new genetic discoveries have been intense and emotional when they have involved questions about the causes of, and solutions for, racial and ethnic health disparities in the United States. The difference in prevalence of diseases, allele frequency and genotype frequency among racial/ethnic groups are well known. The genomic profile for a given disease could have different genetic variants for different racial/ethnic groups. Do these results indicate that we have to consider different genetic tests and different genomic medicine for different racial/ethnic groups? If we do this, what is the impact on ethnic and class disparities in health care services in the United States? Current advances in genetic medicine are very promising; however, we must consider the possible impacts of these findings on health disparities, and how genetic medicine can be extended to everyone, not just those who can pay the often high price. If genomic medicine is to be a valid and reliable technology for all citizens regardless of wealth, race, ethnicity, or other determinants of social disadvantage, public health policymakers have to consider a number of policy issues and implications.

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

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Evaluating the ecological association of casino industry economic development on community health status: a natural experiment in the Mississippi delta region

Objectives of this study were to examine for associations of casino industry economic development on improving community health status and funding for public health services in two counties in the Mississippi Delta Region of the United States. An ecological approach was used to evaluate whether two counties with casino gaming had improved health status and public health funding in comparison with two noncasino counties in the same region with similar social, racial, and ethic backgrounds. Variables readily available from state health department records were used to develop a logic model for guiding analytical work. A linear regression model was built using a stepwise approach and hierarchical regression principles with many dependent variables and a set of fixed and nonfixed independent variables. County-level data for 23 variables over an 11-year period were used. Overall, this study found a lack of association between the presence of a casino and desirable health outcomes or funding for public health services. Changes in the environment were made to promote health by utilizing gaming revenues to build state-of-the-art community health and wellness centers and sports facilities. However, significant increases in funding for local public health services were not found in either of the counties with casinos. These findings are relevant for policy makers when debating economic development strategies. Analysis similar to this should be combined with other routine public health assessments after implementation of development strategies to increase knowledge of health outcome trends and shifts in socioeconomic position that may be expected to accrue from economic development projects.