Ramal Moonesinghe

Institutional and Economic Determinants of Public Health System Performance

OBJECTIVES Although a growing body of evidence demonstrates that availability and quality of essential public health services vary widely across communities, relatively little is known about the factors that give rise to these variations. We examined the association of institutional, financial, and community characteristics of local public health delivery systems and the performance of essential services. METHODS Performance measures were collected from local public health systems in 7 states and combined with secondary data sources. Multivariate, linear, and nonlinear regression models were used to estimate associations between system characteristics and the performance of essential services. RESULTS Performance varied significantly with the size, financial resources, and organizational structure of local public health systems, with some public health services appearing more sensitive to these characteristics than others. Staffing levels and community characteristics also appeared to be related to the performance of selected services. CONCLUSIONS Reconfiguring the organization and financing of public health systems in some communities-such as through consolidation and enhanced intergovernmental coordination-may hold promise for improving the performance of essential services.

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 (≥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.

Required sample size and nonreplicability thresholds for heterogeneous genetic associations

Many gene–disease associations proposed to date have not been consistently replicated across different populations. Nonreplication often reflects false positives in the original claims. However, occasionally, nonreplication may be due to heterogeneity due to biases or even genuine diversity of the genetic effects in different populations. Here, we propose methods for estimating the required sample size to replicate an association across many studies with different amounts of between-study heterogeneity, when data are summarized through metaanalysis. We demonstrate thresholds of between-study heterogeneity (τ02) above which one cannot reach adequate power to replicate a proposed association at a specified level of statistical significance when k studies are performed (regardless of how large these studies are). Based on empirical evidence from 91 proposed gene–disease associations (50 on candidate genes and 41 from genome-wide association efforts), the observed between-study heterogeneity is often close to or even surpasses nonreplicability thresholds. With more modest between-study heterogeneity, the required sample size increases considerably compared with when no between-study heterogeneity exists. Increases are steep as τ02 is approached. Therefore, some true associations may not be practically possible to replicate with consistency, no matter how large studies are conducted. Efforts should be made to minimize between-study heterogeneity in targeted genetic effects.

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.

Getting what you pay for: public health spending and the performance of essential public health services.

Governmental spending in public health varies widely across communities, raising questions about how these differences may affect the availability of essential services and infrastructure. This study used data from local public health systems that participated in the National Public Health Performance Standards Program pilot tests between 1999 and 2001 to examine the association between public health spending and the performance of essential public health services. Results indicated that performance varies significantly with both local and federal spending levels, even after controlling for other system and community characteristics. Some public health services appear more sensitive to these expenditures than others, and all services appear more sensitive to local spending than to state or federal spending. These findings can assist public health decision makers in identifying public health financing priorities during periods of change in the resources available to support local public health infrastructure.

Evaluation of family history as a risk factor and screening tool for detecting undiagnosed diabetes in a nationally representative survey population.

Purpose: We examined the utility of a three-level familial risk stratification system as a screening tool for diabetes in a nationally representative sample of the U.S. adult population.Methods: National Health and Nutrition Examination Survey data were used to assess the prevalence and distribution of familial risk for diabetes, the association between three levels of familial risk and undiagnosed diabetes, and the use of familial risk as a screening tool for diabetes, alone and in combination with body mass index and age.Results: The prevalence of undiagnosed diabetes was 3% and increased with increasing familial risk (average = 2%, moderate = 4%, high = 10%). High familial risk was significantly associated with undiagnosed diabetes (adjusted odds ratio = 4.6; 95% confidence interval: 1.9–11.3). The use of a three-tiered familial risk stratification for diabetes screening yielded higher specificity (94%) and positive predictive value (9.9%) for high familial risk than body mass index ≥25 (specificity = 38%, positive predictive value = 4.2%). High familial risk and body mass index ≥25 combined had higher specificity (97%) and positive predictive value (13.4%); the addition of age ≥45 years further improved positive predictive value (21.0%) without reducing specificity.Conclusions: There was a strong and proportional association between familial risk and undiagnosed diabetes, suggesting that a three-tiered assessment of familial diabetes risk may increase the effectiveness of diabetes screening.

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.

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.

Practices in public health finance: an investigation of jurisdiction funding patterns and performance.

A field of study for public health finance has never been adequately developed. Consequently, very little is known about the relationships, types, and amount of finances that fund the public health system in America. This research was undertaken to build on the sparse knowledge of public health finance by examining the value of performance measurement systems to financial analysis. A correlational study was conducted to examine the associations between public health system performance of the 10 essential public health services and funding patterns of 50 local health departments in a large state. The specific objectives were to investigate if different levels and types of revenues, expenditures, and other demographic variables in a jurisdiction are correlated to performance. Pearson correlation analysis did not conclusively show strong associations; however, statistically significant positive associations primarily between higher levels of performance and jurisdiction taxes per capita were found.

The association between family history of asthma and the prevalence of asthma among US adults: National Health and Nutrition Examination Survey, 1999–2004

Purpose: To assess the overall prevalence of asthma and the association between family history of asthma and the prevalence of asthma among US adults.Methods: We analyzed National Health and Nutrition Examination Survey data from 1999 to 2004 for 15,008 respondents aged 20 years or older with no history of emphysema. We divided respondents into three familial risk groups (high, moderate, and average) on the basis of the number and closeness of relatives, that they reported as having asthma and then assessed the asthma prevalence in each. We also assessed associations between asthma prevalence and age, sex, race/ethnicity, income, body mass index, smoking status, household smoking exposure, and physical activity.Results: By our definitions, 2.3% of respondents were at high, 13.0% at moderate, and 84.7% at average familial risk for asthma. The crude prevalence of self-reported lifetime asthma was 11.5% (95% confidence interval [CI]: 10.7–12.3%) among all respondents, and 37.6% (95% CI: 30.4–45.4%), 20.4% (95% CI: 18.2–22.7%), and 9.4% (95% CI: 8.7–10.2%) among those at high, moderate, and average familial risk, respectively. Among all risk factors we looked at, family history had the strongest association with lifetime asthma prevalence, and the association remained significant after adjustments for other risk factors. Compared with average familial risk, the adjusted odds ratios for lifetime asthma were 2.4 (95% CI: 2.0–2.8) for moderate and 4.8 (95% CI: 3.5–6.7) for high familial risk.Conclusion: Our findings showed that a family history of asthma is an important risk factor for asthma and that familial risk assessments can help identify people at highest risk for developing asthma. Additional research is needed to assess how health care professionals can use family history information in the early detection and management of asthma.