Background The US Centers for Disease Control and Prevention (CDC) recommends annual chlamydia screening for sexually active females aged <26 years. Community health centers (CHCs) have been a focal point for Health Care Reform in the US and have traditionally served as safety-net providers, however little is known about CT screening practices in CHCs or CT prevalence among CHC clients. The Region II Infertility Prevention Project (IPP) supports chlamydia and gonorrhoea (CT/GC) prevalence monitoring in participating facilities throughout New Jersey, New York, Puerto Rico and the US Virgin Islands, including a small number of CHCs. Methods We reviewed Region II IPP CT/GC prevalence monitoring data for females aged 15–25 years tested in CY2009 attending CHCs for non-prenatal visits by age, test result, and laboratory test type, and compared with data for females attending family planning (FP) clinics for non-prenatal visits in the same counties. A total of 3103 CT and 2890 GC test records were associated with 18 CHCs in 12 counties in New Jersey, New York, and the US Virgin Islands; 35 FP clinics in the same counties reported 32 905 CT and 19 882 GC tests. Results CT positivity among females aged 15–19 and 20–25 years in CHCs was 11.4% (n=640) and 5.7% (n=2463), respectively, compared with 8.5% (n=10 946) and 4.6% (n=21 959) in FP clinics in the same counties. GC positivity in CHCs was 1.3% (n=594) and 0.2% (n=2296) among females aged 15–19 and 20–25 years, respectively, compared with 1.0% (n=6548) and 0.3% (n=13 334) in FP clinics (Abstract P5-S7.11 table 1). Over 99% of tests in CHCs were performed using highly sensitive nucleic acid amplification tests (NAATs), vs 55% of tests performed in FP. Abstract P5-S7.11 Table 1 Chlamydia and gonorrhoea testing and positivity among females aged 15-25 years attending community health centers and family planning clinics for non-prenatal visits, CY2009, Region II Infertility Prevention Project, USA Test type Age Group (Years) Community health centers Family planning clinics # Tests % Pos # Tests % Pos Chlamydia 15–19 640 11.4% 10 946 8.5% 20–25 2463 5.7% 21 959 4.6% Total 3103 6.9% 32 905 5.9% Gonorrhoea 15–19 594 1.3% 6548 1.0% 20–25 2296 0.2% 13 334 0.3% Total 2890 0.4% 19 882 0.6% Conclusion The burden of chlamydia and gonorrhoea among females aged 15–25 years attending CHCs is comparable to that observed in FP clinics, and highest among teens. As state and local health departments face mounting budget deficits and impending cuts to public health infrastructure—including cuts to the delivery of direct clinical services, CHCs may play an increasingly integral role in providing screening to the most at risk populations. CHCs are required to report to HRSA (the federal agency that funds the CHC program) on their performance using the measures defined in the Uniform Data System (UDS); however, the UDS does not currently include a measure for the proportion of clients screened for CT/GC. State and local health departments should consider opportunities to partner with CHCs in high morbidity areas to ensure and expand access to CT/GC screening and treatment for at risk populations, and leverage existing infrastructure to incorporate CHCs into ongoing prevalence monitoring efforts.

The PyPop (Python for Population Genetics) analysis package is a suite of population genetic analyses and forms the base of the biostatistics core. It is in an object-oriented framework implemented in the programming language Python. The object-oriented approach allows us to implement individual analysis modules which can be inserted or removed without affecting other modules. Python is a flexible scripting language which allows rapid prototyping of code and has powerful features for interfacing with other languages, such as C (in which we have already implemented many routines and which is particularly suited to computationally intensive tasks). The output of the analyses are stored in the XML format (XML is the eXtensible Markup Language devised by the World Wide Web Consortium, and is a platform-independent, vendor-neutral, non-proprietary, open standard for storing data (2). These output files can then be transformed using standard tools into many other data formats suitable for machine input (such as PHYLIP (6) or input for spreadsheet programs such as Excel or statistical packages, such as R (8)), plain text, or HTML for ‘‘human-readable’’ format. Storing the output in XML allows the final viewable output format to be redesigned at will, without requiring the (often timeconsuming) re-running of the analyses themselves. Data from the anthropology, transplantation, disease and cytokine components have been analyzed using PyPop.

Haplotype analyses are an important area in the study of the genetic components of human disease. Associations between markers and disease loci that are not evident with a single marker locus may be identified in multi-locus marker analyses using estimated haplotype frequencies (HFs). Procedures that make use of the expectation-maximization (EM) algorithm to estimate HFs from unphased genotype data are in common use in genetic studies. The EM algorithm uses these unphased genotype frequencies along with the assumption of Hardy-Weinberg proportions (HWP) to converge on HF estimates. In this paper, we assess the accuracy of EM estimates of HFs in patients with type I diabetes for whom the true haplotypes are known, but the data are analyzed ignoring family information to allow comparison between estimated and true frequencies. The data consist of six HLA loci with high levels of polymorphism and a range of departures from HWP and linkage equilibrium. While the overall accuracy of the EM estimates is good, there can be large over- and underestimates of particular HFs, even for common haplotypes, especially when the loci involved deviate significantly from HWP. Estimating HFs for three or more loci and then collapsing over loci so as to generate two locus haplotypes can improve the accuracy of the estimation. The collapsing procedure is most beneficial when one of the loci in the two-locus haplotype of interest deviates significantly from HWP and the locus collapsed over is in linkage disequilibrium with the other loci.

Software to analyze multi-locus genotype data for entire populations is useful for estimating haplotype frequencies, deviation from Hardy-Weinberg equilibrium and patterns of linkage disequilibrium. These statistical results are important to both those interested in human genome variation and disease predisposition as well as evolutionary genetics. As part of the 13th International Histocompatibility and Immunogenetics Working Group (IHWG), we have developed a software framework (PyPop). The primary novelty of this package is that it allows integration of statistics across large numbers of data-sets by heavily utilizing the XML file format and the R statistical package to view graphical output, while retaining the ability to inter-operate with existing software. Largely developed to address human population data, it can, however, be used for population based data for any organism. We tested our software on the data from the 13th IHWG which involved data sets from at least 50 laboratories each of up to 1000 individuals with 9 MHC loci (both class I and class II) and found that it scales to large numbers of data sets well.

Genomic screening to map disease loci by association requires automation, pooling of DNA samples, and 3,000-6,000 highly polymorphic, evenly spaced microsatellite markers. Case-control samples can be used in an initial screen, followed by family-based data to confirm marker associations. Association mapping is relevant to genetic studies of complex diseases in which linkage analysis may be less effective and to cases in which multigenerational data are difficult to obtain, including rare or late-onset conditions and infectious diseases. The method can also be used effectively to follow up and confirm regions identified in linkage studies or to investigate candidate disease loci. Study designs can incorporate disease heterogeneity and interaction effects by appropriate subdivision of samples before screening. Here we report use of pooled DNA amplifications-the accurate determination of marker-disease associations for both case-control and nuclear family-based data-including application of correction methods for stutter artifact and preferential amplification, These issues, combined with a discussion of both statistical power and experimental design to define the necessary requirements for detecting of disease loci while virtually eliminating false positives, suggest the feasibility and efficiency of association mapping using pooled DNA screening.