Health Care Providers' Training Needs Related to Human Trafficking: Maximizing the Opportunity to Effectively Screen and Intervene

Human trafficking is a complex and multifaceted problem that takes the form of economic, physical and sexual exploitation of people, both adults and children, who are reduced to simple products for commerce. Human trafficking in the United States also has both a domestic and an international aspect. Health care providers are in a unique position to screen for victims of trafficking and may provide important medical and psychological care for victims while in captivity and thereafter. Trafficked persons are likely to suffer a wide spectrum of health risks that reflect the unique circumstances and experiences in a trafficked victim’s life. Although trafficked victims typically have experienced inadequate medical care, once contact is made by the victim with the health care professionals, the opportunity then exists to identify, treat, and assist such victims. The range of services and supports required to appropriately respond to human trafficking victims once identified is broad and typically goes beyond just what is immediately provided by the health care professional and includes safe housing, legal advice, income support, and, for international victims, immigration status related issues. An informed and responsive community is necessary to serve both the international and domestic victims of human trafficking, and needs assessments demonstrated a number of barriers that hindered the delivery of effective services to human trafficking victims. One of the consistent needs identified to combat these barriers was enhanced training among all professionals who might come in contact with human trafficking victims. We highlight the efforts of the Houston Rescue and Restore Coalition (HRRC), a local grassroots non-profit organization whose mission focuses on raising awareness of human trafficking in the Greater Houston Metropolitan area. HRRC responded to the consistent recommendation from various community needs assessments for additional training of front line professionals who would have the opportunity to identify human trafficking victims and supported the design and pilot testing of a health professions training program around human trafficking. Dissemination of this type of training along with careful evaluation and continued refinement will be one way for health care professionals to engage in a positive manner with human trafficking victims.

Maximizing efficacy of the hypomethylating drug 5-aza-2'-deoxycytidine in human leukemia

5-aza-2'-deoxycytidine (DAC) is a cytidine analogue that strongly inhibits DNA methylation, and was recently approved for the treatment of myelodysplastic syndromes (MDS). To maximize clinical results with DAC, we investigated its use as an anti-cancer drug. We also investigated mechanisms of resistance to DAC in vitro in cancer cell lines and in vivo in MDS patients after relapse. We found DAC sensitized cells to the effect of 1-β-D-Arabinofuranosylcytosine (Ara-C). The combination of DAC and Ara-C or Ara-C following DAC showed additive or synergistic effects on cell death in four human leukemia cell lines in vitro, but antagonism in terms of global methylation. RIL gene activation and H3 lys-9 acetylation of short interspersed elements (Alu). One possible explanation is that hypomethylated cells are sensitized to cell killing by Ara-C. Turning to resistance, we found that the IC50 of DAC differed 1000 fold among and was correlated with the dose of DAC that induced peak hypomethylation of long interspersed nuclear elements (LINE) (r=0.94, P<0.001), but not with LINE methylation at baseline (r=0.05, P=0.97). Sensitivity to DAC did not significantly correlate with sensitivity to another hypomethylating agent 5-azacytidine (AZA) (r=0.44, P=0.11). The cell lines most resistant to DAC had low dCK, hENT1, and hENT2 transporters and high cytosine deaminase (CDA). In an HL60 leukemia cell line, resistance to DAC could be rapidly induced by drug exposure, and was related to a switch from monoallelic to biallelic mutation of dCK or a loss of wild type DCK allele. Furthermore, we showed that DAC induced DNA breaks evidenced by histone H2AX phosphorylation and increased homologous recombination rates 7-10 folds. Finally, we found there were no dCK mutations in MDS patients after relapse. Cytogenetics showed that three of the patients acquired new abnormalities at relapse. These data suggest that in vitro spontaneous and acquired resistance to DAC can be explained by insufficient incorporation of drug into DNA. In vivo resistance to DAC is likely due to methylation-independent pathways such as chromosome changes. The lack of cross resistance between DAC and AZA is of potential clinical relevance, as is the combination of DAC and Ara-C. ^

Empowering Medical Professionals to Help Victims of Trafficking

Purpose: to provide commentary of two review articles that discuss the role of medical professionals in combating human trafficking: “Health Care Providers' Training Needs Related to Human Trafficking: Maximizing the Opportunity to Effectively Screen and Intervene” by Isaac, Solak, and Giardino, and “Human Trafficking: What is the Role of the Health Care Provider?” by Crane and Moreno. Findings: Both articles provide a good introduction and explanation of the psychosocial and medical issues faced by many trafficking victims; however, they succeed only to varying degrees in describing all the gaps in the medical system and the vital next steps forward. Conclusion: The key next steps in the fight against human trafficking include: multidisciplinary teams need to improve coordination on all forms of human maltreatment; schools for all medical professions and social work need to significantly strengthen their curriculum on diagnosing and treating human maltreatment; and groups that provide training on human trafficking should partner with other agencies and organizations that provide training on child maltreatment and domestic violence.

Survey of VFC provider knowledge of catch-up regimens and contraindications to vaccination in Houston, Texas

In 2004, Houston had one of the lowest childhood immunization levels among major metropolitan cities in the United States at 65% for the 4:3:1:3:3 vaccination series. Delays in the receipt of scheduled vaccinations may be related to missed opportunities due to health care provider lack of knowledge about catch-up regimens and contraindications for pediatric vaccination. The objectives of this study are to identify, measure, and report on VFC provider-practice characteristics, knowledge of catch-up regimens and contraindications, and use of Reminder recall (R/R) and moved or gone elsewhere (MOGE) practices among providers with high (>80%) and low (<70%) immunization coverage among 19-35 month old children. The sampling frame consists of 187 Vaccines for Children (VFC) providers with 2004 clinic assessment software application (CASA) scores. Data were collected by personal interview with each participating practice provider. Only ten VFC providers were successful at maximizing vaccinations for every vignette and no provider administered the maximum possible number of vaccinations at visit 2 for all six vignettes. Both coverage groups administered polio conjugate vaccine (PCV), haemophilus influenza type b (Hib), and diphtheria, tetanus and acellular pertussis (DTaP) most frequently and omitted most frequently varicella zoster vaccine (VZV) and measles, mumps, and rubella (MMR) vaccine. ^

HEALTH MAINTENANCE ORGANIZATION ENVIRONMENT: AN EMPIRICAL STUDY OF THE RELATIONSHIP OF EXTERNAL CONTEXT AND PERFORMANCE

The research project is an extension of a series of administrative science and health care research projects evaluating the influence of external context, organizational strategy, and organizational structure upon organizational success or performance. The research will rely on the assumption that there is not one single best approach to the management of organizations (the contingency theory). As organizational effectiveness is dependent on an appropriate mix of factors, organizations may be equally effective based on differing combinations of factors. The external context of the organization is expected to influence internal organizational strategy and structure and in turn the internal measures affect performance (discriminant theory). The research considers the relationship of external context and organization performance.^ The unit of study for the research will be the health maintenance organization (HMO); an organization the accepts in exchange for a fixed, advance capitation payment, contractual responsibility to assure the delivery of a stated range of health sevices to a voluntary enrolled population. With the current Federal resurgence of interest in the Health Maintenance Organization (HMO) as a major component in the health care system, attention must be directed at maximizing development of HMOs from the limited resources available. Increased skills are needed in both Federal and private evaluation of HMO feasibility in order to prevent resource investment and in projects that will fail while concurrently identifying potentially successful projects that will not be considered using current standards.^ The research considers 192 factors measuring contextual milieu (social, educational, economic, legal, demographic, health and technological factors). Through intercorrelation and principle components data reduction techniques this was reduced to 12 variables. Two measures of HMO performance were identified, they are (1) HMO status (operational or defunct), and (2) a principle components factor score considering eight measures of performance. The relationship between HMO context and performance was analysed using correlation and stepwise multiple regression methods. In each case it has been concluded that the external contextual variables are not predictive of success or failure of study Health Maintenance Organizations. This suggests that performance of an HMO may rely on internal organizational factors. These findings have policy implications as contextual measures are used as a major determinant in HMO feasibility analysis, and as a factor in the allocation of limited Federal funds. ^

Network-based genome-wide association study of Crohn's disease

Genome-wide association studies (GWAS) have rapidly become a standard method for disease gene discovery. Many recent GWAS indicate that for most disorders, only a few common variants are implicated and the associated SNPs explain only a small fraction of the genetic risk. The current study incorporated gene network information into gene-based analysis of GWAS data for Crohn's disease (CD). The purpose was to develop statistical models to boost the power of identifying disease-associated genes and gene subnetworks by maximizing the use of existing biological knowledge from multiple sources. The results revealed that Markov random field (MRF) based mixture model incorporating direct neighborhood information from a single gene network is not efficient in identifying CD-related genes based on the GWAS data. The incorporation of solely direct neighborhood information might lead to the low efficiency of these models. Alternative MRF models looking beyond direct neighboring information are necessary to be developed in the future for the purpose of this study.^

Association of ALLHAT investigator characteristics with participant recruitment

Clinical trials are often not successful because of the inability to recruit a sufficient number of patients. The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT), the largest antihypertensive trial ever conducted, provided highly generalized results and successful recruitment of over 42,000 participants. The overall purpose of this study was to examine the association of investigator characteristics with anti-hypertensive (AHT) participant recruitment in ALLHAT. This secondary data analyses collected data from the ALLHAT investigator profile survey and related investigator characteristics to recruitment success. The sample size was 502 investigators, with recruitment data from 37,947AHT participants. Recruitment was dichotomized by categorizing all sites with recruitment numbers at or above the overall median recruitment number of 46 as "Successful Recruitment". Frequency distributions and univariate and multivariate logistic regression were conducted. When adjusting for all other factors, Hispanic ethnicity, suburban setting, Department of Veterans Affairs Medical Centers (VAMC) site type, number of clinical site staff working on the trial, study coordinator hours per week, medical conference sessions attended, the investigator's primary goal and the likelihood that a physician will convince a patient to continue on randomized treatment, have significant impacts on the recruitment success of ALLHAT investigators. Most of the ALLHAT investigators described their primary commitment as being towards their patients and not to scientific knowledge alone. However, investigators that distinguished themselves as leaders in research had greater recruitment success than investigators who were leaders in clinical practice. ALLHAT was a highly successful trial that proved that community based cardiovascular trials can be implemented on a large scale. Exploring characteristics of ALLHAT investigators provides data that can be generalized to sponsors, sites, and others interested in maximizing clinical trial recruitment numbers. Future studies should further evaluate investigator and study coordinator factors that impact cardiovascular clinical trial recruitment success.^

Data quality in trauma transfusion studies and the impact of missing data on predicting massive transfusion

Maximizing data quality may be especially difficult in trauma-related clinical research. Strategies are needed to improve data quality and assess the impact of data quality on clinical predictive models. This study had two objectives. The first was to compare missing data between two multi-center trauma transfusion studies: a retrospective study (RS) using medical chart data with minimal data quality review and the PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study with standardized quality assurance. The second objective was to assess the impact of missing data on clinical prediction algorithms by evaluating blood transfusion prediction models using PROMMTT data. RS (2005-06) and PROMMTT (2009-10) investigated trauma patients receiving ≥ 1 unit of red blood cells (RBC) from ten Level I trauma centers. Missing data were compared for 33 variables collected in both studies using mixed effects logistic regression (including random intercepts for study site). Massive transfusion (MT) patients received ≥ 10 RBC units within 24h of admission. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation based on the multivariate normal distribution. A sensitivity analysis for missing data was conducted to estimate the upper and lower bounds of correct classification using assumptions about missing data under best and worst case scenarios. Most variables (17/33=52%) had <1% missing data in RS and PROMMTT. Of the remaining variables, 50% demonstrated less missingness in PROMMTT, 25% had less missingness in RS, and 25% were similar between studies. Missing percentages for MT prediction variables in PROMMTT ranged from 2.2% (heart rate) to 45% (respiratory rate). For variables missing >1%, study site was associated with missingness (all p≤0.021). Survival time predicted missingness for 50% of RS and 60% of PROMMTT variables. MT models complete case proportions ranged from 41% to 88%. Complete case analysis and multiple imputation demonstrated similar correct classification results. Sensitivity analysis upper-lower bound ranges for the three MT models were 59-63%, 36-46%, and 46-58%. Prospective collection of ten-fold more variables with data quality assurance reduced overall missing data. Study site and patient survival were associated with missingness, suggesting that data were not missing completely at random, and complete case analysis may lead to biased results. Evaluating clinical prediction model accuracy may be misleading in the presence of missing data, especially with many predictor variables. The proposed sensitivity analysis estimating correct classification under upper (best case scenario)/lower (worst case scenario) bounds may be more informative than multiple imputation, which provided results similar to complete case analysis.^