VA residential substance use disorder treatment program providers’ perceptions of facilitators and barriers to performance on pre-admission processes

Abstract Background In the U.S. Department of Veterans Affairs (VA), residential treatment programs are an important part of the continuum of care for patients with a substance use disorder (SUD). However, a limited number of program-specific measures to identify quality gaps in SUD residential programs exist. This study aimed to: (1) Develop metrics for two pre-admission processes: Wait Time and Engagement While Waiting, and (2) Interview program management and staff about program structures and processes that may contribute to performance on these metrics. The first aim sought to supplement the VA’s existing facility-level performance metrics with SUD program-level metrics in order to identify high-value targets for quality improvement. The second aim recognized that not all key processes are reflected in the administrative data, and even when they are, new insight may be gained from viewing these data in the context of day-to-day clinical practice. Methods VA administrative data from fiscal year 2012 were used to calculate pre-admission metrics for 97 programs (63 SUD Residential Rehabilitation Treatment Programs (SUD RRTPs); 34 Mental Health Residential Rehabilitation Treatment Programs (MH RRTPs) with a SUD track). Interviews were then conducted with management and front-line staff to learn what factors may have contributed to high or low performance, relative to the national average for their program type. We hypothesized that speaking directly to residential program staff may reveal innovative practices, areas for improvement, and factors that may explain system-wide variability in performance. Results Average wait time for admission was 16 days (SUD RRTPs: 17 days; MH RRTPs with a SUD track: 11 days), with 60% of Veterans waiting longer than 7 days. For these Veterans, engagement while waiting occurred in an average of 54% of the waiting weeks (range 3–100% across programs). Fifty-nine interviews representing 44 programs revealed factors perceived to potentially impact performance in these domains. Efficient screening processes, effective patient flow, and available beds were perceived to facilitate shorter wait times, while lack of beds, poor staffing levels, and lengths of stay of existing patients were thought to lengthen wait times. Accessible outpatient services, strong patient outreach, and strong encouragement of pre-admission outpatient treatment emerged as facilitators of engagement while waiting; poor staffing levels, socioeconomic barriers, and low patient motivation were viewed as barriers. Conclusions Metrics for pre-admission processes can be helpful for monitoring residential SUD treatment programs. Interviewing program management and staff about drivers of performance metrics can play a complementary role by identifying innovative and other strong practices, as well as high-value targets for quality improvement. Key facilitators of high-performing facilities may offer programs with lower performance useful strategies to improve specific pre-admission processes

Identification of cancer risk and associated behaviour: implications for social marketing campaigns for cancer prevention

Background Community misconception of what causes cancer is an important consideration when devising communication strategies around cancer prevention, while those initiating social marketing campaigns must decide whether to target the general population or to tailor messages for different audiences. This paper investigates the relationships between demographic characteristics, identification of selected cancer risk factors, and associated protective behaviours, to inform audience segmentation for cancer prevention social marketing. Methods Data for this cross-sectional study (n = 3301) are derived from Cancer Council New South Wales’ 2013 Cancer Prevention Survey. Descriptive statistics and logistic regression models were used to investigate the relationship between respondent demographic characteristics and identification of each of seven cancer risk factors; demographic characteristics and practice of the seven ‘protective’ behaviours associated with the seven cancer risk factors; and identification of cancer risk factors and practising the associated protective behaviours, controlling for demographic characteristics. Results More than 90% of respondents across demographic groups identified sun exposure and smoking cigarettes as moderate or large cancer risk factors. Around 80% identified passive smoking as a moderate/large risk factor, and 40–60% identified being overweight or obese, drinking alcohol, not eating enough vegetables and not eating enough fruit. Women and older respondents were more likely to identify most cancer risk factors as moderate/large, and to practise associated protective behaviours. Education was correlated with identification of smoking as a moderate/large cancer risk factor, and with four of the seven protective behaviours. Location (metropolitan/regional) and country of birth (Australia/other) were weak predictors of identification and of protective behaviours. Identification of a cancer risk factor as moderate/large was a significant predictor for five out of seven associated cancer-protective behaviours, controlling for demographic characteristics. Conclusions These findings suggest a role for both audience segmentation and whole-of-population approaches in cancer-prevention social marketing campaigns. Targeted campaigns can address beliefs of younger people and men about cancer risk factors. Traditional population campaigns can enhance awareness of being overweight, alcohol consumption, and poor vegetable and fruit intake as cancer risk factors

Treatment of cancer with cryochemotherapy

Cryosurgery employs freezing to destroy solid tumours. However, frozen cells can survive and cause cancer recurrence. Bleomycin, an anticancer drug with a huge intrinsic cytotoxicity is normally not very effective because it is nonpermeant. We report that freezing facilitates bleomycin penetration into cells making it toxic to cryosurgery surviving cells at concentrations that are non-toxic systemically

Scaled momentum distributions for K-S(0) and Λ /̄ Λ in DIS at HERA

Scaled momentum distributions for the strange hadrons K0S and Λ/Λ¯ were measured in deep inelastic ep scattering with the ZEUS detector at HERA using an integrated luminosity of 330 pb−1. The evolution of these distributions with the photon virtuality, Q 2, was studied in the kinematic region 10 < Q 2  < 40000 GeV2 and 0.001 < x < 0.75, where x is the Bjorken scaling variable. Clear scaling violations are observed. Predictions based on different approaches to fragmentation were compared to the measurements. Leading-logarithm parton-shower Monte Carlo calculations interfaced to the Lund string fragmentation model describe the data reasonably well in the whole range measured. Next-to-leading-order QCD calculations based on fragmentation functions, FFs, extracted from e + e − data alone, fail to describe the measurements. The calculations based on FFs extracted from a global analysis including e + e −, ep and pp data give an improved description. The measurements presented in this paper have the potential to further constrain the FFs of quarks, anti-quarks and gluons yielding K0S and Λ/Λ¯ strange hadrons

Combined measurement and QCD analysis of the inclusive e(+/-)p scattering cross sections at HERA

A combination is presented of the inclusive deep inelastic cross sections measured by the H1 and ZEUS Collaborations in neutral and charged current unpolarised e ± p scattering at HERA during the period 1994-2000. The data span six orders of magnitude in negative four-momentum-transfer squared, Q 2, and in Bjorken x. The combination method used takes the correlations of systematic uncertainties into account, resulting in an improved accuracy. The combined data are the sole input in a NLO QCD analysis which determines a new set of parton distributions, HERAPDF1.0, with small experimental uncertainties. This set includes an estimate of the model and parametrisation uncertainties of the fit result