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Abstract Background The Community Health Information System (CHIS) online mapping system was first launched in 1998. Its overarching goal was to provide researchers, residents and organizations access to health related data reflecting the overall health and well-being of their communities within the Greater Houston area. In September 2009, initial planning and development began for the next generation of CHIS. The overarching goal for the new version remained to make health data easily accessible for a wide variety of research audiences. However, in the new version we specifically sought to make the CHIS truly interactive and give the user more control over data selection and reporting. Results In July 2011, a beta version of the next-generation of the application was launched. This next-generation is also a web based interactive mapping tool comprised of two distinct portals: the Breast Health Portal and Project Safety Net. Both are accessed via a Google mapping interface. Geographic coverage for the portals is currently an 8 county region centered on Harris County, Texas. Data accessed by the application include Census 2000, Census 2010 (underway), cancer incidence from the Texas Cancer Registry (TX Dept. of State Health Services), death data from Texas Vital Statistics, clinic locations for free and low-cost health services, along with service lists, hours of operation, payment options and languages spoken, uninsured and poverty data. Conclusions The system features query on the fly technology, which means the data is not generated until the query is provided to the system. This allows users to interact in real-time with the databases and generate customized reports and maps. To the author's knowledge, the Breast Health Portal and Project Safety Net are the first local-scale interactive online mapping interfaces for public health data which allow users to control the data generated. For example, users may generate breast cancer incidence rates by Census tract, in real time, for women aged 40-64. Conversely, they could then generate the same rates for women aged 35-55. The queries are user controlled.

Introduction: The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients., Methods: Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis., Results: One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation., Conclusion: Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy., Competing Interests: The authors have no competing interests to declare., (© The Author(s) 2024.)

Minority populations will continue to grow in the United States. Such pluralism necessitates iterative, geospatial measurements of cultural contexts. Our objective in this study was to create a measure of social determinants of health in geographic areas with varying ethnic, linguistic, and religious diversity in the United States. We extracted geographic information systems data based on community characteristics that have known associations with population health disparities from 2015 to 2019. We used principal component analysis to construct a Cultural Context Index (CCI). We created the CCI for 73,682 census tracts across 50 states and five inhabited territories. We identified hot and cold spots that are the highest and lowest CCI quintile, respectively. Hot spots census tracts were mostly located in metropolitan areas (84.8%), in the Southern census region (41.5%), and also had larger Black and Hispanic populations. The census tracts with the greatest need for culturally competent health care also had the sickest populations. Census tracts with a CCI rank of 5 ('greatest need') had higher prevalences of self-reported poor physical health (17.2%) and poor mental health (17.4%), compared to either the general population (13.9% and 14.5%) or to CCI rank of 1 ('lowest need') (11.9% and 10.8%). The CCI can pinpoint census tracts with a need for culturally competent health care and inform supply-side policy planning as healthcare and social service providers will inevitably come in contact with consumers from different backgrounds., (© 2023 Published by Elsevier Ltd.)

Context: Provider-based human papillomavirus (HPV) vaccine promotion interventions have been widely implemented; however, the effectiveness of these approaches is unclear., Objectives: This systematic review assessed the effects and costs of provider-based interventions designed to increase HPV vaccination coverage. A meta-analysis was conducted to estimate pooled effect sizes of the interventions., Data Sources: We searched PubMed, Medline, Embase, and the Cochrane Library., Study Selection: We identified articles on provider-based HPV vaccine promotion interventions published in English between 2007 and 2021., Data Extraction: Information about the interventions' effects and costs was extracted from the studies. A meta-analysis was performed to estimate the pooled intervention effects, including changes in the HPV vaccine initiation rate, HPV vaccine completion rate, and the percentage of patients receiving the next needed dose., Results: We identified 32 studies of provider-based interventions to improve the HPV vaccination rate. The reported intervention strategies included provider training, provider reminders, and provider assessment and feedback. In the meta-analysis, we found significant improvements in the HPV vaccine initiation rate (3.7%) and the percentage of patients receiving the next needed dose (9.4%)., Limitations: Because of the limited number of studies, we were unable to compare the same outcomes between different provider-based approaches. We found the high heterogeneity across studies. The random effects method was applied to adjust for study heterogeneity, the heterogeneity remined high for certain outcomes., Conclusions: Provider-based interventions were effective in improving HPV vaccination rates. Sustainability and continuous implementation are the central factors that contributed to intervention success., (Copyright © 2023 by the American Academy of Pediatrics.)

Food insecurity is prevalent and associated with poor health outcomes, but little is known about its geographical nature. The aim of this study is to utilize geospatial modeling of individual-level food insecurity screening data ascertained in health care settings to test for neighborhood hot and cold spots of food insecurity in a large metropolitan area, and then compare these hot spot neighborhoods to cold spot neighborhoods in terms of the CDC's Social Vulnerability Index. In this cross-sectional secondary data analysis, we geocoded the home addresses of 6,749 unique participants screened for food insecurity at health care locations participating in CMS's Accountable Health Communities (AHC) Model, as implemented in Houston, TX. Next, we created census-tract level incidence profiles of positive food insecurity screens per 1,000 people. We used Anselin's Local Moran's I statistic to test for statistically significant census tract-level hot/cold spots of food insecurity. Finally, we utilized a Mann-Whitney-U test to compare hot spot tracts to cold spot tracts in relation to the CDC's Social Vulnerability Index. We found that hot spot tracts had higher overall social vulnerability index scores (P <0.001), higher subdomain scores, and higher percentages of individual variables like poverty (P <0.001), unemployment (P <0.001), limited English proficiency (P <0.001), and more. The combination of robust food insecurity screening data, geospatial modeling, and the CDC's Social Vulnerability Index offers a solid method to understand neighborhood food insecurity., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Ramphul et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The Peace of Mind Program is an evidence-based intervention to improve mammography appointment adherence in underserved women. The aim of this study was to assess effectiveness of the intervention and implementation of the intervention in safety net clinics. The intervention was implemented through a non-randomized stepped wedge cluster hybrid study design with 19 Federally Qualified Health Centers and charity care clinics within the Greater Houston area. A multivariable generalized estimating equation logistic regression was conducted to examine mammography appointment adherence. A survey assessing Consolidated Framework for Implementation Research constructs was also conducted with clinic staff prior to adoption and eight weeks post implementation. One-sided t-tests were conducted to analyze mean score changes between the surveys. A total of 4402 women (baseline period = 2078; intervention period = 2324) were included in the final regression analysis. Women in the intervention period were more likely to attend or reschedule their mammography appointment (OR = 1.30; p < 0.01) than those in the baseline period receiving usual care. Women who completed the intervention were more likely to attend or reschedule their mammography appointment than those who did not complete the intervention (OR = 1.62; p < 0.01). The mammography appointment no-show rates for those in the baseline period, in the intervention period, and who completed the intervention were, respectively, 22%, 19%, and 15%. A total of 15 clinics prior to adoption and eight clinics completed the survey at 8 weeks post implementation A statistically significant mean score decrease was observed in Inner Setting and in two Inner Setting CFIR constructs, Culture-Effort, and Implementation Climate. While the intervention improved mammography appointment adherence, there are opportunities to further integrate Consolidated Framework for Implementation Research constructs. Trial registration: Clinical trials registration number: NCT02296177., (© 2021. The Author(s).)

Teach Back is a commonly used communication method to improve patient understanding and retention of health information. The method has been shown to be effective in improving patient and healthcare system outcomes, including patient health literacy and hospital readmissions. Community health workers (CHWs) are frontline healthcare workers who can help address patient health and social needs associated with hospital readmissions. However, a gap exists in Teach Back curricula and training methods reflecting the scope of work for CHWs. The objective of this training was to provide CHWs with didactic information and skill building practice curriculum focused on the integration of Teach Back into clinical patient interactions, care coordination, and follow-up support. A multidisciplinary team of academic and clinical partners at a large academic health university developed, implemented, and evaluated a 3-week pilot Teach Back training with CHWs through a quality improvement approach. The CHWs reported overall satisfaction with the training and instructors. The academic clinical partnership allowed the training to be tailored to the daily clinical workflow as reflected in the CHWs agreement that the training was relevant and practical. With the repeated exposure to Teach Back each week, the CHWs also reported an increase in confidence and conviction in using Teach Back. Additional implementation and evaluation of the training curriculum for CHWs is needed to gain further insights into Teach Back and training best practices and translation into practice., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Holcomb, Ferguson, Thornton and Highfield.)

Introduction: A multitude of HRSN interventions are undergoing testing in the U.S., with the CMS Accountable Health Communities (AHC) Model as the largest. HRSN interventions typically include screening for social needs, referral to community resources, and patient navigation to ensure needs are met. There is currently a paucity of evidence on implementation of HRSN interventions. The Consolidated Framework for Implementation Research (CFIR) is a determinant framework widely used to plan and assess implementation. To the authors knowledge, there are no published studies assessing CFIR constructs for HRSN intervention implementation in the U.S. In the Assessment step of the Strengthening Peer AHC Navigation (SPAN) model, a between-site qualitative assessment methodology was used to examine implementation within and between AHC bridge organizations (BOs) within six ERIC implementation strategies identified by the authors based on AHC Model requirements., Objective: Our aim was to identify and present between-site barriers and facilitators to AHC Model implementation strategies., Design: A multi-site qualitative analysis methodology was used. CFIR determinants were linked to six Expert Recommendations for Implementing Change (ERIC) strategies: staff training, identify and prepare champions, facilitation, community resource engagement (alignment through advisory boards and working groups), data systems, and quality monitoring and assurance. Interviews were analyzed using thematic content analysis in NVivo 12 (QSR International)., Setting: Five health-related bridge organizations participating in the AHC Model., Results: Fifty-eight interviews were completed with 34 staff and 24 patients or patient proxies. Facilitators were identified across five of the six ERIC strategies. Barriers were identified across all six. While organizations found the AHC Model compatible and facilitators to implementation included previous experience, meeting patient needs and resources, and leadership engagement and support, a number of barriers presented challenges to implementation. Issues with adequate staff training, staff skills to resolve HRSN, including patient communication and boundary spanning, setting staff goals, beneficiary caseloads and measurement of progress, data infrastructure (including EHR), available resources to implement and differences in perceptions between clinical delivery site (CDS), and CSP of how to measure and resolve HRSN., Conclusions and Relevance: The conduct of a pre-implementation readiness assessment benefited from identifying CFIR determinants linked to various ERIC implementation strategies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Highfield, Ferguson and Holcomb.)

Background: Integration of health-related social needs (HRSNs) data into clinical care is recognized as a driver for improving healthcare. However, few published studies on HRSNs and their impact are available. CMS sought to fill this gap through the Accountable Health Communities (AHC) Model, a national RCT of HRSN screening, referral, and navigation. Data from the AHC Model could significantly advance the field of HRSN screening and intervention in the USA., Objective: To present data from the Greater Houston AHC (GH-AHC) Model site on HRSN frequency and the association between HRSNs, sociodemographic factors, and self-reported ED utilization using a cross-sectional design. Analyses included descriptive statistics and multinomial logistic regression., Participants (or Patients or Subjects): All community-dwelling Medicare, Medicaid, or dually covered beneficiaries at participating GH-AHC clinical delivery sites were eligible., Main Measures: Self-reported ED utilization in the previous 12 months served as the outcome; demographic characteristics including race, ethnicity, age, sex, income, education level, number of people living in the household, and insurance type were treated as covariates. HRSNs included food insecurity, housing instability, transportation, difficulty paying utility bills, and interpersonal safety. Clinical delivery site type was used as the clustering variable., Key Results: Food insecurity was the most common HRSN identified (38.7%) followed by housing instability (29.0%), transportation (28.0%), and difficulty paying utility bills (26.7%). Interpersonal safety was excluded due to low prevalence. More than half of the beneficiaries (56.9%) reported at least one of the four HRSNs. After controlling for covariates, having multiple co-occurring HRSNs was strongly associated with increased risk of two or more ED visits (OR 1.8-9.47 for two to four needs, respectively; p < 0.001). Beneficiaries with four needs were at almost 10 times higher risk of frequent ED utilization (p < 0.001)., Conclusions: To our knowledge, this is only the second published study to report screening data from the AHC Model. Future research focused on the impact of multiple co-occurring needs on health outcomes is warranted., (© 2022. The Author(s).)

Multi-level organizational stakeholder engagement plays an important role across the research process in a clinical setting. Stakeholders provide organizational specific adaptions in evidence-based interventions to ensure effective adoption, implementation, and sustainability. Stakeholder engagement strategies involve building mutual trust, providing clear communication, and seeking feedback. Using constructs from the Consolidated Framework for Implementation Research and The International Association for Public Participation spectrum, a conceptual framework was created to guide stakeholder engagement in an evidence-based intervention to increase mammography appointment adherence in underserved and low-income women. A document review was used to explore the alignment of the conceptual framework with intervention activities and stakeholder engagement strategies. The results indicate an alignment with the conceptual framework constructs and a real-world application of stakeholder engagement in a mammography evidence-based intervention. The conceptual framework and stakeholder engagement strategies can be applied across a range of community-based cancer programs and interventions, organizations, and clinical settings., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Background: Effective provider communication skills are important for patient decision-making and understanding, particularly for those with low health literacy. A gap exists in training methods and curriculum for community health workers (CHWs). Brief description of activity: Through a clinical and academic partnership, pilot training curriculum focused on patient communication skills was developed to align with CHW scope of work., Implementation: The curriculum was implemented in three 2-hour training sessions over WebEx with seven state-certified CHWs. The goal was for CHWs to understand the key elements and application of active listening, Teach Back, and action planning in a clinical setting. The sessions included didactic and skills practice modules for each skill., Results: A survey was distributed to CHWs to evaluate knowledge, skills, and attitudes and reactions to training methods, instructors, and relevance using the Kirkpatrick's evaluation model (Reaction and Learning). Although CHWs agreed that they had actively participated in the training and that the instructors were well-prepared, there was less agreement that the course was relevant. CHWs reported an increase in understanding of active listening and action planning, capability of using Teach Back and providing social support, and ability to teach, whereas a decrease was reported in the capability to use action planning. When probed about training relevance, CHWs felt action listening and Teach Back were relevant, but that action planning was not relevant to their responsibilities. This gap in responsibilities was also acknowledged by the clinical leadership., Lessons Learned: The training allowed the CHWs to build on subsequent skills from previous sessions and to discuss struggles. A need for tools for integrating the skills in the clinical workflow were requested by CHWs and clinical leadership. These tools offer the opportunity to tailor future trainings on communication skills or patient scenarios. Future trainings should include CHWs to provide insight into scope of work. [ HLRP: Health Literacy Research and Practice . 2022;6(2):e142-e150. ] Plain Language Summary: It is important for community health workers to communicate with patients so that patients can understand information and make their own decisions. There is not enough known about the best way to train CHWs in patient communication. This training was created to help CHWs use three patient communication skills in their clinic.

Providers currently rely on universal screening to identify health-related social needs (HRSNs). Predicting HRSNs using EHR and community-level data could be more efficient and less resource intensive. Using machine learning models, we evaluated the predictive performance of HRSN status from EHR and community-level social determinants of health (SDOH) data for Medicare and Medicaid beneficiaries participating in the Accountable Health Communities Model. We hypothesized that Medicaid insurance coverage would predict HRSN status. All models significantly outperformed the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68 CI 0.66-0.70) was achieved by the "any HRSNs" outcome, which is the most useful for screening prioritization. Community-level SDOH features had lower predictive performance than EHR features. Machine learning models can be used to prioritize patients for screening. However, screening only patients identified by our current model(s) would miss many patients. Future studies are warranted to optimize prediction of HRSNs., (© 2022. The Author(s).)

Through an academic-community partnership, an evidence-based intervention to reduce mammography appointment no-show rates in underserved women was expanded to safety net clinics. The partnership implemented four strategies to improve the adoption and scale-up of evidence-based interventions with Federally Qualified Health Centers and charity care clinics: (1) an outreach email blast targeting the community partner member clinics to increase program awareness, (2) an adoption video encouraging enrollment in the program, (3) an outreach webinar educating the community partner member clinics about the program, encouraging enrollment and outlining adoption steps, and (4) an adoption survey adapted from Consolidated Framework for Implementation Research constructs from the Cancer Prevention and Control Research Network for cancer control interventions with Federally Qualified Health Centers. The development of academic-community partnerships can lead to successful adoption of evidence-based interventions particularly in safety net clinics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Holcomb, Ferguson, Roth, Walton and Highfield.)

Objectives: Complementary and integrative medicine (CIM) therapies show clinical benefits with minimal side effects, yet challenges to effective integration in hospital settings remain. The current study aimed to better understand the process of integration of CIM therapies at a large urban pediatric hospital from the perspectives of providers, parents, and administrators., Design: The study employed an applied medical ethnography., Setting: The ethnography was conducted before, during, and after an Integrative Medicine Pain Consult Service (IM Pilot) was implemented at a large urban pediatric hospital during the spring of 2017., Main Outcome Measures: Fieldwork interviews, participant observations, and document review captured aspects of the integration of CIM over a 6-month study period. Ethnographic analysis included thematic content analysis. Participants included providers (n = 10), administrators (n = 5), and parents of patients (n = 11)., Results: Emergent themes from analysis of the interviews and field notes were organized according to the socio-ecological model. Themes included facilitating factors for CIM pain management at the intrapersonal and community levels (Alignment with Parental Perceptions of Child Needs and Provider Desire to Offer Care, Alignment of CIM with Spiritual Beliefs and Community Norms) and barriers at the interpersonal, organizational, and political levels (Inter-professional Challenges, Lack of Logistics in Place for Referrals and Triaging Patients with Pain, Lack of Remuneration/Insurance Reimbursement for Care)., Conclusions: To address barriers, future efforts to implement integrative pain management programs in pediatric hospital settings may consider testing implementation strategies, including engaging program champions and family advocates, providing education on CIM professions and therapies to hospital staff, and billing for provider time rather than individual CIM therapies., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: This study explored the contributions of social determinants of health (SDOH) to measures of population health-specifically cost, hospitalization rates, rate of emergency department utilization, and health status-in Texas., Study Design: The study associated common SDOH metrics from public data sources (county specific) with health plan enrollment data (including demographics, counties, and zip codes) and medical and pharmaceutical annual claims data., Methods: Following correlation analyses to reduce variables, the contribution of each SDOH individually and by category to the health outcomes was evaluated. Separate matrices for age populations (under age 19, general population [all ages], and ≥ 65 years) were created with assigned weights of influence for categories and the factors within each category., Results: The contributions of the categories varied by population, confirming that different SDOH influence populations to varying degrees. This was reflected in each model. The largest contributor to cost for the general population and for the group 65 years and older was factors grouped as health outcomes (such as perceived health), at 43.5% contribution and 37.7% contribution, respectively. Yet for the population younger than 19 years, the largest contributor to cost was socioeconomic factors (such as unemployment rate), at 40.2%. The other performance measures also varied by population and the mix and weight of determinants., Conclusions: This study and the developed population-based matrices can provide a valuable framework for reporting the impact of SDOH on health care quality. The variation suggests the need for further research on how age groups react to the social environment.

Introduction: The Accountable Health Communities (AHC) Model was designed to address the health-related social needs of Centers for Medicare & Medicaid Services beneficiaries. Bridge organizations across the AHC Model have identified lack of technical assistance and peer planning as potential barriers to Model success, particularly around patient navigation. The technical assistance and peer planning literature lacks an organizing, conceptual framework, but implementation science frameworks could serve as useful guides. The Strengthening Peer AHC Navigation (SPAN) research protocol seeks to fill this gap and will apply three implementation science frameworks, Consolidated Framework for Implementation Research, Intervention Mapping, and the Expert Recommendations for Implementing Change compilation, to develop a multi-level quality improvement intervention and evaluate the impact of peer planning on Model outcomes. The aims of the SPAN study are to implement and evaluate a novel multi-level quality improvement intervention to improve AHC implementation and navigation milestones through structured peer planning and to provide successful technical assistance for the AHC Model. Methods and Analysis: The quality improvement intervention is outlined in four Tasks: (1) Assessment - to conduct an assessment of each bridge organization's current implementation, needs, and readiness in AHC Model navigation activities; (2) Planning - to engage in a peer planning approach to build capacity for AHC Model navigation activities; (3) Implementation with technical assistance - Co-creation of a quality improvement protocol for AHC Model navigation activities; and (4) Evaluation - measure the impact of the peer planning and technical assistance approach. Alongside the development and implementation of the quality improvement intervention, this protocol describes a mixed method, convergent parallel study design which will be used to evaluate whether the quality improvement intervention will lead to better outcomes. Tasks will be replicated with five bridge organizations participating in the AHC Model. Discussion: This research protocol provides a framework that can be used to conduct structured peer planning with technical assistance for social needs programs. This study will provide data on both implementation and outcomes which eventually may impact healthcare cost and utilization., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Holcomb, Walton, Sokale, Ferguson, Schick and Highfield.)

Objective: Among high-risk, underserved populations, such as children living along the US-México border, suffering from asthma-like symptoms without an asthma diagnosis can result in a high burden of illness. We estimated the prevalence of physician-diagnosed and possible undiagnosed asthma among students with histories of wheezing in the US-México border community of El Paso, Texas, and evaluated their burden of illness, primary care use, and medication utilization., Methods: We analyzed cross-sectional survey data collected in May 2012. The survey included validated International Study of Asthma and Allergies in Childhood (ISAAC) items. We performed bivariate and logistic regression analyses on data from 307 students who wheezed., Results: Forty-two percent of students had possible undiagnosed asthma and 58% had physician-diagnosed asthma based on primary caretaker reports. Children of Mexican origin were more likely to report undiagnosed vs. diagnosed asthma (p < 0.05). Children with an asthma diagnosis were more likely to report any medication use for wheezing/asthma and to experience a higher burden of illness in the last year compared to students with possible undiagnosed asthma (p < 0.05)., Conclusions: The burden of illness among these children was high; however, children with asthma-like symptoms were not faring worse than children with asthma symptoms and a diagnosis. Undiagnosed children were being evaluated and receiving treatment for their symptoms; however, by not receiving a diagnosis, they were eliciting an ameliorative rather than preventive treatment strategy.

Background: Complementary and Integrative Medicine (CIM) shows positive clinical benefit with minimal side effects, yet, challenges to effective integration of CIM providers in biomedical health care settings remain. This study aimed to better understand the role evidence played in the process of integration of complementary therapies into a large urban pediatric hospital from the perspective of patients, caregivers, providers, and administrators through applied medical ethnography., Methods: An ethnography was conducted over the course of 6 months in a large urban pediatric hospital in the Southern United States. At the time, the hospital was piloting an integrative medicine (IM) pain consult service. Purposive sampling was used to select providers, patients, administrators, and caregivers to follow as they engaged with both the preexisting pain service and pilot IM pain services. Field observation and interviews were conducted with 34 participants. Thematic content analysis was used to analyze field notes, interview transcripts, and documents collected., Findings: Analysis of the data revealed five themes regarding the role of evidence in the process of integration: Anecdotal Evidence and Personal Experience Effecting CIM Use, Open to Trying Whatever Works, Resistance to the Unfamiliar, Patients and Parents Trusting Doctors as Experts, and Importance of Scientific Evidence to Biomedicine. All themes address the role of evidence in relationship to acceptance of complementary therapies and correspond with three thought processes: critical thinking, relying on others for evidence, and reactionary thinking., Conclusion: Both scientific and anecdotal evidence informed CIM use in this context. Biomedical environments seeking to integrate CIM into pain treatment regimens will need to engage all relevant stakeholders in building education, clinical training, and research programs that are critically informed about the context and evidence for CIM.

Aims: Investigate the interactions of organic acids (OAs), acetic, butyric, citric, formic, lactic and propionic acid against 50 Gram-positive vancomycin-resistant Enterococcus faecium (VRE) strains to determine whether pH, undissociated or dissociated acid forms correlate with bacterial inhibition., Methods and Results: Concentrations of undissociated and dissociated OAs at the molar minimum inhibitory concentrations (MIC M s) of the VRE were calculated using the Henderson-Hasselbalch equation. The pH at the MIC M s of all VRE strains against acetic, butyric, formic and propionic acids was similar, 4·66 ± 0·07, but there was a 1·1 pH unit difference for all six OAs. Inhibition of VRE by all six OAs did not appear to be solely dependent on pH or on the undissociated OA species. The inhibition of VRE by all six dissociated acids was within Δ = 3·1 mmol l -1 ., Conclusions: Vancomycin-resistant Enterococcus faecium inhibition correlated with the dissociated OA species. A small decrease in the concentration of the dissociated OAs from optimum may result in allowing VRE strains to escape disinfection., Significance and Impact of the Study: When an OA is used to disinfect VRE strains, the concentration of the dissociated OA should be carefully controlled. A concentration of at least 20 mmol l -1 dissociated OA should be maintained when disinfecting VRE., (© 2018 The Society for Applied Microbiology.)

Background: Although much work has begun to elucidate contextual factors influencing implementation, the specific processes that facilitate and hinder adoption, implementation, and maintenance of evidence-based interventions (EBIs) in clinical settings remains poorly understood. Intervention Mapping (IM) is a systematic process that facilitates planning and design for dissemination, implementation and maintenance of EBIs in practice. IM has been used to guide the design of many health interventions, focusing on program implementation. Less studied is its use to adapt and scale screening interventions within the healthcare clinic setting. This paper describes the development of an implementation intervention using IM to facilitate the adoption, implementation, and maintenance of an EBI designed to increase mammography adherence in healthcare clinics, the adapted Peace of Mind Program (PMP). Methods: IM framework, Step 5, was used to guide the implementation intervention planning. IM guided identification of specific adoption, implementation, and maintenance performance objectives. We formed an implementation intervention planning group consisting of members of the academic team, our community partner and community health workers (CHWs) with substantial experience working on mammography screening programs in federally qualified health centers (FQHCs) and charity clinics. Results: Results are presented by Intervention Mapping task for Step 5 (Program Implementation Plan). We describe how the consolidated framework for implementation research (CFIR) informed the selection of performance objectives, determinants, methods, and practical applications in the final implementation intervention. Conclusions: This paper provides an example of the use of Intervention Mapping Step 5 and CFIR to create an implementation intervention to support EBI scale up of an evidence-based mammography intervention within a specific setting. Clinical trials registration number: NCT02296177.

Some researchers posit that the lower prevalence of asthma among those of Mexican descent may result from an under-diagnosis and recommend a critical appraisal of factors related to race/ethnicity, like acculturation, and its influence on asthma status. Survey data were analyzed using multinomial logistic regression to examine the association of child's asthma status (no wheezing/no asthma symptoms, possible undiagnosed and diagnosed asthma) with measures of acculturation among Hispanic students (n = 1095). In this population, the prevalence of diagnosed asthma (15%) was higher than both national (7.6%) and state (6.8%) averages for Hispanic children in 2012. While bivariate analyses showed significant associations for asthma status and measures of acculturation, multivariate analyses did not. There is an underestimated burden of illness among Hispanic children in El Paso County. More research on the suitability of acculturation constructs is needed to delineate what they actually measure and how acculturation influences asthma status.

Purpose: Liver cancer (hepatocellular carcinoma (HCC)) incidence and mortality rates are increasing in the United States. New York City (NYC) has a high burden of liver cancer risk factors, including hepatitis C (HCV) and hepatitis B (HBV) infection, which disproportionately affect persons of low socioeconomic position. Identifying neighborhoods with HCC disparities is essential to effectively define targeted cancer control strategies., Methods: New York State Cancer Registry data from 1 January 2001 through 31 December 2012 were matched with NYC HCV and HBV surveillance data. HCC data were aggregated to NYC Zip Code Tabulation Areas (ZCTAs). Moran's I cluster analysis, Poisson regression, and geographically weighted Poisson regression were used to identify hotspots in HCC incidence and to examine the spatial associations with viral hepatitis rates, poverty, and uninsured status., Results: Among NYC residents, 8,827 HCC cases were diagnosed during 2001-2012. Significant clustering was detected in the HCC rates (Moran's I = 0.25) with the strongest clustering found in HCC patients with comorbid HCV infection (Moran's I = 0.47). Poverty and uninsured status were associated (p < 0.05) with increased rates of HCC patients with HBV or HCV infection. Neighborhoods with high rates of HCC without viral hepatitis infection had lower rates of poverty and uninsured status., Conclusions: The geographic variation in HCC highlights the need for neighborhood-targeted interventions to address risk factors and barriers to care. The clusters of HCC by viral hepatitis status may serve as a basis for healthcare policymakers and practitioners to prioritize neighborhoods for cancer screening and control efforts.

Background: Most patients with malaria seek treatment first in retail drug shops. Myriad studies have examined retailer behaviours and characteristics to understand the determinants to these behaviours. Geospatial methods are helpful in discovering if geographic location plays a role in the relationship between determinants and outcomes. This study aimed to discover if spatial autocorrelation exists in the relationship between determinants and retailer behaviours, and to provide specific geographic locations and target behaviours for tailoring future interventions., Methods: Retailer behaviours and characteristics captured from a survey deployed to medicine retailers in the Webuye Demographic and Health Surveillance Site were analysed using geographic weighted regression to create prediction models for three separate outcomes: recommending the first-line anti-malarial therapy to adults, recommending the first-line anti-malarial therapy to children, and selling that therapy more than other anti-malarials. The estimated regression coefficients for each determinant, as well as the pseudo R 2 values for each final model, were then mapped to assess spatial variability and local areas of best model fit., Results: The relationships explored were found to be non-stationary, indicating that spatial heterogeneity exist in the data. The association between having a pharmacy-related health training and recommending the first-line anti-malarial treatment to adults was strongest around the peri-urban centre: comparing those with training in pharmacy to those without training (OR = 5.75, p = 0.021). The association between knowing the first-line anti-malarial and recommending it to children was strongest in the north of the study area compared to those who did not know the MOH-recommended anti-malarial (OR = 2.34, p = 0.070). This is also the area with the strongest association between attending a malaria workshop and selling the MOH-recommended anti-malarial more than other anti-malarials, compared to retailers who did not attend a workshop (OR = 2.38, p = 0.055)., Conclusion: Evidence suggests that spatial heterogeneity exists in these data, indicating that the relationship between determinants and behaviours varies across space. This is valuable information for intervention design, allowing efforts to focus on those factors that have the strongest relationship with their targeted behaviour within that geographic space, increasing programme efficiency and cost-effectiveness.

Background: Efforts to improve malaria case management in sub-Saharan Africa have shifted focus to private antimalarial retailers to increase access to appropriate treatment. Demands to decrease intervention cost while increasing efficacy requires interventions tailored to geographic regions with demonstrated need. Cluster analysis presents an opportunity to meet this demand, but has not been applied to the retail sector or antimalarial retailer behaviors. This research conducted cluster analysis on medicine retailer behaviors in Kenya, to improve malaria case management and inform future interventions., Methods: Ninety-seven surveys were collected from medicine retailers working in the Webuye Health and Demographic Surveillance Site. Survey items included retailer training, education, antimalarial drug knowledge, recommending behavior, sales, and shop characteristics, and were analyzed using Kulldorff's spatial scan statistic. The Bernoulli purely spatial model for binomial data was used, comparing cases to controls. Statistical significance of found clusters was tested with a likelihood ratio test, using the null hypothesis of no clustering, and a p value based on 999 Monte Carlo simulations. The null hypothesis was rejected with p values of 0.05 or less., Results: A statistically significant cluster of fewer than expected pharmacy-trained retailers was found (RR = .09, p = .001) when compared to the expected random distribution. Drug recommending behavior also yielded a statistically significant cluster, with fewer than expected retailers recommending the correct antimalarial medication to adults (RR = .018, p = .01), and fewer than expected shops selling that medication more often than outdated antimalarials when compared to random distribution (RR = 0.23, p = .007). All three of these clusters were co-located, overlapping in the northwest of the study area., Conclusion: Spatial clustering was found in the data. A concerning amount of correlation was found in one specific region in the study area where multiple behaviors converged in space, highlighting a prime target for interventions. These results also demonstrate the utility of applying geospatial methods in the study of medicine retailer behaviors, making the case for expanding this approach to other regions.

Breast cancer mortality disparities continue, particularly for uninsured and minority women. A number of effective evidence-based interventions (EBIs) exist for addressing barriers to mammography screening; however, their uptake and use in community has been limited. Few cancer-specific studies have evaluated adapted EBIs in new contexts, and fewer still have considered implementation. This study sought to (1) evaluate the effectiveness of an adapted mammography EBI in improving appointment keeping in African American women and (2) describe processes of implementation in a new practice setting. We used the type 1 hybrid design to test effectiveness and implementation using a quasi-experimental design. Logistic regression and intent-to-treat analysis were used to evaluate mammography appointment attendance. The no-show rate was 44% (comparison) versus 19% (intervention). The adjusted odds of a woman in the intervention group attending her appointment were 3.88 (p < 0.001). The adjusted odds of a woman attending her appointment in the intent-to-treat analysis were 2.31 (p < 0.05). Adapted EBI effectiveness was 3.88 (adjusted OR) versus 2.10 (OR) for the original program, indicating enhanced program effect. A number of implementation barriers and facilitators were identified. Our findings support previous studies noting that sequentially measuring EBI efficacy and effectiveness, followed by implementation, may be missing important contextual information.

This paper describes and demonstrates the use of the systematic planning process, Intervention Mapping, to adapt an evidence-based public health intervention (EBI). We used a simplified version of Intervention Mapping (IM Adapt) to increase an intervention's fit with a new setting and population. IM Adapt guides researchers and practitioners in selecting an EBI, making decisions about whether and what to adapt, and executing the adaptation while guarding the EBI's essential elements (those responsible for effectiveness). We present a case study of a project in which we used IM Adapt to find, adapt, implement, and evaluate an EBI to improve mammography adherence for African American women in a new practice setting in Houston, Texas. IM Adapt includes the following (1) assess needs and organizational capacity; (2) find EBIs; (3) plan adaptations based on fit assessments; (4) make adaptations; (5) plan for implementation; and (6) plan for evaluation of the adapted EBI. The case study shows an example of how public health researchers and practitioners can use the tool to make it easier to find and use EBIs, thus encouraging greater uptake. IM Adapt adds to existing dissemination and adaptation models by providing detailed guidance on how to decide on effective adaptation, while maintaining the essential elements of the EBI.

When community health planners select an evidence-based intervention that has been developed and tested in one situation and adapt it for use in a different situation or community, best practice suggests needs assessment and formative research in the new setting. Cancer prevention planners who are interested in adopting and adapting evidence-based approaches need to base their choices on a sound understanding of the health or behavioral risk problem in which they mean to intervene. This requires a balancing act of weighing community information against a broader perspective from the scientific literature and using the combination to identify and adapt an evidence-based intervention program that is likely to be effective in the new setting. This report is a case study of a community and organizational assessment conducted as a foundation for selecting and recommending adaptation of an evidence-based intervention for improving mammography appointment attendance. We used an inductive sequential exploratory mixed-methods design to inform this process. The process provides a model for formative research grounding evidence-based practice for cancer control planners. Future studies that incorporate findings from needs assessment into the adaptation of the selected intervention program may promote the effective dissemination of evidence-based programs., (© 2014 Society for Public Health Education.)

Risk heightens motivation and, if used appropriately, may have the potential to improve engagement in the classroom. We have developed a risk-based learning game for school pupils in order to test whether such learning games can improve later recall of information. The study was performed during a series of public engagement workshops delivered by undergraduate students. Undergraduate neuroscience students delivered 90-minute science workshops to 9-10 year old school pupils (n = 448) that were divided into 'Risk', 'No risk' and 'Control' classes. 'Risk' classes received periodic multiple-choice questions (MCQs) during the workshops which required small teams of pupils to assign tokens to the answer(s) they believed to be correct. Tokens assigned to the correct answer were returned to the group and an equal number given back as a prize; tokens assigned to incorrect answers were lost. Participation was incentivised by the promise of a brain-related prize to the team with the most tokens at the end of the workshop. 'No risk' classes received MCQs without the risk component whilst the 'Control' classes received no MCQs. When presented with a neuroscience quiz based on workshop content at the end of the workshop, pupils in the 'Risk' classes exhibited significantly greater recall of information one week later. Quiz scores were higher than scores from the day of the workshop which suggested pupils may have discussed the workshop content outside of the classroom, thereby increasing knowledge over and above what was learned during the workshop. This is supported by feedback from pupils in 'Risk' classes which indicated that 'Risk' workshops were more interesting than 'No risk' and 'Control' workshops. These data suggest that there is a role for risk in the classroom but further investigations are required to elucidate the causal mechanisms of improved retention of information.

This article presents a case study in the redesign, development, and implementation of a web-based healthcare clinic search tool for virtual patient navigation in underserved populations in Texas. It describes the workflow, assessment of system requirements, and design and implementation of two online portals: Project Safety Net and the Breast Health Portal. The primary focus of the study was to demonstrate the use of health information technology for the purpose of bridging the gap between underserved populations and access to healthcare. A combination of interviews and focus groups was used to guide the development process. Interviewees were asked a series of questions about usage, usability, and desired features of the new system. The redeveloped system offers a multitier architecture consisting of data, business, and presentation layers. The technology used in the new portals include Microsoft .NET Framework 3.5, Microsoft SQL Server 2008, Google Maps JavaScript API v3, jQuery, Telerik RadControls (ASP.NET AJAX), and HTML. The redesigned portals have 548 registered clinics, and they have averaged 355 visits per month since their launch in late 2011, with the average user visiting five pages per visit. Usage has remained relatively constant over time, with an average of 142 new users (40 percent) each month. This study demonstrates the successful application of health information technology to improve access to healthcare and the successful adoption of the technology by targeted end users. The portals described in this study could be replicated by health information specialists in other areas of the United States to address disparities in healthcare access.

Breast cancer is the most common cancer and second leading cause of cancer mortality in women in the United States. Women who lack insurance have mammography screening rates that are suboptimal. Our objective was to spatially correlate incidence rates of breast cancer and uninsured women aged 40-64 years and identify outliers-areas where women may be underscreened due to poor access. The eight-county consolidated metropolitan statistical area centered on Harris County, Texas was selected as the study region. Breast cancer incidence data from 1995 to 2004 were acquired from the State of Texas Cancer Registry as individual case data geocoded at the census tract level. A bivariate local indicator of spatial autocorrelation was used to evaluate the spatial pattern of breast cancer incidence and uninsured. Statistically significant negative spatial autocorrelation was observed between breast cancer incidence and uninsured status in women aged 40-64 (Moran's I -0.2065, p < 0.001), indicating that as breast cancer incidence increased, uninsured rates decreased globally. Statistically significant local clusters of low breast cancer incidence and high incidence of uninsured were found. Future research is needed to assess mammography screening behaviors and barriers to screening at the local level., (© 2013 Wiley Periodicals, Inc.)

The role that wild and feral animal populations might play in the incursion and spread of important transboundary animal diseases, such as foot and mouth disease (FMD), has received less attention than is warranted by the potential impacts. An artificial life model (Sirca) has been used to investigate this issue in studies based on spatially referenced data sets from southern Texas. An incursion of FMD in which either feral pig or deer populations were infected could result in between 698 and 1557 infected cattle and affect an area of between 166 km2 and 455 km2 after a 100-day period. Although outbreak size in deer populations can be predicted bythe size of the local deer population initially infected, the resulting outbreaks in feral pig populations are less predictable. Also, in the case of deer, the size of potential outbreaks might depend on the season when the incursion occurs. The impact of various mitigation strategies on disease spread has also been investigated. The approach used in the studies reviewed here explicitly incorporates the spatial distribution and relationships between animal populations, providing a new framework to explore potential impacts, costs, and control strategies.

Modeling potential disease spread in wildlife populations is important for predicting, responding to and recovering from a foreign animal disease incursion. To make spatial epidemic predictions, the target animal species of interest must first be represented in space. We conducted a series of simulation experiments to determine how estimates of the spatial distribution of white-tailed deer impact the predicted magnitude and distribution of foot-and-mouth disease (FMD) outbreaks. Outbreaks were simulated using a susceptible-infected-recovered geographic automata model. The study region was a 9-county area (24 000 km(2)) of southern Texas. Methods used for creating deer distributions included dasymetric mapping, kriging and remotely sensed image analysis. The magnitudes and distributions of the predicted outbreaks were evaluated by comparing the median number of deer infected and median area affected (km(2)), respectively. The methods were further evaluated for similar predictive power by comparing the model predicted outputs with unweighted pair group method with arithmetic mean (UPGMA) clustering. There were significant differences in the estimated number of deer in the study region, based on the geostatistical estimation procedure used (range: 385 939-768 493). There were also substantial differences in the predicted magnitude of the FMD outbreaks (range: 1 563-8 896) and land area affected (range: 56-447 km(2)) for the different estimated animal distributions. UPGMA clustering indicated there were two main groups of distributions, and one outlier. We recommend that one distribution from each of these two groups be used to model the range of possible outbreaks. Methods included in cluster 1 (such as county-level disaggregation) could be used in conjunction with any of the methods in cluster 2, which included kriging, NDVI split by ecoregion, or disaggregation at the regional level, to represent the variability in the model predicted outbreak distributions. How animal populations are represented needs to be considered in all spatial disease spread models.