Does Climate Change Affect Health? Beliefs from the Health Information National Trends Survey.

Climate change is currently and will continue impacting human health, however, beliefs about the level of threat vary by demographics, region, and ideology. The purpose of this study was to assess factors related to climate change and health beliefs using cross-sectional data from the Health Information National Trends Survey (HINTS). Data from 5,075 respondents in the 2022 iteration of HINTS was used for this study. Chi-square tests were used to evaluate demographic differences among those who believe climate change will harm health a lot compared to some, a little, or not at all. Generalized ordinal logistic regression models were used to examine the relationship between the belief that climate change will harm health and independent variables regarding trust in scientists, health recommendations from experts, and demographic characteristics. Female, Black, Hispanic, and college graduate respondents had higher odds and people in the Southern U.S. those aged 35-49, 50-64, and 75years or older had significantly lower odds of believing climate change would harm their health. Those who trust information about cancer from scientists and those that believe health recommendations from experts conflict or change had higher odds of believing climate change would harm health. Our analysis highlights factors that impact climate change and health beliefs, which may provide targets for tailoring public health messages to address this issue.

An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions-Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes.

Mobile health (mHealth) apps can be an evidence-based approach to improve health behavior and outcomes. Prior literature has highlighted the need for more research on mHealth personalization, including in diabetes and pregnancy. Critical gaps exist on the impact of personalization of mHealth apps on patient engagement, and in turn, health behaviors and outcomes. Evidence regarding how personalization, engagement, and health outcomes could be aligned when designing mHealth for underserved populations is much needed, given the historical oversights with mHealth design in these populations. This viewpoint is motivated by our experience from designing a personalized mHealth solution focused on Medicaid-enrolled pregnant individuals with uncontrolled type 2 diabetes, many of whom also experience a high burden of social needs. We describe fundamental components of designing mHealth solutions that are both inclusive and personalized, forming the basis of an evidence-based framework for future mHealth design in other disease states with similar contexts.

Multicomponent provider-patient intervention to improve glycaemic control in Medicaid-insured pregnant individuals with type 2 diabetes: clinical trial protocol for the ACHIEVE study.

INTRODUCTION: Type 2 diabetes (T2D) is one of the most frequent comorbid medical conditions in pregnancy. Glycaemic control decreases the risk of adverse pregnancy outcomes for the pregnant individual and infant. Achieving glycaemic control can be challenging for Medicaid-insured pregnant individuals who experience a high burden of unmet social needs. Multifaceted provider-patient-based approaches are needed to improve glycaemic control in this high-risk pregnant population. Mobile health (mHealth) applications (app), provider dashboards, continuous glucose monitoring (CGM) and addressing social needs have been independently associated with improved glycaemic control in non-pregnant individuals living with diabetes. The combined effect of these interventions on glycaemic control among pregnant individuals with T2D remains to be evaluated. METHODS AND ANALYSIS: In a two-arm randomised controlled trial, we will examine the combined effects of a multicomponent provider-patient intervention, including a patient mHealth app, provider dashboard, CGM, a community health worker to address non-medical health-related social needs and team-based care versus the current standard of diabetes and prenatal care. We will recruit 124 Medicaid-insured pregnant individuals living with T2D, who are ≤20 weeks of gestation with poor glycaemic control measured as a haemoglobin A1c ≥ 6.5% assessed within 12 weeks of trial randomisation or within 12 weeks of enrolling in prenatal care from an integrated diabetes and prenatal care programme at a tertiary care academic health system located in the Midwestern USA. We will measure how many individuals achieve the primary outcome of glycaemic control measured as an A1c<6.5% by the time of delivery, and secondarily, adverse pregnancy outcomes; patient-reported outcomes (eg, health and technology engagement, literacy and comprehension; provider-patient communication; diabetes self-efficacy; distress, knowledge and beliefs; social needs referrals and utilisation; medication adherence) and CGM measures of glycaemic control (in the intervention group). ETHICS AND DISSEMINATION: The Institutional Review Board at The Ohio State University approved this study (IRB: 2022H0399; date: 3 June 2023). We plan to submit manuscripts describing the user-designed methods and will submit the results of the trial for publication in peer-reviewed journals and presentations at international scientific meetings. TRIAL REGISTRATION NUMBER: NCT05662462.

Developing and testing an integrated patient mHealth and provider dashboard application system for type 2 diabetes management among Medicaid-enrolled pregnant individuals based on a user-centered approach: Mixed-methods study.

Background: A tailored and integrated technology solution (patient mHealth application and provider dashboard) can provide a 360° view of Medicaid-enrolled patients with type 2 diabetes (T2D) during pregnancy that could improve health outcomes and address health inequities. Objectives: To develop a set of user specifications for the mHealth and dashboard applications, develop prototypes based on user needs, and collect initial impressions of the prototypes to subsequently develop refined tools that are ready for deployment. Methods: Study activities followed a double-diamond framework with a participatory design mindset. Activities were divided into two phases focused on a qualitative inquiry about participant needs and values (phase 1) and design, development, and usability testing of low and high-fidelity prototypes (phase 2). Results: We identified themes that exemplified pregnancy experience among Medicaid-enrolled individuals with T2D. Patients (n = 7) and providers (n = 7) expressed a core set of expectations for the mHealth and dashboard applications. Participants provided feedback to improve the mHealth and dashboard. For both applications, participants reported scores for the NASA Task load Survey (TLX) that were in the 20th percentile of national TLX scores. Conclusions: Digital health tools have the ability to transform health care among Medicaid-enrolled patients with T2D during pregnancy, with the goal of managing their blood glucose levels, which is a precursor to experiencing a successful pregnancy and birth. Distilling patient and provider needs and preferences-then using them, along with prior studies and theory, to develop applications-holds great potential in tackling complicated health care issues.

Diversity of Participation in Clinical Trials and Influencing Factors: Findings from the Health Information National Trends Survey 2020.

BACKGROUND: Clinical trial diversity is critical to advance health and health equity. Research addressing the discrepancy between goals of achieving clinical trial diversity and realities of study enrollment remains underdeveloped. OBJECTIVE: This study aims to examine the association between race/ethnicity and clinical trial invitation, participation, knowledge, and sources of influence on clinical trial participation. DESIGN AND PARTICIPANTS: A cross-sectional, observational study using nationally representative data from 3689 US adults (≥ 18 years of age) who responded to the Health Information National Trends Survey fielded from February 24 to June 15, 2020. MAIN MEASURES: Primary outcomes included clinical trial invitation, participation, knowledge, and sources of influence on participation. The independent variable of interest is self-reported race/ethnicity. KEY RESULTS: Respondents identifying as non-Hispanic Black (relative to non-Hispanic White) had higher odds of being invited into a clinical trial (adjusted odds ratio: 2.0, 95% confidence interval (CI): 1.1, 3.7), but no differences in odds of participation were observed by race/ethnicity. Respondents from all races/ethnicities reported that personal healthcare providers were the most trusted source of clinical trial information. Hispanic (marginal effect (ME): - 0.09; 95% CI: - 0.16, - 0.03), non-Hispanic Black (ME: - 0.11; 95% CI: - 0.18, - 0.04), and non-Hispanic other (ME: - 0.11; 95% CI: - 0.19, - 0.02) respondents had lower odds than non-Hispanic White respondents of saying they would be influenced "a lot" by their doctor encouraging participation. Non-Hispanic Black respondents had significantly lower odds (relative to non-Hispanic White) of indicating family encouragement would influence their clinical trial participation decision "a lot" (ME: - 0.09; 95%: CI: - 0.14, - 0.03). CONCLUSION: While personal healthcare providers were trusted sources of information, racial/ethnic minority populations noted lower odds of clinical trial participation influence from providers and family. Thus, it is imperative for the healthcare, government, and industry organizations to build trust in medicine and science.

Attitudes towards and sociodemographic determinants of genetic test usage in the USA; data from the Health Information National Trend Survey, 2020.

The purpose of this study was to examine the trends in who obtains genetic tests, and opinions about how genes affect health. Cross-sectional survey data from Health Information National Trends Survey (HINTS) 5, Cycle 4 was used. This data was collected from adults 18 years of age or older who completed mailed surveys sent by the National Cancer Institute between January and April 2020. The sample consisted of 2,947 respondents who answered the question 'Have you ever had a genetic test'? 727 had a test and 2,220 did not have a test. The measures used included survey questions that asked whether respondents obtained certain kinds of genetic tests, who they shared test results with, whether they believed genes affect health status, and their demographic and cancer status information. Multivariate logistic regression models were developed to assess which demographic variables were associated with having different kinds of genetic tests, and whether those who had genetic tests had different opinions about genetic testing and the influence of genes on health. We found that female respondents [OR: 1.9; CI: (1.2-3.1)] had higher odds of having any genetic tests while Hispanic [OR: 0.5; CI: (0.2-1.0)] respondents had lower odds. Our findings indicate that there are demographic disparities in who received genetic tests, and that cancer risk alone does not explain the differences in prevalence of genetic testing.

Construction of the Ohio Children's Opportunity Index.

Objective: To describe the development of an area-level measure of children's opportunity, the Ohio Children's Opportunity Index (OCOI). Data Sources/Study Setting: Secondary data were collected from US census based-American Community Survey (ACS), US Environmental Protection Agency, US Housing and Urban Development, Ohio Vital Statistics, US Department of Agriculture-Economic Research Service, Ohio State University Center for Urban and Regional Analysis, Ohio Incident Based Reporting System, IPUMS National Historical Geographic Information System, and Ohio Department of Medicaid. Data were aggregated to census tracts across two time periods. Study Design: OCOI domains were selected based on existing literature, which included family stability, infant health, children's health, access, education, housing, environment, and criminal justice domains. The composite index was developed using an equal weighting approach. Validation analyses were conducted between OCOI and health and race-related outcomes, and a national index. Principal Findings: Composite OCOI scores ranged from 0-100 with an average value of 74.82 (SD, 17.00). Census tracts in the major metropolitan cities across Ohio represented 76% of the total census tracts in the least advantaged OCOI septile. OCOI served as a significant predictor of health and race-related outcomes. Specifically, the average life expectancy at birth of children born in the most advantaged septile was approximately 9 years more than those born in least advantaged septile. Increases in OCOI were associated with decreases in proportion of Black (48 points lower in the most advantaged vs. least advantaged septile), p < 0.001) and Minority populations (54 points lower in most advantaged vs. least advantaged septile, p < 0.001). We found R-squared values > 0.50 between the OCOI and the national Child Opportunity Index scores. Temporally, OCOI decreased by 1% between the two study periods, explained mainly by decreases in the children health, accessibility and environmental domains. Conclusion: As the first opportunity index developed for children in Ohio, the OCOI is a valuable resource for policy reform, especially related to health disparities and health equity. Health care providers will be able to use it to obtain holistic views on their patients and implement interventions that can tackle barriers to childhood development using a more tailored approach.

Co-establishing an infrastructure for routine data collection to address disparities in infant mortality: planning and implementation.

BACKGROUND: Efforts to address infant mortality disparities in Ohio have historically been adversely affected by the lack of consistent data collection and infrastructure across the community-based organizations performing front-line work with expectant mothers, and there is no established template for implementing such systems in the context of diverse technological capacities and varying data collection magnitude among participating organizations. METHODS: Taking into account both the needs and limitations of participating community-based organizations, we created a data collection infrastructure that was refined by feedback from sponsors and the organizations to serve as both a solution to their existing needs and a template for future efforts in other settings. RESULTS: By standardizing the collected data elements across participating organizations, integration on a scale large enough to detect changes in a rare outcome such as infant mortality was made possible. Datasets generated through the use of the established infrastructure were robust enough to be matched with other records, such as Medicaid and birth records, to allow more extensive analysis. CONCLUSION: While a consistent data collection infrastructure across multiple organizations does require buy-in at the organizational level, especially among participants with little to no existing data collection experience, an approach that relies on an understanding of existing barriers, iterative development, and feedback from sponsors and participants can lead to better coordination and sharing of information when addressing health concerns that individual organizations may struggle to quantify alone.

Developing Dashboards to Address Children's Health Disparities in Ohio.

OBJECTIVES: Social determinants of health (SDoH) can be measured at the geographic level to convey information about neighborhood deprivation. The Ohio Children's Opportunity Index (OCOI) is a composite area-level opportunity index comprised of eight health domains. Our research team has documented the design, development, and use cases of a dashboard solution to visualize OCOI. METHODS: The OCOI is a multidomain index spanning the following eight domains: (1) family stability, (2) infant health, (3) children's health, (4) access, (5) education, (6) housing, (7) environment, and (8) criminal justice. Information on these eight domains is derived from the American Community Survey and other administrative datasets. Our team used the Tableau Desktop visualization software and applied a user-centered design approach to developing the two OCOI dashboards-main OCOI dashboard and OCOI-race dashboard. We also performed convergence analysis to visualize the census tracts where different health indicators simultaneously exist at their worst levels. RESULTS: The OCOI dashboard has multiple, interactive components as follows: a choropleth map of Ohio displaying OCOI scores for a specific census tract, graphs presenting OCOI or domain scores to compare relative positions for tracts, and a sortable table to visualize scores for specific county and census tracts. A case study using the two dashboards for convergence analysis revealed census tracts in neighborhoods with low infant health scores and a high proportion of minority population. CONCLUSION: The OCOI dashboards could assist health care leaders in making decisions that enhance health care delivery and policy decision-making regarding children's health particularly in areas where multiple health indicators exist at their worst levels.

Patient Portals: Useful for Whom and for What? A Cross-Sectional Analysis of National Survey Data.

BACKGROUND: Patients who use patient portals may be more engaged and empowered in their care; however, differences in who accesses patient portals remain. The characteristics of who uses patient portals more frequently and who perceives them as useful may also differ, as well as which functions people use. OBJECTIVE: We assessed the characteristics of patient portal users to examine who uses them more frequently and who perceives them as useful. In addition, we wanted to see if those who use them more frequently or perceive them to be more useful use different functions or more functions of patient portals. METHODS: Pooled cross-sectional data from 2017 to 2018 Health Information National Trends Survey (HINTS) were used. Ordinal regression models were developed to assess frequency of use and perceived usefulness by demographics, and multivariable logistic regression models were used to examine the association between the use of 10 patient portal functions and frequency of use and perceived usefulness of patient portals. RESULTS: The odds of using patient portals more frequently were higher among those with Bachelor's degrees, incomes between $35,000 and $75,000, and those with two or more chronic conditions. Respondents with three or more chronic conditions had higher odds of rating patient portals as useful. Those who used their patient portal 10 or more times in the past year had higher odds of using all functions except for viewing test results compared with those who used their patient portal one to two times per year. Those who rated patient portals as "very useful" had higher odds of using seven of the functions compared with those who rated them "not very"/"not at all useful." CONCLUSION: It is important to continue to assess usefulness, frequency of use, and overall patient portal function use to identify opportunities to increase patient engagement with patient portals.

Socioeconomic Factors Influence Health Information Seeking and Trust Over Time: Evidence From a Cross-Sectional, Pooled Analyses of HINTS Data.

PURPOSE: Assessed socioeconomic factors in health information seeking behavior and trust of information sources from 2007 to 2017. DESIGN: Pooled cross-sectional survey data. SETTING: Health Information National Trends Survey. PARTICIPATION: Data included 6 iterations of U.S. adults (Pooled: N = 19,496; 2007: N = 3,593; 2011: N = 3,959; 2013: N = 3,185; FDA 2015: N = 3,738; 2017: N = 3,285; and FDA 2017: N = 1,736). MEASURES: Outcome variables were health information seeking, high confidence, and high trust of health information from several sources. Independent variables were education and income group, controlling for other sociodemographic variables. ANALYSIS: Weighted descriptive and multivariate logistic regression for the pooled sample assessed associations by education and income. Fully interacted models with education/income-survey year interactions compared differences in outcomes between years. RESULTS: We found information seeking, confidence, and trust were associated with income and education, which supported previously reported findings. Additionally, our findings indicated low-and medium-income groups had significantly lower odds of seeking health information compared to those in a high-income group. Regarding trust of information, a high school education was associated with higher odds of trust in family and friends. We also found that, over time, information seeking, confidence, and trust behavior differed by income and education, with some differences persisting. CONCLUSION: Disparities by income and education in trust of information sources remained across time. Understanding optimal information sources, their reach, and their credibility among groups could enable more targeted interventions and health messaging. We also describe the implications for our findings in the context of COVID-19.

U.S. COVID-19 State Government Public Dashboards: An Expert Review.

BACKGROUND: In the United States, all 50 state governments deployed publicly viewable dashboards regarding the novel coronavirus disease 2019 (COVID-19) to track and respond to the pandemic. States dashboards, however, reflect idiosyncratic design practices based on their content, function, and visual design and platform. There has been little guidance for what state dashboards should look like or contain, leading to significant variation. OBJECTIVES: The primary objective of our study was to catalog how information, system function, and user interface were deployed across the COVID-19 state dashboards. Our secondary objective was to group and characterize the dashboards based on the information we collected using clustering analysis. METHODS: For preliminary data collection, we developed a framework to first analyze two dashboards as a group and reach agreement on coding. We subsequently doubled coded the remaining 48 dashboards using the framework and reviewed the coding to reach total consensus. RESULTS: All state dashboards included maps and graphs, most frequently line charts, bar charts, and histograms. The most represented metrics were total deaths, total cases, new cases, laboratory tests, and hospitalization. Decisions on how metrics were aggregated and stratified greatly varied across dashboards. Overall, the dashboards were very interactive with 96% having at least some functionality including tooltips, zooming, or exporting capabilities. For visual design and platform, we noted that the software was dominated by a few major organizations. Our cluster analysis yielded a six-cluster solution, and each cluster provided additional insights about how groups of states engaged in specific practices in dashboard design. CONCLUSION: Our study indicates that states engaged in dashboard practices that generally aligned with many of the goals set forth by the Centers for Disease Control and Prevention, Essential Public Health Services. We highlight areas where states fall short of these expectations and provide specific design recommendations to address these gaps.

Persistent digital divide in health-related internet use among cancer survivors: findings from the Health Information National Trends Survey, 2003-2018.

PURPOSE: Prior research on the use of the internet among cancer survivors indicates a digital divide. The online landscape and patterns of information consumption, however, have notably changed over the past decade necessitating an updated examination of health-related internet use (HRIU) among cancer survivors. METHODS: Using survey data from 2003, 2005, 2007, 2011, 2013, 2017, and 2018 Health Information National Trends Survey (HINTS) iterations, the objectives of this study were to report prevalence, trends, and user profiles in HRIU in terms of emailing doctors, buying medicine online, and support group participation. Descriptive analyses and weighted multivariate logistic regression analyses were performed. RESULTS: Cancer survivors who reported not using the internet were more likely to be older, belong to ethnic minorities, be less educated, and reside in rural areas as compared with those who reported using the internet. Except for participation in online support groups, all other types of HRIU increased in prevalence across the years. CONCLUSIONS: Consistent with the increased penetration of the internet and the altered online health communication landscape, we found increased prevalence of HRIU among cancer survivors. However, the digital divide persists in terms of internet access. These findings can inform initiatives to bridge the gap among survivors of varying profiles in using the internet for their health needs. IMPLICATIONS FOR CANCER SURVIVORS: There is increased reliance on online platforms to obtain and communicate health-related information. The risk with this approach is potential oversight of ensuring equity in terms of internet access and technology literacy among survivors.

Odds of Meeting Cancer Prevention Behavior Recommendations by Health Information Seeking Behavior: a Cross-Sectional HINTS Analysis.

People who seek health information frequently may be more likely to meet health behavior goals; however, people use many different information sources. The purpose of this paper is to assess how different sources of health information influence likelihood of meeting cancer prevention behavior guidelines. Logistic regression of cross-sectional data from 6 years of the Health Information National Trends Survey (HINTS) was conducted. Independent variables included first source of health information, gender, age, race, education level, income, cancer history, general health, and data year; dependent variables were fruit and vegetable intake, exercise, smoking, mammography, Pap test, and colon cancer screening. Those who seek health information from doctors, the internet, or publications had higher odds of meeting more cancer prevention guidelines than those who do not seek health information. Those who used healthcare providers as an initial information source had higher odds of meeting diet, cervical, and colon cancer screening recommendations, while using the internet as an initial source of health information was associated with higher odds of meeting diet, smoking, and colon cancer screening recommendations. No health information source was associated with meeting either exercise or mammography recommendations. People should be encouraged to seek health information to help them meet their behavior goals, especially from sources that are more likely to be accurate and encourage cancer prevention behavior. Future research is needed to understand the accuracy of health information and what kinds of health information have positive influences on cancer prevention behavior.

Differences Between Races in Health Information Seeking and Trust Over Time: Evidence From a Cross-Sectional, Pooled Analyses of HINTS Data.

PURPOSE: Assessed racial disparities in health information-seeking behavior and trust of information sources from 2007 to 2017. DESIGN: Pooled cross-sectional survey data. SETTING: Health Information National Trends Survey (HINTS). PARTICIPATION: Data included 6 iterations of HINTS (pooled: N = 19 496; 2007: n = 3593; 2011: n = 3959; 2013: n = 3185; Food and Drug Administration [FDA] 2015: n = 3738; 2017: n = 3285; and FDA 2017: n = 1736). MEASURES: Outcome variables were health information seeking, high confidence, and high trust of health information from several sources. Independent variable was race group, controlling for other sociodemographic and socioeconomic variables. ANALYSIS: Weighted descriptive and multivariate logistic regression for the pooled sample assessed associations by race. Fully interacted models with race-survey year interactions compared differences in outcomes between years. RESULTS: Black respondents, relative to white, had greater odds of having high confidence in their ability to attain health information, trust of health information from newspapers and magazines, radio, internet, television, government, charitable organizations, and religious organizations. Hispanic respondents, relative to white, had lower odds of seeking health information and trusting health information from doctors. They had higher odds of trusting health information from the radio, the internet, television, charitable organizations, and religious organizations. CONCLUSION: Disparities between races in trust of information sources remained across time. Understanding optimal information media, their reach, and credibility among racial groups could enable more targeted approaches to developing interventions. Our analytical approach minimized limitations present in the HINTS.

Visualizing Opportunity Index Data Using a Dashboard Application: A Tool to Communicate Infant Mortality-Based Area Deprivation Index Information.

BACKGROUND: An area deprivation index (ADI) is a geographical measure that accounts for socioeconomic factors (e.g., crime, health, and education). The state of Ohio developed an ADI associated with infant mortality: Ohio Opportunity Index (OOI). However, a powerful tool to present this information effectively to stakeholders was needed. OBJECTIVES: We present a real use-case by documenting the design, development, deployment, and training processes associated with a dashboard solution visualizing ADI data. METHODS: The Opportunity Index Dashboard (OID) allows for interactive exploration of the OOI and its seven domains-transportation, education, employment, housing, health, access to services, and crime. We used a user-centered design approach involving feedback sessions with stakeholders, who included representatives from project sponsors and subject matter experts. We assessed the usability of the OID based on the effectiveness, efficiency, and satisfaction dimensions. The process of designing, developing, deploying, and training users in regard to the OID is described. RESULTS: We report feedback provided by stakeholders for the OID categorized by function, content, and aesthetics. The OID has multiple, interactive components: choropleth map displaying OOI scores for a specific census tract, graphs presenting OOI or domain scores between tracts to compare relative positions for tracts, and a sortable table to visualize scores for specific county and census tracts. Changes based on parameter and filter selections are described using a general use-case. In the usability evaluation, the median task completion success rate was 83% and the median system usability score was 68. CONCLUSION: The OID could assist health care leaders in making decisions that enhance care delivery and policy decision making regarding infant mortality. The dashboard helps communicate deprivation data across domains in a clear and concise manner. Our experience building this dashboard presents a template for developing dashboards that can address other health priorities.

A Community Health Worker Home Visit Program: Facilitators and Barriers of Program Implementation.

Home visit programs have long been used as a means of intervention specifically among vulnerable, at-risk populations including: chronically ill, impoverished, rural, or homebound individuals. Understanding barriers and facilitators to the implementation of home visit programs is essential to inform these efforts. Home visit programs led by community health workers (CHWs) are becoming more common and pose specific challenges. The Ohio Infant Mortality Reduction Initiative is a home visit program led by CHWs with the purpose of reducing infant mortality among high-risk populations. The intervention included health education, referrals to health services, and provision of supplies and social support to expectant mothers. This study examined qualitative interviews with program managers and administrators to describe facilitators and barriers associated with this home visit program from a managerial perspective. Findings highlight the importance of initial and ongoing CHW training, appropriate caseloads, effective communication, and adequate funding, which can inform future CHW programs.

The effect of cancer treatment summaries on patient-centered communication and quality of care for cancer survivors: A pooled cross-sectional HINTS analysis.

OBJECTIVE: Provision of cancer treatment summaries to patients is recommended to improve patient-centered communication (PCC). The objective of this study is to assess relationships between cancer treatment summary receipt, PCC, and quality of care (QOC). METHODS: Linear and logistic regression of cross-sectional data from the Health Information National Trends Survey (HINTS) was conducted using data from years 2012, 2014, and 2017. The independent variable was receipt of treatment summary; the dependent variables were overall PCC score, six domains of PCC, and QOC. RESULTS: In the pooled sample, 36.9% of patients with cancer treatment history reported receiving a treatment summary. There was a significant positive association between overall PCC score and treatment summary receipt, and higher odds of high scores for the PCC domains "responding to emotions" and "managing uncertainty." We did not observe significant associations between treatment summary receipt and other PCC domains or QOC. CONCLUSION: Providing patients cancer treatment summaries may improve PCC, but fewer than half of patients reported receiving one of these summaries. PRACTICE IMPLICATIONS: Providing cancer treatment summaries is important, however, providing them without engaging in additional communication may be insufficient to improve all patient-centered care domains or quality of care.

Patient Engagement as Measured by Inpatient Portal Use: Methodology for Log File Analysis.

BACKGROUND: Inpatient portals (IPPs) have the potential to increase patient engagement and satisfaction with their health care. An IPP provides a hospitalized patient with similar functions to those found in outpatient portals, including the ability to view vital signs, laboratory results, and medication information; schedule appointments; and communicate with their providers. However, IPPs may offer additional functions such as meal planning, real-time messaging with the inpatient care team, daily schedules, and access to educational materials relevant to their specific condition. In practice, IPPs have been developed as websites and tablet apps, with hospitals providing the required technology as a component of care during the patient's stay. OBJECTIVE: This study aimed to describe how inpatients are using IPPs at the first academic medical center to implement a system-wide IPP and document the challenges and choices associated with this analytic process. METHODS: We analyzed the audit log files of IPP users hospitalized between January 2014 and January 2016. Data regarding the date/time and duration of interactions with each of the MyChart Bedside modules (eg, view lab results or medications and patient schedule) and activities (eg, messaging the provider and viewing educational videos) were captured as part of the system audit logs. The development of a construct to describe the length of time associated with a single coherent use of the tool-which we call a session-provides a foundational unit of analysis. We defined frequency as the number of sessions a patient has during a given provision day. We defined comprehensiveness in terms of the percentage of functions that an individual uses during a given provision day. RESULTS: The analytic process presented data challenges such as length of stay and tablet-provisioning factors. This study presents data visualizations to illustrate a series of data-cleaning issues. In the presence of these robust approaches to data cleaning, we present the baseline usage patterns associated with our patient panel. In addition to frequency and comprehensiveness, we present considerations of median data to mitigate the effect of outliers. CONCLUSIONS: Although other studies have published usage data associated with IPPs, most have not explicated the challenges and choices associated with the analytic approach deployed within each study. Our intent in this study was to be somewhat exhaustive in this area, in part, because replicability requires common metrics. Our hope is that future researchers in this area will avail themselves of these perspectives to engage in critical assessment moving forward.

Likelihood of Smoking Among Cancer Survivors: An Updated Health Information National Trends Survey Analysis.

INTRODUCTION: Cancer survivors are at high risk for cancer reoccurrence, highlighting the importance of managing behavioral risk factors for cancer. Despite this risk, many cancer survivors continue to smoke cigarettes. This article describes the relationship between smoking behavior and demographic and clinical factors in cancer survivors. METHODS: Multinomial logistic regression of cross-sectional data from the Health Information National Trends Survey was conducted using combined data from years 2003, 2005, 2007, 2011, 2012, 2013, and 2014. Independent variables included age, cancer history, race, education level, marital status, insurance status, and data year; the dependent variable was smoking status (current vs. former or never). RESULTS: Cancer survivors were less likely to be current smokers but more likely to be former smokers than those with no history of cancer. Cancer survivors that currently smoked were more likely to have lower education levels, be divorced, separated, or single, or not have health insurance. Older cancer survivors, Hispanic, and non-Hispanic black survivors were less likely to smoke. Among cancer subgroups, prostate cancer survivors had the lowest rate (8.8%) of current smoking from 2011 to 2014, and cervical cancer survivors had the highest rate (31.1%). CONCLUSIONS: Although those with no history of cancer had higher rates of current smoking, many subgroups of cancer survivors continued to smoke at higher rates than average cancer survivors. Cancer survivors that were younger, had lower education levels, were any marital status other than married or widowed, were uninsured, or survived cervical cancer were more likely to be smokers than other survivors. IMPLICATIONS: It is important to understand which types of cancer survivors are at high risk of continued smoking to better inform tobacco dependence treatment interventions among those at high risk of cancer reoccurrence. Our findings suggest targeted tobacco dependence treatment efforts among cancer survivors should focus on survivors of cervical cancer and survivors that are young, unmarried, uninsured, or have lower education levels.