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[This corrects the article DOI: 10.1371/journal.pone.0272558.].

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OBJECTIVES: This study examines the contents of official communication from United States governors' offices related to the COVID-19 pandemic to assess patterns in communication and to determine if they correlate with trends for COVID cases and deaths. METHODS: We collected text data for all COVID-19 related press releases between March 1 and December 31, 2020 from the US governors' office websites in all 50 states. An automated parsing and sentiment analyzer assessed descriptive statistics and trends in tone, including positivity and negativity. RESULTS: We included a total of 7,720 press releases in this study. We found that both positive and negative sentiments were homogenous across states at the beginning of the pandemic but became heterogeneous as the pandemic evolved. The same trend applied to the frequency and tone of press releases. Sentiments across states were overall positive with a small level of negativity. We observed a reactive official communication to the evolution of the number of COVID-19 cases rather than responsive or preventive. CONCLUSIONS: The findings of both positivity and negativity in press communications suggest that the effect of discounted importance was present in official communications. Our findings support a state-dependent optimal communication frequency and tone, agreeing with the curvilinear communication model of organizational theory and implying that feedback cycles between government officials and public response should be shortened to rapidly maximize communication efficacy during the pandemic. Future research should identify and evaluate the drivers of the large differences in communication tone across states and validate the reactive characteristics of COVID-19 official communications.

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INTRODUCTION: This study compared costs of care among colorectal surgery patients who received liposomal bupivacaine versus those who did not (control) from a health institution perspective. MATERIAL AND METHODS: This pharmacoeconomic evaluation was conducted among adults undergoing open or minimally invasive colorectal resection at an academic medical center from May 2016 to February 2018. Healthcare resource utilization was derived from the electronic health record. Total cost of care (2018 USD) was analyzed using a generalized linear model adjusted for American Society of Anesthesiologists score, enhanced recovery after surgery management, open surgery, opioid use before surgery, height, cancer, and age. The primary analysis used public costs. A sensitivity analysis used internal costs from the hospital to maximize internal validity. RESULTS: Of 486 included patients, 286 (59%) received liposomal bupivacaine. Total cost of care using public costs included perioperative local anesthetics (mean ± standard deviation [SD]: $392 ± 74 liposomal bupivacaine versus $8 ± 13 control), analgesics within 48 h after surgery (mean ± SD: $132 ± 99 liposomal bupivacaine versus $117 ± 127 control), postoperative ileus management (mean ± SD: $5 ± 51 liposomal bupivacaine versus $65 ± 284 control), and hospital length of stay (mean ± SD: $4459 ± 3576 liposomal bupivacaine versus $7769 ± 7082 control). Liposomal bupivacaine was associated with an adjusted absolute difference in total cost of care of -$1435 (95% confidence interval -$2401 to -$470; P = 0.004) using public costs and -$1345 (95% confidence interval -$2215 to -$476; P = 0.002) using internal costs. CONCLUSIONS: Use of liposomal bupivacaine in colorectal surgery was associated with a significant reduction in total cost of care that was predominately driven by reduced costs for hospital stay and postoperative ileus management despite higher medication costs.

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BACKGROUND: The COVID-19 pandemic has exacerbated the challenges of meaningful health care digitization. The need for rapid yet validated decision-making requires robust data infrastructure. Organizations with a focus on learning health care (LHC) systems tend to adapt better to rapidly evolving data needs. Few studies have demonstrated a successful implementation of data digitization principles in an LHC context across health care systems during the COVID-19 pandemic. OBJECTIVE: We share our experience and provide a framework for assembling and organizing multidisciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of health care digitization, in the context of LHC systems across a complex health care organization. METHODS: Houston Methodist (HM) comprises eight tertiary care hospitals and an expansive primary care network across Greater Houston, Texas. During the early phase of the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and established the retrospective research task force (RRTF). We describe an account of the structure, functioning, and productivity of the RRTF. We further elucidate the technical and structural details of a comprehensive data repository-the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard health care digitization principles in the LHC context. RESULTS: The HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. The RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research; to date, it has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR, which in itself was IRB-approved in April 2020. CURATOR is a relational structured query language database that is directly populated with data from electronic health records, via largely automated extract, transform, and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 cases and controls before and after COVID-19 testing. CURATOR has been set up following the SSoT principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. It currently hosts deeply phenotyped sociodemographic, clinical, and outcomes data of approximately 200,000 individuals tested for COVID-19. It supports more than 30 IRB-approved protocols across several clinical domains and has generated numerous publications from its core and associated data sources. CONCLUSIONS: A data-driven decision-making strategy is paramount to the success of health care organizations. Investment in cross-disciplinary expertise, health care technology, and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future health care catastrophes by providing timely and validated decision support.

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INTRODUCTION: Type 2 diabetes prevalence is increasing in the USA, especially in underserved populations. Patient outcomes can be improved by providing access to specialty care within Federally Qualified Health Centers, possibly improving the cost-effectiveness of diabetes care. METHODS AND ANALYSIS: A new model of diabetes care based on multidisciplinary teams of clinical fellows, supported by an endocrinologist for underserved adult populations, is presented. The study uses a retrospective, non-randomised cohort of patients with diabetes who visited the community clinic between 1 January 2012 and 31 December 2018. A quasi-experimental method to analyse the causal evidence of the effect of the new model is presented. Discontinuity regression is used to compare two interventions, the intervention by a Clinical Fellow Endocrinology Programme and usual care by a primary care physician. Patients are referred to the Clinical Fellow Endocrinology Programme in case of uncontrolled diabetes (glycated haemoglobin (HbA1c)≥9%). The regression discontinuity design allows the construction of a treatment group for patients with an HbA1c equal or above the threshold in comparison with a control group for patients with an HbA1c below the threshold. The patient outcomes and cost-effectiveness of the new model are analysed. Regression models will be used to assess the differences between treatment and control groups. ETHICS AND DISSEMINATION: Quantitative patient data are received by the study team in a de-identified format for analysis via an institutional review board-approved protocol. The quantitative study has been approved by the Houston Methodist Research Institute Institutional Review Board, Houston, Texas, USA. Anticipated results will not only provide evidence about the impact of patient outcomes in underserved diabetic populations, but also give an idea of the cost-effectiveness of the new model and whether or not cost savings can be attained for patients, third-party payers and society. The results will help set up evidence-based policy guidelines in diabetes care. Results will be disseminated through papers, conferences and public health/policy fora.

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BACKGROUND: This study explores the relationship between armed conflict and breastfeeding practices of Iraqi mothers. To date, the relationship between violent conflict and breastfeeding is surprisingly understudied. Especially in the Middle East, which is conflict-prone and has a young population, research on war and household behavior is critical for promoting recovery and sustainable development. METHODS: This study employs a unique pairing of the Iraq Body Count Database and the 2006 and 2011 Multiple Indicator Cluster Surveys for Iraq. We use probit models to explore the association between armed conflict and several breastfeeding outcomes - whether a child was ever breastfed, whether a child was breastfed within 1 h after birth, whether a child is currently breastfed, and whether an infant under 6 months of age is exclusively breastfed. Our proxies for conflict intensity are the average rate of conflict-related casualties across the 3 years prior to survey administration and the rate of casualties averaged across the 2 years prior to the birth of the child, in the governorate in which the family resides. We employ a number of other independent variables important for breastfeeding status, including health controls and characteristics of the household, child and mother. We also use a Cox proportional hazards model to study the association between conflict and breastfeeding duration. We complement this analysis with various robustness checks, including disaggregation by year, controls for household wealth and an analysis of breastmilk substitutes and their potential for an interaction with household wealth. RESULTS: We find in our main results that increases in conflict-related casualties are associated with a significant decline in the probability that a child was ever breastfed and a decline in the probability that a child is currently breastfeeding. There is no significant association with exclusive breastfeeding or with initiation of breastfeeding within 1 h after birth. This result is robust to alternative measures of conflict, although some coefficients from estimation based on the 2006 subsample are positive and not significant, and reverse causation is a potential source of bias in interpreting cross-sectional feeding patterns. Results on breastfeeding duration are mixed. Our results also suggest an increase in the use of breastfeeding substitutes like formula concurrent to higher levels of conflict among wealthier households. CONCLUSION: The results are informative in the context of designing policy aimed at stabilizing the long-term health and productivity of populations in conflict areas. Infant formula provided with the objective of offering temporary relief creates risks, including reducing the probability and duration of breastfeeding. Attention to the supply of health care and to support systems for women, especially skilled breastfeeding support and targeted support to infants dependent on formula, are matters of the utmost urgency during and after conflict periods.

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BACKGROUND: Although the importance of nurse leadership stability and participation in decision making in nursing homes is well established, scarce literature exists on determinants of intent to leave among directors of nursing (DONs) in nursing homes. PURPOSE: : The purpose of this study was to examine factors associated with DON intent to leave in nursing homes. METHODOLOGY: We examined potential factors associated with DON intent to leave at three levels: individual DON characteristics, facility, and county-level market factors. A survey of nurse supervisors in Texas nursing homes, the 2003 Texas Nursing Facility Medicaid Cost Report, and the Area Resource File were merged. We only included respondents who identified themselves as DONs in this study (572 observations). We examined bivariate differences in individual DON characteristics on the basis of facility ownership (for-profit versus not-for-profit homes) and geographic location (urban versus rural location). We constructed three alternative logit models to explore the relationships between DON intent to leave and DON, facility, and market characteristics. FINDINGS: DONs working in for-profit homes were more inclined to leave, less satisfied with their job, and had lower levels of perceived empowerment in terms of autonomy. Educational level and intention to leave were significantly higher for DONs working in urban areas. Job satisfaction was significantly and inversely associated with intent to leave in all three models. Higher perceived salary competitiveness and level of empowerment were associated with reduced odds of intending to leave. Higher educational levels were associated with higher odds of intentions to leave. PRACTICE IMPLICATIONS: Nursing homes should focus on improving DON job satisfaction, empowerment in decision making, and salary competitiveness when designing retention strategies for DONs.

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PURPOSE: We examined the effects of facility and market-level characteristics on staffing levels and turnover rates for direct care staff, and we examined the effect of staff turnover on staffing levels. DESIGN AND METHODS: We analyzed cross-sectional data from 1,014 Texas nursing homes. Data were from the 2002 Texas Nursing Facility Medicaid Cost Report and the Area Resource File for 2003. After examining factors associated with staff turnover, we tested the significance and impact of staff turnover on staffing levels for registered nurses (RNs), licensed vocational nurses (LVNs) and certified nursing assistants (CNAs). RESULTS: All three staff types showed strong dependency on resources, such as reimbursement rates and facility payor mix. The ratio of contracted to employed nursing staff as well as RN turnover increased LVN turnover rates. CNA turnover was reduced by higher administrative expenditures and higher CNA wages. Turnover rates significantly reduced staffing levels for RNs and CNAs. LVN staffing levels were not affected by LVN turnover but were influenced by market factors such as availability of LVNs in the county and women in the labor force. IMPLICATIONS: Staffing levels are not always associated with staff turnover. We conclude that staff turnover is a predictor of RN and CNA staffing levels but that LVN staffing levels are associated with market factors rather than turnover. Therefore, it is important to focus on management initiatives that help reduce CNA and RN turnover and ultimately result in higher nurse staffing levels in nursing homes.

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