To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Background: The COVID-19 pandemic disrupted essential health services (EHS) delivery worldwide; however, there are limited data for healthcare facility (HCF)–level EHS disruptions in low- and middle-income countries. We surveyed HCFs in 3 counties in Kenya to understand the extent of and reasons for EHS disruptions occurring during February 2020–May 2021. Methods: We included 3 counties in Kenya with high burden of COVID-19 at the time of study initiation. Stratified sampling of HCFs occurred by HCF level. HCF administrators were interviewed to collect information on types of EHS disruptions that occurred and reasons for disruptions, including those related to infection prevention and control (IPC). Analyses included descriptive statistics with proportions for categorical variables and median with interquartile range (IQR) for continuous variables. Results: In total, 59 HCFs in Kenya provided complete data. All 59 HCFs (100%) reported EHS disruptions due to COVID-19. Among all HCFs, limiting patient volumes was the most common disruption reported (97%), while 56% of HCFs reduced staffing of EHS and 52% suspended EHS. Median duration of disruptions ranged from 7 weeks (IQR, 0–15) for inpatient ward closures to 25 weeks (IQR, 14–37) for limiting patient volumes accessing EHS. Among HCFs that reported disruptions, the most cited reason (ie, 95% of HCFs) was fewer patients receiving services. The most common IPC-related reason for disruption was diversion of resources to accommodate physical distancing measures (76%) followed by COVID-19 outbreaks among patients or staff (34%); staff shortages due to COVID-19 illness (25%) or perceived infection risk (19%); and lack of adequate personal protective equipment (20%). Conclusions: Most HCFs reported disruptions to EHS during the pandemic, including many that were related to IPC. Some disruptions may be mitigated by strengthening IPC infrastructure and practices, including protecting healthcare personnel to prevent staffing shortages.
Background: Antibiotics are frequently prescribed in nursing homes, often inappropriately. Data sources are needed to facilitate measurement and reporting of antibiotic use to inform antibiotic stewardship efforts. Previous analyses have shown that the type of nursing-home stay, that is, short stay (<100 days), is a strong predictor of high antibiotic use compared to longer nursing-home stays. The study objective was to compare 2 different data sources, electronic health record (EHR) and long-term care (LTC) pharmacy data, for surveillance of antibiotic use and type of nursing-home stay. Methods: EHR and pharmacy data during 2017 were included from 1,933 and 1,348 US-based nursing homes, respectively. We compared data elements available in each data source for antibiotic use reporting. In each data set, we attempted to describe antibiotic use as the proportion of residents on an antibiotic, days-of-therapy (DOT) per 1,000 resident days (RD), and distribution of antibiotic course duration, overall and at the facility level. Facility proportion of short-stay and long-stay (>100 days) nursing-home residents were calculated using admission dates and census data in the EHR data set and a payor variable in the pharmacy data set (Figure 1). The 2 data sources also provided antibiotic characteristics, including antibiotic class, agent, and route of administration. The deidentified nature of facility data prevented direct comparison of antibiotic use measures between facilities. Results: The EHR and pharmacy data sets contained 381,382 and 326,713 residents, respectively (Table 1). Within the EHR, 51% of residents were prescribed an antibiotic in 2017, at a median rate of 77 DOT per 1,000 RD. In the LTC pharmacy, 46% of residents were prescribed an antibiotic at a median rate of 79 DOT per 1,000 RD (Table 1). Short-stay residents contributed a smaller proportion of total RDs in the EHR relative to the pharmacy cohort (21% vs 50%, respectively). Conclusions: Nursing-home antibiotic use data obtained from EHR and pharmacy vendors can be used for calculating antibiotic use measures, which is important for antibiotic use reporting and facility-level tracking to identify opportunities for improving prescribing practices and provide facility-level benchmarks. Further validation of both data sources in the same facilities is needed to compare antibiotic use rates and to determine the most appropriate proxy for type of nursing-home stay for facility-level risk adjustment of antibiotic use rates.
Colleges and universities around the world engaged diverse strategies during the COVID-19 pandemic. Baylor University, a community of ˜22,700 individuals, was 1 of the institutions which resumed and sustained operations. The key strategy was establishment of multidisciplinary teams to develop mitigation strategies and priority areas for action. This population-based team approach along with implementation of a “Swiss Cheese” risk mitigation model allowed small clusters to be rapidly addressed through testing, surveillance, tracing, isolation, and quarantine. These efforts were supported by health protocols including face coverings, social distancing, and compliance monitoring. As a result, activities were sustained from August 1 to December 8, 2020. There were 62,970 COVID-19 tests conducted with 1435 people testing positive for a positivity rate of 2.28%. A total of 1670 COVID-19 cases were identified with 235 self-reports. The mean number of tests per week was 3500 with approximately 80 of these positive (11/d). More than 60 student tracers were trained with over 120 personnel available to contact trace, at a ratio of 1 per 400 university members. The successes and lessons learned provide a framework and pathway for similar institutions to mitigate the ongoing impacts of COVID-19 and sustain operations during a global pandemic.
Conduct an environmental scan of Marion County (Indianapolis) neighborhoods using electronic medical record data, state health data, and social and economic data
Develop strong network of community collaborators
Conduct a thorough assessment for each targeted neighborhood by listening and understanding the pressing health issues in the community and working together to design and deliver solutions
Identify measures in the 3 domains of vulnerability, health and assets for the targeted neighborhoods and conduct bivariate descriptive statistics and multivariable regression analyses to investigate association between measures of vulnerability and health outcomes.
Initiate relationships with leaders and residents in targeted neighborhoods
Locate organizations working in targeted neighborhoods through online mapping software and word-of-mouth at neighborhood events, and created a spreadsheet with contact information.
Conduct multidisciplinary assessment (i.e. key informant interviews, focus groups, town hall meetings) of the targeted neighborhood.
Iteratively synthesize assessments to develop areas of interest and relevance to the community.
Develop a road map for solutions identified by the community.
RESULTS/ANTICIPATED RESULTS: The results from the environmental scan conducted will be displayed in a report and visual “map” of health outcomes and health determinants, including assets and barriers for the targeted neighborhoods. The research team will use results from the environmental scan coupled with listening activities including attendance at community events, key informant interviews and focus groups to develop relationships and strong collaborations with the targeted neighborhood stakeholders. The relationship building between the research team and community will provide increased trust and engagement that will further enhance the effectiveness of the assessments completed with the targeted neighborhood. The assessments will help to empower communities to develop sustainable solutions and drive future work that will lead to future grant applications and larger-scale implementation in other community impact hub neighborhoods. DISCUSSION/SIGNIFICANCE OF IMPACT: Through the community impact hub work, we will develop collaborative efforts with targeted neighborhoods with the greatest health inequities in the Marion County area. In partnership with these neighborhoods, we will build a foundation – a network of community collaborators and a focused plan – upon which we will improve the health outcomes of residents while learning best practices on how to do so.
This chapter summarises the main themes of the book, placing individual chapters within diverse thematic frameworks. After a brief discussion of the evolution of human violence, it introduces the Palaeolithic and Neolithic beginnings of human violence before examining prehistoric and ancient warfare. This includes considerations of the role of farming in the Neolithic, the more specialised warfare of the Bronze and Iron Ages, the era of classical antiquity and the growing importance of osteoarchaeology in understanding early violence. The discussion then continues with the other themes of the volume: intimate and collective violence; religion, ritual and violence; violence, crime and the state; and representations and constructions of violence.
The first in a four-volume set, The Cambridge World History of Violence, volume I provides a comprehensive examination of violence in prehistory and the ancient world. Covering the period through to the end of classical antiquity, the chapters take a global perspective spanning sub-Saharan Africa, the Near East, Europe, India, China, Japan and Central America. Unlike many previous works, this book does not focus only on warfare but examines violence as a broader phenomenon. The historical approach complements, and in some cases critiques, previous research on the anthropology and psychology of violence in the human story. Written by a team of contributors who are experts in each of their respective fields, this volume will be of particular interest to anyone fascinated by archaeology and the ancient world.
Different diagnostic interviews are used as reference standards for major depression classification in research. Semi-structured interviews involve clinical judgement, whereas fully structured interviews are completely scripted. The Mini International Neuropsychiatric Interview (MINI), a brief fully structured interview, is also sometimes used. It is not known whether interview method is associated with probability of major depression classification.
To evaluate the association between interview method and odds of major depression classification, controlling for depressive symptom scores and participant characteristics.
Data collected for an individual participant data meta-analysis of Patient Health Questionnaire-9 (PHQ-9) diagnostic accuracy were analysed and binomial generalised linear mixed models were fit.
A total of 17 158 participants (2287 with major depression) from 57 primary studies were analysed. Among fully structured interviews, odds of major depression were higher for the MINI compared with the Composite International Diagnostic Interview (CIDI) (odds ratio (OR) = 2.10; 95% CI = 1.15–3.87). Compared with semi-structured interviews, fully structured interviews (MINI excluded) were non-significantly more likely to classify participants with low-level depressive symptoms (PHQ-9 scores ≤6) as having major depression (OR = 3.13; 95% CI = 0.98–10.00), similarly likely for moderate-level symptoms (PHQ-9 scores 7–15) (OR = 0.96; 95% CI = 0.56–1.66) and significantly less likely for high-level symptoms (PHQ-9 scores ≥16) (OR = 0.50; 95% CI = 0.26–0.97).
The MINI may identify more people as depressed than the CIDI, and semi-structured and fully structured interviews may not be interchangeable methods, but these results should be replicated.
Declaration of interest
Drs Jetté and Patten declare that they received a grant, outside the submitted work, from the Hotchkiss Brain Institute, which was jointly funded by the Institute and Pfizer. Pfizer was the original sponsor of the development of the PHQ-9, which is now in the public domain. Dr Chan is a steering committee member or consultant of Astra Zeneca, Bayer, Lilly, MSD and Pfizer. She has received sponsorships and honorarium for giving lectures and providing consultancy and her affiliated institution has received research grants from these companies. Dr Hegerl declares that within the past 3 years, he was an advisory board member for Lundbeck, Servier and Otsuka Pharma; a consultant for Bayer Pharma; and a speaker for Medice Arzneimittel, Novartis, and Roche Pharma, all outside the submitted work. Dr Inagaki declares that he has received grants from Novartis Pharma, lecture fees from Pfizer, Mochida, Shionogi, Sumitomo Dainippon Pharma, Daiichi-Sankyo, Meiji Seika and Takeda, and royalties from Nippon Hyoron Sha, Nanzando, Seiwa Shoten, Igaku-shoin and Technomics, all outside of the submitted work. Dr Yamada reports personal fees from Meiji Seika Pharma Co., Ltd., MSD K.K., Asahi Kasei Pharma Corporation, Seishin Shobo, Seiwa Shoten Co., Ltd., Igaku-shoin Ltd., Chugai Igakusha and Sentan Igakusha, all outside the submitted work. All other authors declare no competing interests. No funder had any role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.