EMPLOYEE MOTIVATION AND PUBLIC SECTOR FRAUD: EVIDENCE FROM KWARA STATE, NIGERIA

This study examines the relationship between fraud and employee motivation in the Kwara State public sector with a view to ascertain what will happen to fraud occurrence if good salary, allowances and perquisites, effective working hours, conducive environment, timely promotion are put in place. Multistage sampling technique was used in obtaining the primary data used from 870 respondents selected from 7 local governments in the state and were analysed using Ordinary Least Square Regression and Friedman ANOVA test but interpreted using R2, adjusted R2, Durbin Watson Statistics, F statistics and t statistics. The results show that employee motivational factors (salary, perquisites and regular promotion) can reduce fraud activities among the state employees; this is in consonance with theoretical expectations (Douglas McGregor’s theory Y, Abraham Maslow’s needs theory and Fredric Herzberg’s two-factor theory). Contrarily, allowances, conducive environment and training show a positive relationship with fraud and this is at variance with a-priori expectations. The study recommends that government should improve the working conditions of its employee including the provision of improved salary structure, prompt payment of entitlements and regular promotions as well as provided adequate training to motivate employees towards efficiency, commitment and inhibit fraud inclinations. If all these are in place and erring officials are made to face the full wrath of the law without bias, the state economy in particular and Nigeria economy in general will be better for it.Â

LIFE-CYCLE COST ANALYSIS OF RECLAIMED ASPHALT PAVEMENT FOR SUSTAINABLE ROAD REHABILITATION

The construction of pavements requires a significant amount of non-renewable materials and energy. Recycling of asphalt pavements is a valuable approach for technical, economic and environmental reasons. The use of reclaimed asphalt pavement (RAP) is being favoured over virgin materials due to increasing cost of asphalt, the scarcity of quality aggregates and the pressing need to preserve the environment. This paper present a life cycle cost analysis of recycled pavement material for sustainable road rehabilitation on Jabulani Selepe road in Bethal, Mpumalanga Province of South Africa.  Long term cost effects of recycled materials was determined in pavement design; life-cycle cost analysis (LCCA) was carried out on recycled materials, alternative recycling materials and conventional method. The present worth of cost (PWOC) for recycling and conventional method was used to determine the most viable option for construction and maintenance. Agency cost, initial rehabilitation, maintenance, future and salvage cost while the users cost which include construction delays, accident cost, time and vehicle operating cost was done. The result showed that LCCA identifies recycled RAP as the lowest cost pavement alternative. The PWOC for RAP and alternative recycling material was 50.90% and 41.48% respectively when compared with conventional method. Thus, using large amount of RAP could turn these asphalt mixes into good alternative to hot mix asphalt in environmental terms and cost-effective means to road rehabilitation than that of a conventional project

IMPROVING THE STRENGTH PROPERTIES OF SUBGRADE SOILS WITH FLY ASH

Subgrade soils encountered during road construction are not always good materials to respond to the imposed stresses which has become a dominating factor for the failure of pavements in Nigeria. The subgrade materials were sourced from three locations within South Africa namely, Heanertsburg Village (A), Laudium, South West of Pretoria (B), and Eskia Mphahele drive to Francis Baard Street, Pretoria (C). Fly Ash (FA) was added to samples A, B, and C at 3-12%, 5-15%, and 9-12% respectively. Sieve analysis, compaction, Unconfined Compressive Strength (UCS) and Indirect Tensile Strength (ITS) tests were conducted on the virgin and stabilized soil samples for a curing time of 1, 7 and 28 days. The soils were classified as A-7-6, A-6 and A-2-6 according to AASHTO for samples A, B and C respectively. UCS and ITS was improved with the addition of FA to all the soil samples. The UCS results for sample A (406 kN/m2) and B (625 kN/m2) falls short of 1710 kN/m2 specified for cement stabilized base materials but 12% and 10% FA treated soils may be used as improved subgrade for flexible pavement construction. However, sample C result meets the requirement of 687-1373 kN/m2 for sub-base at 100% relative compaction. All the stabilized samples increase in ITS with respect to increasing curing days and FA. Only sample C attained to the specified ITS value. Therefore, FA can be used to improve the subgrade in order to withstand the imposed stress

Classifying Complications: Assessing Adult Spinal Deformity 2-Year Surgical Outcomes.

STUDY DESIGN: Retrospective review of prospective database. OBJECTIVE: Complication rates for adult spinal deformity (ASD) surgery vary widely because there is no accepted system for categorization. Our objective was to identify the impact of complication occurrence, minor-major complication, and Clavien-Dindo complication classification (Cc) on clinical variables and patient-reported outcomes. METHODS: Complications in surgical ASD patients with complete baseline and 2-year data were considered intraoperatively, perioperatively (\u3c6 \u3eweeks), and postoperatively (\u3e6 weeks). Primary outcome measures were complication timing and severity according to 3 scales: complication presence (yes/no), minor-major, and Cc score. Secondary outcomes were surgical outcomes (estimated blood loss [EBL], length of stay [LOS], reoperation) and health-related quality of life (HRQL) scores. Univariate analyses determined complication presence, type, and Cc grade impact on operative variables and on HRQL scores. RESULTS: Of 167 patients, 30.5% (n = 51) had intraoperative, 48.5% (n = 81) had perioperative, and 58.7% (n = 98) had postoperative complications. Major intraoperative complications were associated with increased EBL ( CONCLUSION: The Cc Scale was most useful in predicting changes in patient outcomes; at 2 years, patients with raised perioperative Cc scores and postoperative complications saw reduced HRQL improvement. Intraoperative and perioperative complications were associated with worse short-term surgical and inpatient outcomes

A Copine family member, Cpne8, is a candidate quantitative trait gene for prion disease incubation time in mouse

Prion disease incubation time in mice is determined by many factors including genetic background. The prion gene itself plays a major role in incubation time; however, other genes are also known to be important. Whilst quantitative trait loci (QTL) studies have identified multiple loci across the genome, these regions are often large, and with the exception of Hectd2 on Mmu19, no quantitative trait genes or nucleotides for prion disease incubation time have been demonstrated. In this study, we use the Northport heterogeneous stock of mice to reduce the size of a previously identified QTL on Mmu15 from approximately 25 to 1.2 cM. We further characterised the genes in this region and identify Cpne8, a member of the copine family, as the most promising candidate gene. We also show that Cpne8 mRNA is upregulated at the terminal stage of disease, supporting a role in prion disease. Applying these techniques to other loci will facilitate the identification of key pathways in prion disease pathogenesis

HECTD2 Is Associated with Susceptibility to Mouse and Human Prion Disease

Prion diseases are fatal transmissible neurodegenerative disorders, which include Scrapie, Bovine Spongiform Encephalopathy (BSE), Creutzfeldt-Jakob Disease (CJD), and kuru. They are characterised by a prolonged clinically silent incubation period, variation in which is determined by many factors, including genetic background. We have used a heterogeneous stock of mice to identify Hectd2, an E3 ubiquitin ligase, as a quantitative trait gene for prion disease incubation time in mice. Further, we report an association between HECTD2 haplotypes and susceptibility to the acquired human prion diseases, vCJD and kuru. We report a genotype-associated differential expression of Hectd2 mRNA in mouse brains and human lymphocytes and a significant up-regulation of transcript in mice at the terminal stage of prion disease. Although the substrate of HECTD2 is unknown, these data highlight the importance of proteosome-directed protein degradation in neurodegeneration. This is the first demonstration of a mouse quantitative trait gene that also influences susceptibility to human prion diseases. Characterisation of such genes is key to understanding human risk and the molecular basis of incubation periods

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Primary caregivers, healthcare workers, teachers and community leaders' perceptions and experiences of their involvement, practice and challenges of disclosure of HIV status to children living with HIV in Malawi: A qualitative study

Background: The World Health Organisation has recommended that healthcare workers, teachers and community leaders work with parents to support children living with HIV. The aim of this study was to assess the perceptions and experiences of primary caregivers and other care providers such as healthcare workers, teachers, and community leaders regarding their involvement, practice and challenges of HIV disclosure to children aged between 6 and 12 years living with HIV in Malawi. Methods: Twelve focus group discussions and 19 one-on-one interviews involving a total of 106 participants were conducted in all three administrative regions of Malawi. The interviews and focus group discussions explored perceptions and experiences regarding involvement, practice and challenges of disclosure of HIV status to children. Data were analysed using thematic analysis. Results: Primary caregivers, healthcare workers, teachers, and community leaders all reported that the disclosure of HIV status to children was not well coordinated because each of the groups of participants was working in isolation instead of working as a team. A "working together" model emerged from the data analysis where participants expressed the need for them to work as a team in order to promote safe and effective HIV status disclosure through talking about HIV, sharing responsibility and open communication. Participants reported that by working together, the team members would ensure that the prevalence of HIV disclosure to young children increases and that there would be a reduction in any negative impact of disclosure. Conclusion: Global resources are required to better support children living with HIV and their families. Healthcare workers and teachers would benefit greatly from training in working together with families living with HIV and, specifically, training in the disclosure process. Resources, in the form of books and other educational materials, would help them explain HIV and its effective management to children and families

a systematic analysis for the Global Burden of Disease Study 2021

Funding Information: Research reported in this publication was supported by the Bill & Melinda Gates Foundation (OPP1152504); Queensland Department of Health, Australia; UK Department of Health and Social Care; the Norwegian Institute of Public Health; St Jude Children's Research Hospital; and the New Zealand Ministry of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. Data for this research was provided by MEASURE Evaluation, funded by the US Agency for International Development (USAID). Views expressed do not necessarily reflect those of USAID, the US Government, or MEASURE Evaluation. This study uses a dataset provided by European Centre for Disease Prevention and Control (ECDC) based on data provided by WHO and Ministries of Health from the affected countries. The views and opinions of the authors expressed herein do not necessarily state or reflect those of the ECDC. The accuracy of the authors' statistical analysis and the findings they report are not the responsibility of ECDC. ECDC is not responsible for conclusions or opinions drawn from the data provided. ECDC is not responsible for the correctness of the data and for data management, data merging, and data collation after provision of the data. ECDC shall not be held liable for improper or incorrect use of the data. Health Behaviour in School-Aged Children (HBSC) is an international study carried out in collaboration with WHO/EURO. The international coordinator of the 1997\u201398, 2001\u201302, 2005\u201306, and 2009\u201310 surveys was Candace Currie and the Data Bank Manager for the 1997\u201398 survey was Bente Wold, whereas for the following survey Oddrun Samda was the databank manager. A list of principal investigators in each country can be found at http://www.hbsc.org. Parts of this material are based on data and information provided by the Canadian institute for Health Information. However, the analyses, conclusions, opinions and statements expressed herein are those of the author and not those of the Canadian Institute for Health information. The data reported here have been supplied by the US Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US Government. The data used in this paper come from the 2009\u201310 Ghana Socioeconomic Panel Study Survey which is a nationally representative survey of over 5,000 households in Ghana. The survey is a joint effort undertaken by the Institute of Statistical, Social and Economic Research (ISSER) at the University of Ghana, and the Economic Growth Centre (EGC) at Yale University. It was funded by the Economic Growth Center. At the same time, ISSER and the EGC are not responsible for the estimations reported by the analyst(s). The harmonised dataset was downloaded from the Global Dietary Database (GDD) website ( https://www.globaldietarydatabase.org/). The Canadian Community Health Survey - Nutrition (CCHS-Nutrition), 2015 is available online ( https://www.globaldietarydatabase.org/management/microdata-surveys/650). The harmonisation of the original dataset was performed by GDD. The data was adapted from Statistics Canada, Canadian Community Health Survey: Public Use Microdata File, 2015/2016 (Statistics Canada. CCHS-Nutrition, 2015); this does not constitute an endorsement by Statistics Canada of this product. The data is used under the terms of the Statistics Canada Open Licence (Statistics Canada. Statistics Canada Open Licence. https://www.statcan.gc.ca/eng/reference/licence). The Health and Retirement Study (HRS) is sponsored by the National Institute on Aging (grant number NIA U01AG009740) and is conducted by the University of Michigan. The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with license no. SLN2014-3-170, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law - 2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data. The results and conclusions are mine and not those of Eurostat, the European Commission, or any of the national statistical authorities whose data have been used. This manuscript is based on data collected and shared by the International Vaccine Institute (IVI) from an original study it conducted with support from the Bill & Melinda Gates Foundation. This paper uses data from SHARE Waves 1, 2, 3 (SHARELIFE), 4, 5 and 6 (dois: 10.6103/SHARE.w1.611,10.6103/SHARE.w2.611, 10.6103/SHARE.w3.611, 10.6103/SHARE.w4.611, 10.6103/SHARE.w5.611, 10.6103/SHARE.w6.611), see B\u00F6rsch-Supan et al. (2013) for methodological details. The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006- 028812) and FP7 (SHARE-PREP: N\u00B0211909, SHARE-LEAP: N\u00B0227822, SHARE M4: N\u00B0261982). Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the US National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C) and from various national funding sources is gratefully acknowledged (see www.share-project.org). This paper uses data from the Algeria - Setif and Mostaganem 2003 STEPS survey, implemented by Ministry of Health, Population and Hospital Reform (Algeria) with the support of WHO. This paper uses data from the Algeria 2016-2017 STEPS survey, implemented by Ministry of Health (Algeria) with the support of WHO. This paper uses data from the American Samoa 2004 STEPS survey, implemented by Department of Health (American Samoa) and Monash University (Australia) with the support of WHO. This paper uses data from the Armenia 2016 STEPS survey, implemented by Ministry of Health (Botswana) with the support of WHO. This paper uses data from the Azerbaijan 2017 STEPS survey, implemented by Ministry of Health (Azerbaijan) with the support of WHO. This paper uses data from the Bangladesh 2018 STEPS survey, implemented by Ministry of Health and Family Welfare (Bangladesh) with the support of WHO. This paper uses data from the Barbados 2007 STEPS survey, implemented by Ministry of Health (Barbados) with the support of WHO. This paper uses data from the Belarus 2016-2017 STEPS survey, implemented by Republican Scientific and Practical Center of Medical Technologies, Informatization, Management and Economics of Public Health (Belarus) with the support of WHO. This paper uses data from the Benin - Littoral 2007 STEPS survey, the Benin 2008 STEPS survey, and the Benin 2015 STEPS survey, implemented by Ministry of Health (Benin) with the support of WHO. This paper uses data from the Bhutan - Thimphu 2007 STEPS survey, implemented by Ministry of Health (Bhutan) with the support of WHO. This paper uses data from the Bhutan 2014 STEPS survey, implemented by Ministry of Health (Bhutan) with the support of the World Health Organization. This paper uses data from the Botswana 2014 STEPS survey, implemented by Ministry of Health (Armenia), National Institute of Health with the support of WHO. This paper uses data from the Brunei 2015-2016 STEPS survey, implemented by Ministry of Health (Brunei) with the support of WHO. This paper uses data from the Cambodia 2010 STEPS survey, implemented by Ministry of Health (Cambodia) with the support of WHO. This paper uses data from the Cameroon 2003 STEPS survey, implemented by Health of Populations in Transition (HoPiT) Research Group (Cameroon) and Ministry of Public Health (Cameroon) with the support of WHO. This paper uses data from the Cape Verde 2007 STEPS survey, implemented by Ministry of Health, National Statistics Office with the support of WHO. This paper uses data from the Central African Republic - Bangui 2010 STEPS survey and Central African Republic - Bangui and Ombella M'Poko 2016 STEPS survey, implemented by Ministry of Health and Population (Central African Republic) with the support of WHO. This paper uses data from the Comoros 2011 STEPS survey, implemented by Ministry of Health (Comoros) with the support of WHO. This paper uses data from the Congo - Brazzaville 2004 STEPS survey, implemented by Ministry of Health, Population and Hospital Reform (Algeria) with the support of WHO. This paper uses data from the Cook Islands 2003\u20132004 survey and Cook Islands 2013\u20132015 STEPS survey, implemented by Ministry of Health (Cook Islands) with the support of WHO. This paper uses data from the Eritrea 2010 STEPS survey, implemented by Ministry of Health (Eritrea) with the support of WHO. This paper uses data from the Fiji 2002 STEPS survey, implemented by Fiji School of Medicine, Menzies Center for Population Health Research, University of Tasmania (Australia), Ministry of Health (Fiji) with the support of WHO. This paper uses data from the Fiji 2011 STEPS survey, implemented by Ministry of Health (Fiji) with the support of WHO. This paper uses data from the Georgia 2016 STEPS survey, implemented by National Center for Disease Control and Public Health (Georgia) with the support of WHO. This paper uses data from the Ghana - Greater Accra Region 2006 STEPS survey, implemented by Ghana Health Service with the support of WHO. This paper uses data from the Guniea 2009 STEPS survey, implemented by Ministry of Public Health and Hygiene (Guinea) with the support of WHO. This paper uses data from the Guyana 2016 STEPS survey, implemented by Ministry of Health (Guyana) with the support of WHO. This paper uses data from the Iraq 2015 STEPS survey, implemented by Ministry of Health (Iraq) with the support of WHO. This paper uses data from the Kenya 2015 STEPS survey, implemented by Kenya National Bureau of Statistics, Ministry of Health (Kenya) with the support of WHO. This paper uses data from the Kiribati 2004\u20132006 STEPS survey and the Kiribati 2016 survey, implemented by Ministry of Health and Medical Services (Kiribati) with the support of WHO. This paper uses data from the Kuwait 2006 STEPS survey and the Kuwait 2014 STEPS survey, implemented by Ministry of Health (Kuwait) with the support of WHO. This paper uses data from the Kyrgyzstan 2013 STEPS survey, implemented by Ministry of Health (Kyrgyzstan) with the support of WHO. This paper uses data from the Laos 2013 STEPS survey, implemented by Ministry of Health (Laos) with the support of WHO. This paper uses data from the Lebanon 2016-2017 STEPS survey, implemented by Ministry of Public Health (Lebanon) with the support of WHO. This paper uses data from the Lesotho 2012 STEPS survey, implemented by Ministry of Health and Social Welfare (Lesotho) with the support of WHO. This paper uses data from the Liberia 2011 STEPS survey, implemented by Ministry of Health and Social Welfare (Liberia) with the support of WHO. This paper uses data from the Libya 2009 STEPS survey, implemented by Secretariat of Health and Environment (Libya) with the support of WHO. This paper uses data from the Malawi 2009 STEPS survey and Malawi 2017 STEPS survey, implemented by Ministry of Health (Malawi) with the support of WHO. This paper uses data from the Mali 2007 STEPS survey, implemented by Ministry of Health (Mali) with the support of WHO. This paper uses data from the Marshall Islands 2002 STEPS survey and the Marshall Islands 2017-2018 STEPS survey, implemented by Ministry of Health (Marshall Islands) with the support of WHO. This paper uses data from the Mauritania- Nouakchott 2006 STEPS survey, implemented by Ministry of Health (Mauritania) with the support of WHO. This paper uses data from the Micronesia - Chuuk 2006 STEPS survey, implemented by Ministry of Health (Palestine) with the support of WHO. This paper uses data from the Micronesia - Chuuk 2016 STEPS survey, implemented by Chuuk Department of Health Services (Micronesia), Department of Health and Social Affairs (Micronesia) with the support of WHO. This paper uses data from the Micronesia - Pohnpei 2002 STEPS survey, implemented by Centre for Physical Activity and Health, University of Sydney (Australia), Department of Health and Social Affairs (Micronesia), Fiji School of Medicine, Micronesia Human Resources Development Center, Pohnpei State Department of Health Services with the support of WHO. This paper uses data from the Micronesia - Pohnpei 2008 STEPS survey, implemented by FSM Department of Health and Social Affairs, Pohnpei State Department of Health Services with the support of WHO. This paper uses data from the Micronesia - Yap 2009 STEPS survey, implemented by Ministry of Health and Social Affairs (Micronesia) with the support of WHO. This paper uses data from the Micronesia- Kosrae 2009 STEPS survey, implemented by FSM Department of Health and Social Affairs with the support of WHO. This paper uses data from the Moldova 2013 STEPS survey, implemented by Ministry of Health (Moldova) with the support of WHO. This paper uses data from the Mongolia 2005 STEPS survey, the Mongolia 2009 STEPS survey, and the Mongolia 2013 STEPS survey, implemented by Ministry of Health (Mongolia) with the support of WHO. This paper uses data from the Morocco 2017 STEPS survey, implemented by Ministry of Health (Morocco) with the support of WHO. This paper uses data from the Mozambique 2005 STEPS survey, implemented by Ministry of Health (Mozambique) with the support of WHO. This paper uses data from the Myanmar 2014 STEPS survey, implemented by Ministry of Health (Myanmar) with the support of WHO. This paper uses data from the Nauru 2004 STEPS survey and the Nauru 2015\u20132016 STEPS survey, implemented by Ministry of Health (Nauru) with the support of WHO. This paper uses data from the Niger 2007 STEPS survey, implemented by Ministry of Health (Niger) with the support of WHO. This paper uses data from the Palau 2011-2013 STEPS survey and the Palau 2016 STEPS survey, implemented by Ministry of Health (Palau) with the support of WHO. This paper uses data from the Palestine 2010-2011 STEPS survey, implemented by Chuuk Department of Health Services (Micronesia), Department of Health and Social Affairs (Micronesia) with the support of WHO. This paper uses data from the Qatar 2012 STEPS survey, implemented by Supreme Council of Health (Qatar) with the support of WHO. This paper uses data from the Rwanda 2012-2013 STEPS survey, implemented by Ministry of Health (Rwanda) with the support of WHO. This paper uses data from the Samoa 2002 STEPS survey and the Samoa 2013 STEPS survey, implemented by Ministry of Health (Samoa) with the support of WHO. This paper uses data from the Sao Tome and Principe 2008 STEPS survey, implemented by Ministry of Health (Sao Tome and Principe) with the support of WHO. This paper uses data from the Seychelles 2004 STEPS survey, implemented by Ministry of Health (Seychelles) with the support of WHO. This paper uses data from the Solomon Islands 2005\u20132006 STEPS survey and the Solomon Islands 2015 STEPS survey, implemented by Ministry of Health and Medical Services (Solomon Islands) with the support of WHO. This paper uses data from the Sri Lanka 2014\u20132015 STEPS survey, implemented by Ministry of Health (Sri Lanka) with the support of WHO. This paper uses data from the Sudan 2016 STEPS survey, implemented by Ministry of Health (Sudan) with the support of WHO. This paper uses data from the Swaziland 2007 STEPS survey and the Swaziland 2014 STEPS survey, implemented by Ministry of Health (Swaziland) with the support of WHO. This paper uses data from the Tajikistan 2016 STEPS survey, implemented by Ministry of Health (Tajikistan) with the support of WHO. This paper uses data from the Tanzania - Zanzibar 2011 STEPS survey, implemented by Ministry of Health (Zanzibar) with the support of WHO. This paper uses data from the Tanzania 2012 STEPS survey, implemented by National Institute for Medical Research (Tanzania) with the support of WHO. This paper uses data from the Timor-Leste 2014 STEPS survey, implemented by Ministry of Health (Timor-Leste) with the support of WHO. This paper uses data from the Togo 2010\u20132011 STEPS survey, implemented by Ministry of Health (Togo) with the support of WHO. This paper uses data from the Tokelau 2005 STEPS survey, implemented by Tokelau Department of Health, Fiji School of Medicine with the support of WHO. This paper uses data from the Tonga 2004 STEPS survey and the Tonga 2011\u20132012 STEPS survey, implemented by Ministry of Health (Tonga) with the support of WHO. This paper uses data from the Tuvalu 2015 STEPS survey, implemented by Ministry of Health (Tuvalu), with the support of WHO. This paper uses data from the Uganda 2014 STEPS survey, implemented by Ministry of Health (Uganda) with the support of WHO. This paper uses data from the Uruguay 2006 STEPS survey and the Uruguay 2013-2014 STEPS survey, implemented by Ministry of Health (Uruguay) with the support of WHO. This paper uses data from the Vanuatu 2011 STEPS survey, implemented by Ministry of Health (Vanuatu) with the support of WHO. This paper uses data from the Viet Nam 2009 STEPS survey and the Viet Nam 2015 STEPS survey, implemented by Ministry of Health (Viet Nam) with the support of WHO. This paper uses data from the Virgin Islands, British 2009 STEPS survey, implemented by Ministry of Health and Social Development (British Virgin Islands) with the support of WHO. This paper uses data from the Zambia - Lusaka 2008 STEPS survey, implemented by Ministry of Health (Zambia) with the support of WHO. This paper uses data from the Zambia 2017 STEPS survey, implemented by Ministry of Health (Zambia) with the support of WHO. This research used data from the Chile National Health Survey 2003, 2009\u201310, and 2016\u201317. The authors are grateful to the Ministry of Health, survey copyright owner, for allowing them to have the database. All results of the study are those of the author and in no way committed to the Ministry. This research used information from the Health Surveys for epidemiological surveillance of the Undersecretary of Public Health. The authors thank the Ministry of Health of Chile, having allowed them to have access to the database. All the results obtained from the study or research are the responsibility of the authors and in no way compromise that institution. This research uses data from Add Health, a program project designed by J Richard Udry, Peter S Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524, USA ( [email protected]). No direct support was received from grant P01-HD31921 for this analysis. This study has been realised using the data collected by the Swiss Household Panel (SHP), which is based at the Swiss Centre of Expertise in the Social Sciences FORS. The project is financed by the Swiss National Science Foundation. We thank the Russia Longitudinal Monitoring Survey, RLMS-HSE, conducted by the National Research University Higher School of Economics and ZAO Demoscope together with Carolina Population Center, University of North Carolina at Chapel Hill, and the Institute of Sociology RAS for making these data available. Editorial note: The Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations. Publisher Copyright: © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Detailed, comprehensive, and timely reporting on population health by underlying causes of disability and premature death is crucial to understanding and responding to complex patterns of disease and injury burden over time and across age groups, sexes, and locations. The availability of disease burden estimates can promote evidence-based interventions that enable public health researchers, policy makers, and other professionals to implement strategies that can mitigate diseases. It can also facilitate more rigorous monitoring of progress towards national and international health targets, such as the Sustainable Development Goals. For three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has filled that need. A global network of collaborators contributed to the production of GBD 2021 by providing, reviewing, and analysing all available data. GBD estimates are updated routinely with additional data and refined analytical methods. GBD 2021 presents, for the first time, estimates of health loss due to the COVID-19 pandemic. Methods: The GBD 2021 disease and injury burden analysis estimated years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries using 100 983 data sources. Data were extracted from vital registration systems, verbal autopsies, censuses, household surveys, disease-specific registries, health service contact data, and other sources. YLDs were calculated by multiplying cause-age-sex-location-year-specific prevalence of sequelae by their respective disability weights, for each disease and injury. YLLs were calculated by multiplying cause-age-se

Genetic Dissection of Acute Ethanol Responsive Gene Networks in Prefrontal Cortex: Functional and Mechanistic Implications

Background Individual differences in initial sensitivity to ethanol are strongly related to the heritable risk of alcoholism in humans. To elucidate key molecular networks that modulate ethanol sensitivity we performed the first systems genetics analysis of ethanol-responsive gene expression in brain regions of the mesocorticolimbic reward circuit (prefrontal cortex, nucleus accumbens, and ventral midbrain) across a highly diverse family of 27 isogenic mouse strains (BXD panel) before and after treatment with ethanol. Results Acute ethanol altered the expression of ~2,750 genes in one or more regions and 400 transcripts were jointly modulated in all three. Ethanol-responsive gene networks were extracted with a powerful graph theoretical method that efficiently summarized ethanol\u27s effects. These networks correlated with acute behavioral responses to ethanol and other drugs of abuse. As predicted, networks were heavily populated by genes controlling synaptic transmission and neuroplasticity. Several of the most densely interconnected network hubs, including Kcnma1 and Gsk3β, are known to influence behavioral or physiological responses to ethanol, validating our overall approach. Other major hub genes like Grm3, Pten and Nrg3 represent novel targets of ethanol effects. Networks were under strong genetic control by variants that we mapped to a small number of chromosomal loci. Using a novel combination of genetic, bioinformatic and network-based approaches, we identified high priority cis-regulatory candidate genes, including Scn1b,Gria1, Sncb and Nell2. Conclusions The ethanol-responsive gene networks identified here represent a previously uncharacterized intermediate phenotype between DNA variation and ethanol sensitivity in mice. Networks involved in synaptic transmission were strongly regulated by ethanol and could contribute to behavioral plasticity seen with chronic ethanol. Our novel finding that hub genes and a small number of loci exert major influence over the ethanol response of gene networks could have important implications for future studies regarding the mechanisms and treatment of alcohol use disorders