Monitoring and evaluation of human resources for health: an international perspective

BACKGROUND: Despite the undoubted importance of human resources to the functions of health systems, there is little consistency between countries in how human resource strategies are monitored and evaluated. This paper presents an integrated approach for developing an evidence base on human resources for health (HRH) to support decision-making, drawing on a framework for health systems performance assessment. METHODS: Conceptual and methodological issues for selecting indicators for HRH monitoring and evaluation are discussed, and a range of primary and secondary data sources that might be used to generate indicators are reviewed. Descriptive analyses are conducted drawing primarily on one type of source, namely routinely reported data on the numbers of health personnel and medical schools as covered by national reporting systems and compiled by the World Health Organization. Regression techniques are used to triangulate a given HRH indicator calculated from different data sources across multiple countries. RESULTS: Major variations in the supply of health personnel and training opportunities are found to occur by region. However, certain discrepancies are also observed in measuring the same indicator from different sources, possibly related to the occupational classification or to the sources' representation. CONCLUSION: Evidence-based information is needed to better understand trends in HRH. Although a range of sources exist that can potentially be used for HRH assessment, the information that can be derived from many of these individual sources precludes refined analysis. A variety of data sources and analytical approaches, each with its own strengths and limitations, is required to reflect the complexity of HRH issues. In order to enhance cross-national comparability, data collection efforts should be processed through the use of internationally standardized classifications (in particular, for occupation, industry and education) at the greatest level of detail possible

Qualified and Unqualified (N-R C) mental health nursing staff - minor differences in sources of stress and burnout. A European multi-centre study

BACKGROUND: Unqualified/non-registered caregivers (N-R Cs) will continue to play important roles in the mental health services. This study compares levels of burnout and sources of stress among qualified and N-R Cs working in acute mental health care. METHODS: A total of 196 nursing staff - 124 qualified staff (mainly nurses) and 72 N-R Cs with a variety of different educational backgrounds - working in acute wards or community mental teams from 5 European countries filled out the Maslach Burnout Inventory (MBI), the Mental Health Professional Scale (MHPSS) and the Psychosocial Work Environment and Stress Questionnaire (PWSQ). RESULTS: (a) The univariate differences were generally small and restricted to a few variables. Only Social relations (N-R Cs being less satisfied) at Work demands (nurses reporting higher demands) were different at the .05 level. (b) The absolute scores both groups was highest on variables that measured feelings of not being able to influence a work situation characterised by great demands and insufficient resources. Routines and educational programs for dealing with stress should be available on a routine basis. (c) Multivariate analyses identified three extreme groups: (i) a small group dominated by unqualified staff with high depersonalization, (ii) a large group that was low on depersonalisation and high on work demands with a majority of qualified staff, and (iii) a small N-R C-dominated group (low depersonalization, low work demands) with high scores on professional self-doubt. In contrast to (ii) the small and N-R C-dominated groups in (i) and (iii) reflected mainly centre-dependent problems. CONCLUSION: The differences in burnout and sources of stress between the two groups were generally small. With the exception of high work demands the main differences between the two groups appeared to be centre-dependent. High work demands characterized primarily qualified staff. The main implication of the study is that no special measures addressed towards N-R Cs in general with regard to stress and burnout seem necessary. The results also suggest that centre-specific problems may cause more stress among N-R Cs compared to the qualified staff (e.g. professional self-doubt)

Extracellular vesicle features are associated with COVID-19 severity

COVID-19 is heterogeneous; therefore, it is crucial to identify early biomarkers for adverse outcomes. Extracellular vesicles (EV) are involved in the pathophysiology of COVID-19 and have both negative and positive effects. The objective of this study was to identify the potential role of EV in the prognostic stratification of COVID-19 patients. A total of 146 patients with severe or critical COVID-19 were enrolled. Demographic and comorbidity characteristics were collected, together with routine haematology, blood chemistry and lymphocyte subpopulation data. Flow cytometric characterization of the dimensional and antigenic properties of COVID-19 patients' plasma EVs was conducted. Elastic net logistic regression with cross-validation was employed to identify the best model for classifying critically ill patients. Features of smaller EVs (i.e. the fraction of EVs smaller than 200 nm expressing either cluster of differentiation [CD] 31, CD 140b or CD 42b), albuminemia and the percentage of monocytes expressing human leukocyte antigen DR (HLA-DR) were associated with a better outcome. Conversely, the proportion of larger EVs expressing N-cadherin, CD 34, CD 56, CD31 or CD 45, interleukin 6, red cell width distribution (RDW), N-terminal pro-brain natriuretic peptide (NT-proBNP), age, procalcitonin, Charlson Comorbidity Index and pro-adrenomedullin were associated with disease severity. Therefore, the simultaneous assessment of EV dimensions and their antigenic properties complements laboratory workup and helps in patient stratification

Complete Blood Count and Monocyte Distribution Width–Based Machine Learning Algorithms for Sepsis Detection: Multicentric Development and External Validation Study

Background: Sepsis is an organ dysfunction caused by a dysregulated host response to infection. Early detection is fundamental to improving the patient outcome. Laboratory medicine can play a crucial role by providing biomarkers whose alteration can be detected before the onset of clinical signs and symptoms. In particular, the relevance of monocyte distribution width (MDW) as a sepsis biomarker has emerged in the previous decade. However, despite encouraging results, MDW has poor sensitivity and positive predictive value when compared to other biomarkers. Objective: This study aims to investigate the use of machine learning (ML) to overcome the limitations mentioned earlier by combining different parameters and therefore improving sepsis detection. However, making ML models function in clinical practice may be problematic, as their performance may suffer when deployed in contexts other than the research environment. In fact, even widely used commercially available models have been demonstrated to generalize poorly in out-of-distribution scenarios. Methods: In this multicentric study, we developed ML models whose intended use is the early detection of sepsis on the basis of MDW and complete blood count parameters. In total, data from 6 patient cohorts (encompassing 5344 patients) collected at 5 different Italian hospitals were used to train and externally validate ML models. The models were trained on a patient cohort encompassing patients enrolled at the emergency department, and it was externally validated on 5 different cohorts encompassing patients enrolled at both the emergency department and the intensive care unit. The cohorts were selected to exhibit a variety of data distribution shifts compared to the training set, including label, covariate, and missing data shifts, enabling a conservative validation of the developed models. To improve generalizability and robustness to different types of distribution shifts, the developed ML models combine traditional methodologies with advanced techniques inspired by controllable artificial intelligence (AI), namely cautious classification, which gives the ML models the ability to abstain from making predictions, and explainable AI, which provides health operators with useful information about the models’ functioning. Results: The developed models achieved good performance on the internal validation (area under the receiver operating characteristic curve between 0.91 and 0.98), as well as consistent generalization performance across the external validation datasets (area under the receiver operating characteristic curve between 0.75 and 0.95), outperforming baseline biomarkers and state-of-the-art ML models for sepsis detection. Controllable AI techniques were further able to improve performance and were used to derive an interpretable set of diagnostic rules. Conclusions: Our findings demonstrate how controllable AI approaches based on complete blood count and MDW may be used for the early detection of sepsis while also demonstrating how the proposed methodology can be used to develop ML models that are more resistant to different types of data distribution shifts

Extração semiautomática de contornos de telhado de edifícios com base em snakes e programação dinâmica

Este trabalho apresenta um método para a extração de contornos de telhado de edifícios a partir de imagens digitais tomadas sobre cenas urbanas complexas. O método proposto é baseado na otimização de uma função de energia snakes, que representa contornos de telhado de edifícios em imagens digitais, através da técnica de otimização por programação dinâmica. Como a grande maioria dos contornos de telhado de edifícios possui lados retilíneos se interceptando em ângulos retos, foram aplicadas restrições à função de energia snakes de modo a atender esta condição geométrica. A principal vantagem de se usar o algoritmo de programação dinâmica para otimizar a função de energia snakes é o aumento do raio de convergência, quando comparado com o que é normalmente obtido na solução original baseada em cálculo variacional. A avaliação experimental foi realizada a partir de dados reais e os resultados obtidos na inspeção visual e análise numérica dos experimentos mostraram o potencial do método para a extração de contornos de telhado de edifícios a partir de imagens digitais.This paper presents a method for building roof contours extraction from digital image taken over complex urban scenes. The proposed method is based on the optimization of a snakes' energy function that represents building roof contours in digital images by using the dynamic programming optimization technique. As most of the building roof contours contains straight edges intercepting at right angles, appropriate geometrics constraints are enforced into the original snakes' energy function. The main advantage of using the dynamic programming algorithm for optimizing the snakes' energy function is the augmentation of the pull-in-range, when compared to the one that is usually obtained in the original solution based on variational approaches. Experimental evaluation, including visual inspection and numeric analysis, was performed by using real data and the obtained results showed the potentiality of the proposed method for extracting building roof contours from digital imagery.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)UNESP FCTUNESP FCT Departamento de CartografiaUNESP FCTUNESP FCT Departamento de Cartografi

Human resources for health challenges of public health system reform in Georgia

<p>Abstract</p> <p>Background</p> <p>Human resources (HR) are one of the most important components determining performance of public health system. The aim of this study was to assess adequacy of HR of local public health agencies to meet the needs emerging from health care reforms in Georgia.</p> <p>Methods</p> <p>We used the Human Resources for Health Action Framework, which includes six components: HR management, policy, finance, education, partnerships and leadership. The study employed: (a) quantitative methods: from September to November 2004, 30 randomly selected district Centers of Public Health (CPH) were surveyed through face-to-face interviews with the CPH director and one public health worker randomly selected from all professional staff; and (b) qualitative methods: in November 2004, Focus Group Discussions (FGD) were held among 3 groups: a) 12 district public health professionals, b) 11 directors of district public health centers, and c) 10 policy makers at central level.</p> <p>Results</p> <p>There was an unequal distribution of public health workers across selected institutions, with lack of professionals in remote rural district centers and overstaffing in urban centers. Survey respondents disagreed or were uncertain that public health workers possess adequate skills and knowledge necessary for delivery of public health programs. FGDs shed additional light on the survey findings that there is no clear vision and plans on HR development. Limited budget, poor planning, and ignorance from the local government were mentioned as main reasons for inadequate staffing. FGD participants were concerned with lack of good training institutions and training programs, lack of adequate legislation for HR issues, and lack of necessary resources for HR development from the government.</p> <p>Conclusion</p> <p>After ten years of public health system reforms in Georgia, the public health workforce still has major problems such as irrational distribution and inadequate knowledge and skills. There is an urgent need for re-training and training programs and development of conducive policy environment with sufficient resources to address these problems and assure adequate functionality of public health programs.</p

Determinação de regiões homólogas para registro de uma série multitemporal de imagens de satélite usando algoritmos genéticos

O presente trabalho faz parte do programa GEOSAFRAS, estimativa e safras no Brasil, coordenado pela Companhia Nacional de Abastecimento e do Programa das Nações Unidas para o Desenvolvimento. Uma aplicação muito comum no gerenciamento de safras é o monitoramento de mudanças nos campos de cultivo usando imagens de satélite adquiridas em diferentes datas. Para isto, a compatibilidade espacial entre essas imagens é um requisito básico, mais ainda quando se trata de imagens de diferentes sensores. Nesse artigo é descrita uma metodologia para a semi-automação do processo de correção geométrica com a finalidade de facilitar o ajuste geométrico entre imagens de diferentes sensores. Inicialmente, uma imagem base é manualmente corrigida a partir de dados extraídos de uma carta topográfica digital. Após essa correção, a imagem corrigida é utilizada como elemento base de referência para corrigir as outras imagens (denominadas neste trabalho de imagens de ajuste), as quais são adquiridas em datas diferentes e/ou de diferentes sensores. Depois de corrigida a imagem base todas as imagens (base e de ajuste) são segmentadas e classificadas. Os segmentos classificados como vegetação florestal são escolhidos para compor a malha relacional. As imagens de ajuste são registradas pelo processo imagem-imagem usando os centróides dos segmentos de vegetação florestal. Os segmentos de vegetação florestal presentes na imagem base são confrontados com os segmentos correspondentes nas imagens de ajuste, para buscar correspondências (matching). O processo de matching é realizado através da aplicação de algoritmos genéticos. Ao obter um resultado satisfatório na busca de correspondência, calcula-se os centróides correspondentes aos segmentos detectados, os quais são utilizados como pontos de controle para o processo de registro das imagens. Os resultados mostram que os algoritmos genéticos encontraram a solução ótima na maior parte dos experimentos realizados. Porém, para a imagem LANDSAT 2002 reamostrada a solução encontrada foi sub-ótima, pois um segmento sofreu grandes variações em relação ao mesmo segmento na imagem base

Reviewing The Benefits of Health Workforce Stability

This paper examines the issue of workforce stability and turnover in the context of policy attempts to improve retention of health workers. The paper argues that there are significant benefits to supporting policy makers and managers to develop a broader perspective of workforce stability and methods of monitoring it. The objective of the paper is to contribute to developing a better understanding of workforce stability as a major aspect of the overall policy goal of improved retention of health workers. The paper examines some of the limited research on the complex interaction between staff turnover and organisational performance or quality of care in the health sector, provides details and examples of the measurement of staff turnover and stability, and illustrates an approach to costing staff turnover. The paper concludes by advocating that these types of assessment can be valuable to managers and policy makers as they examine which policies may be effective in improving stability and retention, by reducing turnover. They can also be used as part of advocacy for the use of new retention measures. The very action of setting up a local working group to assess the costs of turnover can in itself give managers and staff a greater insight into the negative impacts of turnover, and can encourage them to work together to identify and implement stability measures

Uses of population census data for monitoring geographical imbalance in the health workforce: snapshots from three developing countries

BACKGROUND: Imbalance in the distribution of human resources for health (HRH), eventually leading to inequities in health services delivery and population health outcomes, is an issue of social and political concern in many countries. However, the empirical evidence to support decision-making is often fragmented, and many standard data sources that can potentially produce statistics relevant to the issue remain underused, especially in developing countries. This study investigated the uses of demographic census data for monitoring geographical imbalance in the health workforce for three developing countries, as a basis for formulation of evidence-based health policy options. METHODS: Population-based indicators of geographical variations among HRH were extracted from census microdata samples for Kenya, Mexico and Viet Nam. Health workforce statistics were matched against international standards of occupational classification to control for cross-national comparability. Summary measures of inequality were calculated to monitor the distribution of health workers across spatial units and by occupational group. RESULTS: Strong inequalities were found in the geographical distribution of the health workforce in all three countries, with the highest densities of HRH tending to be found in the capital areas. Cross-national differences were found in the magnitude of distributional inequality according to occupational group, with health professionals most susceptible to inequitable distribution in Kenya and Viet Nam but less so in Mexico compared to their associate professional counterparts. Some discrepancies were suggested between mappings of occupational information from the raw data with the international system, especially for nursing and midwifery specializations. CONCLUSIONS: The problem of geographical imbalance among HRH across countries in the developing world holds important implications at the local, national and international levels, in terms of constraints for the effective deployment, management and retention of HRH, and ultimately for the equitable delivery of health services. A number of advantages were revealed of using census data in health research, notably the potential for producing detailed statistics on health workforce characteristics at the sub-national level. However, lack of consistency in the compilation and processing of occupational information over time and across countries continues to hamper comparative analyses for HRH policy monitoring and evaluation

"More money for health - more health for the money": a human resources for health perspective

<p>Abstract</p> <p>Background</p> <p>At the MDG Summit in September 2010, the UN Secretary-General launched the Global Strategy for Women's and Children's Health. Central within the Global Strategy are the ambitions of "more money for health" and "more health for the money". These aim to leverage more resources for health financing whilst simultaneously generating more results from existing resources - core tenets of public expenditure management and governance. This paper considers these ambitions from a human resources for health (HRH) perspective.</p> <p>Methods</p> <p>Using data from the UK Department for International Development (DFID) we set out to quantify and qualify the British government's contributions on HRH in developing countries and to establish a baseline.. To determine whether activities and financing could be included in the categorisation of 'HRH strengthening' we adopted the Agenda for Global Action on HRH and a WHO approach to the 'working lifespan' of health workers as our guiding frameworks. To establish a baseline we reviewed available data on Official Development Assistance (ODA) and country reports, undertook a new survey of HRH programming and sought information from multilateral partners.</p> <p>Results</p> <p>In financial year 2008/9 DFID spent £901 million on direct 'aid to health'. Due to the nature of the Creditor Reporting System (CRS) of the Organisation for Economic Co-operation and Development (OECD) it is not feasible to directly report on HRH spending. We therefore employed a process of imputed percentages supported by detailed assessment in twelve countries. This followed the model adopted by the G8 to estimate ODA on maternal, newborn and child health. Using the G8's model, and cognisant of its limitations, we concluded that UK 'aid to health' on HRH strengthening is approximately 25%.</p> <p>Conclusions</p> <p>In quantifying DFID's disbursements on HRH we encountered the constraints of the current CRS framework. This limits standardised measurement of ODA on HRH. This is a governance issue that will benefit from further analysis within more comprehensive programmes of workforce science, surveillance and strategic intelligence. The Commission on Information and Accountability for Women's and Children's Health may present an opportunity to partially address the limitations in reporting on ODA for HRH and present solutions to establish a global baseline.</p