Assessing energy security in Africa based on multi-dimensional approach of principal composite analysis

The concept of energy security has become an increasingly challenging issue in Africa, forcing energy-deficient countries to forge mutual partnerships with energy sufficient countries to access it for their domestic consumption. This study formulates a composite index of energy security in Africa as well as evaluates its impacts and trends using a sample of 28 countries on the continent, during the 2000-2018 period by using a principal composite factor analysis (PCA), with the series of 13 variables. Further interpretation was carried out using these tests: Kaiser-Meyer-Olkin measure of sampling adequacy and Bartlett's test of sphericity, Pearson correlation test, and Cronbach's alpha test. The key results show a trend of energy insecurity among the countries studied, as energy imports loads high in most countries as well as per capita emission, together with fossil fuel source consumption correlating high. These results validate the stark reality on the African continent. The inference from the results of the anaylsis conclude that the principal component analysis (PCA) results of the energy index were considered fit and reliable for the analysis, with the most important Cronbach's alpha test coefficient of 0.8797, far above the standard 0.6 model reliability level. Based on this study, the paper proffers there should be increased intra-regional trading of energy among the various power pools on the continent and increased regional renewable energy investments as well as investment in energy infrastructure, measures to reduce electricity system losses, environmental sustainability, and the adoption of energy in efficiency on the continent.

How energy insecurity leads to energy poverty? Do environmental consideration and climate change concerns matters

The aim of the study is to estimate the nexus between energy insecurity and energy poverty with the role of climate change and other environmental concerns. We used DEA like WP methods and properties of MCDA, a most common form of data envelopment analysis (DEA) to estimate the nexus between constructs. This paper presents a measurement and analysis of G7 countries' energy, economic, social, and environmental performance associated with energy poverty indexes. The study used the multiple, comprehensive, and relevant set of indicators, including energy economics and environmental consideration of energy poverty. The net energy consumption of al G7 economies is equal to 34 percent of the entire world along with the net estimate GDP score of around 50 percent. Using DEA modelling and estimation technique, our research presented valuable insights for readers, theorists and policy makers on energy, environment, energy poverty and climate change mitigation. For this reasons, all these indicators combined in a mathematical composite indicator to measure energy, economic, social, and environmental performance index (EPI). Results show that Canada has the highest EPII score, which shows that Canada's capacity to deal with energy self-sufficiency, economic development, and environmental performance is greater than the other G7 countries. France and Italy rank second and third. Japan comes next with 0.50 EPI scores, while the USA has the lowest average EPI score environment vulnerable even though have higher economic development among the G7 group countries. We suggest a policy framework to strengthen the subject matter of the study.

Smart respiratory monitoring: clinical development and validation of the IPI™ (Integrated Pulmonary Index) algorithm

Continuous electronic monitoring of patient respiratory status frequently includes PetCO₂ (end tidal CO₂), RR (respiration rate), SpO₂ (arterial oxygen saturation), and PR (pulse rate). Interpreting and integrating these vital signs as numbers or waveforms is routinely done by anesthesiologists and intensivists but is challenging for clinicians in low acuity areas such as medical wards, where continuous electronic respiratory monitoring is becoming more common place. We describe a heuristic algorithm that simplifies the interpretation of these four parameters in assessing a patient’s respiratory status, the Integrated Pulmonary Index (IPI). The IPI algorithm is a mathematical model combining SpO₂, RR, PR, and PetCO₂ into a single value between 1 and 10 that summarizes the adequacy of ventilation and oxygenation at that point in time. The algorithm was designed using a fuzzy logic inference model to incorporate expert clinical opinions. The algorithm was verified by comparison to experts’ scoring of clinical scenarios. The validity of the index was tested in a retrospective analysis of continuous SpO₂, RR, PR, and PetCO₂ readings obtained from 523 patients in a variety of clinical settings. IPI correlated well with expert interpretation of the continuous respiratory data (R = 0.83, p <<< 0.001), with agreement of −0.5 ± 1.4. Receiver operating curves analysis resulted in high levels of sensitivity (ranging from 0.83 to 1.00), and corresponding specificity (ranging from 0.96 to 0.74), based on IPI thresholds 3−6. The IPI reliably interpreted the respiratory status of patients in multiple areas of care using off-line continuous respiratory data. Further prospective studies are required to evaluate IPI in real time in clinical settings.

Geographical distribution of typhoid risk factors in low and middle income countries

Background

While the global burden of typhoid fever has been often brought up for attention, the detailed surveillance information has only been available for the limited number of countries. As more efficacious vaccines will be available in the near future, it is essential to understand the geographically diverse patterns of typhoid risk levels and to prioritize the right populations for vaccination to effectively control the disease.

Methods

A composite index called the typhoid risk factor (TRF) index was created based on data with the Global Positioning System (GPS). Demographic and Health Surveys (DHS) and National Geographical Data Center (NGDC) satellite lights data were used for this analysis. A count model was adopted to validate the TRF index against the existing surveillance burden data. The TRF index was then re-estimated for 66 countries using the most recent data and mapped out for two geographical levels (sub-national boundary and grid-cell levels).

Results

The TRF index which consists of drinking water sources, toilet facility types, and population density appeared to be statistically significant to explain variation in the disease burden data. The mapping analysis showed that typhoid risk levels vary not only by country but also by sub-national region. The grid-cell level analysis highlighted that the distribution of typhoid risk factors is uneven within the sub-national boundary level. Typhoid risk levels are geographically heterogeneous.

Conclusions

Given the insufficient number of surveillance studies, the TRF index serves as a useful tool by capturing multiple risk factors of the disease into a single indicator. This will help decision makers identify high risk areas for typhoid as well as other waterborne diseases. Further, the study outcome can guide researchers to find relevant places for future surveillance studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-2074-1) contains supplementary material, which is available to authorized users.

COVID-19: District level vulnerability assessment in India

Objectives

COVID-19 Pandemic has brought a threatening challenge to the world and as well as for Indian society and economy. In India, it has become a public health disaster and its' intensity increasing continuously. For the disaster risk reduction, and capacity building against the COVID-19 pandemic understanding of the relationship between socio-environmental conditions with the pandemic is very necessary. The objective of the present work is to construct a socio-environmental vulnerability index of the potential risk of community spread of COVID-19 using socio-economic and environmental variables.

Methodology

In this, cross-sectional study principal component analyses have been used to drive SoEVI. 4 uncorrelated sub-index has been extracted from 16 sub-indicators which reflects 59% of the variance. Aggregation of 4 Sub-Index has been done to obtain the final vulnerability Index.

Results

Results show that there is spatial variability in vulnerability based on environmental and socio-economic conditions. Districts of north and central India found more vulnerable then south India. Statistical significance has been tested using regression analysis, positive relation has been found between vulnerability index and confirmed and active cases.

Conclusion

The vulnerability index has highlighted environmentaly and socioeconomicallybackward districts. These areas will suffer more critical problems against COVID-19 pandemic for their socio-environmental problem.

Development of an objective water security index and assessment of its association with quality of life in urban areas of developing countries

A composite metric assessing water security’s physical dimension at the micro/ community level is lacking but is essential for setting priorities for program and policy implementations. We prepared an objective index (OI) of water security to measure the physical dimension using a model centered on household water-use behavior in developing countries’ urban areas. A cross-sectional household survey (n = 1500) with multi-stage cluster design was conducted from December 2015 to February 2016 in the Kathmandu Valley, which has faced long-term, severe water shortage. A structured questionnaire probed socio-demographic characteristics, water sources, frequency and quantity of water use, cost related to water, etc. A 15-item water insecurity scale was used to measure subjective and experiential dimension of water insecurity. The World Health Organization Quality of Life – BREF was used to measure quality of life (QoL). The QoL has been considered as proxy of well-being in this study. The OI measured differential water security within small cities, the utility’s service areas for instance, and identified area-specific key dimensions that need improvement. Overall, the OI and its key dimensions can be useful measures to design water-scarcity averting programs and policies, specific to a particular community’s needs. The increased OI values were significantly and positively associated with better physical and psychological health and better social relationship domains of QoL suggesting health implications of water security.

•

A composite water security metric focused on micro level and physical dimension is rare.

•

The proposed objective index is centred on household water-use behaviour.

•

The proposed objective index is based on quantifiable indicators.

•

The objective index was useful in measuring differential water security within cities.

•

Water security was significantly associated with well-being of people.

A Review of Continuous Glucose Monitoring-Based Composite Metrics for Glycemic Control

We performed a literature review of composite metrics for describing the quality of glycemic control, as measured by continuous glucose monitors (CGMs). Nine composite metrics that describe CGM data were identified. They are described in detail along with their advantages and disadvantages. The primary benefit to using composite metrics in clinical practice is to be able to quickly evaluate a patient's glycemic control in the form of a single number that accounts for multiple dimensions of glycemic control. Very little data exist about (1) how to select the optimal components of composite metrics for CGM; (2) how to best score individual components of composite metrics; and (3) how to correlate composite metric scores with empiric outcomes. Nevertheless, composite metrics are an attractive type of scoring system to present clinicians with a single number that accounts for many dimensions of their patients' glycemia. If a busy health care professional is looking for a single-number summary statistic to describe glucose levels monitored by a CGM, then a composite metric has many attractive features.

Assessment of composite index methods for agricultural vulnerability to climate change

A common way of quantifying and communicating climate vulnerability is to calculate composite indices from indicators, visualizing these as maps. Inherent methodological uncertainties in vulnerability assessments, however, require greater attention. This study examines Swedish agricultural vulnerability to climate change, the aim being to review various indicator approaches for assessing agricultural vulnerability to climate change and to evaluate differences in climate vulnerability depending on the weighting and summarizing methods. The reviewed methods are evaluated by being tested at the municipal level. Three weighting and summarizing methods, representative of climate vulnerability indices in general, are analysed. The results indicate that 34 of 36 method combinations differ significantly from each other. We argue that representing agricultural vulnerability in a single composite index might be insufficient to guide climate adaptation. We emphasize the need for further research into how to measure and visualize agricultural vulnerability and into how to communicate uncertainties in both data and methods.

Explaining Accessibility and Satisfaction Related to Healthcare: A Mixed-Methods Approach

Accessibility and satisfaction related to healthcare services are conceived as multidimensional concepts. These concepts can be studied using objective and subjective measures. In this study, we created two indices: a composite healthcare accessibility index (CHCA) and a composite healthcare satisfaction index (CHCS). To calculate the CHCA index we used three indicators based on three components of multidimensional healthcare accessibility: availability, acceptability and accessibility. In the indicator based on the component of accessibility, we included an innovative perceived time-decay parameter. The three indicators of the CHCA index were weighted through the application of a principal components analysis. To calculate the CHCS index, we used three indicators: the waiting time after the patient arrives at the healthcare service, the quality of the healthcare, and the healthcare service supply. These three indicators making up the CHCA index were weighted by applying an analytical hierarchy process. Three kinds of regressions were subsequently applied in order to explain the CHCA and CHCS indices: namely the Linear Least Squares, Ordinal Logistic, and Random Forests regressions. In these regressions, we used different independent social and health-related variables. These variables represented the predisposing, enabling, and need factors of people´s behaviors related to healthcare. All the calculations were applied to a study area: the city of Quito, Ecuador. Results showed that there are health-related inequalities in regard to healthcare accessibility and healthcare satisfaction in our study area. We also identified specific social factors that explained the indices developed. The present work is a mixed-methods approach to evaluate multidimensional healthcare accessibility and healthcare satisfaction, incorporating a pluralistic perspective, as well as a multidisciplinary framework. The results obtained can also be considered as tools for healthcare and urban planners, for more integrative social analyses that can improve the quality of life in urban residents.

Conducting gender-based analysis of existing databases when self-reported gender data are unavailable: the GENDER Index in a working population

OBJECTIVES

Growing attention has been given to considering sex and gender in health research. However, this remains a challenge in the context of retrospective studies where self-reported gender measures are often unavailable. This study aimed to create and validate a composite gender index using data from the Canadian Community Health Survey (CCHS).

METHODS

According to scientific literature and expert opinion, the GENDER Index was built using several variables available in the CCHS and deemed to be gender-related (e.g., occupation, receiving child support, number of working hours). Among workers aged 18-50 years who had no missing data for our variables of interest (n = 29,470 participants), propensity scores were derived from a logistic regression model that included gender-related variables as covariates and where biological sex served as the dependent variable. Construct validity of propensity scores (GENDER Index scores) were then examined.

RESULTS

When looking at the distribution of the GENDER Index scores in males and females, they appeared related but partly independent. Differences in the proportion of females appeared between groups categorized according to the GENDER Index scores tertiles (p < 0.0001). Construct validity was also examined through associations between the GENDER Index scores and gender-related variables identified a priori such as choosing/avoiding certain foods because of weight concerns (p < 0.0001), caring for children as the most important thing contributing to stress (p = 0.0309), and ability to handle unexpected/difficult problems (p = 0.0375).

CONCLUSION

The GENDER Index could be useful to enhance the capacity of researchers using CCHS data to conduct gender-based analysis among populations of workers.

Benchmarking road safety performance: Identifying a meaningful reference (best-in-class)

For road safety improvement, comparing and benchmarking performance are widely advocated as the emerging and preferred approaches. However, there is currently no universally agreed upon approach for the process of road safety benchmarking, and performing the practice successfully is by no means easy. This is especially true for the two core activities of which: (1) developing a set of road safety performance indicators (SPIs) and combining them into a composite index; and (2) identifying a meaningful reference (best-in-class), one which has already obtained outstanding road safety practices. To this end, a scientific technique that can combine the multi-dimensional safety performance indicators (SPIs) into an overall index, and subsequently can identify the 'best-in-class' is urgently required. In this paper, the Entropy-embedded RSR (Rank-sum ratio), an innovative, scientific and systematic methodology is investigated with the aim of conducting the above two core tasks in an integrative and concise procedure, more specifically in a 'one-stop' way. Using a combination of results from other methods (e.g. the SUNflower approach) and other measures (e.g. Human Development Index) as a relevant reference, a given set of European countries are robustly ranked and grouped into several classes based on the composite Road Safety Index. Within each class the 'best-in-class' is then identified. By benchmarking road safety performance, the results serve to promote best practice, encourage the adoption of successful road safety strategies and measures and, more importantly, inspire the kind of political leadership needed to create a road transport system that maximizes safety.

Visitor arrivals forecasts amid COVID-19: A perspective from the Europe team

In a context in which the tourism industry is jeopardised by the COVID-19 pandemic, and potentially by other pandemics in the future, the capacity to produce accurate forecasts is crucial to stakeholders and policy-makers. This paper attempts to forecast the recovery of tourism demand for 2021 in 20 destinations worldwide. An original scenario-based judgemental forecast based on the definition of a Covid-19 Risk Exposure index is proposed to overcome the limitations of traditional forecasting methods. Three scenarios are proposed, and ex ante forecasts are generated for each destination using a baseline forecast, the developed index and a judgemental approach. The limitations and potential developments of this new forecasting model are then discussed.

Inconsistencies of the Disease Activity Assessment Tools for Psoriatic Arthritis: Challenges to Rheumatologists

OBJECTIVE

Currently, concerning the evaluation of psoriatic arthritis (PsA), there is no agreement on a standardized composite index for disease activity that includes all relevant domains. The present study sought to assess the rates of remission (REM)/low disease activity (LDA) and disease states [minimal disease activity (MDA), very low disease activity (VLDA)] as defined by diverse activity scales (DAPSA, DAS28-ESR) in an attempt to display discrepancies across these assessment tools for peripheral PsA.

METHODS

The study involved 758 patients (496 females, 262 males; mean age 47,1 years) with peripheral PsA who were registered to the Turkish League Against Rheumatism (TLAR) Network. The patients were assessed using the DAS28-ESR, DAPSA, MDA, and VLDA. The overall yield of each scale was assessed in identifying REM and LDA. The presence or absence of swollen joints was separately analysed.

RESULTS

The median disease duration was 4 years (range 0-44 years). According to DAPSA and DAS28-ESR, REM was achieved in 6.9% and 19.5% of the patients, respectively. The rates of MDA and VLDA were 16% and 2.9%, respectively. Despite the absence of swollen joints, a significant portion of patients were not considered to be in REM (296 (39.1%) patients with DAS28-ESR, 364 (48%) with DAPSA, and 394 (52%) with VLDA).

CONCLUSION

Patients with peripheral PsA may be assigned to diverse disease activity levels when assessed with the DAS28-ESR, DAPSA, MDA and VLDA, which would inevitably have clinical implications. In patients with PsA a holistic approach seems to be necessary which includes other domains apart from joint involvement, such as skin involvement, enthesitis, spinal involvement, and patient-reported outcomes.

Hotspots identification and ranking for road safety improvement: an alternative approach

During the last decade, the concept of composite performance index, brought from economic and business statistics, has become a popular practice in the field of road safety, namely for the identification and classification of worst performing areas or time slots also known as hotspots. The overall quality of a composite index depends upon the complexity of phenomena of interest as well as the relevance of the methodological approach used to aggregate the various indicators into a single composite index. However, current aggregation methods used to estimate the composite road safety performance index suffer from various deficiencies at both the theoretical and operational level; these include the correlation and compensability between indicators, the weighting of the indicators as well as their high "degree of freedom" which enables one to readily manipulate them to produce desired outcomes (Munda and Nardo, 2003, 2005, 2009). The objective of this study is to contribute to the ongoing research effort on the estimation of road safety composite index for hotspots' identification and ranking. The aggregation method for constructing the composite road safety performance index introduced in this paper, strives to minimize the aforementioned deficiencies of the current approaches. Furthermore, this new method can be viewed as an intelligent decision support system for road safety performance evaluation, in order to prioritize interventions for road safety improvement.

Assessing inequalities and regional disparities in child nutrition outcomes in India using MANUSH - a more sensitive yardstick

BACKGROUND

India is strongly committed to reducing the burden of child malnutrition, which has remained a persistent concern. Findings from recent surveys indicate co-existence of child undernutrition, micronutrient deficiency and overweight/obesity, i.e. the triple burden of malnutrition among children below 5 years. While considerable efforts are being made to address this challenge, and several composite indices are being explored to inform policy actions, the methodology used for creating such indices, i.e., linear averaging, has its limitations. Briefly put, it could mask the uneven improvement across different indicators by discounting the 'lagging' indicators, and hence not incentivising a balanced improvement. Signifying negative implications on policy discourse for improved nutrition. To address this gap, we attempt to develop a composite index for estimating the triple burden of malnutrition in India, using a more sensitive measure, MANUSH.

METHODOLOGY

Data from publicly available nation-wide surveys - National Family Health Survey (NFHS) and Comprehensive National Nutrition Survey (CNNS), was used for this study. First, we addressed the robustness of MANUSH method of composite indexing over conventional aggregation methods. Second, using MANUSH scores, we assessed the triple burden of malnutrition at the subnational level over different periods NHFS- 3(2005-06), NFHS-4 (2015-16) and CNNS (2106-18). Using mapping and spatial analysis tools, we assessed neighbourhood dependency and formation of clusters, within and across states.

RESULT

MANUSH method scores over other aggregation measures that use linear aggregation or geometric mean. It does so by fulfilling additional conditions of Shortfall and Hiatus Sensitivity, implicitly penalising cases where the improvement in worst-off dimension is lesser than the improvement in best-off dimension, or where, even with an overall improvement in the composite index, the gap between different dimensions does not reduce. MANUSH scores helped in revealing the gaps in the improvement of nutrition outcomes among different indicators and, the rising inequalities within and across states and districts in India. Significant clusters (p < 0.05) of high burden and low burden districts were found, revealing geographical heterogeneities and sharp regional disparities. A MANUSH based index is useful in context-specific planning and prioritising different interventions, an approach advocated by the newly launched National Nutrition Mission in India.

CONCLUSION

MANUSH based index emphasises balanced development in nutritional outcomes and is hence relevant for diverse and unevenly developing economy like India.

Method for selection of optimal road safety composite index with examples from DEA and TOPSIS method

Concept of composite road safety index is a popular and relatively new concept among road safety experts around the world. As there is a constant need for comparison among different units (countries, municipalities, roads, etc.) there is need to choose an adequate method which will make comparison fair to all compared units. Usually comparisons using one specific indicator (parameter which describes safety or unsafety) can end up with totally different ranking of compared units which is quite complicated for decision maker to determine "real best performers". Need for composite road safety index is becoming dominant since road safety presents a complex system where more and more indicators are constantly being developed to describe it. Among wide variety of models and developed composite indexes, a decision maker can come to even bigger dilemma than choosing one adequate risk measure. As DEA and TOPSIS are well-known mathematical models and have recently been increasingly used for risk evaluation in road safety, we used efficiencies (composite indexes) obtained by different models, based on DEA and TOPSIS, to present PROMETHEE-RS model for selection of optimal method for composite index. Method for selection of optimal composite index is based on three parameters (average correlation, average rank variation and average cluster variation) inserted into a PROMETHEE MCDM method in order to choose the optimal one. The model is tested by comparing 27 police departments in Serbia.

Quality of maternal obstetric and neonatal care in low-income countries: development of a composite index

BACKGROUND

In low-income countries, studies demonstrate greater access and utilization of maternal and neonatal health services, yet mortality rates remain high with poor quality increasingly scrutinized as a potential point of failure in achieving expected goals. Comprehensive measures reflecting the multi-dimensional nature of quality of care could prove useful to quality improvement. However, existing tools often lack a systematic approach reflecting all aspects of quality considered relevant to maternal and newborn care. We aim to address this gap by illustrating the development of a composite index using a step-wise approach to evaluate the quality of maternal obstetric and neonatal healthcare in low-income countries.

METHODS

The following steps were employed in creating a composite index: 1) developing a theoretical framework; 2) metric selection; 3) imputation of missing data; 4) initial data analysis 5) normalization 6) weighting and aggregating; 7) uncertainty and sensitivity analysis of resulting composite score; 8) and deconstruction of the index into its components. Based on this approach, we developed a base composite index and tested alternatives by altering the decisions taken at different stages of the construction process to account for missing values, normalization, and aggregation. The resulting single composite scores representing overall maternal obstetric and neonatal healthcare quality were used to create facility rankings and further disaggregated into sub-composites of quality of care.

RESULTS

The resulting composite scores varied considerably in absolute values and ranges based on method choice. However, the respective coefficients produced by the Spearman rank correlations comparing facility rankings by method choice showed a high degree of correlation. Differences in method of aggregation had the greatest amount of variation in facility rankings compared to the base case. Z-score standardization most closely aligned with the base case, but limited comparability at disaggregated levels.

CONCLUSIONS

This paper illustrates development of a composite index reflecting the multi-dimensional nature of maternal obstetric and neonatal healthcare. We employ a step-wise process applicable to a wide range of obstetric quality of care assessment programs in low-income countries which is adaptable to setting and context. In exploring alternative approaches, certain decisions influencing the interpretation of a given index are highlighted.

Evaluating the effect of care around labor and delivery practices on early neonatal mortality in the Global Network's Maternal and Newborn Health Registry

BACKGROUND

Neonatal deaths in first 28-days of life represent 47% of all deaths under the age of five years globally and are a focus of the United Nation's (UN's) Sustainable Development Goals. Pregnant women are delivering in facilities but that does not indicate quality of care during delivery and the postpartum period. The World Health Organization's Essential Newborn Care (ENC) package reduces neonatal mortality, but lacks a simple and valid composite index that measures its effectiveness.

METHODS

Data on 5 intra-partum and 3 post-partum practices (indicators) recommended as part of ENC, routinely collected in NICHD's Global Network's (GN) Maternal Newborn Health Registry (MNHR) between 2010 and 2013, were included. We evaluated if all 8 practices (Care around Delivery - CAD), combined as an index was associated with reduced early neonatal mortality rates (days 0-6 of life).

RESULTS

A total of 150,848 live births were included in the analysis. The individual indicators varied across sites. All components were present in 19.9% births (range 0.4 to 31% across sites). Present indicators (8 components) were associated with reduced early neonatal mortality [adjusted RR (95% CI):0.81 (0.77, 0.85); p < 0.0001]. Despite an overall association between CAD and early neonatal mortality (RR < 1.0 for all early mortality): delivery by skilled birth attendant; presence of fetal heart and delayed bathing were associated with increased early neonatal mortality.

CONCLUSIONS

Present indicators (8 practices) of CAD were associated with a 19% reduction in the risk of neonatal death in the diverse health facilities where delivery occurred within the GN MNHR. These indicators could be monitored to identify facilities that need to improve compliance with ENC practices to reduce preventable neonatal deaths. Three of the 8 indicators were associated with increased neonatal mortality, due to baby being sick at birth. Although promising, this composite index needs refinement before use to monitor facility-based quality of care in association with early neonatal mortality. Trial registration The identifier of the Maternal Newborn Health Registry at ClinicalTrials.gov is NCT01073475.

Benchmarking road safety development across OECD countries: An empirical analysis for a decade

Benchmarking performance, monitoring progress and then recalibrating interventions is widely recognized as a valuable process for achieving continuous improvement in road safety. In this study, a systematic and effective methodology, IV-VIKOR with FNBC, is developed to perform the benchmarking of road safety development in an integrative manner for OECD (Organisation for Economic Co-operation and Development) countries. Linking to other methods and measures as the references, 36 OECD Member countries are ranked and grouped into several classes based on their overall achievement regarding road safety from the past decade (2009-2018). This provides government officials and policymakers, across the OECD Member countries, with a flexible tool to comprehensively benchmark road safety development. Providing the ability to identify delays in action plan implementations and proactively redistribute resources toward more effective measures where required. Such a tool can also serve to increase political will and stakeholder accountabilities, at the highest level of government and the private sector for all OECD members: Thereby keeping the implementation of action plans on schedule. It helps OECD Member countries to establish the capacity for sustainable safety management; supporting them in developing future strategies and reforms to create better policies for better lives.

Exposure to multiple air pollutants and the incidence of coronary heart disease: A fine-scale geographic analysis

Geographical variations in cardiovascular disease rates have been linked to individual air pollutants. Investigating the relation between cardiovascular disease and exposure to a complex mixture of air pollutants requires holistic approaches. We assessed the relationship between exposure to multiple air pollutants and the incidence of coronary heart disease (CHD) in a general population sample. We collected data in the Lille MONICA registry (2008-2011) on 3268 incident cases (age range: 35-74). Based on 20 indicators, we derived a composite environmental score (SEnv) for cumulative exposure to air pollution. Poisson regression models were used to analyse associations between CHD rates on one hand and SEnv and each single indicator on the other (considered in tertiles, where T3 is the most contaminated). We adjusted models for age, sex, area-level social deprivation, and neighbourhood spatial structure. The incidence of CHD was a spatially heterogeneous (p=0.006). There was a significant positive association between SEnv and CHD incidence (trend p=0.0151). The relative risks [95%CI] of CHD were 1.08 [0.98-1.18] and 1.16 [1.04-1.29] for the 2nd and 3rd tertile of SEnv exposure. In the single pollutant analysis, PM₁₀, NO₂, cadmium, copper, nickel, and palladium were significantly associated with CHD rates. Multiple air pollution was associated with an increased risk of CHD. Single pollutants reflecting road traffic pollution were the most strongly associated with CHD. Our present results are consistent with the literature data on the impact of road traffic on the CHD risk in urban areas.

Developing an effectiveness index for biomedical waste management in Indian states using a composite indicators approach

The waste from healthcare facilities (HCFs) is most devastating as they induce health hazards and pollute the environment. The effective management of biomedical waste is an essential function of the state governments, depending on state policies and facilities in HCFs. The performance assessment at the state level provides explanatory information for the decision-makers to dispose of biomedical waste. Therefore, this paper aims to establish an effectiveness index for assessing the performance of biomedical waste management for the Indian states. The designed conceptual framework, which acts as the building block for the index, interlinks the technical, managerial, and sustainability dimensions. To assess the existing waste management practices, significant sub-indicators are analyzed for India's northern and southern states. The indicators are transformed into comparable units using the proportionate normalization technique. The weight to the respective indicators follows the entropy method and additive aggregation to form the indices for various states. The developed index allows comparing management practices among the states and highlights the alarming situation. Based on the magnitude of indices values, states are categorized as red, yellow, and green zones. The robustness of the model is validated by performing sensitivity analysis and the cluster analysis tests the reliability of indicators and categorization of states with the existing methodology. The analysis will be useful to the decision-makers of state pollution boards by providing special attention to capacity building and waste prevention technologies.

Measurement and mapping of maternal health service coverage through a novel composite index: a sub-national level analysis in India

BACKGROUND

Expansion of maternal health service coverage is crucial for the survival and wellbeing of both mother and child. To date, limited literature exists on the measurement of maternal health service coverage at the sub-national level in India. The prime objectives of the study were to comprehensively measure the maternal health service coverage by generating a composite index, map India by categorizing it into low, medium and high zones and examine its incremental changes over time.

METHODS

Utilising a nationally representative time series data of 15 key indicators spread across three domains of antenatal care, intranatal care and postnatal care, we constructed a novel 'Maternal Health Service Coverage Index' (MHSI) for 29 states and 5 union territories of India for the base (2017-18) and reference (2019-20) years. Following a rigorous procedure, MHSI scores were generated using both arithmetic mean and geometric mean approaches. We categorized India into low, medium and high maternal health service coverage zones and further generated geospatial maps to examine the extent and transition of maternal health service coverage from base to reference year.

RESULTS

India registered the highest mean percentage coverage (93.7%) for 'institutional delivery' and the lowest for 'treatment for obstetric complications' (9.3%) among all the indicators. Depending on the usage of arithmetic mean and geometric mean approaches, the maternal health service coverage index score for India exhibited marginal incremental change (between 0.015-0.019 index points) in the reference year. West zone exhibited an upward transition in the coverage of maternal health service indicators, while none of the zones recorded a downward movement. The states of Mizoram (east zone) and the Union Territory of Puducherry (south zone) showed a downward transition. Union territories of Dadra & Nagar Haveli (west zone) and Chandigarh (north zone), along with the states of Maharashtra (west zone), Assam, as well as Jharkhand (both from the east & north east zone), showed upward transition.

CONCLUSION

Overall, maternal health service coverage is increasing across India. Our study offers a novel summary measure to comprehensively quantify the coverage of maternal health services, which can momentously help India identify lagged indicators and low performing regions, thereby warranting the targeted interventions and concentrated programmatic efforts to bolster the maternal health service coverage at the sub-national level.

Sensitivity of environmental performance index based on stochastic dominance

The Environmental Performance Index (EPI) is a popular sustainability index. It is a composite index which ranks 180 countries based on their environmental performance in 32 indicators. Using the EPI data and stochastic dominance efficiency methodology, this paper examines the sensitivity to the subjective weights assigned to the indicators and categories of environmental performance. The findings show a remarkable variation in environmental performance based on alternative weights which are selected using Stochastic Dominance criteria. Except for 2020, the environmental health category in EPI gets relatively higher importance in the optimal scenario, and ecosystem vitality gets relatively higher weights in the inferior scenario, suggesting that the environmental health category achievements have been relatively higher for most countries over time. The ranking analysis also shows major variations in country rankings with alternative weights. Two countries, Maldives and Gabon, would have experienced more than 100 position changes in their rankings with alternative weights. Furthermore, 67 countries would have experienced 30 or more position changes in their rankings and 37 countries experienced an EPI score change of more than 50 (out of 100) with alternative weights. Overall, the results illustrate the importance of sensitivity analysis of composite indices to increase reliability and transparency.

Health indices for the evaluation and monitoring of health in children and adolescents in prevention and health promotion: a scoping review

BACKGROUND

Health indicators are used in different settings to monitor health outcomes. Child and adolescent health is arguably one of the most important areas for the application of indices and indicators in prevention and health promotion. Although single health indicators may be better suited to display the complexity of the health status and its determinants, a selected set of indicators will still offer a complex picture. Therefore, it is argued that a group of indicators combined into an index may offer a pragmatic tool that is easier to use in order to inform stakeholders.

METHODS

A scoping review was conducted to identify and describe health indices that monitor and evaluate health of children and adolescents and to appraise the quality and value of the identified indices that may guide the further applications of these indices in particular settings. The three bibliographic databases MEDLINE, EMBASE and PsycINFO were searched and a double screening of titles and abstracts as well as double screening of full texts was performed. Indices contained in these studies were analysed in terms of focus and composition and evaluated in terms of quality criteria.

RESULTS

The scoping review identified 36 eligible studies with 18 health indices in six thematic categories. Of the identified indices, seven indices focus on anthropometrical variables, three indices focus on special aspects of newborns and five indices focus on oral health. One index assesses "healthy lifestyle" and one "functional ability" whereas one index a combination of different aspects. Most indices are calculated by using primary health data.

CONCLUSIONS

Alone or in combination with single sets of indicators, indices in six major thematic domains may be used as pragmatic tools for monitoring children's and adolescents´ health and the evaluation of interventions in health promotion and prevention settings.

A new comprehensive quantitative index for the assessment of essential amino acid quality in beef using Vis-NIR hyperspectral imaging combined with LSTM

The quality of beef is usually predicted by measuring a single index rather than a comprehensive index. To precisely determine the essential amino acid (EAA) contents in 360 beef samples, the feasibility of optimized spectral detection techniques based on the comprehensive EAA index (CEI) and comprehensive weight index (CWI) constructed by factor analysis was explored. Two-dimensional correlation spectroscopy (2D-COS) was used to analyse the mechanisms of spectral peak shifts in complex disturbance systems with CEI and CWI contents, and 15 sensitive feature variables were extracted to establish a quantitative analysis model of a long short-term memory network (LSTM). The results indicated that 2D-COS had good predictive performance in both CEI-LSTM (R2P of 0.9095 and RPD of 2.76) and CWI-LSTM (R2P of 0.8449 and RPD of 2.45), which reduced data information by 88%. This indicates that utilizing 2D-COS can eliminate collinearity and redundant information among variables while achieving data dimensionality reduction and simplification of calibration models. Furthermore, a spatial distribution map of the comprehensive EAA content was generated by combining the optimal prediction model. This study demonstrated that the comprehensive index method furnishes a new approach to rapidly evaluate EAA content.