Our study showed there is low awareness and knowledge about dementia among urban Xhosa-speaking people. Although most people surveyed were tolerant towards people with dementia, nearly one in five knew of an older person who had been abused because of dementia. Abuse included being locked in the house, stolen from, starved, verbally and physically abused, neglected and raped.
Our study revealed that highly skilled and local community health outreach workers are crucial to successfully screen, provide health care services and follow up dementia cases. Also, an innovative speaking book, in isiXhosa and English, helps us share educational information across all age groups about understanding and caring for persons living with dementia. To prevent and reduce dementia, the South African government should introduce policies to modify lifestyle factors. Any research should be in line with international perspectives. It aimed to:.
In South Africa we are a few steps behind the UK. Dementia Knowledge and awareness is lower, while stigma and abuse appear higher. People need to know how and where to find help for relatives. Better access to clinics providing diagnostic services and informed care is also needed. Low pay, earnings mobility and policy — Manchester, Lancashire. Edition: Available editions United Kingdom. Celeste de Jager , University of Cape Town. Developing country challenges The increase in non-communicable diseases such as diabetes and hypertension in Africa over the last few decades come as people adopt aspects of the Western diet - including high sugar and fat content.
Understanding dementia Dementia is an umbrella term describing problems people with various brain disorders have that affect their ability to conduct activities of daily living independently. Research to help people understand the disorder In South Africa, there is little data on dementia. A community health worker has a discussion on dementia with a woman in the Eastern Cape.
Government should step in To prevent and reduce dementia, the South African government should introduce policies to modify lifestyle factors. It aimed to: make sure health and social care systems could deal with the crisis radically step up research into cures and treatments get society involved. You might also like Stigma about HIV can be deadly. In this photo Aaron Laxton of St. Louis, Missouri, takes part in a demonstration in front of the White House in Wendy Holmes. This could be achieved by establishing quantitative measures of performance where each assessing authority would choose their own benchmarks.
However this may not produce desirable results; the use of self-assessments for resource allocation could potentially bias them towards assessing their performance as lower than reality to enable greater access to resources. The number and variety of composite indicator methodologies that have been developed clearly indicate their potential end use for decision makers working in disaster risk reduction, humanitarian and emergency response, civil protection or other fields related to disaster resilience. However the limitations of the present literature have a number of implications for end users and there is a risk that biases and uncertainty may lead to inappropriate decisions.
To counter this risk end users should consider multiple techniques when attempting to understand community vulnerability and resilience. To gain the broadest understanding this should include qualitative and quantitative techniques that go beyond composite indicators, for example using tools that are part of the Vulnerability and Capacity Assessment process developed by the International Federation of Red Cross and Red Crescent Societies.
End users can also take steps to ensure that composite indicator frameworks they are using are of high quality and reliability when they are selecting an existing index for use or commissioning the development of one either internally or by an external team of experts.
Composite indicator frameworks with high quality and reliability are likely to have:. Consideration of the purpose of the composite indicator framework, in particular whether it is needed for comparison of many areas or for local self-assessment. Demonstration that the disaster specific index adds value to the discussion of risk, vulnerability or resilience. This may come from the inclusion of multiple variables that directly relate to the phenomenon of interest or a comparison of the index with a generic socio-economic status index.
Full publication of the methodology and results. Interested third parties should be able to replicate, evaluate and build-upon the results of composite indicators. Particular attention should have been paid to clearly specifying data sources, including agency, year and the wording of any survey questions used. This may be particularly important for increasing transparency in government decision making. The results in a range of formats. Results should be published as tables, graphs and maps to enhance understanding and available in downloadable machine readable formats.
Interactive displays and dashboards may also be highly useful to end users. Adequate sensitivity and uncertainty analysis. This should incorporate, as far as possible, global analysis of sensitivity to understand which construction choices contribute most to possible variance in index values and uncertainty estimates for all index values. Attempts to validate the index values. Although relational indices are internally validated, efforts should be made to relate other indices to outputs or outcomes relevant to the phenomena of concern.
This may include disaster impacts or surveys of experts or community members on their opinion of overall community disaster risk, vulnerability or resilience. An extensive review of disaster risk, vulnerability and resilience composite indicator methodologies has been conducted drawing on a range of sources in both the academic and grey literature. The review has revealed a broad diversity of practice with implementations at both the global and local level and within many different countries.
The significant increase in the number of methodologies being implemented over recent years demonstrates greater availability of composite indicators for use by researchers and policy makers. However present practice has two key limitations that may restrict their use or potentially lead to poor decisions being made in their implementation - low use of direct measures of disaster resilience and low use of sensitivity and uncertainty analysis. Very few studies are implementing comprehensive sensitivity and uncertainty analysis, nor communicating it to end users. This may lead policy-makers to believe that index results are more precise and accurate than is actually the case.
Were a comparative index to be used by a government to allocate resources for disaster risk reduction, without consideration of its reliability, it could lead to waste of government resources or possibly even increased risks if existing resources are shifted away from high risk areas. The lack of sensitivity and uncertainty analysis may be compounded by the low use of variables directly related to disaster risk reduction, preparedness and resilience.
This low use of more direct variables may limit the explanatory power of these tools. Indices lacking direct measures of disaster resilience may be indistinguishable from more general measures of socioeconomic status, such as the Human Development Index, and thus may not offer increased value to researchers investigating disaster vulnerability. Lack of sensitivity analysis means that the exclusion of disaster related variables may go unquestioned by policy makers or researchers using such an index, increasing the risk of inappropriate use.
Policy makers and others who wish to use composite indices to inform decision making need to critically evaluate their quality and reliability before their use. Consideration of the features of high quality and reliable indices, as outlined in the discussion, would assist decision makers to commission or select an appropriate index for their needs.
Similarly, researchers developing these indices need to make greater efforts to ensure that they are relevant to the needs of decision makers, are of high quality, and add value to the understanding of vulnerability and resilience. In particular they should demonstrate that their index has greater explanatory power of disaster risk, vulnerability or resilience than generic socioeconomic status indicators and incorporate robust sensitivity and uncertainty analysis.
Furthermore, the low use of direct measures of disaster resilience may be related to the limited agreement between the methodologies of which direct measures to use. This limited agreement appears to reflect a broader gap in disaster research on the drivers of disaster resilience. It is unclear which variables, in which situations, matter most to disaster resilience. Current approaches appear to be largely tailored to individual contexts and broadly incompatible with one and other.
This could be a significant barrier for achievement of the Sendai Framework for Disaster Risk Reduction, disaster related targets of the Sustainable Development Goals and other elements of the post development agenda as parties seek to agree indicators to measure performance towards targets in these agreements. Further research is needed to better identify which variables are most predictive of disaster risk, vulnerability and resilience and in which contexts they apply. This would enable the construction of more relevant and targeted composite indicators, which combined with improvements in practices related to their construction would lead to indices that are robust, fit for purpose and comparable improving the understanding of disaster risk, vulnerability and resilience and providing decision makers with tools to better monitor progress towards a disaster resilient society.
Link to external file. The author would like to thank Dr. Christopher Burton and Prof. Alberto Monti for their feedback on drafts of this paper. His research interests include the drivers and barriers of local government activity on DRR, measurement of DRR progress and the learning processes of disaster reduction organisations. National Center for Biotechnology Information , U. Version 1.
A Comparative Analysis of Disaster Risk, Vulnerability and Resilience Composite Indicators
PLoS Curr. Published online March Benjamin Beccari Find articles by Benjamin Beccari. Author information Copyright and License information Disclaimer. Benjamin Beccari,. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. This article has been corrected. See PLoS Curr. Abstract Introduction: In the past decade significant attention has been given to the development of tools that attempt to measure the vulnerability, risk or resilience of communities to disasters.
Introduction Increasing attention is being given to issues of vulnerability, capacity and resilience in disaster management. Three key motivations for developing indices and indicator frameworks have been identified: Ranking relative performance Influencing or driving change in performance Understanding and diagnosing performance The choice, type and manipulation of the data vary for each different motivation and an approach developed for one motivation, for example to measure relative performance, is not likely to be appropriate for another motivation, for example planning and goal setting within a single city.
Methods The wide deployment of indicators and related methodologies to study a range of phenomena related to disaster risk, vulnerability and resilience requires a strict set of criteria to enable an extensive review. The following criteria for the review were selected: Composite indicator including those on a spatial basis or scorecard approach. Framework has been tested or implemented. Open in a separate window. Map of Authors. Location of lead authors or their institution for included methodologies.
Classification Schema. Schema used for classifying variables in the composite indicator methodologies. Stakeholder-focussed Methods These methods have been mostly developed for the use of communities or governments as a self-assessment tool and as such focus on explicit elicitation of disaster preparedness and risk reduction outputs. Relational Analysis Methods These methods generate an index based on analysing the relationship between vulnerability inputs and disaster impacts using either simple or multiple linear regression or Data Envelopment Analysis DEA.
Novel Statistical Techniques Four methods in the literature used more advanced construction methods, which have not been broadly deployed. They are: The Local Disaster Index. Results - Growth in Number of Methodologies Figure 3 shows the year of development or submission for publication of each methodology.
Growth in Number of Methodologies. Results - Geographic Level Table 1 shows the geographic level at which each methodology has been applied. Results - Geographic Coverage Of the 25 national or multiple level methodologies only 23 directly compare nations, with two of the multiple level methodologies taking a gridded approach to mapping the index value. Table 2 Location of single country sub-national methods.
Location and number of composite indicators implemented at the sub-national level in single countries and multiple countries. Results - Variable Selection Methods In the vast majority of methodologies 90 the variables were chosen by expert judgement relying on the literature, theory models and stakeholder knowledge. Results - Data Collection Methods A variety of methods were used to collect data which are summarised in Table 3.
Table 3 Number of methodologies using different data collection approaches. Results - Imputation Methods The majority of methods 96 did not perform any imputation with a small number using either case deletion 2 or some form of single imputation 8 to deal with missing data. Results - Normalisation Methods Many methodologies applied no normalisation to the data, either because it was not relevant to the aggregation method or because the data types were already consistent.
Table 4 Number of methodologies that use different normalisation approaches. Results - Weighting Methods A broad variety of methods for weighting variables in index construction have been deployed, including a number of bespoke methods. Table 5 Use of different weighting approaches by composite indicator methodologies. Results - Index Construction and Aggregation A variety of inductive and deductive approaches were used to construct the indices. Table 6 Numbers of variables in deductive methodologies with different numbers of intermediate levels between the variables and the reported index.
Number of levels Number of Methods Smallest number of variables Median number of variables Largest number of variables 0 20 2 7 56 1 28 4 Results - Presentation of Outputs Almost all methodologies provided some display of the results with maps and tables being the most popular as summarised in Table 7. Table 7 Number of methods presenting outputs in different forms.
Results - Sensitivity and Uncertainty Analysis Although many papers discussed the potential limitations of the methodology developed, only twenty have any explicit analysis of uncertainty or sensitivity. Results - Number of Variables There was a large variation in the number of variables each methodology used with the minimum being 2 and the maximum being , however most methodologies used relatively few with two thirds using less than Number of Variables.
Frequency of use of different numbers of variables in composite indicators. Results - Prevalence of Variables, Sub-Indicators, Indicators and Categories The methodologies used variables of which were unique. Table 8 Most commonly used variables across all the methodologies.
Table 9 Most commonly used sub-indicators across all the methodologies. Table 10 Most commonly used indicators across all the methodologies. Table 11 Number of methodologies using variables in each of the 15 categories.
Table 12 Number of Indicators, Sub-Indicators and Variables in each category and the proportion of variables in each category that are used in more than one methodology. Table 13 Number of methodologies containing variables in each environment. Results - Composition of Indices Although the prevalence of different variables provides some insight into their popularity in disaster risk, vulnerability and resilience indices it does not reveal the make-up of the individual indices.
Composition of Indices. Table 14 Proportion of variables from each environment that comprise each index, on average, for all methodologies and for methodologies that only include variables in that environment. Table 16 Proportion of variables from each environment present in methods using the three most common construction approaches. Results - Commonality between the variable sets chosen in each methodology It is desirable to know whether the large number of composite indicator methodologies is actually adding new explanatory power to understanding of vulnerability, risk or resilience or whether they are repeatedly using the similar sets of variables and only varying the construction method.
Table 17 Overlapping score calculated for each level in the classification hierarchy. Level Overlapping Score X Variable 0. Discussion This review has revealed a broad range of practice in the development of composite indicators for the measurement of disaster risk, vulnerability and resilience.
The Broad Methodology Types Although the review found considerable diversity in the methodologies of index construction the majority take a fairly standard deductive or hierarchical approach with a weighted sum of the variables included in the index. Problems with Repeatability The review of the literature found 24 methodologies that were not sufficiently well described to include in the analysis. Sensitivity and Uncertainty Analysis Numerous researchers have been pointing out flaws in index construction and calling for greater use of sensitivity and uncertainty analysis for quite some time as outlined in the introduction.
Outputs Most studies communicated results for example by using maps and summary tables. Direct Measurement of Risk, Resilience and Vulnerability This study aimed to review composite indices that claim to measure disaster risk, vulnerability and resilience. The Tension between Comparison and Self-Assessment Two key motivations have emerged from this analysis of composite index and dashboard methodologies of disaster risk, vulnerability and resilience.
Lessons for End-Users The number and variety of composite indicator methodologies that have been developed clearly indicate their potential end use for decision makers working in disaster risk reduction, humanitarian and emergency response, civil protection or other fields related to disaster resilience. Composite indicator frameworks with high quality and reliability are likely to have: Consideration of the purpose of the composite indicator framework, in particular whether it is needed for comparison of many areas or for local self-assessment.
Conclusion An extensive review of disaster risk, vulnerability and resilience composite indicator methodologies has been conducted drawing on a range of sources in both the academic and grey literature. Competing Interest The author has declared that no competing interests exist. Annex 1 - List of Methods Analysed Link to external file. Annex 2 - Excluded Methods Link to external file. Acknowledgments The author would like to thank Dr.
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