Emerging Themes in Epidemiology - Latest articles

Precision, time, and cost: a comparison of three sampling designs in an emergency setting

Megan Deitchler, Hedwig Deconinck and Gilles Bergeron — 2008-05-02

The conventional method to collect data on the health, nutrition, and food security status of a population affected by an emergency is a 30 × 30 cluster survey. This sampling method can be time and resource intensive and, accordingly, may not be the most appropriate one when data are needed rapidly for decision making. In this study, we compare the precision, time and cost of the 30 × 30 cluster survey with two alternative sampling designs: a 33 × 6 cluster design (33 clusters, 6 observations per cluster) and a 67 × 3 cluster design (67 clusters, 3 observations per cluster). Data for each sampling design were collected concurrently in West Darfur, Sudan in September-October 2005 in an emergency setting. Results of the study show the 30 × 30 design to provide more precise results (i.e. narrower 95% confidence intervals) than the 33 × 6 and 67 × 3 design for most child-level indicators. Exceptions are indicators of immunization and vitamin A capsule supplementation coverage which show a high intra-cluster correlation. Although the 33 × 6 and 67 × 3 designs provide wider confidence intervals than the 30 × 30 design for child anthropometric indicators, the 33 × 6 and 67 × 3 designs provide the opportunity to conduct a LQAS hypothesis test to detect whether or not a critical threshold of global acute malnutrition prevalence has been exceeded, whereas the 30 × 30 design does not. For the household-level indicators tested in this study, the 67 × 3 design provides the most precise results. However, our results show that neither the 33 × 6 nor the 67 × 3 design are appropriate for assessing indicators of mortality. In this field application, data collection for the 33 × 6 and 67 × 3 designs required substantially less time and cost than that required for the 30 × 30 design. The findings of this study suggest the 33 × 6 and 67 × 3 designs can provide useful time- and resource-saving alternatives to the 30 × 30 method of data collection in emergency settings.

The role of causal reasoning in understanding Simpson's paradox, Lord's paradox, and the suppression effect: covariate selection in the analysis of observational studies

Onyebuchi A Arah — 2008-02-26

Tu et al present an analysis of the equivalence of three paradoxes, namely, Simpson's, Lord's, and the suppression phenomena. They conclude that all three simply reiterate the occurrence of a change in the association of any two variables when a third variable is statistically controlled for. This is not surprising because reversal or change in magnitude is common in conditional analysis. At the heart of the phenomenon of change in magnitude, with or without reversal of effect estimate, is the question of which to use: the unadjusted (combined table) or adjusted (sub-table) estimate. Hence, Simpson's paradox and related phenomena are a problem of covariate selection and adjustment (when to adjust or not) in the causal analysis of non-experimental data. It cannot be overemphasized that although these paradoxes reveal the perils of using statistical criteria to guide causal analysis, they hold neither the explanations of the phenomenon they depict nor the pointers on how to avoid them. The explanations and solutions lie in causal reasoning which relies on background knowledge, not statistical criteria.

Cars, corporations, and commodities: Consequences for the social determinants of health

James Woodcock and Rachel Aldred — 2008-02-21

Social epidemiologists have drawn attention to health inequalities as avoidable and inequitable, encouraging thinking beyond proximal risk factors to the causes of the causes. However, key debates remain unresolved including the contribution of material and psychosocial pathways to health inequalities. Tools to operationalise social factors have not developed in tandem with conceptual frameworks, and research has often remained focused on the disadvantaged rather than on forces shaping population health across the distribution. Using the example of transport, we argue that closer attention to social processes (capital accumulation and motorisation) and social forms (commodity, corporation, and car) offers a way forward. Corporations tied to the car, primarily oil and vehicle manufacturers, are central to the world economy. Key drivers in establishing this hegemony are the threat of violence from motor vehicles and the creation of distance through the restructuring of place. Transport matters for epidemiology because the growth of mass car ownership is environmentally unsustainable and affects population health through a myriad of pathways. Starting from social forms and processes, rather than their embodiment as individual health outcomes and inequalities, makes visible connections between road traffic injuries, obesity, climate change, underdevelopment of oil producing countries, and the huge opportunity cost of the car economy. Methodological implications include a movement-based understanding of how place affects health and a process-orientated integration of material and psychosocial explanations that, while materially based, contests assumptions of automatic benefits from economic growth. Finally, we identify car and oil corporations as anti-health forces and suggest collaboration with them creates conflicts of interest.

Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries

Laura D Howe, James R Hargreaves and Sharon RA Huttly — 2008-01-30

Background: Epidemiological studies often require measures of socio-economic position (SEP). The application of principal components analysis (PCA) to data on asset-ownership is one popular approach to household SEP measurement. Proponents suggest that the approach provides a rational method for weighting asset data in a single indicator, captures the most important aspect of SEP for health studies, and is based on data that are readily available and/or simple to collect. However, the use of PCA on asset data may not be the best approach to SEP measurement. There remains concern that this approach can obscure the meaning of the final index and is statistically inappropriate for use with discrete data. In addition, the choice of assets to include and the level of agreement between wealth indices and more conventional measures of SEP such as consumption expenditure remain unclear. We discuss these issues, illustrating our examples with data from the Malawi Integrated Household Survey 2004–5. Methods: Wealth indices were constructed using the assets on which data are collected within Demographic and Health Surveys. Indices were constructed using five weighting methods: PCA, PCA using dichotomised versions of categorical variables, equal weights, weights equal to the inverse of the proportion of households owning the item, and Multiple Correspondence Analysis. Agreement between indices was assessed. Indices were compared with per capita consumption expenditure, and the difference in agreement assessed when different methods were used to adjust consumption expenditure for household size and composition. Results: All indices demonstrated similarly modest agreement with consumption expenditure. The indices constructed using dichotomised data showed strong agreement with each other, as did the indices constructed using categorical data. Agreement was lower between indices using data coded in different ways. The level of agreement between wealth indices and consumption expenditure did not differ when different consumption equivalence scales were applied. Conclusion: This study questions the appropriateness of wealth indices as proxies for consumption expenditure. The choice of data included had a greater influence on the wealth index than the method used to weight the data. Despite the limitations of PCA, alternative methods also all had disadvantages.

Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon – the reversal paradox

Yu-Kang Tu, David Gunnell and Mark S Gilthorpe — 2008-01-22

This article discusses three statistical paradoxes that pervade epidemiological research: Simpson's paradox, Lord's paradox, and suppression. These paradoxes have important implications for the interpretation of evidence from observational studies. This article uses hypothetical scenarios to illustrate how the three paradoxes are different manifestations of one phenomenon – the reversal paradox – depending on whether the outcome and explanatory variables are categorical, continuous or a combination of both; this renders the issues and remedies for any one to be similar for all three. Although the three statistical paradoxes occur in different types of variables, they share the same characteristic: the association between two variables can be reversed, diminished, or enhanced when another variable is statistically controlled for. Understanding the concepts and theory behind these paradoxes provides insights into some controversial or contradictory research findings. These paradoxes show that prior knowledge and underlying causal theory play an important role in the statistical modelling of epidemiological data, where incorrect use of statistical models might produce consistent, replicable, yet erroneous results.

Open access for the non-English-speaking world: overcoming the language barrier

Isaac CH Fung — 2008-01-04

This editorial highlights the problem of language barrier in scientific communication in spite of the recent success of Open Access Movement. Four options for English-language journals to overcome the language barrier are suggested: 1) abstracts in alternative languages provided by authors, 2) Wiki open translation, 3) international board of translator-editors, and 4) alternative language version of the journal. The Emerging Themes in Epidemiology announces that with immediate effect, it will accept translations of abstracts or full texts by authors as Additional files.Editorial note:In an effort towards overcoming the language barrier in scientific publication, ETE will accept translations of abstracts or the full text of published articles. Each translation should be submitted separately as an Additional File in PDF format. ETE will only peer review English-language versions. Therefore, translations will not be scrutinized in the review-process and the responsibility for accurate translation rests with the authors.

Estimating the role of casual contact from the community in transmission of Bordetella pertussis to young infants

Aaron M Wendelboe, Michael G Hudgens, Charles Poole and Annelies Van Rie — 2007-10-19

The proportion of infant pertussis cases due to transmission from casual contact in the community has not been estimated since before the introduction of pertussis vaccines in the 1950s. This study aimed to estimate the proportion of pertussis transmission due to casual contact using demographic and clinical data from a study of 95 infant pertussis cases and their close contacts enrolled at 14 hospitals in France, Germany, Canada, and the U.S. between February 2003 and September 2004. A complete case analysis was conducted as well as multiple imputation (MI) to account for missing data for participants and close contacts who did not participate. By considering all possible close contacts, the MI analysis estimated 66% of source cases were close contacts, implying the minimum attributable proportion of infant cases due to transmission from casual contact with community members was 34% (95% CI = 24%, 44%). Estimates from the complete case analysis were comparable but less precise. Results were sensitive to changes in the operational definition of a source case, which broadened the range of MI point estimates of transmission from casual community contact to 20%–47%. We conclude that casual contact appears to be responsible for a substantial proportion of pertussis transmission to young infants.Medical subject headings (MeSH): multiple imputation, pertussis, transmission, casual contact, sensitivity analysis, missing data, community.

Geographic variation and localised clustering of congenital anomalies in Great Britain

2007-07-06

Background: Environmental pollution as a cause of congenital anomalies is sometimes suspected because of clustering of anomalies in areas of higher exposure. This highlights questions around spatial heterogeneity (clustering) in congenital anomaly rates. If spatial variation is endemic, then any one specific cluster is less remarkable, though the presence of uncontrolled geographically clustered risk factors is suggested. If rates are relatively homogeneous across space other than around specific hazards, then evidence for these hazards causing the clusters is strengthened. We sought to estimate the extent of spatial heterogeneity in congenital anomaly rates in the United Kingdom. Methods: The study population covered about one million births from five registers in Britain from 1991–1999. We estimated heterogeneity across four geographical levels: register area, hospital catchment, electoral ward, and enumeration district, using a negative binomial regression model. We also sought clusters using a circular scan statistic. Results: Congenital anomaly rates clearly varied across register areas and hospital catchments (p < 0.001), but not below this level (p > 0.2). Adjusting for socioeconomic deprivation and maternal age made little difference to the extent of geographical variation for most congenital anomaly subtypes. The two most significant circular clusters (of four ano-rectal atresias and six congenital heart diseases) contained two or more siblings. Conclusion: The variation in rates between registers and hospital catchment area may have resulted in part from differences in case ascertainment, and this should be taken into account in geographical epidemiological studies of environmental exposures. The absence of evidence for variation below this level should be interpreted cautiously in view of the low power of general heterogeneity tests. Nevertheless, the data suggest that strong localised clusters in congenital anomalies are uncommon, so clusters around specific putative environmental hazards are remarkable when observed. Negative binomial models applied at successive hierarchical levels provide an approach of intermediate complexity to characterising geographical heterogeneity.

Methods for health surveys in difficult settings: charting progress, moving forward

2007-06-01

Health surveys are a very important component of the epidemiology toolbox, and play a critical role in gauging population health, especially in developing countries. Research on health survey methods, however, is sparse. In particular, current sampling methods are not well adapted for certain 'difficult' settings, such as emergencies, remote regions without easily available sampling frames, hidden and vulnerable population groups, urban slums and populations living under strong political pressure. This special issue of Emerging Themes in Epidemiology is entirely devoted to survey methods in such settings, and builds upon a successful conference in London highlighting problems with current approaches and possible ways forward. Greater investment in research on health survey methods is needed and will have beneficial effects for populations in need.

Who should be undertaking population-based surveys in humanitarian emergencies?

Paul B Spiegel — 2007-06-01

Background: Timely and accurate data are necessary to prioritise and effectively respond to humanitarian emergencies. 30-by-30 cluster surveys are commonly used in humanitarian emergencies because of their purported simplicity and reasonable validity and precision. Agencies have increasingly used 30-by-30 cluster surveys to undertake measurements beyond immunisation coverage and nutritional status. Methodological errors in cluster surveys have likely occurred for decades in humanitarian emergencies, often with unknown or unevaluated consequences.DiscussionMost surveys in humanitarian emergencies are done by non-governmental organisations (NGOs). Some undertake good quality surveys while others have an already overburdened staff with limited epidemiological skills. Manuals explaining cluster survey methodology are available and in use. However, it is debatable as to whether using standardised, 'cookbook' survey methodologies are appropriate. Coordination of surveys is often lacking. If a coordinating body is established, as recommended, it is questionable whether it should have sole authority to release surveys due to insufficient independence. Donors should provide sufficient funding for personnel, training, and survey implementation, and not solely for direct programme implementation.SummaryA dedicated corps of trained epidemiologists needs to be identified and made available to undertake surveys in humanitarian emergencies. NGOs in the field may need to form an alliance with certain specialised agencies or pool technically capable personnel. If NGOs continue to do surveys by themselves, a simple training manual with sample survey questionnaires, methodology, standardised files for data entry and analysis, and manual for interpretation should be developed and modified locally for each situation. At the beginning of an emergency, a central coordinating body should be established that has sufficient authority to set survey standards, coordinate when and where surveys should be undertaken and act as a survey repository. Technical expertise is expensive and donors must pay for it. As donors increasingly demand evidence-based programming, they have an obligation to ensure that sufficient funds are provided so organisations have adequate technical staff.

Mortality and nutrition surveys by Non-Governmental organisations. Perspectives from the CE-DAT database

Olivier Degomme and Debarati Guha-Sapir — 2007-06-01

In this paper we explore the strengths and gaps among NGO surveys based on an analysis of the records held in the CE-DAT database at CRED. We conclude by recommending the priority areas for strengthening NGO capacity to undertake surveys and ways to improve data quality in general.

A review of methodology and analysis of nutrition and mortality surveys conducted in humanitarian emergencies from October 1993 to April 2004

Claudine Prudhon and Paul B Spiegel — 2007-06-01

Background: Malnutrition prevalence and mortality rates are increasingly used as essential indicators to assess the severity of a crisis, to follow trends, and to guide decision-making, including allocation of funds. Although consensus has slowly developed on the methodology to accurately measure these indicators, errors in the application of the survey methodology and analysis have persisted. The aim of this study was to identify common methodological weaknesses in nutrition and mortality surveys and to provide practical recommendations for improvement. Methods: Nutrition (N = 368) and crude mortality rate (CMR; N = 158) surveys conducted by 33 non-governmental organisations and United Nations agencies in 17 countries from October 1993 to April 2004 were analysed for sampling validity, precision, quality of measurement and calculation according to several criteria. Results: One hundred and thirty (35.3%) nutrition surveys and 5 (3.2%) CMR surveys met the criteria for quality. Quality of surveys varied significantly depending on the agency. The proportion of nutrition surveys that met criteria for quality rose significantly from 1993 to 2004; there was no improvement for mortality surveys during this period. Conclusion: Significant errors and imprecision in the methodology and reporting of nutrition and mortality surveys were identified. While there was an improvement in the quality of nutrition surveys over the years, the quality of mortality surveys remained poor. Recent initiatives aimed at standardising nutrition and mortality survey quality should be strengthened. There are still a number of methodological issues in nutrition and mortality surveys in humanitarian emergencies that need further study.

Wanted: studies on mortality estimation methods for humanitarian emergencies, suggestions for future research

Working Group for Mortality Estimation in Emergencies — 2007-06-01

Measuring rates and circumstances of population mortality (in particular crude and under-5 year mortality rates) is essential to evidence-based humanitarian relief interventions. Because prospective vital event registration is absent or deteriorates in nearly all crisis-affected populations, retrospective household surveys are often used to estimate and describe patterns of mortality. Originally designed for measuring vaccination coverage, the two-stage cluster survey methodology is frequently employed to measure mortality retrospectively due to limited time and resources during humanitarian emergencies. The method tends to be followed without considering alternatives, and there is a need for expert advice to guide health workers measuring mortality in the field.In a workshop in France in June 2006, we deliberated the problems inherent in this method when applied to measure outcomes other than vaccine coverage and acute malnutrition (specifically, mortality), and considered recommendations for improvement. Here we describe these recommendations and outline outstanding issues in three main problem areas in emergency mortality assessment discussed during the workshop: sampling, household data collection issues, and cause of death ascertainment. We urge greater research on these issues. As humanitarian emergencies become ever more complex, all agencies should benefit from the most recently tried and tested survey tools.

Don't spin the pen: two alternative methods for second-stage sampling in urban cluster surveys

Rebecca F Grais, Angela MC Rose and Jean-Paul Guthmann — 2007-06-01

In two-stage cluster surveys, the traditional method used in second-stage sampling (in which the first household in a cluster is selected) is time-consuming and may result in biased estimates of the indicator of interest. Firstly, a random direction from the center of the cluster is selected, usually by spinning a pen. The houses along that direction are then counted out to the boundary of the cluster, and one is then selected at random to be the first household surveyed. This process favors households towards the center of the cluster, but it could easily be improved. During a recent meningitis vaccination coverage survey in Maradi, Niger, we compared this method of first household selection to two alternatives in urban zones: 1) using a superimposed grid on the map of the cluster area and randomly selecting an intersection; and 2) drawing the perimeter of the cluster area using a Global Positioning System (GPS) and randomly selecting one point within the perimeter. Although we only compared a limited number of clusters using each method, we found the sampling grid method to be the fastest and easiest for field survey teams, although it does require a map of the area. Selecting a random GPS point was also found to be a good method, once adequate training can be provided. Spinning the pen and counting households to the boundary was the most complicated and time-consuming. The two methods tested here represent simpler, quicker and potentially more robust alternatives to spinning the pen for cluster surveys in urban areas. However, in rural areas, these alternatives would favor initial household selection from lower density (or even potentially empty) areas. Bearing in mind these limitations, as well as available resources and feasibility, investigators should choose the most appropriate method for their particular survey context.

Optimisation of the T-square sampling method to estimate population sizes

Kristof Bostoen, Zaid Chalabi and Rebecca F Grais — 2007-06-01

Population size and density estimates are needed to plan resource requirements and plan health related interventions. Sampling frames are not always available necessitating surveys using non-standard household sampling methods. These surveys are time-consuming, difficult to validate, and their implementation could be optimised. Here, we discuss an example of an optimisation procedure for rapid population estimation using T-Square sampling which has been used recently to estimate population sizes in emergencies. A two-stage process was proposed to optimise the T-Square method wherein the first stage optimises the sample size and the second stage optimises the pathway connecting the sampling points. The proposed procedure yields an optimal solution if the distribution of households is described by a spatially homogeneous Poisson process and can be sub-optimal otherwise. This research provides the first step in exploring how optimisation techniques could be applied to survey designs thereby providing more timely and accurate information for planning interventions.

Sampling in health geography: reconciling geographical objectives and probabilistic methods. An example of a health survey in Vientiane (Lao PDR)

2007-06-01

Background: Geographical objectives and probabilistic methods are difficult to reconcile in a unique health survey. Probabilistic methods focus on individuals to provide estimates of a variable's prevalence with a certain precision, while geographical approaches emphasise the selection of specific areas to study interactions between spatial characteristics and health outcomes. A sample selected from a small number of specific areas creates statistical challenges: the observations are not independent at the local level, and this results in poor statistical validity at the global level. Therefore, it is difficult to construct a sample that is appropriate for both geographical and probability methods. Methods: We used a two-stage selection procedure with a first non-random stage of selection of clusters. Instead of randomly selecting clusters, we deliberately chose a group of clusters, which as a whole would contain all the variation in health measures in the population. As there was no health information available before the survey, we selected a priori determinants that can influence the spatial homogeneity of the health characteristics. This method yields a distribution of variables in the sample that closely resembles that in the overall population, something that cannot be guaranteed with randomly-selected clusters, especially if the number of selected clusters is small. In this way, we were able to survey specific areas while minimising design effects and maximising statistical precision.ApplicationWe applied this strategy in a health survey carried out in Vientiane, Lao People's Democratic Republic. We selected well-known health determinants with unequal spatial distribution within the city: nationality and literacy. We deliberately selected a combination of clusters whose distribution of nationality and literacy is similar to the distribution in the general population. Conclusion: This paper describes the conceptual reasoning behind the construction of the survey sample and shows that it can be advantageous to choose clusters using reasoned hypotheses, based on both probability and geographical approaches, in contrast to a conventional, random cluster selection strategy.

The use of personal digital assistants for data entry at the point of collection in a large household survey in southern Tanzania

2007-06-01

Background: Survey data are traditionally collected using pen-and-paper, with double data entry, comparison of entries and reconciliation of discrepancies before data cleaning can commence. We used Personal Digital Assistants (PDAs) for data entry at the point of collection, to save time and enhance the quality of data in a survey of over 21,000 scattered rural households in southern Tanzania. Methods: Pendragon Forms 4.0 software was used to develop a modular questionnaire designed to record information on household residents, birth histories, child health and health-seeking behaviour. The questionnaire was loaded onto Palm m130 PDAs with 8 Mb RAM. One hundred and twenty interviewers, the vast majority with no more than four years of secondary education and very few with any prior computer experience, were trained to interview using the PDAs. The 13 survey teams, each with a supervisor, laptop and a four-wheel drive vehicle, were supported by two back-up vehicles during the two months of field activities. PDAs and laptop computers were charged using solar and in-car chargers.Logical checks were performed and skip patterns taken care of at the time of data entry. Data records could not be edited after leaving each household, to ensure the integrity of the data from each interview. Data were downloaded to the laptop computers and daily summary reports produced to evaluate the completeness of data collection. Data were backed up at three levels: (i) at the end of every module, data were backed up onto storage cards in the PDA; (ii) at the end of every day, data were downloaded to laptop computers; and (iii) a compact disc (CD) was made of each team's data each day.A small group of interviewees from the community, as well as supervisors and interviewers, were asked about their attitudes to the use of PDAs. Results: Following two weeks of training and piloting, data were collected from 21,600 households (83,346 individuals) over a seven-week period in July-August 2004. No PDA-related problems or data loss were encountered.Fieldwork ended on 26 August 2004, the full dataset was available on a CD within 24 hours and the results of initial analyses were presented to district authorities on 28 August. Data completeness was over 99%.The PDAs were well accepted by both interviewees and interviewers. Conclusion: The use of PDAs eliminated the usual time-consuming and error-prone process of data entry and validation. PDAs are a promising tool for field research in Africa.

Assessing household wealth in health studies in developing countries: a comparison of participatory wealth ranking and survey techniques from rural South Africa

2007-06-01

Background: Accurate tools for assessing household wealth are essential for many health studies in developing countries. Household survey and participatory wealth ranking (PWR) are two approaches to generate data for this purpose. Methods: A household survey and PWR were conducted among eight villages in rural South Africa. We developed three indicators of household wealth using the data. One indicator used PWR data only, one used principal components analysis to combine data from the survey, while the final indicator used survey data combined in a manner informed by the PWR. We assessed internal consistency of the indices and assessed their level of agreement in ranking household wealth. Results: Food security, asset ownership, housing quality and employment were important indicators of household wealth. PWR, consisting of three independent rankings of 9671 households, showed a high level of internal consistency (intraclass correlation coefficient 0.81, 95% CI 0.79–0.82). Data on 1429 households were available from all three techniques. There was moderate agreement in ranking households into wealth tertiles between the two indicators based on survey data (spearman rho = 0.69, kappa = 0.43), but only limited agreement between these techniques and the PWR data (spearman rho = 0.38 and 0.31, kappa = 0.20 and 0.17). Conclusion: Both PWR and household survey can provide a rapid assessment of household wealth. Each technique had strengths and weaknesses. Reasons for differences might include data inaccuracies or limitations in the methods by which information was weighted. Alternatively, the techniques may measure different things. More research is needed to increase the validity of measures of socioeconomic position used in health studies in developing countries.

Age standardisation – an indigenous standard?

Bridget Robson, Gordon Purdie, Fiona Cram and Shirley Simmonds — 2007-05-14

The study of inequities in health is a critical component of monitoring government obligations to uphold the rights of Indigenous Peoples. In Aotearoa/New Zealand the indigenous Māori population has a substantially younger age structure than the non-indigenous population making it necessary to account for age differences when comparing population health outcomes. An age-standardised rate is a summary measure of a rate that a population would have if it had a standard age structure. Changing age standards have stimulated interest in the potential impact of population standards on disparities data and consequently on health policy.This paper compares the age structure of the Māori and non-Māori populations with two standard populations commonly used in New Zealand: Segi's world and WHO world populations. The performance of these standards in Māori and non-Māori mortality data was then measured against the use of the Māori population as a standard. It was found that the choice of population standard affects the magnitude of mortality rates, rate ratios and rate differences, the relative ranking of causes of death, and the relative width of confidence intervals. This in turn will affect the monitoring of trends in health outcomes and health policy decision-making. It is concluded that the choice of age standard has political implications and the development and utilisation of an international indigenous population standard should be considered.