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Filemask translations/*/3-8-1.po
Translation file translations/fr/3-8-1.po
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SDG Metadata / 3-8-1French

Resource update 12 days ago
User avatar NelMed

Comment added

SDG Metadata / 3-8-1French

aucune traduction disponible "complète"

13 days ago
User avatar NelMed

New translation

SDG Metadata / 3-8-1French

<h2>Sources of discrepancies:</h2>
<p>The service coverage index draws on existing, publicly available data and estimates for tracer indicators. These numbers have already been through a country consultation process (e.g., for immunization coverage), or are taken directly from country reported data. </p>
<h2> Sources des écarts : </h2>
<p> L'indice de couverture des services s'appuie sur des données et des estimations disponibles publiquement pour les indicateurs de suivi. Ces chiffres ont déjà fait l'objet d'un processus de consultation dans le pays (par exemple, pour la couverture vaccinale) ou sont tirés directement des données déclarées par le pays. </p>
13 days ago
User avatar NelMed

New translation

SDG Metadata / 3-8-1French

<h1>Data availability</h1>
<h2>Description:</h2>
<p>Summarizing data availability for the UHC service coverage index is not straightforward, as different data sources are used across the 14 tracer indicators. Additionally, for many indicators comparable estimates have been produced, in many cases drawing on different types of underlying data sources to inform the estimates while also using projections to impute missing values. Based on the underlying data sources for each of the tracer indicators (i.e., ignoring estimates and projections), the average proportion of indicators used to compute the index with underlying data available since 2010 is around 70% across countries globally.</p>
<h2>Time series:</h2>
<p>A baseline value for the UHC service coverage index for 2015 across 183 countries was published in late 2017. As part of this process, data sources going back to 2000 were assembled. In 2019, it is anticipated to publish a time series from 2000 to 2017.</p>
<h2>Disaggregation:</h2>
<p>Equity is central to the definition of UHC, and therefore the UHC service coverage index should be used to communicate information about inequalities in service coverage within countries. This can be done by presenting the index separately for the national population vs disadvantaged populations to highlight differences between them. </p>
<p>For countries, geographic location is likely the most feasible dimension for sub-national disaggregation based on average coverage levels measured with existing data sources. To do this, the UHC index can be computed separately by, e.g., province or urban vs rural residence, which would allow for subnational comparisons of service coverage. Currently, the most readily available data for disaggregation on other dimensions of inequality, such as household wealth, is for indicators of coverage within the reproductive, maternal, newborn and child health services category. Inequality observed in this dimension can be used as a proxy to understand differences in service coverage across key inequality dimensions. This approach should be replaced with full disaggregation of all 14 tracer indicators once data are available to do so.</p>
<h1> Disponibilité des données </h1>
<h2> Description : </h2>
<p> Résumer la disponibilité des données pour l'indice de couverture des services de CSU n'est pas simple, car différentes sources de données sont utilisées pour les 14 indicateurs traceurs. En outre, pour de nombreux indicateurs, des estimations comparables ont été produites, dans de nombreux cas en s'appuyant sur différents types de sources de données sous-jacentes pour éclairer les estimations tout en utilisant également des projections pour imputer les valeurs manquantes. Sur la base des sources de données sous-jacentes pour chacun des indicateurs traceurs (c'est-à-dire en ignorant les estimations et les projections), la proportion moyenne d'indicateurs utilisés pour calculer l'indice avec les données sous-jacentes disponibles depuis 2010 est d'environ 70% dans l'ensemble des pays. </p>
<h2> Série chronologique : </h2>
<p> Une valeur de référence de l'indice de couverture des services de CSU pour 2015 dans 183 pays a été publiée fin 2017. Dans le cadre de ce processus, des sources de données remontant à 2000 ont été rassemblées. En 2019, il est prévu de publier une série chronologique de 2000 à 2017. </p>
<h2> Désagrégation : </h2>
<p> L'équité est au cœur de la définition de la CSU, et par conséquent, l'indice de couverture des services de CSU devrait être utilisé pour communiquer des informations sur les inégalités de couverture des services dans les pays. Cela peut être fait en présentant l'indice séparément pour la population nationale par rapport aux populations défavorisées afin de mettre en évidence les différences entre elles. </p>
<p> Pour les pays, l'emplacement géographique est probablement la dimension la plus réalisable pour la désagrégation infranationale basée sur les niveaux de couverture moyens mesurés avec les sources de données existantes. Pour ce faire, l'indice de CSU peut être calculé séparément, par exemple, par province ou résidence urbaine vs rurale, ce qui permettrait des comparaisons infranationales de la couverture des services. À l'heure actuelle, les données les plus facilement disponibles pour la ventilation sur d'autres dimensions de l'inégalité, telles que la richesse des ménages, concernent les indicateurs de couverture dans la catégorie des services de santé reproductive, maternelle, néonatale et infantile. Les inégalités observées dans cette dimension peuvent être utilisées comme approximation pour comprendre les différences de couverture des services entre les dimensions clés de l'inégalité. Cette approche devrait être remplacée par une désagrégation complète des 14 indicateurs traceurs une fois que les données seront disponibles pour ce faire. </p>
13 days ago
User avatar NelMed

New translation

SDG Metadata / 3-8-1French

<h2>Regional aggregates:</h2>
<p>Regional and global aggregates are computed by using national population sizes to compute a weighted average of country-specific values for the index. This is justified on the grounds that UHC is a property of countries, and the index of essential services is a summary measure of access to essential services for each country&#x2019;s population.</p>
<h2> Agrégats régionaux : </h2>
<p> Les agrégats régionaux et mondiaux sont calculés en utilisant la taille de la population nationale pour calculer une moyenne pondérée des valeurs spécifiques au pays pour l'indice. Cela se justifie par le fait que la CSU est une propriété des pays et que l'indice des services essentiels est une mesure sommaire de l'accès aux services essentiels pour la population de chaque pays &#x2019;. </p>
13 days ago
User avatar NelMed

Translation changed

SDG Metadata / 3-8-1French

<h2>Treatment of missing values:</h2>
<ul>
<li><strong><em>At country level:</em></strong></li>
</ul>
<p>The starting point for computing the index is to assemble existing information for each tracer indicator. In many cases, this involves using country time series that have been produced or collated by UN agencies in consultation with country governments (e.g., immunization coverage, access to sanitation, HIV treatment coverage, etc). Some of these published time series involve mathematical modelling to reconcile multiple data sources or impute missing values, and these details are summarized in Annex 1. </p>
<p>After assembling these inputs, there are still missing values for some country-years for some indicators. Calculating the UHC service coverage index requires values for each tracer indicator for a country, so some imputation is necessary to fill these data gaps. The current approach involves a simple imputation algorithm. For each indicator:</p>
<ul>
<li>
<ul>
<li>If a country has missing values between two years with values, linear interpolation is used to fill missing values for the intervening years</li>
<li>If a country has historical years with values, but no current value, constant extrapolation is used to fill missing values to the current year</li>
<li>If a country has no values, a value is imputed. For pneumonia care-seeking and density of surgeons, a regression is fit to impute missing values (see Annex 1 for details). For all other indicators, a regional median is calculated to impute missing values. Regions are based on World Bank geographic regions, with a separate grouping of traditional high-income countries<sup><a href="#footnote-1" id="footnote-ref-1">[1]</a></sup> </li>
</ul>
</li>
</ul>
<p>Given the timing and distribution of various health surveys and other data collection mechanisms, countries do not collect and report on all 14 tracer indicators of health service coverage on an annual basis. In addition, monitoring at country level is most suitably done at broader time intervals, e.g., every 5 years, to allow for new data collection across indicators. Therefore, the extent to which imputation has been used to fill missing information should be communicated along with the index value. </p>
<ul>
<li><strong><em>At regional and global levels:</em></strong></li>
</ul>
<p>Any needed imputation is done at country level. These country values can then be used to compute regional and global ones.</p><div class="footnotes"><div><sup class="footnote-number" id="footnote-1">1</sup><p> Argentina, Australia, Austria, Belgium, Brunei Darussalam, Canada, Chile, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Luxembourg, Malta, Netherlands, New Zealand, Norway, Poland, Portugal, Republic of Korea, Singapore, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States of America, Uruguay <a href="#footnote-ref-1">&#x2191;</a></p></div></div>
<h2> Traitement des valeurs manquantes :</h2>
<ul>
<li> <strong> <em> Au niveau national :</em></strong></li>
</ul>
<p> Le point de départ de l’informatique de l’index est d’assembler les informations existantes pour chaque indicateur traceur. Dans de nombreux cas, il s’agit d’utiliser des séries de temps par pays qui ont été produites ou rassemblées par les agences des Nations Unies en consultation avec les gouvernements des pays (p. ex., couverture vaccinale, accès à l’assainissement, couverture du traitement du VIH, etc.). Certaines de ces séries de temps publiées impliquent une modélisation mathématique pour réconcilier plusieurs sources de données ou imputer les valeurs manquantes, et ces détails sont résumés à l’annexe 1. </p>
<p> Après avoir rassemblé ces intrants, il manque encore des valeurs pour certaines années par pays pour certains indicateurs. Le calcul de l’indice UHC de couverture des services nécessite des valeurs pour chaque indicateur traceur pour un pays, de sorte qu’une certaine imputation est nécessaire pour combler ces lacunes de données. L’approche actuelle implique un algorithme d’imputation simple. Pour chaque indicateur :</p>
<ul>
<li>
<ul>
<li> Si un pays a des valeurs manquantes entre deux ans avec des valeurs, l’interpolation linéaire est utilisée pour remplir les valeurs manquantes pour les années intermédiaires</li>
<li> Si un pays a des années historiques avec des valeurs, mais aucune valeur actuelle, l’extrapolation constante est utilisée pour remplir les valeurs manquantes à l’année en cours</li>
<li> Si un pays n’a pas de valeurs, une valeur est imputée. Pour la recherche de soins et la densité des chirurgiens en cas de pneumonie, une régression est apte à imputer les valeurs manquantes (voir l’annexe 1 pour plus de détails). Pour tous les autres indicateurs, une médiane régionale est calculée pour imputer les valeurs manquantes. Les régions sont basées sur les régions géographiques de la Banque mondiale, avec un groupe distinct de pays traditionnels à revenu élevé<sup><a href="#footnote-1" id="footnote-ref-1">[1]</a></sup> </li>
</ul>
</li>
</ul>
<p> Compte tenu du calendrier et de la distribution de diverses enquêtes sur la santé et d’autres mécanismes de collecte de données, les pays ne recueillent pas et ne font pas rapport sur les 14 indicateurs traceurs de la couverture des services de santé sur une base annuelle. En outre, la surveillance au niveau des pays se fait le mieux à des intervalles de temps plus larges, par exemple tous les cinq ans, afin de permettre la collecte de nouvelles données entre les indicateurs. Par conséquent, la mesure dans laquelle l’imputation a été utilisée pour remplir les informations manquantes doit être communiquée avec la valeur de l’indice. </p>
<ul>
<li> <strong> <em> Aux niveaux régional et mondial :</em></strong></li>
</ul>
<p> Toute imputation nécessaire se fait au niveau des pays. Ces valeurs nationales peuvent ensuite être utilisées pour calculer les valeurs régionales et mondiales. </p> <div class="footnotes"> <div> <sup class="footnote-number" id="footnote-1">1</sup><p> Argentine, Australie, Autriche, Belgique, Brunei Darussalam, Canada, Chili, Chypre, Czechia, Danemark, Estonie, Finlande, France, Allemagne, Grèce, Islande, Irlande, Israël, Italie, Japon, Luxembourg, Malte, Pays-Bas, Nouvelle-Zélande, Norvège, Pologne, Portugal, République de Corée, Singapour, Slovaquie, Slovénie, Espagne, Suède, Suisse, Royaume-Uni, États-Unis d’Amérique, Uruguay <a href="#footnote-ref-1">&#x2191;</a></p></div></div></div></div>
13 days ago
User avatar NelMed

New translation

SDG Metadata / 3-8-1French

<h2>Treatment of missing values:</h2>
<ul>
<li><strong><em>At country level:</em></strong></li>
</ul>
<p>The starting point for computing the index is to assemble existing information for each tracer indicator. In many cases, this involves using country time series that have been produced or collated by UN agencies in consultation with country governments (e.g., immunization coverage, access to sanitation, HIV treatment coverage, etc). Some of these published time series involve mathematical modelling to reconcile multiple data sources or impute missing values, and these details are summarized in Annex 1. </p>
<p>After assembling these inputs, there are still missing values for some country-years for some indicators. Calculating the UHC service coverage index requires values for each tracer indicator for a country, so some imputation is necessary to fill these data gaps. The current approach involves a simple imputation algorithm. For each indicator:</p>
<ul>
<li>
<ul>
<li>If a country has missing values between two years with values, linear interpolation is used to fill missing values for the intervening years</li>
<li>If a country has historical years with values, but no current value, constant extrapolation is used to fill missing values to the current year</li>
<li>If a country has no values, a value is imputed. For pneumonia care-seeking and density of surgeons, a regression is fit to impute missing values (see Annex 1 for details). For all other indicators, a regional median is calculated to impute missing values. Regions are based on World Bank geographic regions, with a separate grouping of traditional high-income countries<sup><a href="#footnote-1" id="footnote-ref-1">[1]</a></sup> </li>
</ul>
</li>
</ul>
<p>Given the timing and distribution of various health surveys and other data collection mechanisms, countries do not collect and report on all 14 tracer indicators of health service coverage on an annual basis. In addition, monitoring at country level is most suitably done at broader time intervals, e.g., every 5 years, to allow for new data collection across indicators. Therefore, the extent to which imputation has been used to fill missing information should be communicated along with the index value. </p>
<ul>
<li><strong><em>At regional and global levels:</em></strong></li>
</ul>
<p>Any needed imputation is done at country level. These country values can then be used to compute regional and global ones.</p><div class="footnotes"><div><sup class="footnote-number" id="footnote-1">1</sup><p> Argentina, Australia, Austria, Belgium, Brunei Darussalam, Canada, Chile, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Luxembourg, Malta, Netherlands, New Zealand, Norway, Poland, Portugal, Republic of Korea, Singapore, Slovakia, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States of America, Uruguay <a href="#footnote-ref-1">&#x2191;</a></p></div></div>
<h2> Traitement des valeurs manquantes :</h2>
<ul>
<li> <strong> <em> Au niveau national :</em></strong></li>
</ul>
<p> Le point de départ de l’informatique de l’index est d’assembler les informations existantes pour chaque indicateur traceur. Dans de nombreux cas, il s’agit d’utiliser des séries de temps par pays qui ont été produites ou rassemblées par les agences des Nations Unies en consultation avec les gouvernements des pays (p. ex., couverture vaccinale, accès à l’assainissement, couverture du traitement du VIH, etc.). Certaines de ces séries de temps publiées impliquent une modélisation mathématique pour réconcilier plusieurs sources de données ou imputer les valeurs manquantes, et ces détails sont résumés à l’annexe 1. </p>
<p> Après avoir rassemblé ces intrants, il manque encore des valeurs pour certaines années par pays pour certains indicateurs. Le calcul de l’indice UHC de couverture des services nécessite des valeurs pour chaque indicateur traceur pour un pays, de sorte qu’une certaine imputation est nécessaire pour combler ces lacunes de données. L’approche actuelle implique un algorithme d’imputation simple. Pour chaque indicateur :</p>
<ul>
<li>
<ul>
<li> Si un pays a des valeurs manquantes entre deux ans avec des valeurs, l’interpolation linéaire est utilisée pour remplir les valeurs manquantes pour les années intermédiaires</li>
<li> Si un pays a des années historiques avec des valeurs, mais aucune valeur actuelle, l’extrapolation constante est utilisée pour remplir les valeurs manquantes à l’année en cours</li>
<li> Si un pays n’a pas de valeurs, une valeur est imputée. Pour la recherche de soins et la densité des chirurgiens en cas de pneumonie, une régression est apte à imputer les valeurs manquantes (voir l’annexe 1 pour plus de détails). Pour tous les autres indicateurs, une médiane régionale est calculée pour imputer les valeurs manquantes. Les régions sont basées sur les régions géographiques de la Banque mondiale, avec un groupe distinct de pays traditionnels à revenu élevé<sup><a href="#footnote-1" id="footnote-ref-1">[1]</a></sup> </li>
</ul>
</li>
</ul>
<p> Compte tenu du calendrier et de la distribution de diverses enquêtes sur la santé et d’autres mécanismes de collecte de données, les pays ne recueillent pas et ne font pas rapport sur les 14 indicateurs traceurs de la couverture des services de santé sur une base annuelle. En outre, la surveillance au niveau des pays se fait le mieux à des intervalles de temps plus larges, par exemple tous les cinq ans, afin de permettre la collecte de nouvelles données entre les indicateurs. Par conséquent, la mesure dans laquelle l’imputation a été utilisée pour remplir les informations manquantes doit être communiquée avec la valeur de l’indice. </p>
<ul>
<li> <strong> <em> Aux niveaux régional et mondial :</em></strong></li>
</ul>
<p> Toute imputation nécessaire se fait au niveau des pays. Ces valeurs nationales peuvent ensuite être utilisées pour calculer les valeurs régionales et mondiales. </p> <div class="footnotes"> <div> <sup class="footnote-number" id="footnote-1">1</sup><p> Argentine, Australie, Autriche, Belgique, Brunei Darussalam, Canada, Chili, Chypre, Czechia, Danemark, Estonie, Finlande, France, Allemagne, Grèce, Islande, Irlande, Israël, Italie, Japon, Luxembourg, Malte, Pays-Bas, Nouvelle-Zélande, Norvège, Pologne, Portugal, République de Corée, Singapour, Slovaquie, Slovénie, Espagne, Suède, Suisse, Royaume-Uni, États-Unis d’Amérique, Uruguay <a href="#footnote-ref-1">&#x2191;</a></p></div></div></div></div>
13 days ago
User avatar NelMed

Comment added

SDG Metadata / 3-8-1French

il y a un epartie manquante à la traduction à partir du lien " SRC=

13 days ago
User avatar NelMed

Marked for edit

SDG Metadata / 3-8-1French

<h1>Methodology</h1>
<p><strong>Computation method:</strong></p>
<p>The index is computed with geometric means, based on the methods used for the Human Development Index. The calculation of the 3.8.1 indicator requires first preparing the 14 tracer indicators so that they can be combined into the index, and then computing the index from those values. </p>
<p>The 14 tracer indicators are first all placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement, e.g., the percentage of infants who have been immunized ranges from 0 to 100 percent. However, for a few indicators additional rescaling is required to obtain appropriate values from 0 to 100, as follows:</p>
<ul>
<li>Rescaling based on a non-zero minimum to obtain finer resolution (this &#x201C;stretches&#x201D; the distribution across countries): prevalence of non-raised blood pressure and prevalence of non-use of tobacco are both rescaled using a minimum value of 50%.<ul>
<li>rescaled value = (X-50)/(100-50)*100</li>
</ul>
</li>
<li>Rescaling for a continuous measure: mean fasting plasma glucose, which is a continuous measure (units of mmol/L), is converted to a scale of 0 to 100 using the minimum theoretical biological risk (5.1 mmol/L) and observed maximum across countries (7.1 mmol/L).<ul>
<li>rescaled value = (7.1 - original value)/(7.1-5.1)*100</li>
</ul>
</li>
<li>Maximum thresholds for rate indicators: hospital bed density and health workforce density are both capped at maximum thresholds, and values above this threshold are held constant at 100. These thresholds are based on minimum values observed across OECD countries.<ul>
<li>rescaled hospital beds per 10,000 = minimum(100, original value / 18*100)</li>
<li>rescaled physicians per 1,000 = minimum(100, original value / 0.9*100)</li>
<li>rescaled psychiatrists per 100,000 = minimum(100, original value / 1*100)</li>
<li>rescaled surgeons per 100,000 = minimum(100, original value / 14*100)</li>
</ul>
</li>
</ul>
<p>Once all tracer indicator values are on a scale of 0 to 100, geometric means are computed within each of the four health service areas, and then a geometric mean is taken of those four values. If the value of a tracer indicator happens to be zero, it is set to 1 (out of 100) before computing the geometric mean. The following diagram illustrates the calculations.</p>
<p><img 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<p>Note that in countries with low malaria burden, the tracer indicator for use of insecticide-treated nets is dropped from the calculation.</p>
<h1>Méthodologie</h1>
<p><strong> méthode de calcul : </strong></p>
<p>L’indice est calculé avec des moyens géométriques, basés sur les méthodes utilisées pour l’indice de développement humain. Le calcul de l’indicateur 3.8.1 nécessite d’abord de préparer les 14 indicateurs traceurs afin qu’ils puissent être combinés dans l’indice, puis de calculer l’indice à partir de ces valeurs. </p>
<p>Les 14 indicateurs traceurs sont d’abord placés sur la même échelle, 0 étant la valeur la plus basse et 100 étant la valeur optimale. Pour la plupart des indicateurs, cette échelle est l’échelle naturelle de mesure, par exemple, le pourcentage de nourrissons vaccinés varie de 0 à 100 p. 100. Toutefois, pour quelques indicateurs, un recalage supplémentaire est nécessaire pour obtenir des valeurs appropriées de 0 à 100, comme suit : </p>
<ul>
<li>Réalisation basée sur un minimum non nul pour obtenir une résolution plus fine (ceci &#x201C;stretches&#x201D; la répartition entre les pays): la prévalence de la pression artérielle non élevée et la prévalence de la non-consommation de tabac sont toutes deux recalées en utilisant une valeur minimale de 50%. <ul>
<li>rééchelonné = (X-50)/(100-50)*100</li>
</ul>
</li>
<li>Réalisation pour une mesure continue : le glucose plasmatique à jeun moyen, qui est une mesure continue (unités de mmol/L), est converti en une échelle de 0 à 100 en utilisant le risque biologique théorique minimum (5,1 mmol/L) et observé au maximum dans tous les pays (7,1 mmol/L). <ul>
<li>rééchelonné = (7,1 - valeur originale)/(7,1-5,1)*100</li>
</ul>
</li>
<li>Maximum pour les indicateurs de taux : la densité des lits d’hôpital et la densité du personnel de santé sont tous deux plafonnés à des seuils maximaux, et les valeurs supérieures à ce seuil sont maintenues constantes à 100. Ces seuils sont basés sur les valeurs minimales observées dans les pays de l’OCDE. <ul>
<li> lits d’hôpital à l’échelle inférieure pour 10 000 = minimum (100, valeur originale / 18*100)</li>
<li> médecins pour 1 000 = minimum (100, valeur originale / 0,9*100)</li>
<li> psychiatres pour 100.000 = minimum (100, valeur originale / 1*100)</li>
<li> chirurgiens per 100,000 = minimum (100, valeur originale / 14*100)</li>
</ul>
</li>
</ul>
<p>Ce que toutes les valeurs des indicateurs traceurs sont sur une échelle de 0 à 100, les moyens géométriques sont calculés dans chacun des quatre domaines des services de santé, puis une moyenne géométrique est prise de ces quatre valeurs. Si la valeur d’un indicateur traceur se trouve être nulle, il est réglé à 1 (sur 100) avant de calculer la moyenne géométrique. Le diagramme suivant illustre les calculs.</p>
<p><img
src=
13 days ago
User avatar NelMed

New translation

SDG Metadata / 3-8-1French

<h2>Comments and limitations:</h2>
<p>These tracer indicators are meant to be indicative of service coverage, not a complete or exhaustive list of health services and interventions that are required for universal health coverage. The 14 tracer indicators were selected because they are well-established, with available data widely reported by countries (or expected to become widely available soon). Therefore, the index can be computed with existing data sources and does not require initiating new data collection efforts solely to inform the index.</p>
<h2> Commentaires et limitations :</h2>
<p> Ces indicateurs traceurs sont censés indiquer la couverture des services, et non une liste complète ou exhaustive des services de santé et des interventions nécessaires à la couverture sanitaire universelle. Les 14 indicateurs traceurs ont été sélectionnés parce qu’ils sont bien établis, et les données disponibles sont largement rapportées par les pays (ou devraient bientôt être largement disponibles). Par conséquent, l’index peut être calculé avec les sources de données existantes et ne nécessite pas d’entreprendre de nouveaux efforts de collecte de données uniquement pour informer l’index. </p>
13 days ago
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Last author Nelcie Medeiros

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