Zen

Translation

<p>The numerator is the number of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method (<em>CP<sub>Mod</sub></em>CPMod). The denominator is the total demand for family planning (the sum of the number of women using any contraceptive method (<em>CP<sub>Any</sub></em>CPAny) and the number of women with unmet need for family planning (<em>UMN</em>UMN)). The quotient is then multiplied by 100 to arrive at the percentage of women (aged 15 to 49 years) who have their need for family planning satisfied with modern methods (<em>NS<sub>Mod</sub></em>NSMod).</p>
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DATA_COMP
English
<p>The numerator is the number of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method (CPMod). The denominator is the total demand for family planning (the sum of the number of women using any contraceptive method (CPAny) and the number of women with unmet need for family planning (UMN)). The quotient is then multiplied by 100 to arrive at the percentage of women (aged 15 to 49 years) who have their need for family planning satisfied with modern methods (NSMod).</p>
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59511/15070 · 1507

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Key English Spanish Actions
DATA_SOURCE
<p>Survey data are obtained from national household surveys that are internationally coordinated&#x2014;such as the Demographic and Health Surveys (DHS), the Reproductive Health Surveys (RHS), and the Multiple Indicator Cluster Surveys (MICS), Gender and Generation Surveys (GGS)&#x2014;and other nationally-sponsored surveys.</p>
<h1>Proveedores de datos</h1>
<p>Los datos de encuestas se obtienen de las encuestas nacionales de hogares coordinadas internacionalmente&#x2014;como las Encuestas Demográficas y de Salud (EDS), las Encuestas de Salud Reproductiva (ESR) y las Encuestas de Indicadores Múltiples por Conglomerados (MICS)&#x2014;y otras encuestas patrocinadas a nivel nacional. Se realizaron búsquedas sistemáticas en estos programas de encuestas internacionales, en las bases de datos de encuestas (por ejemplo, la base de datos de la Red Integrada de Encuestas de Hogares (RIEH)), en las plataformas de presentación de informes nacionales sobre los ODS y en las consultas ad hoc, además de utilizar las respuestas específicas de cada país a los cuestionarios sobre datos administrados por UNICEF (Presentación de informes nacionales sobre indicadores para los objetivos de desarrollo del Milenio (CRING)) y la información de las oficinas de campo del UNFPA.</p>
COMPILING_ORG
<p>This indicator is produced at the global level by the Population Division, Department of Economic and Social Affairs, United Nations in collaboration with the United Nations Population Fund (UNFPA).</p>
<h1>Compiladores de datos</h1>
<p>Este indicador es elaborado a nivel mundial por la División de Población del Departamento de Asuntos Económicos y Sociales de las Naciones Unidas en colaboración con el Fondo de Población de las Naciones Unidas (UNFPA).</p>
INST_MANDATE
<p>The Population Division of the Department of Economic and Social Affairs conducts demographic research in the area of population and development and assists countries in developing their capacity to produce and analyse population data and information. The Population Division compiles global datasets of family planning indicators and provides analysis of levels and trends in contraceptive use and the need for family planning. The Population Division monitors progress in ensuring universal access to sexual and reproductive health-care services, as called for in the 2030 Agenda for Sustainable Development, and is the custodian agency for Sustainable Development Goal (SDG) indicator 3.7.1.</p>
 
RATIONALE
<p>The proportion of demand for family planning satisfied with modern methods is useful in assessing overall levels of coverage for family planning programmes and services. Access to and use of an effective means to prevent pregnancy helps enable women and their partners to exercise their rights to decide freely and responsibly the number and spacing of their children and to have the information, education and means to do so. Meeting demand for family planning with modern methods also contributes to maternal and child health by preventing unintended pregnancies and closely spaced pregnancies, which are at higher risk for poor obstetrical outcomes. </p>
<p>Levels of demand for family planning satisfied with modern methods of 75 percent or more are generally considered high, and values of 50 percent or less are generally considered as very low. The indicator has no global numerical &#x2018;target&#x2019; value set to be achieved by 2030. Looking at the highest values of the indicator, in 22 countries representing regions such as Europe and Northern America, Latin America and the Caribbean and Eastern and South-Eastern Asia, more than 85 percent of women who want to avoid pregnancy are using a modern contraceptive method but for no country is this estimate above 91 percent. </p>
<p>Even in these countries, specific sub-populations (for example, adolescents or the poor) can still face barriers of access to family planning information and services. It should also be recognized that reaching 100 percent may not be a necessary or even desirable outcome with respect to reproductive rights. Some women may prefer to use a traditional method, even while having access to a full range of modern methods and being aware of the typical differences in effectiveness of methods in preventing pregnancies. Other women might have ambivalent preferences regarding their next pregnancy which may influence their contraceptive choice.</p>
<h2>Justificación:</h2>
<p>La proporción de la demanda de planificación familiar satisfecha con métodos modernos es útil para evaluar los niveles generales de cobertura de los programas y servicios de planificación familiar. El acceso y la utilización de un medio eficaz para prevenir el embarazo contribuye a que las mujeres y sus parejas puedan ejercer su derecho a decidir libre y responsablemente el número y el espaciamiento de sus hijos y a disponer de la información, la educación y los medios para hacerlo. Satisfacer la demanda de planificación familiar con métodos modernos también contribuye a la salud materno-infantil al prevenir los embarazos no deseados y los embarazos poco espaciados, que presentan un mayor riesgo de resultados obstétricos deficientes. Los niveles de demanda de planificación familiar satisfechos con métodos modernos del 75% o más se consideran generalmente altos, y los valores del 50% o menos se consideran generalmente como muy bajos.</p>
REC_USE_LIM
<p>Differences in the survey design and implementation, as well as differences in the way survey questionnaires are formulated and administered can affect the comparability of the data. The most common differences relate to the range of contraceptive methods included and the characteristics (age, sex, marital or union status) of the persons for whom contraceptive prevalence is estimated (base population). The time frame used to assess contraceptive prevalence can also vary. In most surveys there is no definition of what is meant by &#x201C;currently using&#x201D; a method of contraception.</p>
<p> </p>
<p>In some surveys, the lack of probing questions, asked to ensure that the respondent understands the meaning of the different contraceptive methods, can result in an underestimation of contraceptive prevalence, in particular for traditional methods. Sampling variability can also be an issue, especially when contraceptive prevalence is measured for a specific subgroup (by age-group, level of educational attainment, place of residence, etc.) or when analysing trends over time. </p>
<p> </p>
<p>When data on women aged 15 to 49 are not available, information for married or in-union women is reported. Illustrations of base populations that are sometimes presented are: married or in-union women aged 15-44, sexually active women (irrespective of marital status), or ever-married women. Notes in the data set indicate any differences between the data presented and the standard definitions of contraceptive prevalence or unmet need for family planning or where data pertain to populations that are not representative of women of reproductive age.</p>
<h2>Comentarios y limitaciones:</h2>
<p>Las diferencias en el diseño y la aplicación de la encuesta, así como las diferencias en la forma de formular y administrar los cuestionarios de la encuesta, pueden afectar a la comparabilidad de los datos. Las diferencias más comunes se refieren a la gama de métodos anticonceptivos incluidos y a las características (edad, sexo, estado civil o de unión) de las personas para las que se estima la prevalencia del uso de anticonceptivos (población base). El marco temporal utilizado para evaluar la prevalencia del uso de anticonceptivos también puede variar. En la mayoría de las encuestas no existe una definición de lo que se entiende por &#x201C;utilizar actualmente&#x201D; un método anticonceptivo.</p>
<p>En algunas encuestas, la falta de preguntas de sondeo, formuladas para asegurarse de que el encuestado entiende el significado de los diferentes métodos anticonceptivos, puede dar lugar a una subestimación de la prevalencia del uso de anticonceptivos, en particular para los métodos tradicionales. La variabilidad del muestreo también puede ser un problema, especialmente cuando la prevalencia del uso de anticonceptivos se mide para un subgrupo específico (por grupo de edad, nivel de educación, lugar de residencia, etc.) o cuando se analizan las tendencias a lo largo del tiempo.</p>
<p>Cuando no se dispone de datos sobre mujeres de 15 a 49 años, se informa sobre las mujeres casadas o unidas. Los ejemplos de poblaciones base que a veces se presentan son: mujeres casadas o en unión libre de 15 a 44 años, mujeres sexualmente activas (independientemente del estado civil) o mujeres alguna vez casadas. Las notas en el conjunto de datos indican cualquier diferencia entre los datos presentados y las definiciones estándar de la prevalencia del uso de anticonceptivos o la necesidad insatisfecha de planificación familiar, o cuando los datos pertenecen a poblaciones que no son representativas de las mujeres en edad reproductiva.</p>
DATA_COMP
<p>The numerator is the number of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method (CPMod). The denominator is the total demand for family planning (the sum of the number of women using any contraceptive method (CPAny) and the number of women with unmet need for family planning (UMN)). The quotient is then multiplied by 100 to arrive at the percentage of women (aged 15 to 49 years) who have their need for family planning satisfied with modern methods (NSMod).</p>
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<h1>Metodología</h1>
<h2>Método de cálculo:</h2>
<p>El numerador es el porcentaje de mujeres en edad reproductiva (15-49 años) que utilizan actualmente, o cuya pareja utiliza actualmente, al menos un método anticonceptivo moderno. El denominador es la demanda total de planificación familiar (la suma de la prevalencia de anticonceptivos (cualquier método) y la necesidad insatisfecha de planificación familiar).</p>
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DATA_VALIDATION
<p>For surveys with microdata sets, the indicators are calculated following the definitions and concepts described above. These results are compared with the indicators published in survey reports, SDG national reporting platforms, or obtained from ad hoc queries. In some cases of discrepancies, the results are consulted with the national institutions that conducted the survey.</p>
<p>For model-based estimates and projections, out-of-sample validation methods are described in Kantorov&#xE1; et al (2020).</p>
 
ADJUSTMENT
<p>Generally, there is no discrepancy between data presented and data published in survey reports. However, some published national data have been adjusted by the Population Division to improve comparability. Notes are used in the data set to indicate when adjustments were made and where data differed from standard definitions. Surveys might differ in the classification of modern and traditional methods. To improve comparability of data over time and across countries, method classifications used in some survey are adjusted to follow the classification described above. </p>
<p>The global indicator represents all women of reproductive age. Some survey estimates represent women who are married or in a union and this is indicated in a note.</p>
 
IMPUTATION
<ul>
<li>At country level </li>
</ul>
<p>There is no attempt to provide estimates for individual countries or areas when country or area data are not available.</p>
<p>For analytical and comparative purposes, country-level model-based estimates and projections are generated using a Bayesian hierarchical model (see references below).</p>
<ul>
<li><strong>At regional and global levels </strong></li>
</ul>
<p>In order to generate regional and global estimates for any given reference year, the Population Division/DESA uses a Bayesian hierarchical model, described in detail in:</p>
<p>Alkema et al (2013) and Kantorov&#xE1; et al (2020).</p>
<p>Country-level, model-based estimates are only used for computing regional and global averages and are not used for global SDG reporting of trends at the country level. However, the model-based estimates are recommended to be used for analytical and comparative purposes. Since the model takes into account the relationship of family planning indicators - contraceptive use of any, modern and traditional methods, unmet need for family planning &#x2013; the information from surveys that only provide data on contraceptive use (and have no information on unmet need for family planning) is considered as well. The model is providing estimates of the indicator for countries and years without direct survey data by extrapolating underlying trends determined using data across all countries. The model implicitly weights observations from other countries such that higher weights are given to observations from more similar countries. The fewer the number of observations for the country of interest, the more its estimates are driven by the experience of other countries, whereas for countries with many observations the results are determined to a greater extent by those empirical observations.</p>
<h2>Tratamiento de valores faltantes:</h2>
<ul>
<li><strong><em>A nivel de país:</em></strong></li>
</ul>
<p>No se intenta proporcionar estimaciones para países o zonas individuales cuando no se dispone de datos de países o zonas.</p>
<ul>
<li><strong><em>A nivel regional y mundial:</em></strong></li>
</ul>
<p>Para generar estimaciones regionales y globales para cualquier año de referencia, la División de Población/DESA utiliza un modelo jerárquico bayesiano, descrito en detalle en: </p>
<p>Alkema L., V. Kantorov&#xE1;, C. Menozzi y A. Biddlecom (2013). Tasas y tendencias nacionales, regionales y mundiales de la prevalencia del uso de anticonceptivos y la necesidad insatisfecha de planificación familiar entre 1990 y 2015: un análisis sistemático y exhaustivo. The Lancet. Vol. 381, Issue 9878, pp. 1642&#x2013;1652 </p>
<p>Wheldon M., V. Kantorov&#xE1;, P. Ueffing y A. N. Z. Dasgupta (2018). Métodos para estimar y proyectar indicadores clave de planificación familiar entre todas las mujeres en edad reproductiva. Naciones Unidas, Departamento de Asuntos Económicos y Sociales, División de Población, Documento técnico nº 2. Nueva York: Naciones Unidas.</p>
<p>Kantorov&#xE1; V., M. Wheldon, P. Ueffing, A. N. Z. Dasgupta (2020). Estimación del progreso hacia la satisfacción de las necesidades anticonceptivas de las mujeres en 185 países: A Bayesian hierarchical modelling study. PLoS Medicine 17(2):e1003026. </p>
<p>Las estimaciones basadas en modelos a nivel de país sólo se utilizan para calcular los promedios regionales y mundiales y no se utilizan para informar sobre las tendencias de los ODS a nivel de país. Cuanto menor sea el número de observaciones para el país de interés, más se guían sus estimaciones por la experiencia de otros países, mientras que para los países con muchas observaciones los resultados están determinados en mayor medida por esas observaciones empíricas.</p>
REG_AGG
<p>The Bayesian hierarchical model is used to generate regional and global estimates and projections of the indicator. Aggregate estimates and projections are weighted averages of the model-based country estimates, using the number of women aged 15-49 for the reference year in each country. The number of women aged 15-49 are taken from United Nations, Department of Economic and Social Affairs, Population Division (2022). World Population Prospects 2022. Numbers of women who are married or in a union are taken from United Nations, Department of Economic and Social Affairs, Population Division (2022). Estimates and Projections of Women of Reproductive Age Who Are Married or in a Union: 2022 Revision. New York: United Nations, which are estimates and projections based on data from United Nations, Department of Economic and Social Affairs, Population Division (2019). World Marriage Data 2019. </p>
<p>Details of the methodology are described in Kantorov&#xE1; et al (2020) and United Nations, Department of Economic and Social Affairs, Population Division (2022).</p>
<h2>Agregados regionales:</h2>
<p>El modelo jerárquico bayesiano se utiliza para generar estimaciones y proyecciones regionales y globales del indicador. Las estimaciones y proyecciones agregadas son medias ponderadas de las estimaciones de los países basadas en el modelo, utilizando el número de mujeres de 15 a 49 años para el año de referencia en cada país. Los detalles de la metodología se describen en: </p>
<p>Wheldon, M., V. Kantorov&#xE1;, P. Ueffing y A. N. Z. Dasgupta (2018). Métodos para estimar y proyectar indicadores clave de planificación familiar entre todas las mujeres en edad reproductiva. Naciones Unidas, Departamento de Asuntos Económicos y Sociales, División de Población, Documento técnico nº 2. Nueva York: Naciones Unidas. </p>
DOC_METHOD
<p>E-Learning video for SDG indicator 3.7.1 on the website of the Population Division (<a href="https://www.un.org/development/desa/pd/file/10712">https://www.un.org/development/desa/pd/file/10712</a>)</p>
<p>Information on the operational definitions and calculations of family planning indicators from surveys, as well as survey questions and statistical programs needed to derive the indicator, can be found at the website of the USAID Demographic and Health Surveys Program: https://dhsprogram.com/topics/Family-Planning.cfm and the website of UNICEF MICS: <a href="https://mics.unicef.org/">https://mics.unicef.org/</a></p>
<h2>Métodos y directrices a disposición de los países para la recopilación de los datos a nivel nacional:</h2>
<p>N.A.</p>
Key English Spanish Actions
COLL_METHOD
<p>Data are compiled based on systematic searches of websites of international survey programmes, survey databases (e.g., the Integrated Household Survey Network (IHSN) database), websites of national statistical offices, SDG national reporting platforms and ad hoc queries in addition to utilization of the country-specific information from UNFPA country offices.</p>
 
COMPARABILITY
<p><strong>Sources of discrepancies:</strong></p>
<p>Generally, there is no discrepancy between data presented and data published in survey reports. However, some published national data have been adjusted by the Population Division to improve comparability. Notes are used in the data set to indicate when adjustments were made and where data differed from standard definitions. Surveys might differ in the classification of modern and traditional methods. To improve comparability of data over time and across countries, method classifications used in some surveys are adjusted to follow the classification described above.</p>
<p>The global indicator represents all women of reproductive age. Some survey estimates represent women who are married or in a union and this is indicated in a note. </p>
<h2>Fuentes de discrepancia:</h2>
<p>En general, no hay discrepancias entre los datos presentados y los publicados en los informes de las encuestas. Sin embargo, algunos datos nacionales publicados han sido ajustados por la División de Población para mejorar la comparabilidad. Se utilizan notas en el conjunto de datos para indicar cuándo se hicieron los ajustes y cuándo los datos difieren de las definiciones estándar. El indicador global representa a todas las mujeres en edad reproductiva. Algunas estimaciones de las encuestas representan a las mujeres casadas o en unión libre, lo que se indica en una nota.</p>
COMPILING_ORG
<p>This indicator is produced at the global level by the Population Division, Department of Economic and Social Affairs, United Nations in collaboration with the United Nations Population Fund (UNFPA).</p>
<h1>Compiladores de datos</h1>
<p>Este indicador es elaborado a nivel mundial por la División de Población del Departamento de Asuntos Económicos y Sociales de las Naciones Unidas en colaboración con el Fondo de Población de las Naciones Unidas (UNFPA).</p>
CONTACT_ORGANISATION
<p>Population Division, Department of Economic and Social Affairs (DESA)</p>
<p>United Nations Population Fund (UNFPA)</p>
 
COVERAGE
<p><strong>Data availability:</strong></p>
<p>Data for the percentage of women of reproductive age (15-49 years) who have their need for family planning satisfied with modern methods are available for 140 countries or areas for the 2000-2021 time period. For 115 countries or areas, there are at least two available data points.</p>
<p>Table 1: The regional breakdown of data availability is as follows:</p>
<table>
<tbody>
<tr>
<td>
<p><strong>Region</strong></p>
</td>
<td>
<p><strong>At least one data point</strong></p>
</td>
<td>
<p><strong>Two or more data points</strong></p>
</td>
</tr>
<tr>
<td>
<p>WORLD</p>
</td>
<td>
<p>140</p>
</td>
<td>
<p>115</p>
</td>
</tr>
<tr>
<td>
<p><em>Central and Southern Asia</em></p>
</td>
<td>
<p>13</p>
</td>
<td>
<p>10</p>
</td>
</tr>
<tr>
<td>
<p>Central Asia</p>
</td>
<td>
<p>4</p>
</td>
<td>
<p>4</p>
</td>
</tr>
<tr>
<td>
<p>Southern Asia</p>
</td>
<td>
<p>9</p>
</td>
<td>
<p>6</p>
</td>
</tr>
<tr>
<td>
<p><em>Eastern and South-Eastern Asia</em></p>
</td>
<td>
<p>12</p>
</td>
<td>
<p>11</p>
</td>
</tr>
<tr>
<td>
<p>Eastern Asia</p>
</td>
<td>
<p>3</p>
</td>
<td>
<p>2</p>
</td>
</tr>
<tr>
<td>
<p>South-Eastern Asia</p>
</td>
<td>
<p>9</p>
</td>
<td>
<p>9</p>
</td>
</tr>
<tr>
<td>
<p><em>Europe and Northern America</em></p>
</td>
<td>
<p>16</p>
</td>
<td>
<p>11</p>
</td>
</tr>
<tr>
<td>
<p>Eastern Europe</p>
</td>
<td>
<p>6</p>
</td>
<td>
<p>4</p>
</td>
</tr>
<tr>
<td>
<p>Northern America</p>
</td>
<td>
<p>1</p>
</td>
<td>
<p>1</p>
</td>
</tr>
<tr>
<td>
<p>Northern Europe</p>
</td>
<td>
<p>3</p>
</td>
<td>
<p>2</p>
</td>
</tr>
<tr>
<td>
<p>Southern Europe</p>
</td>
<td>
<p>5</p>
</td>
<td>
<p>4</p>
</td>
</tr>
<tr>
<td>
<p>Western Europe</p>
</td>
<td>
<p>1</p>
</td>
<td>
<p>0</p>
</td>
</tr>
<tr>
<td>
<p><em>Latin America and the Caribbean</em></p>
</td>
<td>
<p>25</p>
</td>
<td>
<p>20</p>
</td>
</tr>
<tr>
<td>
<p>Caribbean</p>
</td>
<td>
<p>8</p>
</td>
<td>
<p>5</p>
</td>
</tr>
<tr>
<td>
<p>Central America</p>
</td>
<td>
<p>8</p>
</td>
<td>
<p>8</p>
</td>
</tr>
<tr>
<td>
<p>South America</p>
</td>
<td>
<p>9</p>
</td>
<td>
<p>7</p>
</td>
</tr>
<tr>
<td>
<p><em>Northern Africa and Western Asia</em></p>
</td>
<td>
<p>17</p>
</td>
<td>
<p>15</p>
</td>
</tr>
<tr>
<td>
<p>Northern Africa</p>
</td>
<td>
<p>6</p>
</td>
<td>
<p>6</p>
</td>
</tr>
<tr>
<td>
<p>Western Asia</p>
</td>
<td>
<p>11</p>
</td>
<td>
<p>9</p>
</td>
</tr>
<tr>
<td>
<p><em>Oceania (excluding Australia and New Zealand)</em></p>
</td>
<td>
<p>9</p>
</td>
<td>
<p>6</p>
</td>
</tr>
<tr>
<td>
<p>Melanesia</p>
</td>
<td>
<p>3</p>
</td>
<td>
<p>2</p>
</td>
</tr>
<tr>
<td>
<p>Micronesia</p>
</td>
<td>
<p>3</p>
</td>
<td>
<p>1</p>
</td>
</tr>
<tr>
<td>
<p>Polynesia</p>
</td>
<td>
<p>3</p>
</td>
<td>
<p>3</p>
</td>
</tr>
<tr>
<td>
<p><em>Sub-Saharan Africa</em></p>
</td>
<td>
<p>48</p>
</td>
<td>
<p>42</p>
</td>
</tr>
<tr>
<td>
<p>Eastern Africa</p>
</td>
<td>
<p>16</p>
</td>
<td>
<p>14</p>
</td>
</tr>
<tr>
<td>
<p>Middle Africa</p>
</td>
<td>
<p>10</p>
</td>
<td>
<p>8</p>
</td>
</tr>
<tr>
<td>
<p>Southern Africa</p>
</td>
<td>
<p>6</p>
</td>
<td>
<p>5</p>
</td>
</tr>
<tr>
<td>
<p>Western Africa</p>
</td>
<td>
<p>16</p>
</td>
<td>
<p>15</p>
</td>
</tr>
<tr>
<td>
<p><em>Landlocked developing countries (LLDCs)</em></p>
</td>
<td>
<p>33</p>
</td>
<td>
<p>29</p>
</td>
</tr>
<tr>
<td>
<p><em>Least developed countries (LDCs)</em></p>
</td>
<td>
<p>47</p>
</td>
<td>
<p>42</p>
</td>
</tr>
<tr>
<td>
<p><em>Small island developing States (SIDS)</em></p>
</td>
<td>
<p>27</p>
</td>
<td>
<p>19</p>
</td>
</tr>
</tbody>
</table>
<p><strong>Time series:</strong></p>
<p>Not applicable</p>
<p><strong>Disaggregation:</strong></p>
<p>Age, marital status, geographic location, socioeconomic status and other categories, depending on the data source and number of observations.</p>
<h1>Disponibilidad de datos</h1>
<p>Se dispone de datos sobre el porcentaje de mujeres en edad reproductiva (15-49 años) que satisfacen su necesidad de planificación familiar con métodos modernos para 130 países o zonas en el período 2000-2019. Para 103 países o áreas, hay al menos dos observaciones disponibles.</p>
<p>El desglose regional de la disponibilidad de datos es el siguiente:</p>
<table>
<tbody>
<tr>
<td>
<p><strong>Regiones del mundo y de los ODS</strong></p>
</td>
<td>
<p><strong>Al menos una observación</strong></p>
</td>
<td>
<p><strong>Dos o más observaciones</strong></p>
</td>
</tr>
<tr>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td>
<p>MUNDO</p>
</td>
<td>
<p>130</p>
</td>
<td>
<p>103</p>
</td>
</tr>
<tr>
<td>
<p>América del Norte y Europa</p>
</td>
<td>
<p>13</p>
</td>
<td>
<p>7</p>
</td>
</tr>
<tr>
<td>
<p>América del Norte</p>
</td>
<td>
<p>1</p>
</td>
<td>
<p>1</p>
</td>
</tr>
<tr>
<td>
<p>Europa</p>
</td>
<td>
<p>12</p>
</td>
<td>
<p>6</p>
</td>
</tr>
<tr>
<td>
<p>América Latina y el Caribe</p>
</td>
<td>
<p>23</p>
</td>
<td>
<p>20</p>
</td>
</tr>
<tr>
<td>
<p>Asia central y Asia meridional</p>
</td>
<td>
<p>13</p>
</td>
<td>
<p>10</p>
</td>
</tr>
<tr>
<td>
<p>Asia Central</p>
</td>
<td>
<p>4</p>
</td>
<td>
<p>4</p>
</td>
</tr>
<tr>
<td>
<p>Asia meridional</p>
</td>
<td>
<p>9</p>
</td>
<td>
<p>6</p>
</td>
</tr>
<tr>
<td>
<p>Asia oriental y sudoriental </p>
</td>
<td>
<p>11</p>
</td>
<td>
<p>10</p>
</td>
</tr>
<tr>
<td>
<p>Asia Oriental</p>
</td>
<td>
<p>3</p>
</td>
<td>
<p>2</p>
</td>
</tr>
<tr>
<td>
<p>Sudeste de Asia</p>
</td>
<td>
<p>8</p>
</td>
<td>
<p>8</p>
</td>
</tr>
<tr>
<td>
<p>Asia Occidental&#xA0;y África del Norte</p>
</td>
<td>
<p>17</p>
</td>
<td>
<p>15</p>
</td>
</tr>
<tr>
<td>
<p>Asia occidental</p>
</td>
<td>
<p>11</p>
</td>
<td>
<p>9</p>
</td>
</tr>
<tr>
<td>
<p>África del Norte</p>
</td>
<td>
<p>6</p>
</td>
<td>
<p>6</p>
</td>
</tr>
<tr>
<td>
<p>África subsahariana</p>
</td>
<td>
<p>44</p>
</td>
<td>
<p>38</p>
</td>
</tr>
<tr>
<td>
<p>Oceanía</p>
</td>
<td>
<p>9</p>
</td>
<td>
<p>3</p>
</td>
</tr>
<tr>
<td>
<p>Oceanía, excepto Australia y Nueva Zelanda</p>
</td>
<td>
<p>9</p>
</td>
<td>
<p>3</p>
</td>
</tr>
<tr>
<td>
<p>Australia y Nueva Zelanda</p>
</td>
<td>
<p>0</p>
</td>
<td>
<p>0</p>
</td>
</tr>
<tr>
<td>
<p>Países en desarrollo sin litoral (PDSL)</p>
</td>
<td>
<p>30</p>
</td>
<td>
<p>25</p>
</td>
</tr>
<tr>
<td>
<p>Países menos adelantados (PMA)</p>
</td>
<td>
<p>45</p>
</td>
<td>
<p>36</p>
</td>
</tr>
<tr>
<td>
<p>Pequeños Estados insulares en desarrollo (PEID)</p>
</td>
<td>
<p>26</p>
</td>
<td>
<p>16</p>
</td>
</tr>
</tbody>
</table>
<h2>Desagregación:</h2>
<p> Edad, estado civil, ubicación geográfica, estatus socioeconómico y otras categorías, según la fuente de datos y el número de observaciones.</p>
DATA_COMP
<p>The numerator is the number of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method (CPMod). The denominator is the total demand for family planning (the sum of the number of women using any contraceptive method (CPAny) and the number of women with unmet need for family planning (UMN)). The quotient is then multiplied by 100 to arrive at the percentage of women (aged 15 to 49 years) who have their need for family planning satisfied with modern methods (NSMod).</p>
<p><math xmlns="http://www.w3.org/1998/Math/MathML">
<msub>
<mrow>
<mi>N</mi>
<mi>S</mi>
</mrow>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msubsup>
<mrow>
<mi>C</mi>
<mi>P</mi>
</mrow>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
</mrow>
<mrow></mrow>
</msubsup>
</mrow>
<mrow>
<mi>U</mi>
<mi>M</mi>
<mi>N</mi>
<msubsup>
<mrow>
<mo>+</mo>
<mi>C</mi>
<mi>P</mi>
</mrow>
<mrow>
<mi>A</mi>
<mi>n</mi>
<mi>y</mi>
</mrow>
<mrow></mrow>
</msubsup>
</mrow>
</mfrac>
<mo>&#xD7;</mo>
<mn>100</mn>
</math></p>
<h1>Metodología</h1>
<h2>Método de cálculo:</h2>
<p>El numerador es el porcentaje de mujeres en edad reproductiva (15-49 años) que utilizan actualmente, o cuya pareja utiliza actualmente, al menos un método anticonceptivo moderno. El denominador es la demanda total de planificación familiar (la suma de la prevalencia de anticonceptivos (cualquier método) y la necesidad insatisfecha de planificación familiar).</p>
<p><img 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77q3LqIb4tOCUpH2XHGnz+U5U/ePjqMfSmVL1d16s+qctXkdVPdtpLI7/8+vX79i8qVn6/p/S+vzMTtn1c2bCgqn021mTdzFlSgKGGDJBgYhbwT8bKKOa8iVyg6MPpKRxVBET9v1cHWn5CWXXT79SyqJL26+ePTlHewjPyylTdlB+m8g2zROvvKvWzZRSepeXmkee08gzT+Uj+OVj8HU7u/+7LsT46KTmj8fl2275X9XpHU79j5bFDDhr1wUZn2+5lv/NC6xjpA+1VePVFUR0je/DHob/a7So//fU2X8SeNSm/eCZw/WdZyi+oJm2ZfX+jkT9tVaVVcoV8+7fotnydaF20zny+at+pE1c6v5TbBlheloUzeBVzVsc6uZ9F+Nsx8s/PaoH0q7s8qN1q2lmXn8b+lNMXf0v8+P6ryT+tjf0Nxu/9GWrbf58vOEex8Nmjb2uOcft8vV+s4COWdXV7RNo58Hivk5UGkvLDz5uWD3b99sNtMafX1hkLZ78uw9+sqdv20fK2D/W3RdvbbVtNl7Lw2T1RutC7aP/Rb2qb+92RY5TmVXZ7yKEXKd7QOdr68tMaypO1RVH7sdlMo4uezQX8bpPz6ZassazmWtrM9Diju5+mH/12VUbtcrZfS59en7DxrGHmlee18Wv8qKvt2uYOU56J1Upr0t5hn+s28Y45Pu63ztb4+D8rSqt+yZUHBpiHStG84q6r7fR2Qyn8vBq2X1i+mTds+rzEvlok8vg5T0O/5sqoy6Y8DCmXni/pdv2xfl+bV2zYMKm97xjrL5ovSpO3s01u1Lyhf7Pxl+25dflvG7W3Trd/TfDHdyss84zjH9Puifstva/1WGZ+/SnMK/z27XVUGlS7No+Xl5Zn9blN1t5Zh51Xw20DxeFyN8+Tx21PrlLeN/DFAy7XrYZXlWawTIqXT18UKdl08raudtyyvLP+9MsMq5z5vbMir0/yyla4mKN12uXHZNi+1fZWGvHrVbkPPzme3vfZj/a5+Q+XJr28dNk2K+/pMac/bNtqGRWz5sOlWUN2odGu9Fc87Xg1jX0o1zHJl80XfU/r9dssrJ5rO44+jKetu80vr4/n1t+XYGmY++WVrPf3217R+J86j9aqzDwzSZj74WVAJGvMxX/kTA+2UZZWSKgY7v0LVgdnu9ApF7HypBwZb8ZZdaPj1LKokvbz8qaq0fCWfR+tmK/aqfI98Rat8yuPzvCrN+m2f7rw88r9fdLJXhLqyHZroBBV/kpBSZvLmiap+L0/qd+x8MajM5+13fv+0QSfXeXxdkXeiHPl5FZSXRfup0uPzuigdPr+r6mdfp+skN09emlPqwzp82lOWn3fxUcbOq3VqgvLMLld5WiSvXOmzIqpj7bx522fY+Wbni6GoXPm0xFCWJr9vluWfnTdlPe1xrSyf4zw2lO07KcfLOvwxu+h8QOtr54uhbPuprrDz5uVZ3f3b1xtlde8o9usq2m9UhxbVm5Yvj2XnOXa+GMrKjTes8pwqLkshtT5M+Y4tz0pzv3y5LDLM8muPvWXzpdTVdWkdVJ6qzrX9PlZWBoeVV36frSrLOkey89e9nrDqrpPfx2Moyjefv2Vl2qelqs7x+VCWbz7dqfLWV+tQdJzxadI6FfHLrqr/tH7aNvY7Revsy1TZsv1xLoZB+e1Zlm+iv9l6WqGsLvBla9BzicjXR8rzsn1Mf9O2zDvWDvsYbueLoWxb+/wtyzOf9rKybPnvxZByDiHKI61Dk3W3vw4sO1dWOdR+rHR4dX5T/DGgqDzn5Zl+vyzPfDkpS4vfF/V7Kfz3ivj0N1nO8/JGoShvVG70+3beQalM+GVWlWdfr+aVp8jOF0NV2apL+at6qmrb+3XVvlPElw+FlG0vfrs2tS+lGma5avq6Scuz81Qt1+dV3vx+/YvKxTDzydYB+k7Z+YGtw8uOidJUm/ngNUeJokTS4I+28xV/SoVv5y+7EIxSTlpUYdh5yjoQLF+hFlU8TZ04peSP/07egcBX7PpOqpQDjf17yjYSn6a8PNL623nqpFtsnUj9OFr95Lcta4p7Onmw5SFvHn/iUbbvVf1entTv2PlSlu8bgRSqTtjsCVPZ8n0dUdWoIj4fi5avk6o4j062qpYrNm+K6gufZoXUejSV30Ypy9f6+RPVvDoxsvPVrb+K6Pfscosugvw2tKHo2OKPcXnrNux8s/MoVOWbb6DS75QdO1OPK36+lItHn+c6zuWx8yjUvZAdtCz5urSorsnbDxWUx0V8nZDHL7dqm4nfzkVGsV83KeVcKLLzKRTVy3mGWZ5T2WWlluGU79htPkgjmC+XRYZVfn3dXpXfdr3rlIUm2PUp++1h5ZUvm1XXcXUag6rkrVPZuY/fxxWqyqmtRxWK2HorZb18XVeWb7Z8KaTy39P2LMsf/c3OX1Se/DZPLfN+exXlvc2XlPNZlWP7HYVB+G2jeFUaxOdL0XFX/LyaboLytKnl+vKTsiyfdwqp55hNnn/5/C2b1/Lfq/PdulLqbn+OOEhahnWd6PNM27/q2JJXTor4eiO1TPvvFRlmOc8rT1VtCr4dInV9i/h8SC1DPl+K0m3nUSgqy6Pi86+IzxeF1Lwe1r6Uqg3lSlKum5Q3dp6q8yN7DNM+lsevf9G6DDOf7Lls2fmbpNZF0lSb+f+Z/aGh+e53vxtiwPx20003hViaq6++OsSK3XjjjSFW7OOPPw6xrq997WshVu6GG24IsS6/nKbVzR/59NNPQ+yyt99+O8S6Zg+IIVbtrrvuCrGu//mf/wmxrmPHjoVY14MPPhhi5b7whS+EWDGt/2xlH6aybMuWLdmGDRuyM2fOhE8wH/zqV78KscFo29uye+TIkezQoUNhqr1Uhl955ZUwlc/XQbMnNp31LXPbbbeFWDcvUr366quVdemqVat69r285Z8/f76zrGj2BDOpjr7//vtDLMvef//9znKqzJ7IddLUFP2mXSeVq5Tla/2eeuqpMNX13nvvhdhoLF26NJu9kAlTWfanP/0pxHr98Y9/DLG5df5vfvObEOv1+uuvh1jW+Q39ljXqfFOZ2rx5c5jK5485L7/8cumx0//tzTffDLFePo8eeOCBECumvJi9eApTc4+9ebSOu3btClP51q1bF2Jdf/vb30KsP3fffXeIdf35z38OsV6vvfZaiPWWoYsXLxbWvb5OSPHWW29Vnu/47Zx3HjAf9+uUc6E8KmdVxxVrVOV5HK666qoQy7L9+/d3ysEoNVV+/fl7Vdnw5+ejlHKsz9NUXqls2nOU3bt3h9hcqqtOnz4dprLsvvvuC7FmaJ3K8sPX3zq2VtX5t9xyS4h1+Wst0XqpLo7+9V//NcSKKZ333HNPmCo+f2iKttHhw4dL80d/s8eXU6dOhVgve04jTz/9dIiVe+SRR3rqsbx1Vv7avEw5n1U5npqaClODe/fdd3vSoGNSVRpE+4LNP5X1UV8n2zzVNk857uYZxznmqM+/Umi9q859+5VSpvw5Ycr5Qh5tz1FdJ2o7Vh1b9Nv+2jqvbh22UZfzPXv2VLYp+HaIv//97yHWH3v9oPpX9XAKX68XXZ9Ydc+Hh+HKK68MsXpS2zdGuS+lGke5kpTrJuWNvQZV3hUdF5UnunaIqtapribzyZ7LVpW5O+64I8RGZ6idoE01KAOTKKVifOedd0Ks63vf+162du3ayqD52ialgvvggw9CrKvqJNHyy//www9DrOvzzz8Psa6mK9zf/va3Idalg5gusHQwqeoA44aRyeNPjp944okQa6/ly5cnnahaKSfTt956a4jV4zu2itiGMjl58mSIdY3yZpKmGwPU2GTZi4Qqvo777//+7xAbHdtIrBNiv23ENko9/PDDPRcDeY2A/qL8O9/5TohdNup88w3BKVLOAWwnchF73FQDReo+bG9O8MfePP2s46ANnFoX22hqL/4ilQddMEdqYKrqEPPH5G9+85shVq7OOUmUd8PXfN+v60htII9GVZ7HwXZqqRPh+uuvzy3Tw9JU+b3iiitCLE3RDRxt1lReyaOPPhpi5Tdm/OEPfwixbmNp0w1cddcpZd9LuaHXX4/5zpoi9phj6/hh6Of81zbyWfa8RdsxtaNNv2/rMS3fNwT7doHU89l+G77zDPOG5WFSXtpt5q8d6pgP55ij6GBO7eAfluPHj4dYt1M79brRG+V14he/+MUQK/fVr341xLr8vj8Koy7nPj9TnD17NsTq89cPqhNSjwO+XrdlsUjd8+E2SW3fGOW+lGrU5aouf2PYG2+8EWK99Lm9Aemxxx4LsWY0mU/2OrxKnbqtqTbzoXaCFqHBH+iPDtRq/K0Kw75YnI/8RXjT1Lhw4sSJnju+RZ2h9957b7ZkyZJs586dcy5oxd4wQv04WsPKb50c284cXXjv3bs3TC0sTTa+5Pmnf/qnEOv6xz/+EWJd8/lmEl9v1TlB9Rdo4+gY8I1jv3FPefkLUDUA2o4C/c035PiLA/+0oMz3fItSLpZ1zI90p3VeWc4Lw3qCsE6DaArb6JLXgWAbaWIjmO08sHckR7azQd/pp8OjiG/4ydPW8qmbFHSc2rZt25zyMqr6sG3luUkql/a8QOV548aNnQ4YnR+O+qmoPCnlV/uLbeAou+lZ62S3adNPheoYojpB+af8HWW5SMkr8U8n2frHGuYd/sOScjOP7wT326goPP/88+Eb7ZFSfv05TR1++b4h+LPPPguxLn88GAV/zKlz/PT7zLCvzS2fl/7aoY62HsObPv8apibqbruv6TjarzZeJ6Z2lg5TG8t5k3WerxPq3mxg97c2tb3qqWHtV5s2bZpTZod9XJ2vbS79lqsmrpt0Y5g9p37hhRdCrNfvfve7EGv+2jVVaj7Zm4xU5vLavyNbJquOYU21mY+lE5QnRIHRUSX55S9/OUxhmHQw0uuR9u3bN+cJHjV26TW5uvM/72moiPpxtIbZAb1jx44Q6/rhD39YehKA0dCFSrxhpCy06YJmvlKdaG8M8U922g7N+Fpb32joOz3tRUDeq3AXMh1n8spyXrB3k7aZ7+T2T6HYDoXYwWTvoM97Atl2NthOqYVKjSVqQLz55puzxx9/vHPs8uVlHPXhJJZnlT29Rt5SGdX5oepKNZyM47V3ddlX3elGAzUA+fMbrYffv/p9XaGnfVoN59dcc03nRkPln9LRxnKhY5TNB70S1+eVOgNsWpu+w79N/DYqCvapPXSN86YsoAnzoe7mOhHzjc4p1Omphy5Wr17d2a90ruHL7KiPq5O6LzV93WRvfNP3/E2R2r72pl77hpE2sm2qKnPf+ta35qyTprXetkzqjWCpBmkzZ0xQYB45evSoRguuFdQpN19fvTAI//TZsJ9Gs1Sh67UYqtTV2GXv7tFJ/Z133tmKO/7Rq+kOaDV82cZObfsXX3wxTGE+4WaS/tnGX39ib8dQiXcNar+xN5HY8T/FdqT6MdnQnzqvuBo1Xx78k522PPzf//t/O//7jlNdjEZqgLONa6mvappEuqheuXJlp7Fkkjoc2lyeZfv27Z381jh9/pVRKqtqwFJjVpvp1Wg27WoAUqN2vANejUNaD9sIpDGlmrhpRR3JanjydUGb+fE9/c09w3w6fRJwswow/83HujsV14kYF13X6CELdXqO8+aBprR5XxrWdZO/8e3AgQMh1uXPGZu6oXBY9MSoPW/TtY22q/JO1wj6X9P2WKCnQG1n8DAxJijQYn6sFT+u5aS56qqrQqzLP71Rxr9+wb+yw0/Xef94v9TYo8auTz75pNPYFekE5T//8z/DFDeMTLLvf//7PQ2FP//5z+c8AYDRWqg3k/j6dVT8eIv2RN4+kWc7o+w4nzpxjvuMf1pmFK/+Gle+9UP5kVd+q0Lbnzqy5UEXnfHcQP/bi9A41pz2VXvx9dprr4VY7yuZVTc3+WqtcRikfOrOXNtJpQvSPXv2dF7t78uI6s1Rm9TyLDo/1Pi1Fy5c6Lw9xNdlaszS05VtpbTlNbapvlaw+6X2M5WrJsbM1tOleoWwpfPrgwcPZufOnZtTFkZxjEihhh17LvjSSy+FWLdRTds7avsd/oPy2ygl2HOFhcSPvzufzkdSjPIG5baatG1aZD7V3Tx00LxJL+f2+D5KOn/QwxX+2ljnlToP8+VUN6ON0qTtS8O6bvJvzvKvLbbnjNq+TdxQOExqr8m72UV5p2sEm4eih0ZeeeWVMFWsqTZzxgQFWuxLX/pSiHX518ANqm0XHzqIW3/5y19CrJpt5BR/B9G1114bYl1//etfQ6xcEx3POpCrscs+zWKfXhnmK1lRbtj5rW1vXxunk1SeBm2W35f9vj6fbybxdfRHH30UYtX8KxXrjnXSFH9iH5/stE/k+c6or3zlKyHWFTtO7TFQyyx6WmYS8i2VPa5M6g0WRU922nME/6SQPZ/QxVZ8Atkee0d1x6nXhvKp/S/mo+iiWuOrqgNxnE+hLYTy7KkcHj58uNNgYhvS9HRlG98aojTF1/0rvWpoUwOG3XaiMqXGIe1/TXVM+xus1YCu82vdANH2Bmh7M6T2vbht59sd/nXdeuutIdY16fu13Yfrjknrx0/1dbGv7/3xYBR8Z0aTNywPk+9Q9uOr1rGQzjGbMoy62+5rg9QrbbxO9GXquuuuC7HRmfRy7uuE1LbByJ5D1x3/uSl6YtB2gOpcTOeTOq8cR0fZJD/AM+zrpieffDLEem/41bmi/d06r4wdlyeeeCLEuh3huhZQflm6ZlB5VQeyHhpKOQ401WbOmKBAi+ngZRuP88aRGcQoLz5S+IbvZ555JsTK6UTL3lGixlBfkfq81N0pVY1LOvh8+9vfDlODs69utHfJW9SPo9XUwbSMnn6wZU9Pg/79738PU/XUbVCZz1LrOtupoQtif9I/7JtJhsnXiW+99VaIVfvjH/8YYl3+wmSU7AD5OpHXtrUn9Pbv4jtO42tz7V2FZa/Hm5R8S6FXykQ6Dk5iA7M/fsebnuyrkv1NVL7jVHWnjvn2XOHrX/96iI1WG8qn3f/k6aefbkUnUtvKc91GsUHoRpCXX345THW18ZhvX9Oljj3tn2rA0DAQ9i54NcSpccgfkwfhX50Vn/6eD/xbEWI+2nGutU5N5lcb/PM//3OIdanRsElte8LIns+ocTq1k9CfF/mbCsR3hKR2SgzS4ecN84blYfKN1HlPqaSa7+eYvvNnFIZRd9uOJ50vVLXrFBnldWJqp5AvU0X7iu+obNKkX0upTrAd6bY9oYqedLP8zT6j4oeM0bnYOM3nNpcqw75u8tetvwlvLrI3yqm8+vnaRu3ysZ1b5zG6ttG1gK4J7DWCrhlUXvvtQB6kzZwxQYGWs3eF6GJK79EeVqOQP2EZNVWC9i4RVaBV4yLp4vIb3/hGmOoqqnv8K6bKlq0K3L9eooiWo+1SdfJtX23g74bB+A2zA/qnP/1piHX3Y504pbINYikNKnptmD9Rm49S6jqtq91H857s8h0oTd9MMkz+Ai3l5g3R+tnX62kZ42ws9p1NOqG3jWF+vDSxnZzazir39uYR36BsTUq+pfjqV78aYl2T+qS5LQ9q7FJ5sPWcvyjUfm8bkdXR4C8kx7Vt21g+qxqjR3U3dxvKsz1epDSKNTl+p9+eZ8+eDTF4KY2ObTrWa7+3dZKuCZQ+2zkwH+7wr8s/IfPss8+GWDPa9oSR7yTcu3dviJXzdZ19DXzk8zLlZmGdP8Unt5vgO0aavGF52Ox5hM4n/dNqqeb7OabSP05N1d1+/G8/jl6qUV4n6ub6qut4lSV7XFDairbZMB9omO/lPIW/aSX11ev+OFZ2TToqeTfOeE3eEJNnPre51NX0dZPyzrYPx33ohRde6Pwvaudqww2r8x1jggItpzsn7EFNFxC3335756LKn0TpIKMTev1NleSSJUtKD+a+8lZla+9sUjz1ZKAp/l31SpOeDFA67EFU675z5845HZV6rL5ojK9HHnmk52RODajqaLHrrOWqQWv16tWXlmu/k0cnhFqWDvr6rr2oUpq1fP2ObcC3J+7cMDL5tD/a/diWhSq+UfhnP/tZiM2l8ufHXJmvVNeV3VwQ91XroYceCrFeo7yZpGn2dcqiRpyytOtvWj9bL/pljJoufm09qrEtbGNY3muE7AWl1mXLli1hqrxBIJqEfEuhusXmrfJp1MftUfANDL486OLRs43Iaryxd0zn3TAxSuMun/7pAf9KTkt1bZNvxSjThvLsG8WKOjG0TXR+ahv7iug4pnPyqg4Rv6533HFHiLWHLTvqXNE6lZXdJtmyYRuJ8+j8wB5n2uDf//3fQ6x7HviLX/wiTHXXre13+PdDdbPtfNL1kj93a5J/QmfU9DpjW05VP1TVYfq7Pabp+3mvRfaNpCpDuhYuon2z6WsCnXsN84blYfI33Ommg6rjrsY/tm8oiCbpHNM/oTsMw6i7fX2pfUhlrYz2CR2L/bYa1XWilq22q6J6Sr9p60upM0500w80TFI5z+PrIZW9qjKkeexNmKoPx3VjgX0TgvaZsk5qHWeavCGmyHxucykziusmeyOc8k71la0Lx/UGozrsmwaUdu0vKTdPpGjsvGFmiJ577rkQA+aX6enpGe0eMaSw888eDMOnxY4ePdrzHU0XOXHixMzsyWPP/KlB61Jm9mQm93sx5K1LP/lTZ3337dvXM29qmJqaCksoVnfZml95EKfz0l2Vhz6sWLEifLOLurIdUreDLQ8p+3rk9wEbyvaHc+fOzdn/VeZUL8S/q5wuW7asZ54YytJYd138OlTVL5JaX/j5YtC662+nT5/uzBfXNy9Pymi/s/Mrv/bs2XMpHyMtX+upv2mZ+h39Xp5+6sK6lB6/bZUmpS/miSieVw58fZPHzp+yTfuh+tn+Tgxl6fPbOIatW7eGOYoNO9/svCl55stK2T4fpe6fBw8e7Fm2gvJbnysfLJV3fa481DpqvYvY5aWWi7p1Sh1+G8VQVB60rnnzKygPqvSzf/s6smg7j2K/LqPl2uXpt31alUblQd5+WFZ+7Xyp5cYaVnlO5behgtYjbhf9X5QvcV7PLlNp1Da121nr4ZepeB7NF+dRKJI6n+XXPW87axsor+18Pujv2v9jUFr8sbYf/jiiaV8mlGZbD8VQVh8NK688pdV+xwatS5P6WSc7f1l+Ral5oLLu9xct3+8HomktR+mP29HPY/n6QmUvljXlt34jL122jKSsq6TmqepxO5+Czif9cUfp8mVawc9n+TxX0DLs/qXv2/XzeT8o/ZZdnoLyXXlt98e8ek2h6hzOr2Pe9uuXzRcFpS2vPlYabbo9recwj+F2XuVhijpl2tfhSnfcdnG7eYNsl1HV3Qr6zO4P2gZaP7sN8tLu80Tz63t2WaJ06/v6W9V1os8zG/L227w0+Hyy9Dc7v9Ji6w/F89Lm862Ilj+scu7zJm+b5LHfSd03ymgb2mUqaP+320b54OtVBeWFzQfPzttEWj3luf0N5bdPj6bzjjMKRVLLR5Fh7EuphlWulI92HqXVL1vro+/qb3ZehZR06Pv2uzauPEyRuv6p83n2O0Vluqi8xaB10b4Ug/Y3W28VaarNfPCzoBJFiaTBH23XT8Vv5y87WYvqVjyqFPMO0lWhqkJRhZ5XUceQty795E/d9dXf/YlUUVD6dfBMpYNr2Tor6LfjgVp5ED/PS7fm8wf7oqBtqG1p2TqR+nG0+slvWx5S9nXLfteGqv1B5Tvve3lBZVEnE3G6LI1118Xvx0UnP1ZqfWHnU1qqTqBs0Dr7/crTflq13xeFovXspy7sR79pT8kXsd9J2ab90LHI/k4MZY1hRWUg5URZhplvdv6UPPNlpWqflzr7Z539xYcidp7UclG3TqnD1ms2lJWHovOIlP2in/3b15Fl23nY+3WVvPy0F6b2c6XT5mXZetnv9VufDKM811HnfFtptfmVt87a1vY7KaGoXKeWy2GWX5W/1HN0G5RPg5TdomuWWGZ9mrSv2HmKDHtftwY9rqXqZ53s/GX5FdXJA99YW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DATA_SOURCE
<p>Survey data are obtained from national household surveys that are internationally coordinated&#x2014;such as the Demographic and Health Surveys (DHS), the Reproductive Health Surveys (RHS), and the Multiple Indicator Cluster Surveys (MICS), Gender and Generation Surveys (GGS)&#x2014;and other nationally-sponsored surveys.</p>
<h1>Proveedores de datos</h1>
<p>Los datos de encuestas se obtienen de las encuestas nacionales de hogares coordinadas internacionalmente&#x2014;como las Encuestas Demográficas y de Salud (EDS), las Encuestas de Salud Reproductiva (ESR) y las Encuestas de Indicadores Múltiples por Conglomerados (MICS)&#x2014;y otras encuestas patrocinadas a nivel nacional. Se realizaron búsquedas sistemáticas en estos programas de encuestas internacionales, en las bases de datos de encuestas (por ejemplo, la base de datos de la Red Integrada de Encuestas de Hogares (RIEH)), en las plataformas de presentación de informes nacionales sobre los ODS y en las consultas ad hoc, además de utilizar las respuestas específicas de cada país a los cuestionarios sobre datos administrados por UNICEF (Presentación de informes nacionales sobre indicadores para los objetivos de desarrollo del Milenio (CRING)) y la información de las oficinas de campo del UNFPA.</p>
DATA_VALIDATION
<p>For surveys with microdata sets, the indicators are calculated following the definitions and concepts described above. These results are compared with the indicators published in survey reports, SDG national reporting platforms, or obtained from ad hoc queries. In some cases of discrepancies, the results are consulted with the national institutions that conducted the survey.</p>
<p>For model-based estimates and projections, out-of-sample validation methods are described in Kantorov&#xE1; et al (2020).</p>
 
DOC_METHOD
<p>E-Learning video for SDG indicator 3.7.1 on the website of the Population Division (<a href="https://www.un.org/development/desa/pd/file/10712">https://www.un.org/development/desa/pd/file/10712</a>)</p>
<p>Information on the operational definitions and calculations of family planning indicators from surveys, as well as survey questions and statistical programs needed to derive the indicator, can be found at the website of the USAID Demographic and Health Surveys Program: https://dhsprogram.com/topics/Family-Planning.cfm and the website of UNICEF MICS: <a href="https://mics.unicef.org/">https://mics.unicef.org/</a></p>
<h2>Métodos y directrices a disposición de los países para la recopilación de los datos a nivel nacional:</h2>
<p>N.A.</p>
FREQ_COLL
<p>Data are compiled in the period from October to April.</p>
<h1>Calendario</h1>
<h2>Recopilación de datos:</h2>
<p>Los datos se recopilan y actualizan anualmente en el primer trimestre del año. </p>

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String updated in the repository

  1. SDG Metadata
  2. 3-7-1
  3. Spanish
<p>The numerator is the number of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method (CPMod). The denominator is the total demand for family planning (the sum of the number of women using any contraceptive method (CPAny) and the number of women with unmet need for family planning (UMN)). The quotient is then multiplied by 100 to arrive at the percentage of women (aged 15 to 49 years) who have their need for family planning satisfied with modern methods (NSMod).</p>
<p><math xmlns="http://www.w3.org/1998/Math/MathML">
<msub>
<mrow>
<mi>N</mi>
<mi>S</mi>
</mrow>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msubsup>
<mrow>
<mi>C</mi>
<mi>P</mi>
</mrow>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
</mrow>
<mrow></mrow>
</msubsup>
</mrow>
<mrow>
<mi>U</mi>
<mi>M</mi>
<mi>N</mi>
<msubsup>
<mrow>
<mo>+</mo>
<mi>C</mi>
<mi>P</mi>
</mrow>
<mrow>
<mi>A</mi>
<mi>n</mi>
<mi>y</mi>
</mrow>
<mrow></mrow>
</msubsup>
</mrow>
</mfrac>
<mo>&#xD7;</mo>
<mn>100</mn>
</math></p>
<h1>Metodología</h1>
<h2>Método de cálculo:</h2>
<p>El numerador es el porcentaje de mujeres en edad reproductiva (15-49 años) que utilizan actualmente, o cuya pareja utiliza actualmente, al menos un método anticonceptivo moderno. El denominador es la demanda total de planificación familiar (la suma de la prevalencia de anticonceptivos (cualquier método) y la necesidad insatisfecha de planificación familiar).</p>
<p><img 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8 months ago
User avatar None

Source string changed

  1. SDG Metadata
  2. 3-7-1
  3. Spanish
<p>The numerator is the number of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method (<em>CP<sub>Mod</sub></em>CPMod). The denominator is the total demand for family planning (the sum of the number of women using any contraceptive method (<em>CP<sub>Any</sub></em>CPAny) and the number of women with unmet need for family planning (<em>UMN</em>UMN)). The quotient is then multiplied by 100 to arrive at the percentage of women (aged 15 to 49 years) who have their need for family planning satisfied with modern methods (<em>NS<sub>Mod</sub></em>NSMod).</p>
<p><math xmlns="http://www.w3.org/1998/Math/MathML">
<msub>
<mrow>
<mi>N</mi>
<mi>S</mi>
</mrow>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msubsup>
<mrow>
<mi>C</mi>
<mi>P</mi>
</mrow>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
</mrow>
<mrow></mrow>
</msubsup>
</mrow>
<mrow>
<mi>U</mi>
<mi>M</mi>
<mi>N</mi>
<msubsup>
<mrow>
<mo>+</mo>
<mi>C</mi>
<mi>P</mi>
</mrow>
<mrow>
<mi>A</mi>
<mi>n</mi>
<mi>y</mi>
</mrow>
<mrow></mrow>
</msubsup>
</mrow>
</mfrac>
<mo>&#xD7;</mo>
<mn>100</mn>
</math></p>
8 months ago
User avatar None

String updated in the repository

  1. SDG Metadata
  2. 3-7-1
  3. Spanish
<p>The numerator is the number of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method (<em>CP<sub>Mod</sub></em>). The denominator is the total demand for family planning (the sum of the number of women using any contraceptive method (<em>CP<sub>Any</sub></em>) and the number of women with unmet need for family planning (<em>UMN</em>)). The quotient is then multiplied by 100 to arrive at the percentage of women (aged 15 to 49 years) who have their need for family planning satisfied with modern methods (<em>NS<sub>Mod</sub></em>).</p>
<p><math xmlns="http://www.w3.org/1998/Math/MathML">
<msub>
<mrow>
<mi>N</mi>
<mi>S</mi>
</mrow>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msubsup>
<mrow>
<mi>C</mi>
<mi>P</mi>
</mrow>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
</mrow>
<mrow></mrow>
</msubsup>
</mrow>
<mrow>
<mi>U</mi>
<mi>M</mi>
<mi>N</mi>
<msubsup>
<mrow>
<mo>+</mo>
<mi>C</mi>
<mi>P</mi>
</mrow>
<mrow>
<mi>A</mi>
<mi>n</mi>
<mi>y</mi>
</mrow>
<mrow></mrow>
</msubsup>
</mrow>
</mfrac>
<mo>&#xD7;</mo>
<mn>100</mn>
</math></p>
<h1>Metodología</h1>
<h2>Método de cálculo:</h2>
<p>El numerador es el porcentaje de mujeres en edad reproductiva (15-49 años) que utilizan actualmente, o cuya pareja utiliza actualmente, al menos un método anticonceptivo moderno. El denominador es la demanda total de planificación familiar (la suma de la prevalencia de anticonceptivos (cualquier método) y la necesidad insatisfecha de planificación familiar).</p>
<p><img 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2 years ago
User avatar None

Source string changed

  1. SDG Metadata
  2. 3-7-1
  3. Spanish
<p>The numerator is the percentagenumber of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method (<em>CP<sub>Mod</sub></em>). The denominator is the total demand for family planning (the sum of contraceptive prevalence (any method) and the number of women using any contraceptive method (<em>CP<sub>Any</sub></em>) and the number of women withe unmet need for family planning).</p>
<table>
<tbody>
<tr>
<td rowspan="3">
<p>Demand (<em>UMN</em>)). The quotient is then multiplied by 100 to arrive at the percentage of women (aged 15 to 49 years) who have their need for family planning satisfied <br>bywith modern methods</p>
</td>
<td rowspan="2">
<p>=</p>
</td>
<td>
<p>Number of women who are currently using a modern method of contraception</p (<em>NS<sub>Mod</sub></em>).</p>
<p><math xmlns="http://www.w3.org/1998/Math/MathML">
<msub>
<mrow>
<mi>N</mi>
<mi>S</mi>
</mrow>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msubsup>
<mrow>
<mi>C</mi>
</td>
</tr>
<tr>
<td></td>
</tr>
<tr>
<td>
<p> </p>
</td>
<td>
<p>Number of women who are using any method of contraception or are having an unmet need for family planning</ <mi>P</mi>
</mrow>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
</mrow>
<mrow></mrow>
</msubsup>
</mrow>
<mrow>
<mi>U</mi>
<mi>M</mi>
<mi>N</mi>
<msubsup>
<mrow>
<mo>+</mo>
<mi>C</mi>
<mi>P</mi>
</mrow>
<mrow>
<mi>A</mi>
<mi>n</mi>
<mi>y</mi>
</mrow>
<mrow></mrow>
</msubsup>
</tdmrow>
</tr>
</tbody>
</tablemfrac>
<mo>&#xD7;</mo>
<mn>100</mn>
</math></p>
2 years ago
User avatar None

String updated in the repository

  1. SDG Metadata
  2. 3-7-1
  3. Spanish
<p>The numerator is the percentage of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method. The denominator is the total demand for family planning (the sum of contraceptive prevalence (any method) and the unmet need for family planning).</p>
<table>
<tbody>
<tr>
<td rowspan="3">
<p>Demand satisfied <br>by modern methods</p>
</td>
<td rowspan="2">
<p>=</p>
</td>
<td>
<p>Number of women who are currently using a modern method of contraception</p>
</td>
</tr>
<tr>
<td></td>
</tr>
<tr>
<td>
<p> </p>
</td>
<td>
<p>Number of women who are using any method of contraception or are having an unmet need for family planning</p>
</td>
</tr>
</tbody>
</table>
<h1>Metodología</h1>
<h2>Método de cálculo:</h2>
<p>El numerador es el porcentaje de mujeres en edad reproductiva (15-49 años) que utilizan actualmente, o cuya pareja utiliza actualmente, al menos un método anticonceptivo moderno. El denominador es la demanda total de planificación familiar (la suma de la prevalencia de anticonceptivos (cualquier método) y la necesidad insatisfecha de planificación familiar).</p>
<p><img 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2 years ago
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Source string changed

  1. SDG Metadata
  2. 3-7-1
  3. Spanish
<h1>Methodology</h1>
<h2>Computation method:</h2>
<p>The numerator is the percentage of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method. The denominator is the total demand for family planning (the sum of contraceptive prevalence (any method) and the unmet need for family planning).</p>
<p><img 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numerator is the percentage of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method. The denominator is the total demand for family planning (the sum of contraceptive prevalence (any method) and the unmet need for family planning).</p>
<table>
<tbody>
<tr>
<td rowspan="3">
<p>Demand satisfied <br>by modern methods</p>
</td>
<td rowspan="2">
<p>=</p>
</td>
<td>
<p>Number of women who are currently using a modern method of contraception</p>
</td>
</tr>
<tr>
<td></td>
</tr>
<tr>
<td>
<p> </p>
</td>
<td>
<p>Number of women who are using any method of contraception or are having an unmet need for family planning</p>
</td>
</tr>
</tbody>
</table>
2 years ago
User avatar hernandanielmunoz

Translation added

  1. SDG Metadata
  2. 3-7-1
  3. Spanish
<p>The numerator is the number of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method (CPMod). The denominator is the total demand for family planning (the sum of the number of women using any contraceptive method (CPAny) and the number of women with unmet need for family planning (UMN)). The quotient is then multiplied by 100 to arrive at the percentage of women (aged 15 to 49 years) who have their need for family planning satisfied with modern methods (NSMod).</p>
<p><math xmlns="http://www.w3.org/1998/Math/MathML">
<msub>
<mrow>
<mi>N</mi>
<mi>S</mi>
</mrow>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msubsup>
<mrow>
<mi>C</mi>
<mi>P</mi>
</mrow>
<mrow>
<mi>M</mi>
<mi>o</mi>
<mi>d</mi>
</mrow>
<mrow></mrow>
</msubsup>
</mrow>
<mrow>
<mi>U</mi>
<mi>M</mi>
<mi>N</mi>
<msubsup>
<mrow>
<mo>+</mo>
<mi>C</mi>
<mi>P</mi>
</mrow>
<mrow>
<mi>A</mi>
<mi>n</mi>
<mi>y</mi>
</mrow>
<mrow></mrow>
</msubsup>
</mrow>
</mfrac>
<mo>&#xD7;</mo>
<mn>100</mn>
</math></p>
<h1>Metodología</h1>
<h2>Método de cálculo:</h2>
<p>El numerador es el porcentaje de mujeres en edad reproductiva (15-49 años) que utilizan actualmente, o cuya pareja utiliza actualmente, al menos un método anticonceptivo moderno. El denominador es la demanda total de planificación familiar (la suma de la prevalencia de anticonceptivos (cualquier método) y la necesidad insatisfecha de planificación familiar).</p>
<p><img 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2 years ago
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Glossary

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String information

Key
DATA_COMP
Flags
ignore-inconsistent
String age
3 years ago
Last updated
8 months ago
Source string age
3 years ago
Translation file
translations-metadata/es/3-7-1.yml, string 21