id,name,unit,description,createdAt,updatedAt,code,coverage,timespan,datasetId,sourceId,shortUnit,display,columnOrder,originalMetadata,grapherConfigAdmin,shortName,catalogPath,dimensions,schemaVersion,processingLevel,processingLog,titlePublic,titleVariant,attributionShort,attribution,descriptionShort,descriptionFromProducer,descriptionKey,descriptionProcessing,licenses,license,grapherConfigETL,type,sort,dataChecksum,metadataChecksum
959829,Proportion of deaths due to maternal causes,%,,2024-07-29 15:05:39,2024-07-29 15:05:39,,,1985-2020,6642,,%,"{""unit"": ""%"", ""shortUnit"": ""%"", ""numDecimalPlaces"": 1}",0,,,pm,grapher/un/2024-07-08/maternal_mortality/maternal_mortality#pm,,2,,,,,,,The proportion of deaths among women of reproductive age (15-49 years old) that are due to maternal causes.,"Data on maternal mortality and other relevant variables are obtained through databases maintained by WHO, UNPD, UNICEF, and the World Bank Group. Data available from countries varies in terms of the source and methods.

Given the variability of the sources of data, different methods are used for each data source in order to arrive at country estimates that are comparable and permit regional and global aggregation.

The current methodology employed by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) in this round
followed an improved approach that built directly upon methods used to produce the previous
rounds of estimates published by the MMEIG since 2008.

Estimates for this round were generated using a Bayesian approach, referred to as the Bayesian maternal mortality estimation model, or BMat model.
This enhanced methodology uses the same core estimation method as in those previous rounds, but adds refinements to optimize the use of country-specific data sources and excludes late maternal deaths.
It therefore provides more accurate estimates, and a more realistic assessment of certainty about those estimates.

The new model still incorporates the same covariates which are;
- the Gross Domestic Product per capita based on purchasing power parity conversion (GDP),
- the general fertility rate (GFR)
- proportion of births attended by a skilled health worker (SAB).

The MMEIG has developed a method to adjust existing data in order to take into account these data quality issues and ensure the comparability of different data sources.
This method involves assessment of data for underreporting and, where necessary, adjustment for incompleteness and misclassification of deaths as well as development of estimates through statistical modelling for countries with no reliable national level data.",[],"- The data shown is the UN MMEIG point estimate - this means there is a 50% chance that the true measure lies above this point,
  and a 50% chance that the true value lies below this point.",,,,float,[],aa1414992a1aa3a38222f315d7a48733,3950af67f9d1f10b2f18735a1edaa820
959828,Estimated maternal deaths,deaths,,2024-07-29 15:05:39,2024-07-29 15:05:39,,,1985-2020,6642,,,"{""unit"": ""deaths"", ""numDecimalPlaces"": 0}",0,,,maternal_deaths,grapher/un/2024-07-08/maternal_mortality/maternal_mortality#maternal_deaths,,2,,,,,,,The estimated number of maternal deaths in a given year.,"Data on maternal mortality and other relevant variables are obtained through databases maintained by WHO, UNPD, UNICEF, and the World Bank Group. Data available from countries varies in terms of the source and methods.

Given the variability of the sources of data, different methods are used for each data source in order to arrive at country estimates that are comparable and permit regional and global aggregation.

The current methodology employed by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) in this round
followed an improved approach that built directly upon methods used to produce the previous
rounds of estimates published by the MMEIG since 2008.

Estimates for this round were generated using a Bayesian approach, referred to as the Bayesian maternal mortality estimation model, or BMat model.
This enhanced methodology uses the same core estimation method as in those previous rounds, but adds refinements to optimize the use of country-specific data sources and excludes late maternal deaths.
It therefore provides more accurate estimates, and a more realistic assessment of certainty about those estimates.

The new model still incorporates the same covariates which are;
- the Gross Domestic Product per capita based on purchasing power parity conversion (GDP),
- the general fertility rate (GFR)
- proportion of births attended by a skilled health worker (SAB).

The MMEIG has developed a method to adjust existing data in order to take into account these data quality issues and ensure the comparability of different data sources.
This method involves assessment of data for underreporting and, where necessary, adjustment for incompleteness and misclassification of deaths as well as development of estimates through statistical modelling for countries with no reliable national level data.",[],"- The data shown is the UN MMEIG point estimate - this means there is a 50% chance that the true measure lies above this point,
  and a 50% chance that the true value lies below this point.",,,,float,[],6a119d0bbb64c05c2bb6419e48fa289c,7b5422bf5aa7a36a14ea2147339f2720
959827,Estimated maternal mortality ratio,"deaths per 100,000 live births",,2024-07-29 15:05:39,2024-07-29 15:05:39,,,1985-2020,6642,,,"{""unit"": ""deaths per 100,000 live births"", ""numDecimalPlaces"": 1}",0,,,mmr,grapher/un/2024-07-08/maternal_mortality/maternal_mortality#mmr,,2,,,,,,,"The estimated maternal mortality ratio (MMR) is the number of maternal deaths per 100,000 live births.","Data on maternal mortality and other relevant variables are obtained through databases maintained by WHO, UNPD, UNICEF, and the World Bank Group. Data available from countries varies in terms of the source and methods.

Given the variability of the sources of data, different methods are used for each data source in order to arrive at country estimates that are comparable and permit regional and global aggregation.

The current methodology employed by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) in this round
followed an improved approach that built directly upon methods used to produce the previous
rounds of estimates published by the MMEIG since 2008.

Estimates for this round were generated using a Bayesian approach, referred to as the Bayesian maternal mortality estimation model, or BMat model.
This enhanced methodology uses the same core estimation method as in those previous rounds, but adds refinements to optimize the use of country-specific data sources and excludes late maternal deaths.
It therefore provides more accurate estimates, and a more realistic assessment of certainty about those estimates.

The new model still incorporates the same covariates which are;
- the Gross Domestic Product per capita based on purchasing power parity conversion (GDP),
- the general fertility rate (GFR)
- proportion of births attended by a skilled health worker (SAB).

The MMEIG has developed a method to adjust existing data in order to take into account these data quality issues and ensure the comparability of different data sources.
This method involves assessment of data for underreporting and, where necessary, adjustment for incompleteness and misclassification of deaths as well as development of estimates through statistical modelling for countries with no reliable national level data.",[],"- The data shown is the UN MMEIG point estimate - this means there is a 50% chance that the true measure lies above this point,
  and a 50% chance that the true value lies below this point.",,,,float,[],ff9b45915c89de43c1db34b8a8e7ab45,c54fcb01800bc2113ce931ec94d46178
959826,Estimated maternal mortality rate,"deaths per 100,000 women",,2024-07-29 15:05:39,2024-07-29 15:05:39,,,1985-2020,6642,,,"{""unit"": ""deaths per 100,000 women"", ""numDecimalPlaces"": 1}",0,,,mm_rate,grapher/un/2024-07-08/maternal_mortality/maternal_mortality#mm_rate,,2,,,,,,,"The estimated maternal mortality rate is the number of maternal deaths per 100,000 women of reproductive age (15-49 years old).","Data on maternal mortality and other relevant variables are obtained through databases maintained by WHO, UNPD, UNICEF, and the World Bank Group. Data available from countries varies in terms of the source and methods.

Given the variability of the sources of data, different methods are used for each data source in order to arrive at country estimates that are comparable and permit regional and global aggregation.

The current methodology employed by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) in this round
followed an improved approach that built directly upon methods used to produce the previous
rounds of estimates published by the MMEIG since 2008.

Estimates for this round were generated using a Bayesian approach, referred to as the Bayesian maternal mortality estimation model, or BMat model.
This enhanced methodology uses the same core estimation method as in those previous rounds, but adds refinements to optimize the use of country-specific data sources and excludes late maternal deaths.
It therefore provides more accurate estimates, and a more realistic assessment of certainty about those estimates.

The new model still incorporates the same covariates which are;
- the Gross Domestic Product per capita based on purchasing power parity conversion (GDP),
- the general fertility rate (GFR)
- proportion of births attended by a skilled health worker (SAB).

The MMEIG has developed a method to adjust existing data in order to take into account these data quality issues and ensure the comparability of different data sources.
This method involves assessment of data for underreporting and, where necessary, adjustment for incompleteness and misclassification of deaths as well as development of estimates through statistical modelling for countries with no reliable national level data.",[],"The data is originally given in deaths per person-years for women of reproductive age. To make the figures comparable with other sources, we multiply it by 100,000 to get deaths per 100,000 person-years (corresponding roughly to 100,000 women of reproductive age).",,,,float,[],445ec3aa123af3dbc1b462fae6c3ab0c,f09d56476ffc1fc384ee593d54583362
959825,Lifetime risk of maternal death (1 in x),,,2024-07-29 15:05:38,2024-07-29 15:05:39,,,1985-2020,6642,,,"{""numDecimalPlaces"": 0}",0,,,lifetime_risk_1_in,grapher/un/2024-07-08/maternal_mortality/maternal_mortality#lifetime_risk_1_in,,2,,,,,,,"Statistically, 1 of x women is expected to die from a maternal cause during her reproductive lifespan.","Data on maternal mortality and other relevant variables are obtained through databases maintained by WHO, UNPD, UNICEF, and the World Bank Group. Data available from countries varies in terms of the source and methods.

Given the variability of the sources of data, different methods are used for each data source in order to arrive at country estimates that are comparable and permit regional and global aggregation.

The current methodology employed by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) in this round
followed an improved approach that built directly upon methods used to produce the previous
rounds of estimates published by the MMEIG since 2008.

Estimates for this round were generated using a Bayesian approach, referred to as the Bayesian maternal mortality estimation model, or BMat model.
This enhanced methodology uses the same core estimation method as in those previous rounds, but adds refinements to optimize the use of country-specific data sources and excludes late maternal deaths.
It therefore provides more accurate estimates, and a more realistic assessment of certainty about those estimates.

The new model still incorporates the same covariates which are;
- the Gross Domestic Product per capita based on purchasing power parity conversion (GDP),
- the general fertility rate (GFR)
- proportion of births attended by a skilled health worker (SAB).

The MMEIG has developed a method to adjust existing data in order to take into account these data quality issues and ensure the comparability of different data sources.
This method involves assessment of data for underreporting and, where necessary, adjustment for incompleteness and misclassification of deaths as well as development of estimates through statistical modelling for countries with no reliable national level data.",[],"- The data shown is the UN MMEIG point estimate - this means there is a 50% chance that the true measure lies above this point,
  and a 50% chance that the true value lies below this point.",,,,float,[],a3a800f08672b3600ebafcca7b0b319d,fbf74895bd7addc7222a7d3ae801bf5c
959824,Lifetime risk of maternal death,percent,,2024-07-29 15:05:38,2024-07-29 15:05:38,,,1985-2020,6642,,%,"{""unit"": ""percent"", ""shortUnit"": ""%"", ""numDecimalPlaces"": 1}",0,,,lifetime_risk,grapher/un/2024-07-08/maternal_mortality/maternal_mortality#lifetime_risk,,2,,,,,,,The probability for one woman to die from a maternal cause during her reproductive lifespan.,"Data on maternal mortality and other relevant variables are obtained through databases maintained by WHO, UNPD, UNICEF, and the World Bank Group. Data available from countries varies in terms of the source and methods.

Given the variability of the sources of data, different methods are used for each data source in order to arrive at country estimates that are comparable and permit regional and global aggregation.

The current methodology employed by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) in this round
followed an improved approach that built directly upon methods used to produce the previous
rounds of estimates published by the MMEIG since 2008.

Estimates for this round were generated using a Bayesian approach, referred to as the Bayesian maternal mortality estimation model, or BMat model.
This enhanced methodology uses the same core estimation method as in those previous rounds, but adds refinements to optimize the use of country-specific data sources and excludes late maternal deaths.
It therefore provides more accurate estimates, and a more realistic assessment of certainty about those estimates.

The new model still incorporates the same covariates which are;
- the Gross Domestic Product per capita based on purchasing power parity conversion (GDP),
- the general fertility rate (GFR)
- proportion of births attended by a skilled health worker (SAB).

The MMEIG has developed a method to adjust existing data in order to take into account these data quality issues and ensure the comparability of different data sources.
This method involves assessment of data for underreporting and, where necessary, adjustment for incompleteness and misclassification of deaths as well as development of estimates through statistical modelling for countries with no reliable national level data.",[],"- The data shown is the UN MMEIG point estimate - this means there is a 50% chance that the true measure lies above this point,
  and a 50% chance that the true value lies below this point.",,,,float,[],d3de8dda6fa67615e7397a5fc987924e,eced571d072e667f4641fddc9ea6a2cf
959823,Estimated HIV-related indirect maternal mortality ratio,"deaths per 100,000 live births",,2024-07-29 15:05:38,2024-07-29 15:05:38,,,1985-2020,6642,,,"{""unit"": ""deaths per 100,000 live births"", ""numDecimalPlaces"": 1}",0,,,hiv_related_indirect_mmr,grapher/un/2024-07-08/maternal_mortality/maternal_mortality#hiv_related_indirect_mmr,,2,,,,,,,The maternal mortality ratio (MMR) of estimated maternal deaths caused by the aggravating effects of pregnancy on HIV.,"- For countries with generalized HIV epidemics and high HIV prevalence, HIV/AIDS is a leading cause of death during pregnancy and after delivery.
Furthermore, evidence suggests that women with HIV infection have an eight times higher risk of pregnancy-related death compared with non-HIV infected women.

When estimating maternal mortality in these countries, it is, thus, important to differentiate between incidental HIV deaths (non-maternal deaths) and HIV-related indirect maternal deaths (maternal deaths caused by the aggravating effects of pregnancy on HIV) among HIV-positive pregnant and postpartum women who have died.
- Data on maternal mortality and other relevant variables are obtained through databases maintained by WHO, UNPD, UNICEF, and the World Bank Group. Data available from countries varies in terms of the source and methods.

Given the variability of the sources of data, different methods are used for each data source in order to arrive at country estimates that are comparable and permit regional and global aggregation.

The current methodology employed by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) in this round
followed an improved approach that built directly upon methods used to produce the previous
rounds of estimates published by the MMEIG since 2008.

Estimates for this round were generated using a Bayesian approach, referred to as the Bayesian maternal mortality estimation model, or BMat model.
This enhanced methodology uses the same core estimation method as in those previous rounds, but adds refinements to optimize the use of country-specific data sources and excludes late maternal deaths.
It therefore provides more accurate estimates, and a more realistic assessment of certainty about those estimates.

The new model still incorporates the same covariates which are;
- the Gross Domestic Product per capita based on purchasing power parity conversion (GDP),
- the general fertility rate (GFR)
- proportion of births attended by a skilled health worker (SAB).

The MMEIG has developed a method to adjust existing data in order to take into account these data quality issues and ensure the comparability of different data sources.
This method involves assessment of data for underreporting and, where necessary, adjustment for incompleteness and misclassification of deaths as well as development of estimates through statistical modelling for countries with no reliable national level data.",[],"- The data shown is the UN MMEIG point estimate - this means there is a 50% chance that the true measure lies above this point,
  and a 50% chance that the true value lies below this point.",,,,float,[],8f280d69476f845f6ec1ade0c39ec141,fec848d05bff0d3ca867cb92b0fbc1c4
959822,Estimated proportion of HIV-related maternal deaths,%,,2024-07-29 15:05:38,2024-07-29 15:05:38,,,1985-2020,6642,,%,"{""unit"": ""%"", ""shortUnit"": ""%"", ""numDecimalPlaces"": 1}",0,,,hiv_related_indirect_percentage,grapher/un/2024-07-08/maternal_mortality/maternal_mortality#hiv_related_indirect_percentage,,2,,,,,,,The proportion of estimated maternal deaths caused by the aggravating effects of pregnancy on HIV.,"- For countries with generalized HIV epidemics and high HIV prevalence, HIV/AIDS is a leading cause of death during pregnancy and after delivery.
Furthermore, evidence suggests that women with HIV infection have an eight times higher risk of pregnancy-related death compared with non-HIV infected women.

When estimating maternal mortality in these countries, it is, thus, important to differentiate between incidental HIV deaths (non-maternal deaths) and HIV-related indirect maternal deaths (maternal deaths caused by the aggravating effects of pregnancy on HIV) among HIV-positive pregnant and postpartum women who have died.
- Data on maternal mortality and other relevant variables are obtained through databases maintained by WHO, UNPD, UNICEF, and the World Bank Group. Data available from countries varies in terms of the source and methods.

Given the variability of the sources of data, different methods are used for each data source in order to arrive at country estimates that are comparable and permit regional and global aggregation.

The current methodology employed by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) in this round
followed an improved approach that built directly upon methods used to produce the previous
rounds of estimates published by the MMEIG since 2008.

Estimates for this round were generated using a Bayesian approach, referred to as the Bayesian maternal mortality estimation model, or BMat model.
This enhanced methodology uses the same core estimation method as in those previous rounds, but adds refinements to optimize the use of country-specific data sources and excludes late maternal deaths.
It therefore provides more accurate estimates, and a more realistic assessment of certainty about those estimates.

The new model still incorporates the same covariates which are;
- the Gross Domestic Product per capita based on purchasing power parity conversion (GDP),
- the general fertility rate (GFR)
- proportion of births attended by a skilled health worker (SAB).

The MMEIG has developed a method to adjust existing data in order to take into account these data quality issues and ensure the comparability of different data sources.
This method involves assessment of data for underreporting and, where necessary, adjustment for incompleteness and misclassification of deaths as well as development of estimates through statistical modelling for countries with no reliable national level data.",[],"- The data shown is the UN MMEIG point estimate - this means there is a 50% chance that the true measure lies above this point,
  and a 50% chance that the true value lies below this point.",,,,float,[],36a5721e51372d1924a86012b23d8427,7f487a8258f7afd6fe37f3f6de694a47
959821,Estimated HIV-related indirect maternal deaths,deaths,,2024-07-29 15:05:38,2024-07-29 15:05:38,,,1985-2020,6642,,,"{""unit"": ""deaths"", ""numDecimalPlaces"": 0}",0,,,hiv_related_indirect_maternal_deaths,grapher/un/2024-07-08/maternal_mortality/maternal_mortality#hiv_related_indirect_maternal_deaths,,2,,,,,,,The estimated number of indirect maternal deaths and late maternal deaths caused by the aggravating effects of pregnancy on HIV.,"- For countries with generalized HIV epidemics and high HIV prevalence, HIV/AIDS is a leading cause of death during pregnancy and after delivery.
Furthermore, evidence suggests that women with HIV infection have an eight times higher risk of pregnancy-related death compared with non-HIV infected women.

When estimating maternal mortality in these countries, it is, thus, important to differentiate between incidental HIV deaths (non-maternal deaths) and HIV-related indirect maternal deaths (maternal deaths caused by the aggravating effects of pregnancy on HIV) among HIV-positive pregnant and postpartum women who have died.
- Data on maternal mortality and other relevant variables are obtained through databases maintained by WHO, UNPD, UNICEF, and the World Bank Group. Data available from countries varies in terms of the source and methods.

Given the variability of the sources of data, different methods are used for each data source in order to arrive at country estimates that are comparable and permit regional and global aggregation.

The current methodology employed by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) in this round
followed an improved approach that built directly upon methods used to produce the previous
rounds of estimates published by the MMEIG since 2008.

Estimates for this round were generated using a Bayesian approach, referred to as the Bayesian maternal mortality estimation model, or BMat model.
This enhanced methodology uses the same core estimation method as in those previous rounds, but adds refinements to optimize the use of country-specific data sources and excludes late maternal deaths.
It therefore provides more accurate estimates, and a more realistic assessment of certainty about those estimates.

The new model still incorporates the same covariates which are;
- the Gross Domestic Product per capita based on purchasing power parity conversion (GDP),
- the general fertility rate (GFR)
- proportion of births attended by a skilled health worker (SAB).

The MMEIG has developed a method to adjust existing data in order to take into account these data quality issues and ensure the comparability of different data sources.
This method involves assessment of data for underreporting and, where necessary, adjustment for incompleteness and misclassification of deaths as well as development of estimates through statistical modelling for countries with no reliable national level data.",[],"- The data shown is the UN MMEIG point estimate - this means there is a 50% chance that the true measure lies above this point,
  and a 50% chance that the true value lies below this point.",,,,float,[],03e72c30bc391c88aab25546b47ab179,3e2f63ec167a9b768dc0b163781ab390
959820,Estimated live births,births,,2024-07-29 15:05:38,2024-07-29 15:05:39,,,1985-2020,6642,,,"{""unit"": ""births"", ""numDecimalPlaces"": 0}",0,,,births,grapher/un/2024-07-08/maternal_mortality/maternal_mortality#births,,2,,,,,,,The estimated number of live births in a given year.,"- Data on maternal mortality and other relevant variables are obtained through databases maintained by WHO, UNPD, UNICEF, and the World Bank Group. Data available from countries varies in terms of the source and methods.

Given the variability of the sources of data, different methods are used for each data source in order to arrive at country estimates that are comparable and permit regional and global aggregation.

The current methodology employed by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) in this round
followed an improved approach that built directly upon methods used to produce the previous
rounds of estimates published by the MMEIG since 2008.

Estimates for this round were generated using a Bayesian approach, referred to as the Bayesian maternal mortality estimation model, or BMat model.
This enhanced methodology uses the same core estimation method as in those previous rounds, but adds refinements to optimize the use of country-specific data sources and excludes late maternal deaths.
It therefore provides more accurate estimates, and a more realistic assessment of certainty about those estimates.

The new model still incorporates the same covariates which are;
- the Gross Domestic Product per capita based on purchasing power parity conversion (GDP),
- the general fertility rate (GFR)
- proportion of births attended by a skilled health worker (SAB).

The MMEIG has developed a method to adjust existing data in order to take into account these data quality issues and ensure the comparability of different data sources.
This method involves assessment of data for underreporting and, where necessary, adjustment for incompleteness and misclassification of deaths as well as development of estimates through statistical modelling for countries with no reliable national level data.",[],"- The data shown is the UN MMEIG point estimate - this means there is a 50% chance that the true measure lies above this point,
  and a 50% chance that the true value lies below this point.",,,,float,[],3515a2177839b0499c893bd3a3f3ba0e,f628041030a8b54c8c69e79b42bbd8bb