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Population Health Metrics

, 16:21 | Cite as

How useful are registered birth statistics for health and social policy? A global systematic assessment of the availability and quality of birth registration data

  • David E. Phillips
  • Tim Adair
  • Alan D. Lopez
Open Access
Research

Abstract

Background

The registration and certification of births has a wide array of individual and societal benefits. While near-universal in some parts of the world, birth registration is less common in many low- and middle-income countries, and the quality of vital statistics vary. We assembled publicly available birth registration records for as many countries as possible into a novel global birth registration database, and we present a systematic assessment of available data.

Methods

We obtained 4918 country-years of data from 145 countries covering the period 1948–2015. We compared these to existing estimates of total births to assess completeness of public data and adapted existing methods to evaluate the quality and timeliness of the data.

Results

Since 1980, approximately one billion births were registered and shared in public databases. Compared to estimates of fertility, this represents only 40.0% of total births in the peak year, 2011. Approximately 74 million births (53.1%) per year occur in countries whose systems do not systematically register them and release the aggregate records. Considering data quality, timeliness, and completeness in country-years where data are available, only about 12 million births per year (8.6%) occur in countries with high-performing registration systems.

Conclusions

This analysis highlights the gaps in available data. Our objective and low-cost approach to assessing the performance of birth registration systems can be helpful to monitor country progress, and to help national and international policymakers set targets for strengthening birth registration systems.

Keywords

Civil registration Vital statistics Birth certificates Data quality 

Abbreviations

CRVS

Civil registration and vital statistics

LMICs

Low- and middle-income countries

UNSD

United Nations Statistical Division

VSPI

Vital Statistics Performance Index

Background

The registration and certification of births, while a near-universal practice in some parts of the world, is far less common in many low- and middle-income countries (LMICs) [1]. Birth registration has a wide array of individual and societal benefits [2], including the identification and facilitation of legal entitlements [1], citizenship and voting rights [3], social security benefits, social inclusion [4], access to health and education services [5], security benefits in times of crisis [6], and proof of age [3]. So fundamental is birth registration to legal identity that it has frequently been described as a basic human right [7, 8, 9]. Additionally, reliable birth registration, compiled and consolidated within a national civil registration and vital statistics (CRVS) system, should be the primary data source for fertility statistics [10]. Such data are necessary to track (often rapid) changes in fertility levels and patterns, to monitor and evaluate family planning programs, to provide the denominator for an array of key maternal and child mortality indicators [11], to project future population size and structure [12], and to inform planning for future health, education, and other social services. Their fundamental and comprehensive importance for a nation’s health and social development underlies calls for universal birth registration, as reflected in the Sustainable Development Goal 16.9 that aims, by 2030, to provide legal identity for all, including birth registration [13].

As interest in universal birth registration continues to grow, it will become increasingly important for countries and development partners alike to understand the performance of birth registration systems and in particular, to have some objective basis to determine whether these systems are ‘fit-for purpose’, as described above. Yet, despite their fundamental importance, the global status of birth registration is not well understood. While multiple studies have described, assessed, and monitored the global landscape of death registration [14, 15, 16], to our knowledge no comparable evaluations of birth registration systems exist. Some partial assessments to guide policy, however, have been undertaken. UNICEF, for example, has estimated that 71% of all children younger than 5 years have had their birth registered [17]. However, this estimate is based on self-reported survey responses that may be biased, especially as data from UNICEF show considerable discrepancies in some countries between reported birth registration and evidence of a birth certificate. For example, for only 10% of births in Rwanda that are reported to be “registered” can the family provide the birth certificate [18]. Moreover, the UNICEF approach does not include information on children who have died, especially neonatal deaths, for whom birth registration is often overlooked [10].

One reason why there has been no systematic assessment of birth registration data and systems could be the absence of a comprehensive and properly maintained and used global database. While agencies such as the World Health Organization annually aggregate and disseminate cause of death statistics based on death registration data from over 150 countries around the world, birth registration data are made public only through information provided by countries to the United Nations Statistical Division via an annual questionnaire, or through country-specific channels (e.g., national statistical offices) and other decentralized sources such as the Human Fertility Collection [19, 20].

To objectively assess the quality of birth registration data, and thus (indirectly) the performance of birth registration systems, it is first necessary to define the essential elements of data quality. While a fundamental measure of the quality of birth registration data is the completeness of registration, i.e., the percentage of all births that occur in a given year that are registered, there is other specific information about the newborn, the mother or the family that is, or should be, routinely collected for each birth and provided along with the birth certificate. Much of this information is likely to be of central importance for public health and demographic purposes, and hence reflects the utility of birth registration data. These characteristics include:
  • age of the mother, to understand the age patterns of fertility and to calculate the total fertility rate, the most common summary measure of fertility levels in a population;

  • sex of the newborn, to monitor the sex ratio at birth, also as an indicator of sex preferences in fertility [21];

  • birth order of the child, to understand fertility behavior (such as stopping behavior and progression patterns from one parity to the next); and

  • birthweight, given its critical role for the survival of the newborn [22].

An objective, reliable, and descriptive low-cost approach to assessing the performance of birth registration systems would enable countries to monitor progress in developing their birth registration and reporting systems, by facilitating international goal-setting, facilitating monitoring of development goals, and assisting in the global efforts to improve birth registration that are already underway by identifying specific aspects of data quality or availability that require attention [23]. This paper advances efforts to improve the monitoring of global birth registration in a number of ways. First, we present the results of what we believe is the first systematic effort to assemble publicly available birth registration records for as many countries as possible into a global birth registration database, similar to what WHO maintains for death registration and causes of death. Second, we present a systematic assessment of birth registration data quality around the world. We do so by adapting an existing framework used to assess the quality and utility of death registration statistics, known as the Vital Statistics Performance Index (VSPI) [15], to the context of birth registration. We expect that the birth registration database, and our findings and framework for assessing its utility, will help enable the measurement and tracking of performance metrics, especially between countries, and thus will be of immediate use by both countries and development partners to facilitate monitoring of progress with global and national development goals.

Methods

Data

We have systematically compiled a global database1 on birth registration statistics, based on 4918 country-years of data from 145 countries covering the period 1948–2015 (Table 1). For each country-year, the number of registered live births2 specified by age of mother, sex of newborn, birth order, and birthweight were compiled, where available. These variables are all recommended core topics to be collected for vital statistics purposes in national civil registration systems as specified in the UN Principles and Recommendations for a Vital Statistics System [21]. The primary source of data was the United Nations Statistical Division (UNSD) database, which provides birth registration data reported by countries in standardized tables in the Demographic Yearbook questionnaire [19, 24]. Because this database covers only a subset of countries likely to have functional birth registration systems, additional data were collected from Eurostat and directly from national statistical offices and ministry of health databases (Table 1). It is important to note that these are the data that are publicly available. Most, if not all, countries are likely to have some form of a birth registration system, but in many countries these data are not published. For example, there are many countries reported by UNICEF as having birth registration data as reported in surveys, but which cannot be found in the UNSD database or country statistical office publications [18].
Table 1

Data availability

Region

Country

Years with dataa

Eastern Europe/Central Asia

Albania

1948, 1950–1967, 1969–1971, 1979–2013

North Africa/Middle East

Algeria

1964–1965, 1978–1980, 1985–1986

East Asia/Pacific

American Samoa

1952–1969, 1971–1973, 1976, 1982, 1984–2014

High Income

Andorra

2002–2012

Latin America/Caribbean

Antigua and Barbuda

1972–1975, 1977–1986, 1993, 1995

High Income

Argentina

1960–1966, 1968–1970, 1979–2014

Eastern Europe/Central Asia

Armenia

1982–1994, 1996–2000, 2002–2004, 2006–2009, 2014

Latin America/Caribbean

Aruba

1993–1995, 1997–2015

High Income

Australia

1948–2014, 2010–2015 [32]

High Income

Austria

1951–2015

Eastern Europe/Central Asia

Azerbaijan

1982–2004, 2006–2010, 2012–2014

Latin America/Caribbean

Bahamas

1968–1977, 1990–1992, 1996

North Africa/Middle East

Bahrain

1977–2014

Latin America/Caribbean

Barbados

1954–1980, 1982–1987, 1990–1991, 2005–2007

Eastern Europe/Central Asia

Belarus

1969–1973, 1986–1999, 2002–2014

High Income

Belgium

1947–1970, 1972–1983, 1986–1987, 1989–2015

Latin America/Caribbean

Bermuda

1962–1965, 1975–1989, 2006–2015

Eastern Europe/Central Asia

Bosnia and Herzegovina

1989–1991, 1996–2010, 2012

Latin America/Caribbean

Brazil

1994–1999, 2000–2015 [33]

High Income

Brunei Darussalam

1969–1974, 1976, 1978, 1981–1992, 1996–2002, 2006–2008, 2011–2014

Eastern Europe/Central Asia

Bulgaria

1949–1990, 1992–2014

Sub-Saharan Africa

Cabo Verde

1979–1985, 1990

High Income

Canada

1948–2009, 2010–2014 [34]

Latin America/Caribbean

Cayman Islands

1981–1983, 1986–1995, 2009, 2011–2014

High Income

Chile

1948–2003, 2005–2014, 1997–1999, 2005–2014 [35]

Latin America/Caribbean

Colombia

1998–2014 [36]

East Asia/Pacific

Cook Islands

1971–1977, 1979–1982

Latin America/Caribbean

Costa Rica

1953–1974, 1976–1991, 1994–1997, 1999–2014

Eastern Europe/Central Asia

Croatia

1988–2014

Latin America/Caribbean

Cuba

1965–1971, 1976–1989, 1991, 1993–2014

Latin America/Caribbean

Curaçao

2009–2015

High Income

Cyprus

1948–2014

Eastern Europe/Central Asia

Czech Republic

1991–2014

High Income

Denmark

1948–1966, 1968–2015

Latin America/Caribbean

Dominica

1960, 1966, 1969, 1985–1989, 2005–2006

Latin America/Caribbean

Ecuador

1992–2007, 2009–2010 [37]

North Africa/Middle East

Egypt

1965–1999, 2006–2012

Latin America/Caribbean

El Salvador

1948–2004, 2005–2007, 2010, 2012

Eastern Europe/Central Asia

Estonia

1986–2015

High Income

Faeroe Islands

1951–1966, 1968–1987, 1989, 2005–2007

East Asia/Pacific

Fiji

1948–1987, 2004, 2008

High Income

Finland

1948–2015

High Income

France

1948–1972, 1974–2009, 2011–2014, 2015 [38]

Latin America/Caribbean

French Guiana

1951–1970, 1972–1976, 1984–1985, 1996, 1998–2003, 2005–2007

East Asia/Pacific

French Polynesia

1968

Eastern Europe/Central Asia

Georgia

1989, 1992, 1994–1997, 1999–2015

High Income

Germany

1991–1997, 1999–2015

High Income

Greece

1956–1985, 1990–2015

High Income

Greenland

1952–1965, 1967–1986

Latin America/Caribbean

Grenada

1951–1969, 1978, 1997, 2000

Latin America/Caribbean

Guadeloupe

1950–1967, 1969–1970, 1975, 1978–1980, 1984–1986, 1991, 1999–2003

East Asia/Pacific

Guam

1949–1986, 1988–1992, 1999, 2001–2004, 2015

Latin America/Caribbean

Guatemala

1948–1973, 1975–1979, 1981–1999, 2006, 2009–2014 [39]

Latin America/Caribbean

Guyana

1954–1956, 1960–1961, 1967–1972,

East Asia/Pacific

Hong Kong

1969–2014

Eastern Europe/Central Asia

Hungary

1948–2015

High Income

Iceland

1948–2015

South Asia

India

2011–2015 [40]

North Africa/Middle East

Iran

2011–2013

High Income

Ireland

1955–2015

High Income

Isle of Man

1955–1961

High Income

Israel

1953–2015

High Income

Italy

1948–1964, 1973, 1980–1997, 1999–2015

Latin America/Caribbean

Jamaica

1948–1964, 1977–1984, 1986–1989,1995–1996, 2000–2004, 2016

High Income

Japan

1948–2010, 2012–2014, 2011, 2015 [41]

North Africa/Middle East

Jordan

1969–1979, 2000–2015 [42]

Eastern Europe/Central Asia

Kazakhstan

1987–2008, 2012–2013

Eastern Europe/Central Asia

Kosovo

2002–2003, 2005, 2008, 2011

North Africa/Middle East

Kuwait

1963–1970, 1972, 1987, 1991–2014

Eastern Europe/Central Asia

Kyrgyzstan

1980, 1982–2015

Eastern Europe/Central Asia

Latvia

1986–2015

North Africa/Middle East

Libya

1972–1977, 1981, 1996, 2000, 2002

High Income

Liechtenstein

1965–1966, 1968, 1978–1983, 1986, 1987, 1993, 2003–2014

Eastern Europe/Central Asia

Lithuania

1970–1977, 1985–2015

High Income

Luxembourg

1948–2014, 2015 [38]

East Asia/Pacific

Macao

1955–2015

East Asia/Pacific

Malaysia

1990–1997, 2001–2009, 2011–2015

East Asia/Pacific

Maldives

1996, 1999–2014

Sub–Saharan Africa

Mali

1897

High Income

Malta

1957–1990, 1992–2015

Latin America/Caribbean

Martinique

1950–1970, 1972–1976, 1984–1992, 1999–2003, 2005–2007

East Asia/Pacific

Mauritius

1990–2003, 2005–2015

Latin America/Caribbean

Mexico

1985–2015 [43]

Eastern Europe/Central Asia

Moldova

1987–1992, 1995–1996, 1998–2014

Eastern Europe/Central Asia

Mongolia

1980, 1990, 1994–2010, 2012–2015

Eastern Europe/Central Asia

Montenegro

1980, 1990, 2000, 2003–2009

Latin America/Caribbean

Montserrat

1982–1986, 1994–1999, 2010–2014

North Africa/Middle East

Morocco

1990–1991, 1993, 1995–2001

East Asia/Pacific

Nauru

1965–1968, 2009–2011

High Income

Netherlands

1948–2014, 2015 [38]

East Asia/Pacific

New Caledonia

1962–1968, 1970–1985, 1987, 1990–1994, 1996–2003, 2005–2007, 2010, 2012

High Income

New Zealand

1962–2015

East Asia/Pacific

Niue

1957–1962, 2009

East Asia/Pacific

Norfolk Island

1948–1972, 1974–1976, 1978–1981, 1983–1984, 1988,

High Income

Norway

1948–2014, 2015 [38]

North Africa/Middle East

Oman

2006–2015 [44]

East Asia/Pacific

Palau

1989–2005

Latin America/Caribbean

Panama

1950, 1952–2000, 2002–2003, 2005–2015

Latin America/Caribbean

Peru

2013–2015 [45]

East Asia/Pacific

Philippines

1990–1993, 1997–2007, 2009–2015

Eastern Europe/Central Asia

Poland

1950–2015

High Income

Portugal

1948–2015

Latin America/Caribbean

Puerto Rico

1948–1962, 1964–1985, 1987–1994, 1996–2000, 2002–2009, 2012–2015

North Africa/Middle East

Qatar

1985–1994, 1996–2010, 2012–2013

High Income

South Korea

1993–2014

East Asia/Pacific

Reunion

1950–1970, 1980, 1982–1986, 1989, 1993–1997, 2002–2003, 2005–2007

Eastern Europe/Central Asia

Romania

1955, 1957–2014, 2015 [38]

Eastern Europe/Central Asia

Russia

1960,1965, 1970, 1975, 1980–1989, 1991–2004, 2006–2011, 2013, 2014 [38]

High Income

Saint Pierre and Miquelon

1948–1952, 1959, 1963–1964, 1967, 1969, 1973–1977

Latin America/Caribbean

Saint Vincent and the Grenadines

1952–1956, 1960–1964, 1977–1984, 1986, 1988, 1992–1994, 1996–2005, 2008–2009, 2013–2014

Latin America/Caribbean

Saint Kitts and Nevis

1956–1972, 1974–1991, 1993–1996

Latin America/Caribbean

St Lucia

1953–1961, 1963, 1975, 1978–1986, 1994–2002, 2004–2005

East Asia/Pacific

Samoa

1993

High Income

San Marino

1960–1989, 1992–1995, 1997–2004, 2011–2014

Sub-Saharan Africa

Sao Tome and Principe

1958, 1974–1979

Eastern Europe/Central Asia

Serbia

2000–2015

East Asia/Pacific

Seychelles

1982,1984–1985, 1990, 1992–1993, 1995–1996, 2004–2015

High Income

Singapore

1948–2015

Eastern Europe/Central Asia

Slovakia

1988–1995, 1997–2015

Eastern Europe/Central Asia

Slovenia

1987–2015

Sub-Saharan Africa

South Africa

1998–2015 [46]

High Income

Spain

1948–1983, 1985–2014, 2015 [38]

East Asia/Pacific

Sri Lanka

1952–1969, 1977–1989, 1991, 1995–1996, 2001, 2006–2010

Latin America/Caribbean

Suriname

1980–1986, 1988–2007, 2012–2014

High Income

Sweden

1948–2014, 2015 [38]

High Income

Switzerland

1948–1982, 1984–2014, 2015 [38]

East Asia/Pacific

Taiwan

1982–2014 [47]

Eastern Europe/Central Asia

Tajikistan

1989–1994, 2001–2003

Eastern Europe/Central Asia

TFYR of Macedonia

1989–2015

East Asia/Pacific

Thailand

1991–1992, 1994, 1997

Sub-Saharan Africa

Tonga

1990, 1993–2000, 2002–2003

Latin America/Caribbean

Trinidad and Tobago

1992–1995, 1997, 2002, 2004–2006, 2008–2009

North Africa/Middle East

Tunisia

1960, 1965–1972, 1974, 1977–1980, 1985–1989, 1992–1995, 1998, 2006–2007, 2011

North Africa/Middle East

Turkey

2009–2015

Eastern Europe/Central Asia

Turkmenistan

1989

Latin America/Caribbean

Turks and Caicos Islands

2001–2005

Eastern Europe/Central Asia

Ukraine

1969–1971, 1973–1975, 1987–1996, 1998, 2001–2004, 2006–2008, 2010–2012, 2014–2015

High Income

United Kingdom

1982–2004, 2007–2014, 2015 [38]

High Income

United States

1948–1989, 1991, 1993–2002, 2003–2015 [48]

Latin America/Caribbean

United States Virgin Islands

1948–1962, 1964–1967, 1969–1972, 1977–1997

High Income

Uruguay

1949–1954, 1963, 1977–1989, 1993, 1996–1997, 1999–2007, 2012–2014 [49]

Eastern Europe/Central Asia

Uzbekistan

1989, 1993–1997, 1999–2000, 2005–2015

Latin America/Caribbean

Venezuela

1990–1991, 1996, 1998–2002, 2005–2007, 2009–2015

East Asia/Pacific

Wallis and Futuna Islands

1969, 1996–2008

Eastern Europe/Central Asia

Yugoslavia

1994–1995

aUnless otherwise specified with a citation, the source for data is [19] UN Statistics Division. UNSD Demographic Statistics [Internet]. United Nations; 2017. Available from: http://data.un.org

Assessment of completeness

In order to assess birth registration completeness, we relied on existing annual estimates of total births produced by the United Nations Population Division [25]. We use these estimates as a measure of the total number of live births that occur each year in a country, recognizing that they are subject to methodological and empirical uncertainty. They are, however, the only estimates of the total numbers of births occurring in countries currently available. The observed number of births reported for each country were divided by estimates of the total number of live born in each country-year.

The resulting figures thus represent a measure of completeness of birth registration data in the public domain. We assume that in a country-year which has made birth registration data available, the data include all registered births for that year, and therefore can be used to assess registration completeness. In country-years where no data are available, we are unable to draw conclusions about registration completeness.

Vital statistics performance index

To evaluate the utility of vital statistics with respect to their accuracy in addition to their completeness and availability, we adapted methods defined by Phillips et al. 2014 [15]. In their study, six empirical indicators were used to create a summary index of death registration data utility known as the VSPI. Comparably, we defined four indicators of data quality: the proportion of registered births with unspecified maternal age, the proportion of registered births with unspecified newborn sex, the proportion of registered births with unspecified birthweight, and the proportion of registered births with unspecified live birth order. Following the VSPI framework, we included two additional components of system performance which, together with the four components of data quality mentioned above define a summary of the overall accuracy of birth registration data.

These indicators of performance were selected on the basis of their suitability for assessing the policy relevance of demographic and fertility statistics (as described above), their availability in many data systems, and their inclusion in global recommendations for vital registration systems [21]. In doing so, we implicitly assume that complete, accurate, and recent information about maternal age, newborn sex, birthweight, and live birth order are useful to describe about the distribution and trends of fertility, and can summarize the overall accuracy of data used to represent those fertility trends.

As detailed in Phillips et al. 2014 [15], simulation techniques were used to combine the six indicators into a composite index. The purpose of the simulation is to assess the distortion in observed fertility trends as compared to the true underlying trends associated with different levels of the above indicators. As an example, if a certain proportion of births are reported with an unknown sex, the simulation approach measures the accuracy of sex ratios in the observed data as compared to the sex ratio of the population from which the data were derived. Each other indicator’s accuracy was assessed using a separate, relevant objective function. Maternal age was evaluated using the fraction of births in each age group (less than 15 years of age, 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and greater than or equal to 45 years of age). Birthweight was evaluated using the fraction of births in each birthweight category (less than 2500 g, 2500–3499, and greater than 3500 g). Birth order was evaluated using the proportion of births in each sibship size (0, 1, 2, or 3 or more livebirth siblings). Like the VSPI framework, we used the population-level accuracy formula defined by Murray et al. 2011 to assess the similarity between observed fractions and that of the underlying simulated population [26].

We used the above-mentioned estimates of birth counts as the population for the simulation [25]. Because these estimates are not disaggregated by sex, birthweight, and live birth order of the newborn, and because no other global estimates are as well, to our knowledge, we developed an approach to disaggregating them based on available data. We combined publicly-available survey data as direct measures of the fraction of births in each birth group (age, sex, birthweight, and birth order). These data included 211 Demographic and Health Surveys from 73 countries and the UK Understanding Society Longitudinal Household Study [27, 28]. We used regression techniques (see Additional file 1 for details) to estimate the fraction of births by birth group from the survey data. Modeled birth fractions were multiplied by the UN estimates of births by country, year, and maternal age to disaggregate them, leaving the total unchanged.

Using these estimates of birth counts as a population of simulated births, we drew a weighted sample of birth certificates in order to simulate progressively less-than-complete registration. Observed patterns of missing data from the birth registration database described above were used as empirical probabilities for weighted sampling. Finally, we computed observed proportions of missing data among the actual data, and simulation results were used to assess the accuracy of those observed proportions.

The separate indicators of data quality were then combined by taking the product of accuracy measures from the simulation. Following Phillips et al. 2014 [15], an exponential smoothing algorithm was applied to the product in order to measure the component of overall utility related to the timeliness of the data. Further details on the exact computation of the VSPI has been described elsewhere [15].

The result of this simulation and smoothing procedure is a single index of the policy utility of birth registration statistics for a given population in a given year, simultaneously capturing data availability, quality, completeness, and timeliness, which we will term VSPI-B. This index quantifies the extent to which registered and available birth data are accurate in reflecting the underlying demographic profile of births in the country.

Results

We analyzed 2680 country-years of data, from 109 countries spanning 1980 to 2015. We found 51 countries with greater than or equal to 30 years of available data since 1980, 75 countries with greater than or equal to 20 years of available data, and 11 countries with less than or equal to five years. Available data came from 32 high-income countries, 29 countries from Eastern Europe or Central Asia, 20 countries from Latin America and the Caribbean, 12 countries from North Africa and the Middle East, 11 countries from East Asia and the Pacific, and four countries from Sub-Saharan Africa (Table 1). Notably, several populous countries (e.g., China, Bangladesh and Pakistan) did not have any birth registration data publicly-available for analysis.

The data we were able to gather represented approximately 27.9 million births per year on average, ranging from 16.8 million births recorded in 1981 to 55.3 million births recorded in 2011, for a total of 1.01 billion births registered since 1980. The available data represented only 20.8% of the estimated total number of birth worldwide. This figure varied from 13.2% in 1990 to 40.0% in 2011, the most recent year for which data was available for most of the reporting countries. In 2015, the most recent year for which data were available, global availability was estimated as 32.6%. The most notable change in global registration completeness occurred in 2011, when India began publicly reporting data. Figure 1 displays the global percentage of births registered based on publicly-available data over time.
Fig. 1

Percentage of global births registered in publicly-available data

Based on their most recent year with available data, 83 countries had estimated completeness greater than 90%, 19 countries had estimated completeness between 80 and 90%, five countries had estimated completeness between 50 and 80%, and two countries had estimated completeness below 50%. Completeness estimates for the most recent year for each country with available data are shown in Table 2. Additional file 2 displays the time series of completeness for each country.
Table 2

Birth registration completeness in most recent year by country

Country

Year

Completeness (%)

Albania

2013

90

Algeria

1986

90

Antigua and Barbuda

1995

97

Argentina

2014

100

Armenia

2014

100

Australia

2014

95

Austria

2015

100

Azerbaijan

2014

85

Bahamas

1996

100

Bahrain

2014

99

Barbados

2007

100

Belarus

2014

100

Belgium

2015

94

Bosnia and Herzegovina

2012

93

Brazil

2015

99

Brunei Darussalam

2014

100

Bulgaria

2014

100

Canada

2014

100

Chile

2014

100

Colombia

2014

90

Costa Rica

2014

100

Croatia

2014

95

Cuba

2014

100

Cyprus

2014

71

Czech Republic

2014

100

Denmark

2015

100

Ecuador

2010

88

Egypt

2012

100

El Salvador

2012

100

Estonia

2015

100

Fiji

2008

91

Finland

2015

98

France

2015

100

Georgia

2015

100

Germany

2015

100

Greece

2015

100

Grenada

2000

96

Guatemala

2014

88

Hong Kong

2014

81

Hungary

2015

100

Iceland

2015

97

India

2015

92

Iran

2013

100

Ireland

2015

91

Israel

2015

100

Italy

2015

100

Jamaica

2006

84

Japan

2015

99

Jordan

2015

93

Kazakhstan

2013

100

Kuwait

2014

80

Kyrgyzstan

2015

94

Latvia

2015

100

Libya

2002

90

Lithuania

2015

100

Luxembourg

2015

96

Macao

2015

89

Macedonia

2015

97

Malaysia

2015

100

Maldives

2014

91

Mali

1987

95

Malta

2015

100

Mauritius

2015

95

Mexico

2015

87

Moldova

2014

89

Mongolia

2015

100

Montenegro

2009

100

Morocco

2001

89

Netherlands

2015

98

New Zealand

2015

100

Norway

2015

100

Oman

2015

85

Panama

2014

100

Peru

2015

85

Philippines

2015

74

Poland

2015

95

Portugal

2015

100

Puerto Rico

2015

73

Qatar

2013

93

Romania

2015

100

Russia

2014

99

Saint Vincent and the Grenadines

2014

100

Samoa

1993

37

Serbia

2015

73

Seychelles

2015

97

Singapore

2015

83

Slovakia

2015

99

Slovenia

2015

92

South Africa

2015

83

South Korea

2014

93

Spain

2015

100

Sri Lanka

2010

100

Suriname

2014

100

Sweden

2015

97

Switzerland

2015

100

Taiwan

2014

100

Tajikistan

2003

49

Thailand

1997

95

Tonga

2003

97

Trinidad and Tobago

2009

89

Tunisia

2011

100

Turkey

2015

100

Turkmenistan

1989

96

Ukraine

2015

85

United Kingdom

2015

96

United States

2015

100

Uruguay

2014

100

Uzbekistan

2015

100

Venezuela

2015

100

Among the indicators of data quality, most country-years reported births by maternal age and the newborn’s sex (95.3 and 73.4% of country-years respectively), when data were available. Fewer countries reported births by live birth order and birthweight, with 55.6 and 51.1%, respectively, of country years containing these indicators. Among countries which did report each indicator, some missing values were observed as well. The indicator with the highest proportion missing was birth weight, with 2.6% of births with unknown birth weight. Maternal age and live birth order had fewer missing values: 1.0% each. Births without a recorded sex were very rare, occurring in only 0.05% of cases. Additional file 2 displays the level of each indicator over time by country.

Combining completeness, quality, and timeliness, Fig. 2 displays the VSPI-B scores for each country for their most recent year with available data. 26 countries had VSPI-B scores in the highest category, between 0.9 and 1. These countries include many high- and middle-income countries with high completeness, and are generally countries which report births by all four data quality indicators. 17 countries had VSPI-B scores in the range 0.8–0.9. These countries also typically included mostly high- and middle-income countries, and were characterized by high completeness but sporadic reporting of the four data quality indicators. 38 countries were in the range 0.6–0.8. Spanning high-, middle-, and lower-middle income countries, these countries’ VSPI-B scores were driven by a mixture of lower completeness and lack of reporting of one or more data quality indicator. 19 countries had VSPI-B scores in the 0.3–0.6 range, characterized by either lower completeness, erratic availability of data, and/or lack of reporting on multiple data quality indicators (i.e., only reporting births by mother’s age or newborn’s sex, but not the others). Finally, nine countries had VSPI-B scores that were lower than 0.3. These countries typically had only few years with available data, low completeness, and/or lack of reporting of multiple indicators of data quality. Additional file 2 displays the time series of VSPI-B scores for each country.
Fig. 2

Vital statistics performance index (most recent year with available data)

The results from the simulation indicate that, all else being equal, the completeness indicator has the highest weight in the VSPI-B. This is evidenced by Additional file 3, which displays the simulated accuracy associated with each indicator at varying levels among simulated samples. At high levels, all five indicators have generally similar accuracy (i.e., similar influence on the VSPI-B score), but at lower levels the indicators have quite different values. This is the result of different empirical simulation probabilities to inform the weighted samples.

Discussion

This paper presents, for the first time to our knowledge, a systematic assessment of the availability and quality of data reported by birth registration systems worldwide. As Mikkelsen et al. 2015 [16] argue, vital registration data quality can be assumed to be an accurate reflection of the performance of the registration system itself. In assessing birth registration data quality, we demonstrate each country’s progress toward strengthening birth registration through an adaptation of the Vital Statistics Performance Index. We also present estimates of the country-level completeness of birth registration based on available data. This assessment is based on the largest database of its kind, containing records of over one billion births since 1980 by country, year, maternal age, sex, birthweight, and live birth order.

Although over 100 countries had at least one data point, the availability of data remains low in many parts of the world. Our assessment of birth registration availability and completeness, where available, demonstrates that sharing of birth registration data is surprisingly low compared with death registration, although it does appear to be increasing. Only around 33% of births worldwide in 2015 were registered with the aggregate records made publicly available. This (and even the 2011 peak of 40% availability) is considerably less than for deaths, where 55–60% of global deaths are now registered in publicly available data systems. Further, we found considerable variance between birth reporting systems in that some countries report births by maternal age, sex, birthweight, and live birth order, while others exclude some or all of this information.

This description of birth registration completeness in country-years where it is possible to assess is in stark contrast to other assessments, particularly UNICEF’s State of the World’s Children report [17]. In their most recent such report, completeness estimates are much higher than those presented here, even in country-years with data available to assess. For example, the available data from the Philippines in 2015 represent only 74% of the estimated births according to our assessment, but the UNICEF report estimates 90% completeness. Notable examples of large discrepancies in other parts of the world include Peru (85% completeness according to 2015 data, as compared with 98% according to UNICEF), and Serbia (73% as compared to 99%). Other countries, such as South Africa, are closer, but still different (83% as compared to 85%). The reasons for the discrepancies are likely twofold. First, the alternative estimates of global birth registration completeness are based on self-report in surveys, not actual records of birth certificates. Issues of recall bias, survival bias, and survey instruments that do not confirm the actual existence of the birth certificates, are likely to lead to over-estimates of completeness via this method. Second, the estimates of completeness we present reflect the accuracy of the estimated denominator data as much as they do the completeness of systems. As already noted, the model estimates of total births include uncertainty intervals within which the total births may fall. While it would have been ideal to propagate that uncertainty into our estimates of completeness, uncertainty estimates were not available to do so at the time of analysis.

Altogether, these findings imply that approximately 74 million births (53.1% of annual global births) per year occur in countries whose systems do not systematically register them and release the records publicly. Conversely, only about 12 million births per year (8.6%) occur in countries with high-performing registration systems, i.e., those with consistent data availability, high completeness and reporting by maternal age, sex, birthweight and live birth order.

The assessment of birth registration is not without limitations however. Primarily, these numbers are based on available data only. This caution is especially salient in that it renders estimates of global registration completeness impossible, as noted above. There are likely more births registered per year that do not get aggregated and reported in order for us to assess them. As such, these availability numbers should be considered as the minimum completeness, and are most useful in countries where data are public. Evidence from China, for example, suggests that about 10 million births per year are registered in the country, which would increase global birth registration completeness to close to 50% were they to be made available for analyses such as that reported here. The assessment of completeness where data are available may also be limited by the assumption that all registered births are reported when a public release is made. The assessment of data quality is also limited to the data that are available. Many countries may have low VSPI-B scores not because their registration systems are functioning poorly, but because the data aren’t released. That includes missing years, but also failure to report certain variables. For example, it is rare to fail to record the sex of a child on their birth certificates, but many countries have not made such information publicly available. Without further data to inform our assessment, it is impossible to distinguish the reasons for lack of reporting. Additionally, some of the details of the VSPI-B simulations are subject to limitations. Principal among them is the fact that the simulations and estimates of disaggregated birth counts are based, in part, on Demographic and Health Surveys and the Understanding Society Survey. With more data, these estimates may have been more accurate. Finally, it could be argued there are other means of measuring the quality of birth registration data; for example, the percentage of births that are registered late or with unspecified type of site of occurrence (e.g., hospital, home etc.). However, given the largest available source of data, the UN database, did not collect these data, we were not able to include them in our analysis.

Conclusion

Our findings have a number of important implications and uses. First, we highlight the gaps in available data. While national policymakers may have unpublished data at their disposal, international and multinational health and development organizations are often reliant on public information of registered births, which we demonstrate are unavailable in many country-years. These findings underscore the significance of open data practices for public policy.

Second, we present an objective and low-cost approach to assess the performance of birth registration systems, wherever data are available. This can be helpful to monitor country progress and benchmark efforts to improve birth registration against national and international goals, especially in an era with significant multilateral, bilateral, and philanthropic investments in strengthening CRVS systems [23]. In addition, we present a set of metrics for completeness and overall system performance that is consistent between countries and over time. As such, these results may be useful for international goal-setting.

An important outcome of this work should be to highlight both the importance of birth registration as a source of fertility statistics and the limitations of the available data. National and subnational governments require routine and timely birth registration data for a range of purposes, not least of which is to lessen reliance on costly sample surveys such as the Demographic and Health Surveys that produce fertility statistics with considerable uncertainty in small areas and which can be 2–5 years out of date once available. More generally, the data generated by civil registration systems are of paramount importance to global health and development efforts, as well as for critical epidemiologic and demographic research [10, 16, 29]. Some authors have even argued that the heightened ability to design and implement effective health policy afforded by greater civil registration has led to a measurable relationship with population health outcomes [30, 31]. This will hopefully encourage stakeholders to collect, consolidate, use, and release more data, release data more promptly, and ensure they maintain a centralized, standardized system for aggregating birth registration data. Considering that birth registration is seen as a fundamental human right, and given the enormous policy relevance of timely, accurate, and complete information on fertility patterns, our findings should be taken as an urgent call for immediate, coordinated, and sustained support to countries to strengthen birth as well as death registration systems, and for greater global efforts to register births and incorporate the minimum demographic and health indicators associated with each of them.

Footnotes

  1. 1.
  2. 2.

    The analysis is restricted to live births.

Notes

Acknowledgements

The authors acknowledge the contribution of Rebecca Kippen of Monash University to initial discussions on the scope of the VSPI-B. The authors wish to thank Juan Cortez and Fatima Marinho from Ministry of Health Brazil for making available data from Brazil and Peru, and Caitlin Rawding of University of Melbourne for assisting with data collection and management.

Funding

This study was funded under an award from Bloomberg Philanthropies to the University of Melbourne to support the Data for Health Initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials

The data used in this analysis can be downloaded from the following URL: http://ghdx.healthdata.org/record/global-birth-registration-database-1948-2015

Authors’ contributions

All authors contributed equally to the conception of the study and drafting of the manuscript. DEP carried out the data analysis, TA carried out data collection and management. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The author declares that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

12963_2018_180_MOESM1_ESM.docx (16 kb)
Additional file 1: Further Statistical Details: Disaggregating Estimated Birth Counts by Sex, Birthweight and Live Birth Order. A two-page document describing the methods applied to estimate birth counts disaggregated by sex, birthweight and live birth order. (DOCX 16 kb)
12963_2018_180_MOESM2_ESM.pdf (600 kb)
Additional file 2: VSPI-B Estimates and their Component Indicators by Country. A figure for every country with available data, displaying the observed data, final VSPI-B estimate, and sub-plots for each of the five components of the VSPI. (PDF 600 kb)
12963_2018_180_MOESM3_ESM.pdf (8 kb)
Additional file 3: Simulated Age-Sex-Parity-Birthweight Fraction Accuracy Associated with Each Indicator. A figure displaying the results of the simulation procedure. The lines demonstrate the accuracy of simulated data in terms of the fraction of births in each birth group, as compared to the underlying population. Each line represents a different component of the VSPI-B at different simulated levels of that component. (PDF 7 kb)

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© The Author(s). 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Institute for Health Metrics and EvaluationUniversity of WashingtonSeattleUSA
  2. 2.Melbourne School of Population and Global HealthUniversity of MelbourneMelbourneAustralia

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