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Archivos argentinos de pediatría

versión impresa ISSN 0325-0075versión On-line ISSN 1668-3501

Arch. argent. pediatr. vol.116 no.4 Buenos Aires ago. 2018 


Evolution of fetal mortality in the setting of Argentine socioeconomic inequalities. Period 2007-2014


Karina V. Loiacono, Magistera

a. Universidad Nacional de Tres de Febrero, Department of Methodology, Statistics, and Mathematics, Caseros, Province of Buenos Aires, Argentina. National Ministry of Health of Argentina, Health Statistics and Information Department (Dirección de Estadísticas e Información de Salud, DEIS), Autonomous City of Buenos Aires, Argentina.

E-mail address: Karina V. Loiacono, Magister:

Funding: None.

Conflict of interest: None.

Received: 7-29-2017
Accepted: 1-23-2018



Introduction. Few studies have established a relationship between adverse social conditions by geographic area (GA) and fetal mortality (FM).
Objective. T o assess health inequalities in relation to FM by GA.
Population and methods. Descriptive, ecological study. The principal components of 525 GAs were analyzed. A socioeconomic status indicator and indices of inequality were developed and estimated, and the FM ratio was calculated.
Results. The Kunst and Mackenbach relative index of inequality ranged from 1.8 to 1.4 in the 2007/2008 and 2013/2014 biennia, and a higher FM ratio was estimated for the highly unfavorable socioeconomic level stratum.
Conclusion. The FM ratio is higher in this stratum. The gap between the ends of the socioeconomic spectrum narrowed towards the latest biennium. Regardless of this, in the GAs with a very unfavorable socioeconomic status, the FM ratio reduced in the latest biennium and increased in those with a very favorable socioeconomic status.

Key words: Fetal mortality; Social inequality; Epidemiological factors.


CI: confidence interval.
DEIS: Health Statistics and Information Department of the Ministry of Health of Argentina.
EPM: extended perinatal mortality.
FM: fetal mortality.
GA: geographic area.
INDEC: National Statistics and Censuses Institute of Argentina.
LBs: live births.
Ln: natural logarithm.
PCA: principal component analysis.
PR: Poisson regression.
Q1: quintile 1; very unfavorable socioeconomic status.
Q2: quintile 2; unfavorable socioeconomic status.
Q3: quintile 3; regular socioeconomic status.
Q4: quintile 4; favorable socioeconomic status.
Q5: quintile 5; very favorable socioeconomic status.
RII: relative index of inequality.
RIIkm: Kunst and Mackenbach relative index of inequality.
RR: relative risk.
SESI: socioeconomic status indicator.
SII: slope index of inequality.
UBNs: unmet basic needs.



Each year, there are more than 2.6 million stillbirths globally in middle-and low-income countries, and three-fourths of the stillbirths occur in south Asia and sub-Saharan Africa.1

In 2014, the World Health Assembly approved a goal of a reduction to 12 or fewer stillbirths per 1000 births by 2030. By 2015, 94 countries had reached this goal,1 including Argentina, where the fetal mortality (FM) rate by weight was 4.8 per 1000 live births (LBs).2

There is no knowledge of national measurements that report on the under-recording of FM, because, although the statistical operational definition by the World Health Organization (WHO) does not establish weight, age or the time of death for a statistical and legal recording, the Health Statistics and Information Department (Dirección de Estadística e Información en Salud, DEIS) provides information that shows that most jurisdictions record deaths occurred as of 22 weeks of gestation.

Health inequality is understood as the unfair disparities of health status among individuals from different social groups that may be associated with various disadvantages, such as poverty, discrimination, and lack of access to goods or services. Health inequalities may be measured and assessed.3 Such measurement is essential for improving a region's health status.4

According to the World Bank, for the 20072013 fiscal period, Argentina was classified as an upper-middle-income economy; for 2014, as a high-income economy; and for 2015, again as an upper-middle-income economy.5

Figure 1 shows the evolution of the overall FM rate in Argentina. Between 2007 and 2010, it decreased and reached 7.6 stillbirths per 1000 LBs, then it started increasing and reached 8.3 per 1000 LBs by 2014.

Figure 1. Evolution of fetal mortality rate (per 1000 live births and stillbirths). 2007-2014 period. Argentina

The objective of this study is to analyze FM behavior in the different geographic areas (GAs) of Argentina as characterized by their socioeconomic status for the 2007-2014 period.


This was a descriptive, ecological study. Analyzed GAs or units corresponded to the 525 administrative subdivisions of the 24 political divisions of Argentina. These GAs were the basis for the development of a socioeconomic status indicator (SESI), which allowed to classify them according to their similar conditions. Using these new groupings, the set of observations related to FM was analyzed biennially for the 2007-2014 period.

According to the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10),6 statistical data should include all fetuses and newborns who weigh at least 500 grams, either live births or stillbirths. If weight is not recorded, deaths occurring as of 22 weeks of gestation should be recorded. When data are available, it is recommended to estimate the overall FM rate and the FM ratio using information systems. Both values were included in this study. The DEIS and the National Statistics and Censuses Institute of Argentina (Instituto Nacional de Estadísticas y Censos de la República Argentina, INDEC) were the secondary sources of data. Databases provided by the DEIS corresponded to statistical reports on stillbirths and live births for the 20072014 period. The variables and sociodemographic indicators corresponded to the 2010 National Population, Households and Housing Census and were retrieved from the INDEC website. The FM rate was defined as the ratio between total fetal deaths and the sum of the births (live births and stillbirths) per 1000; whereas the FM ratio was the ratio between total fetal deaths and the total live births per 1000 (criteria adopted by the DEIS).


Principal component analysis (PCA). This technique is used to reduce the dimensionality of a data set; it converts a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called "principal components." The PCA7-9 was performed on the variables detailed below in the 525 GAs to determine as much homogeneous areas as possible from a structural perspective. When deciding which variables should be used in indirect measurements of socioeconomic status, the suggestion is to use those that summarize household wealth based on living conditions, access to utilities, level of education, and employment status.3

The variables used to develop the SESI were obtained from the 2010 National Population, Households and Housing Census as percentage of the population who lived in an urban area, had no access to running water or sewer system, had gas connection, met any indicator of unmet basic needs (UBNs), completed only primary education, owned a house, and were employed.


The relationship between health and socioeconomic status is an important topic that calls for the measurement of such inequality.3 Socioeconomic status categories were classified in a scale from the lowest to the highest. Each category had a proportional value (or relative weight) according to the size of the variable "number of live births per socioeconomic inequality stratum;" an x value that was divided by 2, thus obtaining a mean. Then, for another variable, the accumulated relative weight was estimated and, lastly, the population relative weight was obtained, which consisted in the accumulation of the relative weight plus the relative weight of the previous column, as shown in Table 1 for the 2007-2008 biennium. A Poisson regression (PR) analysis was performed here as in different studies.10-11 The slope index of inequality (SII) (represented by alpha = regression slope coefficient) and the Kunst and Mackenbach relative index of inequality (RIIKM) (defined as the ratio between the alpha regression slope coefficient and the same alpha coefficient plus the beta coefficient from the population relative weight variable) were estimated. Some authors refer to these as relative indices of inequality.12-16 Both were estimated at an alpha significance level= 0.05, together with their 95% confidence intervals (CIs).

Table 1. Population relative weight estimated based on the number of live births and the socioeconomic status indicator. 20072008 biennium. Argentina

In the PR analysis, the variable "population relative position" was used as an independent variable; the number of fetal deaths, as a dependent variable; and the number of live births, as an independent adjustment variable for the model (also called offset variable), and although it was not represented by any coefficient included in the model, it was used to model the FM ratio when time periods differed. The Poisson model equation was Ln (number of fetal deaths) = Ln (number of live births) + alpha + beta (population relative position) + ei (random error). The number of recorded fetal deaths and live births was estimated for the department where the mother lived. Collected data were processed using Microsoft Office; the PR model was developed using the GLM.1 package from the R. software, version 3.3.1; and the PCA was done using the IBM software SPSS 17.0. The map of Argentina was prepared using the gvSIG software, version 2.1.0. The studied variables from the 2010 National Population, Households and Housing Census conducted by the INDEC were processed using the Redatam SP statistical software package.


Once the PCA was completed, homogeneous groups made up of 105 GAs were established; each represented quintiles 1 to 5 (Q1-Q5). These groups corresponded to the SESI strata defined as Q1: very unfavorable socioeconomic status; Q2: unfavorable socioeconomic status; Q3: regular socioeconomic status; Q4: favorable socioeconomic status; Q5: very favorable socioeconomic status.

Table 2 shows the median percentages for the different SESI strata based on the studied variables after the PCA was performed. For Q1, population living in an urban area: 53%; households with no running water: 39.3%; households without sewer system: 100%; households with gas connection: 0.0%; households with any UBN: 21.6%; population who completed only primary education: 52.9%; population who owned a house: 69.6%; and employment rate: 47.7%.

Table 2. Median values corresponding to the variables selected to develop the socioeconomic status indicator by quintiles

Figure 2 shows the representation of the 525 GAs of Argentina into SESI quintiles (the darkest shade represents Q1). The frequency of GAs in Q1 may be observed in a descending order. In the province of Santiago del Estero, 22 GAs were classified as having a very unfavorable socioeconomic status, in the province of Chaco, 15; in the province of Misiones, 10; for the rest of the provinces, GAs are shown in the table accompanying the figure.

Figure 2. Representation of geographic areas by quintiles of the socioeconomic status indicator and frequency distribution table by province departments with a very unfavorable socioeconomic status. Argentina. 2010

Table 3 shows the SII and RIIKM, which decrease towards the 2013/2014 biennium. The SII for the 2007/2008 biennium was -0.617 (95% CI: -0.682:-0.551), and the RIIKM was 1.853 (95% CI: 1.207:2.499). Towards the end of the series, in the 2013/2014 biennium, the SII was -0.345 (95% CI: -0.409:-0.281), and the RIIKM was 1.412 (95% CI: 1.306:1.519). Both indices showed a significant alpha value = 0.05 for the entire series. The gap between Q1 and Q5 in terms of health inequality in relation to FM, comparing the 2007/2008 biennium to the 2013/2014 biennium, narrowed by 44.1%, as estimated using the SII. The negative sign in the SII means that the higher FM ratio values are observed in the most vulnerable group (Q1). Another interpretation may be done based on the RIIKM: for each unit of increment of the RIIkm, the FM ratio increased from 1.853 to 1.412.

Table 3. Poisson regression coefficients and 95% confidence intervals for the slope index of inequality and the Kunst and Mackenbach relative index of inequality. Argentina. 2007-2014 period

Figure 3 shows that, in all socioeconomic strata (Q1-Q5), the FM ratio tended to decrease towards the 2009/2010 biennium and then started to increase slightly towards the 2013/2014 biennium. The FM ratio estimated based on the very unfavorable socioeconomic status Q1 compared to the very favorable socioeconomic status Q5 shows that, in the beginning of the series, the gap was wide (it accounted for 57.67%) and for the subsequent biennia, it accounted for 41.32%, 36.55%, and 29.21%, always showing a greater inequality in Q1. The gap between the ends of the socioeconomic spectrum narrowed towards the latest biennium.

Figure 3. Fetal mortality ratio estimated according to the socioeconomic status indicator. Argentina. 2007-2014 period

The FM ratio estimated for the 2007/2008 biennium per 1000 LBs was 10.75 for Q1 and 6.82 for Q5; and for the 2013/2014 biennium, it was 9.52 for Q1 and 7.37 for Q5.

The FM ratio is higher in the GAs with a very unfavorable socioeconomic status across Argentina. Regardless of this, in the GAs with a very unfavorable socioeconomic condition, the FM ratio reduced in the latest biennium and increased in those with a very favorable socioeconomic status.


This study was useful to observe and measure the existing health inequality. The set of the more unfavorable GAs of Argentina -Q1- had the highest FM ratio, and differences were observed in the different GAs described based on the SESI. These conditions were highly heterogeneous: more unfavorable areas accounting for inequality were in the north and central regions of Argentina; and the more favorable GAs were in the south of the country, with some exceptions. It is worth noting that, although the SII reduced by 44.1% when comparing the first and the latest biennia, such reduction may not have been completely accompanied by the reduction in the FM ratio, given that, as in many biological events, multiple variables were involved that were not included in this study.

Outside of the developed world, the lowest FM rates were observed in Eurasia, South-East Asia, and Latin America and the Caribbean (12, 13, and 13 per 1000 LBs, respectively). India has the highest FM rate worldwide, which ranges from 20 to 66 per 1000 LBs.17 In a simple comparison with the United States of America, the FM rate was 6.05 per 1000 LBs in 2006 and increased slightly by 2007, for which only deaths occurred as of 20 weeks of gestation were taken into consideration.18 In Argentina, the overall FM rate was 8.3 per 1000 LBs in 2007, and at present it meets the WHO goal for 2030.

Among high-income countries, 90% of deaths take place in the antenatal period, in association with different risk factors.19 Some authors have conducted research to prevent these.20-21

Few studies analyzed the overall FM by GA and socioeconomic status. Pearson et al. described the ethnic and socioeconomic conditions of fetal and neonatal mortality in a region of London and found that the rate of stillbirths and neonatal mortality among parents from unfavorable social situations was twice as much as that among parents from a favorable social condition (16.8 versus 8.6 per 1000 LBs), so they concluded that the working class population had needs that were not addressed by the government.22 These problems may also be present in certain GAs of Argentina. The FM ratio was Q1= 10.75 and Q5= 6.82 for the 2007/2008 biennium and Q1= 9.52 and Q5 = 7.37 for the 2013/2014 biennium (both per 1000 LBs). The relative differences between quintile estimations were high, 57.67% and 29.24%, respectively, indicating the measurement of health inequality observed in this study. In addition, the FM ratio was highest for Q1 over the entire study period.

A study carried out in Sweden several years ago showed that a low socioeconomic level was associated with a higher risk for FM.23 Another study conducted in Spain concluded that the FM rate ranged from 1.0 to 4.7 per 1000 LBs and that the risk for FM among mothers who had completed, at most, secondary education was double that among mothers who had completed tertiary education, relative risk (RR) = 2.13 (95% CI: 1.74-2.60).24

In England, the socioeconomic inequality in relation to the overall FM rate and the cause of death specific rate was assessed using a deprivation index and showed that the FM rates were twice as high in the areas with a higher deprivation, RR = 2.1 (95% CI: 2.0-2.2).25

This study described the persistent inequality in FM ratio among the SESI groups and found similar results, although different methodologies were used.

The recommendation for the future is to implement a probability sample at a national level of pregnant women living in the 105 most vulnerable GAs and to study the situation of access to the public health system and the risk factors associated with pregnancy or whether there are other conditions that have hindered the narrowing of the present gap in the FM ratio observed in Argentina over the past years.

It is worth noting that the ecological nature of this study prevented us from establishing the reasons in more depth.26,27 Different actions are required, including the one mentioned above, to reduce stillbirths.28

The analyzed death records are of compulsory registration. These records allow the continuity of national statistical series and provide information developed in accordance with internationally comparable conventions.29 Further research on the under-recording of FM in Argentina is required. In other countries, some authors30-34have proposed how to carry it out.

One of the limitations of this study was that only the overall FM ratio was estimated. In a follow-up of this study, the following rates should probably be estimated: FM by weight, FM by gestational age and/or perinatal mortality.


The FM ratio is higher in the GAs with a very unfavorable socioeconomic status across Argentina. The gap between the ends of the socioeconomic spectrum narrowed towards the latest biennium. Regardless of this, the comparison between the 2007/2008 and the 2013/2014 biennia shows that, in the GAs with a very unfavorable socioeconomic status, the FM ratio reduced in the latest biennium and increased in those with a very favorable socioeconomic status.


I would like to thank Carlos G. Guevel, B.S., Boris Groisman, M.D., Irene Kliewer, Magister, and Norberto V. Rodríguez, B.S. for their huge and invaluable support and for their generosity in giving their time and expertise. And also to Leonardo A. Santamaría and Mathieu A. Santamaría Loiacono for their wholehearted collaboration.


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