Corresponding author: Alexandra A. Moskaleva (
Academic editor:
Assisted reproductive technologies (
Two key concepts used in this paper are infertility and assisted reproductive technologies (
According to survey estimates, the level of primary infertility in the Russian Federation in 1990 was 2.2%, in 2010 – 2.9%, and level of secondary infertility – 18.2 and 19.2% respectively (
The importance of IVF as a tool of public policy in the field of demography is marked by the inclusion of the procedure in the passport of the national project
The inclusion of IVF in the government guarantees programme should increase the availability of the technology to all groups of the population and change the set of factors affecting the use of the procedure, reducing the role of economic ones. Increased access to
Publications on the topic of this research are scarce both in domestic and foreign literature.
The first group of factors for the use of
The second group of factors for the use of
The third group of factors for the use of
The fourth group of factors in the use of
Several publications highlight the impact of reproductive technology policy, its effectiveness and benefits to the State. Many existing studies are based on the US data, as measures to regulate the application of the technology differ across the states and were introduced at different times. In the US, health insurance mandates – legislatively enshrined requirements for the employer or individual to acquire health insurance – are arranged differently. This creates an opportunity to use the difference-in-difference method and compare the effectiveness of various regulations. In the work of Marianne P. Bitler and Lucie Schmidt (Bitler, Schmidt 2012), the comparative effectiveness of different formats of health insurance for IVF use is evaluated on the basis of survey data. Basing on the logit model, the authors find that the introduction of insurance mandates did not affect inequalities in access by race and socioeconomic status.
An important question in research on the US is whether the state policy affects treatment programme choices. The choice is compared between a more “aggressive” but more effective treatment option, where more than two embryos are transplanted during an IVF procedure, and a safer one when only one embryo is transplanted. The impact of the health insurance options is analyzed using the least squares method (OLS) with two-way fixed effects at clinic and market levels (Hamilton, McManus 2011). Insurance mandates increase the availability of the technology and can reduce the number of multiple births, but their effectiveness varies widely depending on the type of insurance. In the study by Kasey S. Buckles (
A number of studies consider
Based on the simulation model of population reproduction, built using the Monte Carlo method on historical data for France, Henri Leridon (
Studies show the presence of a significant positive relationship between the number of
Basing on the literature review results, the author of this study systemized factors affecting the use of
Three-level scheme of influence of factors on the decision on the use of
At the first level, the patient’s medical diagnosis is determined: under the influence of environmental factors and previous reproductive behaviour, the reproductive health status is defined, and it determines the occurrence of the infertility diagnosis.
The awareness of the need for using
The third level is the accessibility level. Factors of the third level have an impact on the ability to take advantage of the technology if a woman has already decided that she would like to use
We are particularly interested in the impact of economic and social factors on the use of
Assessing infertility levels is a difficult task. Apart from differences in the definition of the concept itself, there is a problem with data quality. Estimates gathered from the population surveys are underestimated because the topic of infertility is difficult for respondents (Thonneau, Spira 1990;
Since 1995 the Russian Association for Human Reproduction (RAHR) has issued reports on the use and development of
Number of children born using
Note that after 2013 there was a small spike in the number of births using
Figure
Distribution of
Analysis of the structure of
State support for IVF in Russia began with the inclusion of this procedure into the programme of state guarantees with funding from the federal budget and regional budgets in 2013. In fact, this change took its effect in 2014, which is reflected by the frequent mention of this year in news reports. The inclusion of IVF in the programme of state guarantees is due to the fact that, firstly, since 2014 the
Since 2016 IVF is included in the basic
This study examined the change in IVF availability after incorporating it into the state guarantee Programme in 2014. It is not currently possible to analyze the change in the availability of IVF after its inclusion in the basic
This study uses panel data gathered by regions or clinics, and individual medical history data. Depending on the available data, in different cases the author exploits three analytical approaches, i.e. difference-in-differences method, a panel model with fixed effects, and a binary choice model. In Russia, the necessary individual data are not available to researchers for several reasons. Firstly, personal medical information is highly confidential. Secondly, in Russian longitudinal surveys, such as the
The study combines data from the RAHR register, Federal Research Institute for Health Organization and Informatics of Ministry of Health of the Russian Federation, and the Federal State Statistic Service (Rosstat). Data on the regional distribution of the number of births using
The control variables are divided into four following groups: 1) infertility incidence; 2) demographics; 3) health care system characteristics; 4) characteristics of the
The first group of variables includes the rate of infertility for a specific year per 10 million population of the corresponding sex in reproductive age. Demographics include the overall marriage rate, the proportion of women in late reproductive ages, i.e. 35 to 49 years old (Hamilton, McManus 2012), as a measure of the proportion of the population potentially having a need for the use of
To characterize the health care system the author uses two classic indicators of the system’s resource provision: number of gynecological beds (i.e. places in specialized hospitals) and number of gynecologists per 10,000 female population. Note that the indicator of provision of gynecologists includes the provision of medical reproductologists who, according to the nomenclature of medical specialties, are part of the group of obstetricians and gynecologists. The indicator of women’s attitude to their health is the proportion of women registered with a doctor at the early stages of pregnancy. Also, as an alternative variable for the development of the health care system, the Index of the mother and child health care system development (HSE), is included in the analysis.
The characteristics of the
The links between groups of factors, their meaning and corresponding variables are given in Table
Description of control variables
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Reproductive health | Prevalence of infertility | Male infertility, female infertility (Ministry of Health) | - |
Demographics | Need for the use of |
Proportion of women over 35 in the total number of women of reproductive age (calculation) | (Bitler, Schmidt 2012; Hamilton, McManus 2012; |
Total marriage rate (Rosstat) | ( |
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Health Care System Characteristics | Development of the service delivery system | Provision of gynecologists (Ministry of Health) | ( |
Provision of gynecological beds (Ministry of Health) | |||
Attitude to health | Proportion of women registered before the 12th week of pregnancy (Ministry of Health) | ||
Quality of functioning | Index of development of the mother and child health care system (HSE) | ||
Available treatment options | Share of state clinics, share of specialized clinics (calculation) | - |
In addition to these factors, the analysis includes a variable characterizing economic recession to take into account the impact of the 2014-2015 economic crisis. The crisis affects the decision on the birth of a child itself, to say nothing of the decision that requires additional costs, such as in the IVF procedure. The cycle phase indicator is calculated on the basis of GRP growth: at a value of less than zero the variable equals 1, and at a value over zero the variable equals 0.
The variables of interest are divided into three groups: the indicator of inclusion of IVF into
The inclusion of IVF in the state insurance programme is modelled by creating a fictitious variable equal to 1 after IVF is included in the
Description of variables of interest
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State policy | Policy impact | Dummy variable equal to 1 for 2014−2017 | - |
Covariates of a fictitious variable with economic factors | - | ||
Economic | Access to paid services | Real average per capita income (calculation) | ( |
Inequality in access | Gini Index, measuring inequality of income (Rosstat) | (Hamilton, McManus 2012) | |
Social | Awareness | Internet access (Rosstat) | ( |
Access to services | Degree of urbanization (Rosstat)/ Percentage of population in cities with |
(Hamilton, McManus 2012) | |
Attitude to use of the technology | Share of employed with higher education in the total number of employed, % (Rosstat) | (Bitler, Schmidt 2012; Hamilton, McManus 2012; |
Economic factors include the level of real income of the population (income in the prices of the base year, 2010, given the consumer price index), and income inequality based on the Gini Index. The group of social factors includes public awareness measured through the indicator of access to the Internet, territorial accessibility measured through the degree of urbanization, or, more precisely, through the proportion of the population in the cities where
The analysis does not include the characteristics of the religious composition of the population, as when using models with fixed effects based on panel data, it is not possible to identify the influence of variables that do not vary greatly in time. The influence of religion will be factored into the fixed effects of the regions.
The IVF procedure is arranged so that more than one embryo is often transplanted to the patient to increase the likelihood of pregnancy. Therefore, the author supposes that the distribution of the use of the technology in regions strongly correlates with the distribution of multiple births, which we know from the Federal State Statistics Service data.
Before verifying this assumption, let’s look at the dynamics of multiple births in Russia (Figure
The relevance of this assumption might also be verified based on the US data, which are more complete. The aggregated data on the number of live births using
Dynamics of the multiple births proportion in the total number of births in Russia in 1985-2018, %.
Correlation between the number of children born using
Having confirmed our assumption, we can construct a dependent variable – the proportion of children born using
where
The following regression equation is proposed to evaluate models:
where
Three methods for evaluating models based on panel data were used for the analysis: a model with fixed effects of time and the region, a model with random effects, and pooled regression. We estimate four following model specifications: model 1 – pooled regression; model 2 – fixed effects of time; model 3 – fixed effects of the region; model 4 – random effects model.
According to the criterion of adjusted R-square(
Table
Panel model evaluation results, dependent variable: proportion of children born with
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Inclusion in |
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− |
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Inclusion in |
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Inclusion in |
−2,49 | −1,74 | 0.38 | −0,53 |
(1,9) | (2,22) | (2,03) | (1,96) | |
ln (real average per capita income) | ||||
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Income inequality | −1,44 | −1,04 | − |
− |
(1,15) | (1,05) |
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Percentage of population in cities where |
−0,02 | −0,04 | −0,02 | 0.09 |
(0,14) | (0,14) | (0,15) | (0,12) | |
Percentage of employment with higher education |
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Percentage of population with access to the Internet |
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Control variables | + | + | + | + |
Number of observations | 577 | 577 | 577 | 577 |
Adjusted ( |
0.73 | 0.34 | 0.8 | 0.8 |
F-statistics | 81,3*** | 18,2*** | 131,0*** | 2267*** |
The directions of influence for most variables correspond to the expectations of the author. The level of education has a significant positive impact on the proportion of births using
The key result of the analysis is an assessment of the impact of IVF inclusion in the HMI programme. Two of the three coefficients illustrating this impact were proved to be significant. Based on the model with fixed effects of the region, which was chosen when comparing the models, we see that when IVF is included in
∆
where ∆
Then ∆
The findings show that inclusion in the programme had a significant positive effect on the proportion of births using
Here we will consider how the use of alternative variables characterizing the same phenomenon will affect the results of model construction. To verify this relation we estimate six additional models using: 1) data on RAHR clinics instead of data on clinics collected by the author; 2) total incidence rate of infertility instead of the indicator of new diagnoses per year; 3) inclusion of maternal and child health rating as characteristics of the quality of system functioning; 4) and 5) environmental factors instead of infertility indicators, which is due to the influence of types of environmental pollution on male and female factors of infertility identified in medical studies; 6) level of urbanization as a proxy variable for territorial accessibility of services instead of the proportion of the population of cities with
The increase in the indicator of the explanatory strength of the
An analysis of information provided on the websites of regional ministries and health care departments has shown that there was mobility between nearby regions to obtain
To take into account these factors, we will use methods of modelling spatial relationships. To do this, we shall carry out the following steps:
We shall choose a method to form a matrix of spatial weights.
We shall carry out statistical tests for the presence of spatial dependence in the data.
If there is a significant relationship, we shall choose a method for estimating spatial panel regression.
A key element in the analysis of spatial relationships is the matrix of spatial weights. Several classes of methods for creating similar matrices are given in the literature.
The basis of the first group of methods is geographic distances between objects. Distances between the centers of spatial objects, calculated as the distance between points on the plane with latitude and longitude coordinates (for objects in one region/district), as the distance between points on a circle (for countries/regions), as the distance by railway (Vakulenko et al. 2012) or by roads (
For this group of methods, several variations in calculating diagonal elements of the spatial weights matrix (
The second group of methods is the creation of an adjacency matrix, for example, a weighted matrix by the number of neighbours. The adjacency matrix is created in different ways based on the use of graph analysis methods. A variant of the matrix, in which the presence of a boundary is denoted as 1 (Kolomac 2010) is common, after which the weighted matrix is calculated.
The third group of methods is to create a proximity matrix in the characteristic space. Distance matrices in physical space and space of characteristics are combined for analysis.
Two sets of distance data have been used to analyze spatial influence. The first array is data on distances between the centers of the regions, the second is data on distances by railway between the centers of the regions (if there are
To construct a matrix of spatial weights, five options for calculating elements are used:
Inverse matrices for distances on railways and between centers in a straight line are very similar. Matrices with large values of the gamma parameter (1 and 0.5) are very sparse, since they do not give more value to closer regions than farther ones. Therefore, in further analysis, these matrices are not used.
Several tests have been developed to verify spatial dependence (
where
Table
In 2013, there is no significant spatial relationship in the values of the proportion of births with the use of
Moran coefficient values for different types of spatial weights matrices
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2017 | 0,019* | 0,030** | 0,002** | 0,006** |
2016 | 0,070*** | 0,071*** | 0,014*** | 0,017*** |
2015 | 0,044*** | 0,051*** | −0,004 | −0,002* |
2014 | 0,028** | 0,037*** | −0,002* | 0,005* |
2013 | 0.005 | 0.013 | −0,007 | −0,006 |
2012 | 0,023** | 0,036** | 0,007*** | 0,009*** |
2011 | 0.008 | 0,015* | −0,003 | −0,006* |
Models on panel data with spatial effects are evaluated either using the maximum likelihood method or generalized moment method (can only be used to estimate models with spatial interaction in errors). We will apply the maximum likelihood method: it is more versatile with respect to spatial evaluations of models because it can be used to evaluate all types of models.
Spatial effects can be taken into account in three ways: including explanatory variables in the spatial lag model, spatial lag of the dependent variable, and calculation of spatial errors. According to these options, there are four main types of spatial models with fixed effects (
Spatial cross model with fixed effects (Spatial cross- regressive (SLX) model with fixed effects):
where α
Spatial lag model (SAM) with fixed effects (Spatial lag model (SAM) with fixed effects):
where is the coefficient at spatial lag;
Spatial error model (SEM) with fixed effects:
where ρ is the spatial factor;
Spatial Durbin model with fixed effects:
For analysis, we decided to build three specifications of spatial models. The first specification is a model with a spatial lag of the dependent variable: the high rate of births using IVF in the region may be, firstly, a signal of the use of the technology, resulting in a network effect (the more people use the technology, the more new people learn about it) and increased demand from those who had previously been in doubt both within the region and in neighbouring regions; it may be a signal of increased use of the technology in the region and attract “reproductive migration”, thirdly, it may reflect hidden characteristics of quality of service delivery that were not considered by us in the basic model specification. The second specification is a model with a spatial lag of the number of clinics: the more clinics in nearby regions, the higher the proportion of births using technology in the region. The third specification is the Darbin model with the inclusion of both spatial lag of the dependent and explanatory variable.
Note that the evaluation of spatial models in
The results of the evaluation of the proposed specifications are given in Table
Results of the evaluation of spatial models on panel data, dependent variable: proportion of children born using
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Dependent variable spatial lag | |||
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Spatial Lag Number of Clinics | |||
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Inclusion in |
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Inclusion in |
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Inclusion in |
−0,34 | 0.24 | −0,3 |
(1,08) | (1,27) | (1,07) | |
Income inequality | −0,22 | 0.22 | −0,35 |
(0,22) | (0,27) | (0,23) | |
Percentage of population in cities where |
−1,47 | − |
−1 |
(1,56) |
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(1,57) | |
Percentage of employment with higher education | 0.93 | ||
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(0,58) | |
Percentage of population with access to the Internet | 0.11 | ||
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(0,15) | |
Control variables | + | + | + |
Number of observations | 560 | 560 | 560 |
LogLik | 152.45 | 156.9 | |
AIC | −104,89 | −111,8 | |
Adj. R2 | 0.806 |
Most of the variables significant in the basic specification remained significant in spatial models as well: the inclusion of IVF in the
The choice of model for interpretation is complicated by the use of both panel data and assessment using the maximum likelihood method (except for the spatial lag model of the explanatory variable, which is rated as a conventional fixed effects model). For comparing the models, the logarithm of maximum likelihood (the larger it is, the better the model) and information criteria of Akaike or Schwartz (the smaller they are, the better) are used. By these criteria, the spatial distribution is marginally better described by the Darbin model with the inclusion of a spatial lag of both the dependent and explanatory variable of the number of clinics.
We interpret the coefficient of the variable of inclusion of IVF in the
∆
(9)
where ∆
The first and main limitation of the study is the lack of data on the real distribution of the number of
The second limitation of the analysis is an insufficiently long time period. The inclusion of IVF in
The third limitation of the analysis is the lack of research into the factors of the use of
The study found that the inclusion of IVF in
The higher the income level in the region, the greater the positive effect of the inclusion of IVF in
The results are stable regarding the choice of method of evaluating the model and replacing some variables with alternate ones. When environmental variables are included, the quality of the model slightly improves, and in some variations, environmental factors have a significant impact. We assume that environmental indicators better reflect the real incidence of infertility than official statistics.
To further expand the availability of IVF covered by
The second direction of impact is the accessibility of information about the procedure and the possibility of obtaining it via
As a further direction of research, the author is interested in the analysis of territorial programmes of state guarantees of the regions of the Russian Federation for 2013-2014 to identify differences in terms of incorporation and financing of IVF with
National Environmental Rating of the Russian regions.
Register of the Russian Association of Human Reproduction.
Federal Research Institute for Health Organization and Informatics of Ministry of Health of the Russian Federation.
Federal State Statistics Service data, Russia.
Centers for Disease Sontrol and Prevention, USA.
National Vital Statistics Report (CDC), USA.
Alexandra Moskaleva, postgraduate student at Mathematical and Instrumental Methods of Economics programme, Engineer of the Admissions Department, Faculty of Economics of Lomonosov Moscow State University. E-mail:
The model evaluation is carried out in RStudio, the code is available at the following link: https://github.com/SaschMosk/master_research/blob/master/dissertation_modeli_itog.R