Introduction {#sec1-1}
============

Maternal mortality reduction efforts around the world took shape during the 80s, and 90s when two milestone ideas emerged, the *Three Delay Model* (TDM)^[@ref1]^ and the proposal of Essential Obstetric Care (EOC).^[@ref2]^ The Safe Motherhood Initiative^[@ref3]^ launched by World Health Organization (1987) developed a well-documented set of information resources, and a toolkit of intervention alternatives^[@ref4]^ spread worldwide.  In 2000 the millennium goals were stated, maternal mortality goal was to *Reduce by three-quarters, between 1990 and 2015, the maternal mortality ratio*.^[@ref5]^ However, maternal mortality in Ecuador and many other Latin American countries is the only millennium goal on which national agencies are still making strong efforts to reach the 2015 target.^[@ref6]^

The foundation for those ideas was based on the hypothesis that health systems development to reach the capacity to provide essential obstetric care would be the rationale to fight against maternal mortality; a plausible approach at that time in an era where the non-developed world was more uniformly poor, socio-economically speaking, than it is nowadays.  National efforts in many cases achieved important reductions;^[@ref7]^ however, the intensity of the reduction in Ecuador has reached a level where additional decreasing seems to be a completely new challenge.  The trend of maternal mortality ratio per 100,000 live births comparing 1990-2003 with 2004-2013 period shows a clear difference in slope inclination (t=5.03, P=0.00006345), to regain a sustained declining trend will require the use of alternative sources of information to made adjustments in the decision-making processes that have been in place for a long time. 

Maternal mortality also has a clear socioeconomic relationship identified worldwide *The risk of a woman in a developing country dying from a maternal-related cause during her lifetime is about 23 times higher compared to a woman living in a developed country.  Maternal mortality is a health indicator that shows very wide gaps between rich and poor, urban and rural areas, both between countries and within them*.^[@ref8]^

The purpose of the present study is to process the national maternal death databases that collect data for statistical purposes (not for research), to provide some empirical evidence on the relationship between the variables that are registered in the death certificate and annually published officially by the national statistics agency.  The idea is to identify a specific pattern of associations in women's characteristics that help to understand better if the rationale of the proposed health systems development to cope essential obstetric care as a strategy to reduce maternal mortality is still applicable today as it was in former times or some adjustments need to be explored.  To improve the strength of this attempt to build possible variable associations TDM, EOC, and socioeconomic determinants were utilised as a framework.  On the other hand, pregnancy-induced hypertension (PIH) has been the leading maternal mortality cause in Ecuador for a long period, then a comparison of PIH with all the rest of causes grouped was included in the analysis. 

One clear limitation for building a model using data registered by the statistics national system is the fact that a cross-sectional/ecological design is only able to offer a relational level of association where only proxy plausible data bindings can be drawn while a causal model would require a different approach.  It was not feasible for the investigators to pool other databases published by the *Instituto Nacional de Estadísticas y Censos* (INEC) (for example, birth register, health activities, *etc*.)  neither the team had resources to perform spatial analysis using geographical frameworks and tools.  It is an exploratory phase of research that hopefully will encourage institutional efforts to mine the huge amount of data available, which are regularly collected but remain underutilised in most non-developed countries.  This endeavor is nowadays possible because data gathered are accessible for everybody. 

Design and methods {#sec1-2}
==================

The study processed mortality databases published yearly by the INEC.^[@ref9]^ Databases (Excel format) were downloaded from INEC's website in March 2015.  It was exclusively those reporting maternal deaths \[classified under the International List of Causes of Death (ICD) code 0 Chapter X\], corresponding to the 2003 to 2013 period, which was already accessible through INEC's website.  The variables available for this study, in the INEC's maternal death set of databases for the mentioned years, included: i) year of death; ii) place of death and place of residence: Ecuador is organised into three levels of administration; in descendent order: province, canton, and parish; for this study, data were re-coded as canton's main town and countryside.  Also, data are reported by area (urban/rural/peripheral); iii) age: re-coded for the present study in under 18 - over 35 years old, and 18 to 35 years old; iv) marital status: registered as common law partner, married, single, divorced, separated, widow.  For comparisons were reclassified into two categories: No partner (separated, widow, divorced, single), and Partner (married, common-law partner); v) level of education: registered as None, Literacy Center, Elementary, High School, Basic Education, Middle level, Postsecondary cycle, High, Postgraduate, Ignored; vi) literacy: registered as Illiterate and Literate; vii) facility where death occurred: registered as Hospital or home/another place; viii) PIH.  Differentiated between those classified with the ICE code *O10-O16*, and the rest of causes (O00-O99). 

The only variable added to the INEC's deaths database was the presence of Ministry of Health and Social Security hospital facility in the canton of residence.^[@ref10]^

Place of residence and place of death were matched to identify those deaths occurred in the same canton of residence and those occurred in a different canton, considering that hospitals are located predominantly in canton's main town.  It is difficult to associate place of death and the presence of a health facility with availability or access to essential obstetric care by the population.  But on the other hand, any given health system should work by a referral system that, once it is accessed, theoretically could offer the level of care needed.  Because of that, to relate place of death and presence of a health facility is only an attempt to associate *ecologically* two variables but not to infer any causal relationship between them. 

Cultural issues are an important part of maternal mortality dynamics that must be taken into consideration when the topic is investigated.^[@ref11]^ Although, an ethnic identification is not present in the data gathered by the certificate of death and is not included in the study. 

The study was performed from March 2015 till January 2016, including variable definition standardisation to have a comparable study period.  Variables were recoded to a dichotomic format grouping subcategories into two well-differentiated groups, which allow the building of 2×2 contingency tables to facilitate the processing and interpretation of statistical tests.  The aim was to identify statistical associations between outcome variables (to die at home, canton of death, and presence of a hospital in the canton of death) with exposure variables (marital status, residence area, age groups, literacy PIH).  The statistical tool used was a 2×2 contingency tables to calculate odds ratio and Chi-square.  Logistic regression analysis was performed to adjust these bivariable calculations to the complete set of variables.  Also, multidimensional scaling analysis was used to provide evidence on the relation between the whole set of variables.  Comparisons are based on number of deaths and not on maternal mortality ratio because the intention was to identify a model of variable association rather than an epidemiological distribution of risks.  On the other hand, data corresponds to a nationwide registration of deaths system that uses the death certificate as the primary data source. 

Data were processed using *Statistical Package for the Social Sciences* (PUCE license), initial drafts were written down, and several revisions were needed to reach the final version. 

Results {#sec1-3}
=======

Trend slopes for the number of maternal deaths classified under the ICD codes O10-O16, compared with the number of deaths classified under other causes, 2003-2013 (t=3.02 P=0.0026) shows statistically significant difference.  The number of deaths classified as other than PIH had a clear tendency to increase (Slope=7.218, Standard Error=1.978), while PIH deaths had a stable trend slightly increasing (Slope=0.609, Standard Error=0.936).  One explanation provided by national health officers is that since 2007 death register has been improving both logistically and technically. 

A total of 1902 maternal deaths is included in the INEC's databases between 2003 and 2013.  Pregnancy induced hypertension coded O10-O16, stands for 599 cases, the remaining 1303 were classified using other codes of the O series (chapter XV) of the ICD.  In 1046 (73.9%) cases there was a coincidence in the register of the place of residence (parish level) with the place of death.  While 1231 (64.7%) deaths, occurred in the same canton where the parish of residence is located and 1631 (85.8%) deaths happened in the main town of the canton.  Mothers dying lived in 198 cantons out of 277 total Cantons of Ecuador.  Also, 50 out of the 198 cantons were, at least, one maternal death was registered in the period does not have a Ministry of Public Health Hospital.  It does not mean that every canton could be considered as a local health system with basic and comprehensive emergency obstetric and new-born care^[@ref12]^ because the distribution of resources in Ecuador is uneven. 

The distribution of deaths for the period is presented in [Table 1](#table001){ref-type="table"}.  Most of the deaths correspond to women of urban residence.  A wide variation in marital status is observed in the case series.  The level of illiteracy is almost three times higher than the national level (2.8%).^[@ref13]^ Every single educational level register deaths but women with education ranging from elementary to high school are over-represented in the series.  Almost one-third of the cases in 11 years are classified under codes O10-O16.  Women under 18 years old and over 35 years old represent a 38.3% of the cases while the number of births in those age groups in 2013 was 19% for the whole country. 

The percentage of births attended by health professionals in Ecuador was 94.7% in 2013,^[@ref9]^ and it was 90% between 2007-2012, estimated by the Demographic and Health Survey.^[@ref13]^ This source also reports 4.7% of home births attended by traditional birth attendants, 4.2% of home births attended by family members, and 0.7% of home births without any person caring the mother directly.  The Ministry of Public Health is the major provider of health care.  Nationwide, it has 80 *Basic* hospitals (provide short stay hospitalisation including basic obstetric care), 30 *General* hospitals (most of them provide hospitalisation for basic and comprehensive obstetric care), and six specialised obstetric hospitals able to manage comprehensive obstetric care ([Table 2](#table002){ref-type="table"}).^[@ref14]^

Once adjusted to the model, to die at home or in a different place than a hospital was related to living in a non-urban area and living outside of the canton's main town.  Additionally, those who died at home or in a different place than a hospital, had a greater chance to die in the same canton of residence, in a place where there is no a MoH hospital, and with a diagnostic different from PIH.  It seems that socioeconomic characteristics of the mothers, cause of death and resources allocation are involved in a nationwide model of maternal mortality ([Table 3](#table003){ref-type="table"}). 

In case a woman dying in a different canton than that of her residences, one possible explanation is because she and her family had to travel (by their initiative or referred by a health personnel) to procure higher level of attention than the resolution capacity of the facility near home.  The results showed no relationship between socio-demographic variables like the place of residence, marital status, and extremes of reproductive age.  However, illiterate women tend to die in the same canton of residence.  Whereas, dying in a hospital and dying in the canton's main town, are clearly related to dying in a Canton different from the usual residence of the mother.  Also, when the death was classified as due to PIH, the likelihood of moving out from the canton of residence was higher than the chance of dying in the same canton. 

In a stratified analysis, the likelihood of dying in a hospital or canton's main town (where hospitals are usually located) showed the same trend of moving out from the canton of residence to die in a different canton whether the mother was finally registered as dying for PIH or by any other cause ([Table 4](#table004){ref-type="table"}). 

The absence of a MoH hospital was related to the probability of dying at home but showed no other plausible association with the rest of variables. 

There is no proximity pattern between part or the complete set of variables included in the analysis which means the absence of a statistical model to explain any particular way to die beyond the dichotomic relationships described before.  It is so, either at the national level (as shown in [Figure 1](#fig001){ref-type="fig"} and [Table 5](#table005){ref-type="table"}) or for the mothers who died at home (Stress=0.058). 

Discussion {#sec1-4}
==========

General agreement on maternal mortality reduction suggested that to reach the millennium target a health system must to be able to provide essential and emergency obstetric care in a well-allocated geographic, ethnic, and socioeconomic distribution of resources.  The results of the present study identified variable association into the maternal mortality national databases showing that to die at home or in a different place than a hospital are closely related to socio-economic conditions and absence of public health facilities.  Also, to die in a different place than the usual residence could mean that the health system is referring patients or women and families are searching for a higher level of attention when they face complications, perhaps too late to save a life.  Cultural traditions and beliefs outstanding role in maternal mortality has been widely discussed; unfortunately, maternal mortality databases do not include variables related neither to cultural background nor their ethnic group. 

Published evidence on current discussion on maternal mortality intervention approaches provides some orientation on comprehensive health systems development as the future to cope maternal mortality.  Using the search engine *Pubmed*, there are no published jobs processing maternal death databases.  The data visualisation tool from University of Washington^[@ref14]^ shows the attributable risk of iron deficiency for maternal hemorrhage and maternal sepsis, and intimate partner violence for complications of abortion. 

Only 28 out of 75 countries are on track to achieve Millennium Development Goal (MDG) 4 by 2015 and only 20 for MDG5.  Additionally, no Latin American countries are on track for MDG5.  It is necessary to adjust the targets for each country according to its economic and political order to evaluate its performance.^[@ref14]^ On the other hand; the *three delay model* suggests that maternal mortality is characterised by factors depending on both, the population and the health system.  One of the major constraints women and their family face when complications arise during pregnancy and mainly during delivery is the decision-making process to seek for help and to arrive at a place where she could be treated. * Addressing gaps in facility readiness and provider competencies for emergency obstetric care, alongside improving coverage of institutional deliveries, are critical to improving maternal outcomes*.^[@ref15]^ All in all, there is no standard formula -- fast-track countries deploy tailored strategies and adapt quickly to change.^[@ref16]^ Eichler and colleagues, assess the evidence on efforts to enhance health initiatives addressed to improve maternal health status, one conclusion is *We found no direct evidence on the impact of performance-based incentives on neonatal health services or mortality of mothers and new-borns.  A number of studies describe approaches to rewarding quality as well as increases in the quantities of services provided, although how quality is defined and monitored is not always clear*.^[@ref17]^ The present study results indicate socioeconomic characteristics of maternal deaths, the cause of death and resources allocation are involved in a nationwide model of deaths registered as occurred at home.  In China, provincial maternal mortality surveillance systems showed that Provinces from remote regions had the highest risk of maternal mortality, followed by provinces from inland regions and coast regions.^[@ref18]^ In a cross-sectional study in 29 countries in Africa, Asia, Latin America and the Middle East showed statistically significant relationship between low education levels and serious maternal outcomes, maternal near miss and death.^[@ref19]^ In Burkina Faso, Cambodia, Indonesia, and Morocco, strategies to improve maternal and neonatal health have focused on the expansion of the network of health facilities with increased uptake of facility birthing, scaling up of the production of midwives, reduction of financial barriers, and late attention for improving the quality of care.^[@ref20]^ World Health Organisation Multicountry Survey on Maternal and Newborn Health in 29 countries from Africa, Asia, Latin America and the Middle East showed Maternal near-miss cases were eight times more frequent in women with pre-eclampsia, and increased 60 times more frequent in women with eclampsia.^[@ref21]^

Paraphrasing the Priorities in Health^[@ref22]^ publication, *income growth and technical progress, in the broadest sense, worked to \[...\] reduce maternal mortality to levels not easy to imagine 30 years ago*.  Perhaps a new approach corresponds to the development level reached for some national health systems, where a mixture of interventions ranging from community-based initiatives to third level health care interventions could contribute to addressing the current situation.  For example, the strategy used in the UK to reduce maternal morbidity and mortality is a warning system for early detection; they surveyed 205 obstetric anaesthetists who agreed with the six most important physiological parameters were: heart rate, respiratory rate, temperature, systolic and diastolic blood pressure and oxygen saturation.^[@ref23]^ Considering that education, training, licensure and regulation of midwives improve the use of resources and outcomes, some strategies include preventive care, promotion of normal reproductive processes and emergency treatment available.^[@ref24]^ The absence of commodities (especially misoprostol), limitations in the scope of practice for midwives, and gaps in the inclusion of maternal health indicators in the national data systems have impeded efforts to scale up programs nationally.^[@ref25]^

The last argument means a new stage in health systems development.  Nevertheless, the idea of local health systems has been in place for decades, but it is hard to find examples of such kind of development oriented to EOC.  In Ecuador the attempt of the Health Authority is clear in this sense and governmental levels of the organisation have been explicitly issued in 2011.^[@ref26]^

The *Patterns of Inequity in Health Status, Health Care Provision and Health Risks*,^[@ref22]^ are still demonstrable in Ecuadorian maternal deaths, but at levels below those two or three decades ago.  It is hard to explain every death and subsets of deaths using national data, but it clearly shows that in-depth evaluation is needed.  However, a predominant factor seems unclear to explain the variable association found processing national databases.  It could mean that every pattern of health systems development played a role in maternal mortality, but also that factors different from those registered by the statistics system may remain hidden and that some random influences are not even considered in an explanatory model yet.  Ideally, a national statistics system should permit a country to merge different databases and analyse data to explain reality effectively and to provide evidence for decision-making. 

The authors would like to thank Mrs. Marie Dahlstrom for her review of the final draft and the PUCE authorities for support research. 

![ Ecuador: multidimensional scaling proximities representation of variables (dichotomic) in INEC's databases for maternal deaths, 2003-2013.](jphr-2016-2-692-g001){#fig001}

###### 

Ecuador: general characteristics of maternal deaths reported by national statistics institute, 2003-2013. 

  Characteristics                                          N. (%)
  -------------------------------------------------------- -------------
  Residence area                                           
     Urban                                                 1254 (65.9)
     Rural                                                 574 (30.2)
     Peripheral^[\*](#table1-tfn1){ref-type="table-fn"}^   65 (3.4)
     No data                                               9 (0.5)
  Marital status                                           
     Common-law partner                                    540 (28.4)
     Single                                                574 (30.2)
     Married                                               599 (31.5)
     Divorced                                              17 (0.9)
     Separated                                             78 (4.1)
     Widow                                                 15 (0.8)
     Ignored                                               57 (3.0)
     No data                                               22 (1.2)
  Literacy                                                 
     Yes                                                   1689 (88.8)
     No                                                    147 (7.7)
     Ignored                                               50 (2.6)
     No data                                               16 (0.8)
  Educational level                                        
     None                                                  164 (8.6)
     Literacy Centre                                       19 (1.0)
     Elementary                                            775 (40.7)
     High School                                           558 (29.3)
     Basic Education                                       111 (5.8)
     Middle level                                          34 (1.8)
     Postsecondary cycle                                   14 (0.7)
     High                                                  93 (4.9)
     Postgraduate                                          2 (0.1)
     Ignored                                               111 (5.8)
     No data                                               21 (1.1)
  Pregnancy induced hypertension                           
     PIH                                                   599 (31.5)
     Others                                                1303 (68.5)
  Extremes of reproductive age                             
     \<18 and \>35                                         728 (38.3)
     Reproductive age                                      1174 (61.7)
  Parish of death same of parish of residence              
     Same                                                  1046 (55.0)
     Different                                             856 (45.0)
  Canton of death same of canton of residence              
     Same                                                  1231 (64.7)
     Different                                             671 (35.3)
  Death in the canton's main town                          
     Main town                                             1631 (85.8)
     Country side                                          271 (14.2)
  Death in a hospital                                      
     Hospital                                              1341 (70.5)
     Home or other                                         561 (29.5)

^\*^Human settlements surrounding cities, but not as scattered as in rural areas.  Source: INEC. 

###### 

Ecuador: comparison of number of maternal deaths at a home or other place than a hospital, related with socioeconomic variables and health facility presence, 2003-2013. 

  Variables                                            Place of death   χ^2^ (P)   OR^[\*](#table2-tfn1){ref-type="table-fn"}^ (IL-SL)   
  ---------------------------------------------------- ---------------- ---------- ----------------------------------------------------- --------------------
  Marital status (n= 1823)                                                                                                               
     No partner (separated, widow, divorced, single)   210              474        1.4 (0.235)                                           1.064 (0.833-1.36)
     Partner (married, common-law partner)             320              819                                                              
  Residence area (n=1893)                                                                                                                
     Urban                                             288 (23.0)       966        80.09 (\<0.001)                                       3.6 (2.05-6.2)
     Rural                                             242 (42.2)       332                                                              4.6 (2.5-8.5)
     Peripheral                                        31 (47.7)        34                                                               
  Age (n=1902)                                                                                                                           
     Under 18 and over 35 years old                    238 (32.7)       490        5.79 (0.016)                                          1.27 (0.995-1.62)
     18 to 35 years old                                323 (22.5)       851                                                              
  Literacy (n=1836)                                                                                                                      
     Illiterate                                        69 (46.9)        78         23.82 (\<0.001)                                       1.385 (0.898-2.14)
     Literate                                          470 (27.8)       1219                                                             
  Canton's main town (n=1902)                                                                                                            
     Main town                                         356 (21.8)       1275       323.7 (\<0.001)                                       0.07 (0.046-0107)
     Countryside                                       205 (75.7)       66                                                               
  Die in same canton of residence (n=1902)                                                                                               
     No                                                97 (14.4)        574        112.76 (\<0.001)                                      0.339 (0.255-0.45)
     Yes                                               464 (37.7)       767                                                              
  MoH hospital in canton of death (n=1902)                                                                                               
     No                                                84 (40.6)        123        13.7 (\<0.001)                                        2.47 (1.7-3.58)
     Yes                                               477 (28.1)       1218                                                             
  Pregnancy induced hypertension (n=1902)                                                                                                
     Yes                                               120 (20.0)       479        37.64 (\<0.001)                                       0.55 (0.42-0.72)
     Others                                            441 (33.8)       862                                                              

^\*^The logistic regression model had an omnibus P\>0.001, a Nagelkerke R Square=0.324, and a Hosmer and Lemeshow Test=0.050. 

###### 

Ecuador: comparison of number of maternal deaths in the same canton of residence or in a different canton with socioeconomic variables and health facility presence, 2003-2013. 

  Variables                                                 Canton of death   χ^2^ (P)   OR^[\*](#table3-tfn1){ref-type="table-fn"}^ (IL-SL)   
  --------------------------------------------------------- ----------------- ---------- ----------------------------------------------------- ------------------
  Residence area                                                                                                                               
     Urban                                                  446               808        2.82 (0.24)                                           2.03 (1.2-3.5)
     Rural                                                  196               378                                                              0.996 (0.6-1.8)
     Peripheral                                             29                36                                                               
  Marital status                                                                                                                               
     No partner (separated, widow, divorced, single)        247               437        0.184 (0.668)                                         1.15 (0.93-1.42)
     Partner (married, common-law partner)                  400               739                                                              
  Age                                                                                                                                          
     Under 18 and more than 35                              248               480        0.760 (0.383)                                         0.97 (0.78-1.2)
     18 to 35 years old                                     423               751                                                              
  Literacy                                                                                                                                     
     Illiterate                                             35                112        9.638 (0.002)                                         0.72 (0.47-1.12)
     Literate                                               618               1071                                                             
  Canton main town                                                                                                                             
     Main town                                              651               980        107.728 (\<0.001)                                     7.7 (4.5-13.3)
     Countryside                                            20                251                                                              
  Place of death                                                                                                                               
     Home or other                                          97                464        112.76 (\<0.001)                                      0.34 (0.25-0.45)
     Hospital                                               574               767                                                              
  Pregnancy induced hypertension                                                                                                               
     Yes                                                    231 (38.6%)       368        4.134 (0.042)                                         1.05 (0.84-1.3)
     Others                                                 440 (33.8%)       863                                                              
  Ministry of Health hospital in canton of death (n=1902)                                                                                      
     No                                                     108               99         29.04 (\<0.001)                                       3.16 (2.26-4.4)
     Yes                                                    563               1132                                                             

^\*^The logistic regression model had an omnibus P\>0.001, a Nagelkerke R Square=0.184, and a Hosmer and Lemeshow Test=0.283. 

###### 

Ecuador: comparison of number of maternal deaths in cantons where ministry of health hospitals are not available related with socioeconomic variables, 2003-2013. 

  Variables                                            Ministry of health hospital available   χ^2^ (P)   OR^[\*](#table4-tfn1){ref-type="table-fn"}^ (IL-SL)   
  ---------------------------------------------------- --------------------------------------- ---------- ----------------------------------------------------- ------------------
  Marital status                                                                                                                                                
     No partner (separated, widow, divorced, single)   70                                      614        0.245 (0.620)                                         0.9 (0.67-1.26)
     Partner (married, common-law partner)             125                                     1014                                                             
  Residence area (n=1893)                                                                                                                                       
     Urban                                             136                                     1118       0.001 (0.999)                                         0.76 (0.34-1.7)
     Rural                                             62                                      512                                                              0.94 (0.39-2.3)
     Peripheral                                        7                                       58                                                               
  Age (n=1902)                                                                                                                                                  
     Under 18 and more than 35                         90                                      638        2.66 (0.103)                                          1.25 (0.91-1.7)
     18 to 35 years old                                117                                     1057                                                             
  Literacy (n=1836)                                                                                                                                             
     Illiterate                                        16                                      131        0.004 (0.95)                                          1.1 (0.63-1.9)
     Literate                                          181                                     1508                                                             
  Canton main town (n=1902)                                                                                                                                     
     Main town                                         177                                     1454       0.011 (0.915)                                         0.93 (0.52-1.65)
     Countryside                                       30                                      241                                                              
  Place of death (n=1902)                                                                                                                                       
     Home                                              84 (15.0%)                              477        13.7 (\<0.001)                                        2.2 (1.67-3.5)
     Hospital                                          123 (9.2%)                              1218                                                             
  Pregnancy induced hypertension (n=1902)                                                                                                                       
     Yes                                               60                                      539        0.677 (0.411)                                         0.92 (0.66-1.3)
     Others                                            147                                     1156                                                             

^\*^The logistic regression model had an omnibus P\>0.001, a Nagelkerke R Square=0.067, and a Hosmer and Lemeshow Test=0.286. 

###### 

Stress and fit measures. 

  Variable                             Value
  ------------------------------------ -------------------------------------------------
  Normalized Raw Stress                0.06449
  Stress-I                             0.25394^[a](#table5-tfn1){ref-type="table-fn"}^
  Stress-II                            0.73482^[a](#table5-tfn1){ref-type="table-fn"}^
  S-Stress                             0.16290^[b](#table5-tfn2){ref-type="table-fn"}^
  Dispersion Accounted For             0.93551
  Tucker's Coefficient of Congruence   0.96722

PROXSCAL minimizes Normalized Raw Stress. 

^a^Optimal scaling factor = 1.069

^b^Optimal scaling factor = 0.918. 

[^1]: Contributions: AP, research management, conception and design, acquisition, analysis, and interpretation of data, drafting the work, revising critically the work for important intellectual content; MA, conception and design, drafting the work, revising critically the work for important intellectual content; AR, acquisition, analysis, and interpretation of data, revising critically the work for important intellectual content; ER, acquisition, analysis, and interpretation of data, revising critically the work for important intellectual content. 

[^2]: Conflict of interest: the authors declare no potential conflict of interest.