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Inter-Ethnic/Racial Facial Variations: A Systematic Review and Bayesian Meta-Analysis of Photogrammetric Studies

  • Yi Feng Wen,

    Affiliation Paediatric Dentistry & Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China

  • Hai Ming Wong ,

    wonghmg@hku.hk

    Affiliation Paediatric Dentistry & Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China

  • Ruitao Lin,

    Affiliation Department of Statistics & Actuarial Science, Faculty of Science, The University of Hong Kong, Hong Kong SAR, China

  • Guosheng Yin,

    Affiliation Department of Statistics & Actuarial Science, Faculty of Science, The University of Hong Kong, Hong Kong SAR, China

  • Colman McGrath

    Affiliation Periodontology & Public Health, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China

Inter-Ethnic/Racial Facial Variations: A Systematic Review and Bayesian Meta-Analysis of Photogrammetric Studies

  • Yi Feng Wen, 
  • Hai Ming Wong, 
  • Ruitao Lin, 
  • Guosheng Yin, 
  • Colman McGrath
PLOS
x

Abstract

Background

Numerous facial photogrammetric studies have been published around the world. We aimed to critically review these studies so as to establish population norms for various angular and linear facial measurements; and to determine inter-ethnic/racial facial variations.

Methods and Findings

A comprehensive and systematic search of PubMed, ISI Web of Science, Embase, and Scopus was conducted to identify facial photogrammetric studies published before December, 2014. Subjects of eligible studies were either Africans, Asians or Caucasians. A Bayesian hierarchical random effects model was developed to estimate posterior means and 95% credible intervals (CrI) for each measurement by ethnicity/race. Linear contrasts were constructed to explore inter-ethnic/racial facial variations. We identified 38 eligible studies reporting 11 angular and 18 linear facial measurements. Risk of bias of the studies ranged from 0.06 to 0.66. At the significance level of 0.05, African males were found to have smaller nasofrontal angle (posterior mean difference: 8.1°, 95% CrI: 2.2°–13.5°) compared to Caucasian males and larger nasofacial angle (7.4°, 0.1°–13.2°) compared to Asian males. Nasolabial angle was more obtuse in Caucasian females than in African (17.4°, 0.2°–35.3°) and Asian (9.1°, 0.4°–17.3°) females. Additional inter-ethnic/racial variations were revealed when the level of statistical significance was set at 0.10.

Conclusions

A comprehensive database for angular and linear facial measurements was established from existing studies using the statistical model and inter-ethnic/racial variations of facial features were observed. The results have implications for clinical practice and highlight the need and value for high quality photogrammetric studies.

Introduction

International migration is occurring at an unprecedented pace in the contemporary world [1]. The past 50 years has witnessed a dynamic increase in the number of international migrants from 92 million in 1960 to 165 million in 2000 [1] and to 214 million in 2010 [2]; The number is estimated to reach 405 million in 2050 [2]. Therefore, it is increasingly important for professionals from various medical and dental specialties whose work involves correction of facial anomalies and achieving aesthetics to be aware of the differences in facial characteristics among ethnic/racial groups.

While inter-ethnic/racial facial variations have long been of interest to the general public, anthropologists, and medical and dental practitioners, studies providing solid evidence on this issue are surprisingly sparse. One of the most comprehensive studies by Farkas and colleagues [3] compared normative facial measurements of a North American white population with data from other regions in the world; however, the generalizability of this study is limited by its small sample size (only 30 males and 30 females) in each participating country. Moreover, the facial features investigated were limited to linear measurements/parameters, and all comparisons were made against the North American white population.

Apart from the direct anthropometric method used by Farkas and colleagues [3], several indirect anthropometric methods exist, e.g. cephalometry, photogrammetry, three-dimensional stereophotogrammetry and surface laser scanning [4,5]. Of these methods, photogrammetry provides unique advantages over other methods from several perspectives [4,5]. First, the measurements are not affected by tissue sensitivity and compressibility, which is ideal for soft tissue analysis. Second, the examination procedure is less uncomfortable from both the subjects’ and examiners’ side and subjects are examined free from radiation exposure. Third, permanent photographic archives allowed flexibility in selection of and objectivity in assessment of facial measurements. Furthermore, equipment for photogrammetry is portable, the examination procedure is time saving and the cost is relatively low [6]. In addition, reliability of photogrammetry proved to be excellent [6]. Therefore, despite the advanced anthropometric methods such as three-dimensional stereophotogrammetry, photogrammetry remains the optimal choice for large epidemiological studies aiming at establishing population norms [6], especially in developing countries where sophisticated equipment is not available.

Results from different anthropometric methods are not directly comparable [7,8]. To date, no meta-analysis of photogrammetric studies has been performed. To fill in this gap, we aimed to conduct a systematic review and apply a statistical model to establish database for population norms of various angular and linear facial measurements for Africans, Asians and Caucasians; and to determine inter-ethnic/racial facial variations.

Methods

This review was conducted according to a predetermined protocol (S1 Text) and was reported in line with recommendations from the MOOSE (Meta-analysis Of Observational Studies in Epidemiology) guidelines (S2 Text) [9].

Data sources and search strategies

We comprehensively searched the electronic databases of PubMed (1997 onward), ISI Web of Science (1956 onward), EMBASE (1947 onward) and Scopus (1995 onward) with no restrictions on language, dates or status of publication. The initial search was updated to 1st December, 2014 using automatic e-mail alerts. One reviewer (YFW) developed the search strategy and conducted the initial search using controlled vocabularies and keywords. The search strategy for all four databases is available in S3 Text. Reference lists of articles that were identified in the screening process were also manually searched.

Study selection

Two trained and calibrated reviewers (YFW and HMW) independently screened titles and abstracts of the identified records during the first round screening. In the second round screening, full texts of those records judged to be potentially eligible were retrieved and assessed for eligibility. Inter-reviewer agreement was assessed using Cohen’s κ. Discrepant opinions between the reviewers were resolved by discussion at the end of each round, and a senior author (CM) was consulted if consensus could not be reached.

This review sought to identify all facial photogrammetric studies regardless of the type of study design. We considered studies for inclusion if they recruited African, Asian or Caucasian subjects between 18 to 45 years old; adopted the well-established definitions of facial landmarks and measurements (S1 and S2 Tables) [1012]; and if standard error (SE) could be extracted or estimated from the report. Studies were excluded if they recruited exclusively the following subjects: attractive/beautiful subjects; subjects with severe malocclusion, developmental craniofacial disfigurement, history of facial trauma/fracture or cosmetic surgery; or patients with systematic disorders known to affect craniofacial development. Furthermore, we required the reported measurements to be accurate to one decimal place for linear measurements in millimeters and angular measurements in degrees. We attempted to acquire missing information by E-mail enquiry of the studies’ correspondence author whenever needed.

Data extraction

Study characteristics and demographics such as name of the first author, year of publication, study location, origin of the subjects, sample source, sample size, age range, and gender were extracted. We also extracted details of the photographic process including the subjects’ body position, head posture, occlusal position, lip/chin posture and the camera-subject distance.

We intended to extract 11 angular and 18 linear facial measurements that have the greatest clinical implications (S3 and S4 Tables). Measurements were recorded by mean and standard deviation (SD); conversions were made if confidence interval or SE was reported. Articles reporting on more than one population group were regarded as many separate studies as the number of heterogeneous populations they contained. Different articles investigating the same group of subjects were considered as one study.

Data extraction was performed by one reviewer (YFW) using a predefined piloted spreadsheet in Microsoft Excel 2013 and the results of extraction were then verified by a second reviewer (HMW). Discrepancies were resolved by consensus or further consultation of a third investigator (CM).

Assessment of risk of bias

To ascertain the validity of each eligible study, risk of bias was assessed based on an instrument [13] that has been used in systematic reviews on craniofacial anthropometrics [4,5]. Further modifications of the instrument were made in view of potential sources of bias unique to photogrammetric studies [14]. We included 17 items assessing four domains of the eligible studies: study design, photo taking process, facial measurements and the appropriateness of statistical analysis (S5 Table).

Our criteria for risk of bias assessment is detailed in S6 Table. A score of 0, 0.5 or 1 was assigned to each item indicating free of bias, partially free of bias and subject to bias, respectively. In cases of inapplicable items, no scores were given. A score was calculated for each study by dividing the sum of item scores by the total number of applicable items. Studies with scores below 0.40 were considered as with low risk of bias. Two trained and calibrated reviewers (YFW and HMW) assessed the studies and a third reviewer (CM) resolved discrepancies.

Statistical analysis

Despite our extensive literature search, data for several facial measurements were still sparse, especially when analyses were stratified by gender. In addition, while we rigorously followed the predefined inclusion and exclusion criteria during article screening, there were still varying degrees of risk of bias among the eligible studies. To fully utilize our extracted data, a Bayesian hierarchical random effects model was constructed, with contrasts established for pairwise comparisons among the ethnic/racial groups.

The multilevel modelling approach naturally applies a hierarchical structure to the extracted data where individual studies were nested within ethnicities/races that in turn were nested within the total population. In addition, the Bayesian approach to multilevel modelling has additional advantages of allowing for greater flexibility in modelling variability at different levels and enabling us to make direct probability statements [15,16]. In the Bayesian hierarchical model, ethnicity/race-specific estimates of a facial measurement were more model-driven when there was substantial uncertainty on the basis of a small number of studies, whereas for ethnicities/races with less uncertainty, the estimates were more data-driven [17].

S4 Text and S1 and S2 Figs details statistical models for each level of the hierarchy. In a single level notation, the overall model to estimate facial measurements from the ith study of the jth ethnicity/race is: where μ00 is the grand mean of the facial measurement across ethnicities/races, η0j and ζij represent ethnicity/race-specific and study-specific random effects that are normally distributed with mean 0 and between-ethnicity/race variance τ2 and between-study variance σ2, respectively, and ϵij denotes sampling error for each individual study.

Non-informative priors were specified for τ and σ using the half-Cauchy distribution with the scale set to be 25. The grand mean μ00 was assigned a non-informative normal prior, i.e. μ00N(0, 104). Linear contrasts were constructed to explore inter-ethnic/racial variations of the measurements [18].

We fitted the Bayesian hierarchical model using the Markov chain Monte Carlo (MCMC) algorithm to generate samples of posterior distributions of all model parameters, including ethnicity/race-specific estimates of facial measurements and the linear contrasts. The analyses were performed separately for males and females. A facial measurement was meta-analysed only if there were data from two or three ethnicities/races with at least one of the ethnicities/races included two or more eligible studies. Estimates of the facial measurements were informed by posterior means and 95% credible intervals (CrIs) of the posterior distributions. Inter-ethnic/racial variations were explored at significance levels of 0.05 and 0.10 by examining whether 0 was included in the 95% and 90% CrIs of the linear contrasts, respectively. The 95% (90%) CrI was obtained by taking the 2.5th (5th) and 97.5th (95th) percentiles of the posterior distributions. The MCMC sampling algorithm was performed using the JAGS software (version 3.4.0) [19] on R version 3.1.1 (R Development Core Team, 2014) [20].

Results

Literature search

Fig 1 summarises the process of study identification and selection. We retrieved 3769 published original articles, abstracts, letters and reviews from the search of electronic databases and additional hand searching. After the first round study selection based on titles and abstracts (κ = 0.97), 308 potentially eligible articles were accessed for full-texts and underwent the second round study selection. Of these, 36 eligible articles [2156] (κ = 0.95) that reported 38 studies were identified.

Study characteristics

Characteristics of the eligible studies are detailed in Table 1. All studies had a cross-sectional design. The year of publication ranged from 1989 to 2014. One study was in Chinese, one in Korean, and the remaining 36 studies were in English. The studies involved 6686 subjects (male: 2944, female: 3742). Following Risch and colleagues’ ethnicity/race classification scheme on the basis of numerous population genetic surveys [57], subjects were considered as Africans if they were African Americans or Afro-Caribbeans originating from the sub-Saharan Africa; Asians if they were from China, Indochina (e.g. Cambodia, Malaysia, and Vietnam), Japan, Korea, the Philippines and Siberia in eastern Asia; and Caucasians if they were from Indian subcontinent, Middle East, North Africa with ancestry in Europe and West Asia. As a result, 1856 (27.8%) of the subjects were Africans (male: 1043; female: 813), 2720 (40.7%) were Asians (male: 1259; female: 1461), 2110 (31.5%) were Caucasians (male: 642; female: 1468).

Risk of bias

Detailed risk of bias ratings are available in S5 Table. Of the 38 studies included in analysis, 23 (60.5%) were deemed low risk of bias, with the rest (39.5%) classed as high risk of bias. Scores of these studies ranged from 0.06 to 0.66. Over 70% of studies on Asians and 66.7% studies on Caucasians were of low risk of bias, whereas 58.3% of the African studies were subject to high risk of bias.

When each item in the instrument is assessed (Fig 2), sampling methods was found under-reported in most studies (57.9%). Regarding the photo taking process, most studies failed to adequately address the subjects’ body posture (63.2%), head position (55.3%) and lip posture (63.2%). Only three studies (8.9%) described the subjects’ occlusal position. Photographic parameters were reported in seven studies (18.4%). As to facial measurements, most studies defined facial landmarks by photo illustration (65.8%) and only eight studies provided written definitions. Measurement reliability was addressed in 16 studies (42.1%) and ten of them (26.3%) reported the reliability measure of method error.

Ethnicity/race-specific population norm of facial measurements

Database for normative values of facial measurements was established at the ethnicity/race level by gender. Posterior means and corresponding 95% CrIs of the facial measurements were summarized in Tables 2 and 3. The number of studies and the number of subjects with which we obtained the posterior distributions were also recorded. Six measurements (angle of the medium facial third, angle of the inferior facial third, height of the upper face, height of the lower lip, vermilion height of the upper lip and vermilion height of the lower lip) were excluded from analysis due to small sample size (S4 Text).

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Table 2. Ethnicity/race-specific population norm of facial measurements for males.

https://doi.org/10.1371/journal.pone.0134525.t002

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Table 3. Ethnicity/race-specific population norm of facial measurements for females.

https://doi.org/10.1371/journal.pone.0134525.t003

Inter-ethnic/racial facial variations

Inter-ethnic/racial facial variations were summarized in Fig 3. Three measurements revealed inter-ethnic/racial variations at the significance level of 0.05. Nasofrontal angle was more obtuse in Caucasians than in Africans (posterior mean difference: 8.1°, 95% CrI: 2.2°–13.5°) among males. Nasolabial angle in Caucasian females was more obtuse than in African (17.4°, 0.2°–35.3°) and Asian (9.1°, 0.4°–17.3°) females. Asian males had on average more acute nasofacial angle compared to African males (7.4°, 0.1°–13.2°).

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Fig 3. Facial measurements with significant inter-ethnic/racial variations by gender.

(A) nasofrontal angle, (B) nasolabial angle, (C) nasofacial angle, (D) width of the face, (E) height of forehead II, (F) physiognomical height of the face. Error bars: 95% CrI. *: significantly different at 0.10 level of significance. **: significantly different at 0.05 level of significance.

https://doi.org/10.1371/journal.pone.0134525.g003

Additional inter-ethnic/racial facial variations were revealed when the statistical significance level was set at 0.10, which indicated a trend toward a significant difference. Caucasian females had larger nasofrontal angle than African females (8.5°, 90% CrI: 0.6°–15.9°). Nasolabial angle in males of Caucasians was on average 12.6° larger than in African males (0.8°–23.2°). As per linear facial measurements, Caucasian females had on average smaller width of the face (14.6°, 2.1°–23.2°), shorter height of forehead II (8.7°, 0.9°–12.8°) and shorter physiognomical height of the face (11.4°, 0.7°–20.2°) compared to Asian females.

Discussion

This systematic review and meta-analysis is the first to collate all available photogrammetric studies to establish a comprehensive database for ethnicity/race-specific population norms of a variety of angular and linear facial measurements. Furthermore, this study for the first time comprehensively explored inter-ethnic/racial facial variations among the three major ethnic/racial groups. Our study provides strong evidence of inter-ethnic/racial variations as per nasofrontal angle, nasolabial angle and nasofacial angle. In addition, we observed substantial inter-ethnic/racial differences for linear measurements including width of the face, height of the forehead II and physiognomical height of the face.

Our meta-analysis updates results of an international anthropometric study [3] and a systematic review [58]. Compared with these studies, the meta-analysis adds to the literature by including both angular and linear facial measurements rather than being restricted to linear measurements related to the neoclassical canons [59]. Besides, our approach to investigating inter-ethnic/racial facial variations were more intuitive than relying on frequency distributions of arbitrarily defined categories [3] or focusing on the variance component of the measurements [58].

Ethnic/racial categorization in medical research is an issue of ongoing debate [57,60,61]. Despite the claim from some medical journals that ethnic/racial categorization is biologically meaningless [62,63], these discussions have been challenged due to a lack of solid scientific basis [57]. Before genetic and environmental determinants of facial characteristics are fully identified, ethnicity/race as a cruder surrogate factor to investigate facial variations remains a useful approach [57].

Our analysis of angular measurements revealed significant inter-ethnic/racial variations for nasofrontal angle, nasolabial angle and nasofacial angle. Nasofrontal and nasofacial angle are both affected by the position of nasion and nasal tip protrusion [12,64]. The smaller nasofrontal angle in African males compared to Caucasian males (posterior mean difference: 8.1°, 95% CrI: 2.2°–13.5°) and larger nasofacial angle in Africans compared to Asians (7.4°, 0.1°–13.2°) may be a reflection of the more inclined nasal bridge in Africans. Nasolabial angle is a critical determinant of nasal tip aesthetics [65]. The larger estimated nasolabial angle in Caucasian females indicates the prognathic feature of Africans and Asians [66]. As per linear facial measurements, our results suggest that width of the face and height of forehead II are significantly larger in Caucasian females than in Asian females, which are consistent with previous preliminary study [46] and systematic review [58]. While previous studies reported moderate inter-ethnic/racial variations regarding the nose [3,58], the present study failed to identify such differences.

The database established in this study provides normative range of facial measurements. Compared to the existing database [3], our database is more comprehensive in terms of the number of subjects used to derive the normative values and the more comprehensive coverage of facial features. Equipped with knowledge about this normal range, plastic and craniofacial surgeons are better informed in determining the amount of surgical corrections needed for a particular patient taking his/her ethnicity/race into consideration. This brings us closer to the ultimate goal of individualized treatment in plastic surgery. Besides, the database provides critical parameters for the manufacture of respirators and oxygen masks, whose design requires taking the consumers’ ethnicities/races into consideration. In addition, the results provide a platform for future genetic, nutritional and environmental studies to identify factors influencing facial morphology.

The strengths of this study rest on several aspects. First, the well-established definitions of landmarks and measurements [10,12] were complied, which ensured homogeneity of the measurements. Second, risk of bias was assessed following priori defined criteria (S6 Table) to enhance objectivity in assessment. Third, the Bayesian hierarchical model provides statistical advantages over traditional subgroup analysis in meta-analysis. The frequentist approach to meta-analysis yields 95% confidence intervals that are in fact narrower than the range of values they intended to cover [67]; besides, the no pooling nature of subgroup analysis tends to overestimate the variation among ethnicities/races [68]. Therefore, the frequentist approach to subgroup analysis tends to result in an inflated type I error rate compared to Bayesian hierarchical modelling. The type I error rate can be further increased when subgroups are pairwise compared post-hoc. In contrast, pairwise comparisons in Bayesian approach do not affect the rate of type I error since there is only one posterior distribution regardless of how comparisons are made [18].

There are several limitations in the current study. First, our meta-analysis inherits the limitations of original research. Since not all of the eligible studies were conducted as ethnicity/race-specific studies, subjects’ ethnicity/race had to be classified according to an external classification scheme. While the scheme proposed by Risch and colleagues [57] is well established, possibilities of ethnicity/race misclassification still could not be completely obviated. The issue could be further complicated by the increasing presence of mixed ethnicity/race. We recommend future photogrammetric studies defining subjects’ ethnicity/race in a more rigorous way by using methods such as ancestral mapping to facilitate inter-ethnic/racial comparisons. Second, we did not adjust our analyses for age or anthropometric indices such as body weight, height or body mass index since they were reported in none of the eligible studies. Possibilities for residual confounding cannot be excluded from our estimates. Third, there is heterogeneity among the eligible studies in terms of the subjects’ posturing, camera-object distance and photographic parameters. Risk of bias of the studies differed and a notably high percentage of African studies (58.3%) were with high risk of bias. We accounted for such heterogeneity by using random effects model in our analysis. However, it should be noted that the use of statistical model in our analyses should not overshadow the importance of a universally adopted photographic set-up. The most detailed descriptions of photogrammetric set-up come from Fernández-Riveiro and colleagues [33,34] and their method has been used by other studies [42]. We recommend its universal usage for future photogrammetric studies. Finally, despite the extensive literature search, there is still scarcity of data for several facial measurements. Estimates derived from a small amount of data may be subject to bias when applied to the population at large. Besides, scarcity of data results in substantial uncertainty in the ethnicity/race-specific estimates as revealed by the wide Bayesian credible intervals. In addition, six measurements were excluded from analysis due to insufficient data. To overcome the challenges of sparse data, we used the Bayesian approach to account for uncertainty in the hierarchical modelling, which proved to be more accurate than the frequentist approach, especially for small sample sizes [68,69]. Generalizability of our findings could be improved by inclusion of more high quality photogrammetric studies.

Our study provides a comprehensive database for various angular and linear facial measurements based on the best available photogrammetric studies. Significant inter-ethnic/racial variations were found for both angular and linear measurements. The results can provide a useful resource to guide research and clinical practice. This study also highlights the need for more high quality photogrammetric studies employing standardized photographic techniques; and preferably from a large randomized sample comprising different ethnic/racial groups.

Supporting Information

S1 Fig. Plot of the hierarchical structure.

Purple, blue and green represent the first, second and third level of the hierarchy, respectively. “P” indicates the total number of ethnicities/races and “m” is the number of studies informing the first ethnicity/race.

https://doi.org/10.1371/journal.pone.0134525.s001

(TIF)

S2 Fig. Path diagram illustrating the Bayesian hierarchical model.

The diagram is plotted borrowing Curran et al.’s path diagramming system [70]. The box represents the dependent variable. Triangles with number “1” inside is used to define the intercept term, and the subscript to “1” reflects specific levels of the hierarchical structure. Circles represent unobserved random coefficients. Solid arrows represent regression parameters. Purple, blue and green color represent the first, second and third level of the hierarchy, repsectively. We incorported distribution of random error terms for each level of the hierarchy using dash dot arrow. Unknown parameters and their prior distributions are illustrated in red with dot arrows.

https://doi.org/10.1371/journal.pone.0134525.s002

(TIF)

S1 Table. Definitions of anthropometric landmarks used in this study.

https://doi.org/10.1371/journal.pone.0134525.s003

(DOCX)

S2 Table. Definitions of standard anthropometric measurements used in this study.

https://doi.org/10.1371/journal.pone.0134525.s004

(DOCX)

S3 Table. Angular measurements extracted for meta-analysis.

https://doi.org/10.1371/journal.pone.0134525.s005

(DOCX)

S4 Table. Linear measurements extracted for meta-analysis.

https://doi.org/10.1371/journal.pone.0134525.s006

(DOCX)

S5 Table. Risk of bias of included studies.

https://doi.org/10.1371/journal.pone.0134525.s007

(DOCX)

S6 Table. Criteria for risk of bias assessment.

https://doi.org/10.1371/journal.pone.0134525.s008

(DOCX)

S1 Text. Protocol of the systematic review.

https://doi.org/10.1371/journal.pone.0134525.s009

(DOCX)

S2 Text. Meta-analysis of Observational Studies in Epidemiology (MOOSE) Checklist.

https://doi.org/10.1371/journal.pone.0134525.s010

(DOCX)

S4 Text. Specification of the Bayesian hierarchical random effects model.

https://doi.org/10.1371/journal.pone.0134525.s012

(DOCX)

Author Contributions

Conceived and designed the experiments: HMW CM. Performed the experiments: YFW HMW. Analyzed the data: YFW GY RL. Contributed reagents/materials/analysis tools: HMW. Wrote the paper: YFW HMW GY RL CM.

References

  1. 1. Özden Ç, Parsons C, Schiff M, Walmsley T. Where on earth is everybody? The evolution of global bilateral migration 1960–2000. World Bank Econ Rev. 2011;25: 12–56.
  2. 2. Koser K, Laczko F. World Migration Report 2010 –The Future of Migration: Building Capacities for Change. In. Geneva: International Organization for Migration. 2010;115.
  3. 3. Farkas LG, Katic MJ, Forrest CR, Alt KW, Bagic I, Baltadjiev G, et al. International anthropometric study of facial morphology in various ethnic groups/races. J Craniofac Surg. 2005;16: 615–646. pmid:16077306
  4. 4. Brons S, van Beusichem M, Bronkhorst E, Draaisma J, Bergé S, Schols J, et al. Methods to quantify soft tissue-based cranial growth and treatment outcomes in children: a systematic review. PLoS One. 2014;9: e89602. pmid:24586904
  5. 5. Brons S, van Beusichem M, Bronkhorst E, Draaisma J, Bergé S, Maal T, et al. Methods to quantify soft-tissue based facial growth and treatment outcomes in children: a systematic review. PLoS One. 2012;7: e41898. pmid:22879898
  6. 6. Zhang X, Hans M, Graham G, Kirchner H, Redline S. Correlations between cephalometric and facial photographic measurements of craniofacial form. Am J Orthod Dentofacial Orthop. 2007;131: 67–71. pmid:17208108
  7. 7. Farkas L, Bryson W, Klotz J. Is photogrammetry of the face reliable? Plast Reconstr Surg. 1980;66: 346–355. pmid:7422721
  8. 8. Phillips C, Greer J, Vig P, Matteson S. Photocephalometry: errors of projection and landmark location. Am J Orthod. 1984;86: 233–243. pmid:6591803
  9. 9. Stroup D, Berlin J, Morton S, Olkin I, Williamson G, Rennie D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283: 2008–2012. pmid:10789670
  10. 10. Farkas LG. Anthropometry of the Head and Face. 2nd ed. New York: Raven Press, Ltd.; 1994.
  11. 11. Naini FB. Facial Aesthetics: Concepts and Clinical Diagnosis. West Sussex, UK: Wiley-Blackwell; 2011.
  12. 12. Powell N, Humphreus B. Proportions of the aesthetic face. New York: Thieme-Stratton Inc.; 1984.
  13. 13. Gordon J, Rosenblatt M, Witmans M, Carey J, Heo G, Major P, et al. Rapid palatal expansion effects on nasal airway dimensions as measured by acoustic rhinometry. A systematic review. Angle Orthod. 2009;79: 1000–1007. pmid:19705938
  14. 14. Arnett G, Bergman R. Facial keys to orthodontic diagnosis and treatment planning. Part I. Am J Orthod Dentofacial Orthop. 1993;103: 299–312. pmid:8480695
  15. 15. Spiegelhalter D, Myles J, Jones D, Abrams K. Bayesian methods in health technology assessment: a review. Health Technol Assess. 2000;4: 1–130.
  16. 16. Sutton A, Abrams K. Bayesian methods in meta-analysis and evidence synthesis. Stat Methods Med Res. 2001;10: 277–303. pmid:11491414
  17. 17. Say L, Chou D, Gemmill A, Tunçalp Ö, Moller A, Daniels J, et al. Global causes of maternal death: a WHO systematic analysis. Lancet Glob Health. 2014;2: e323–333. pmid:25103301
  18. 18. Kruschke J. Doing Bayesian Data Analysis: A Tutorial with R, Jags, and Stan. 2nd ed. San Diego, CA, USA: Academic Press Inc; 2014.
  19. 19. Plummer M. JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003): 2003; Vienna, Austria; 2003.
  20. 20. R Development Core Team: R: A Language and Environment for Statistical Computing [http://www.R-project.org]
  21. 21. Akhter Z, Banu M, Alam M, Hossain S, Nazneen M. Photo-anthropometric study on face among Garo adult females of Bangladesh. Bangladesh Med Res Counc Bull. 2013;39: 61–64. pmid:24930193
  22. 22. Anibor E. Photometric facial analysis of the Ibo ethnic group in Nigeria. Arch Appl Sci Res. 2010a;2: 219–222.
  23. 23. Anibor E. Photometric facial analysis of the Urhobo ethnic group in Nigeria. Arch Appl Sci Res. 2010b;2: 28–32.
  24. 24. Anibor E. Photometric facial analysis of the Itsekiri ethnic group in Nigeria. Adv Appl Sci Res. 2011;2: 145–148.
  25. 25. Anic-Milosevic S, Lapter-Varga M, Slaj M. Analysis of the soft tissue facial profile by means of angular measurements. Eur J Orthod. 2008a;30: 135–140. pmid:18263886
  26. 26. Anic-Milosevic S, Lapter-Varga M, Slaj M. Analysis of the soft tissue facial profile of Croatians using of linear measurements. J Craniofac Surg. 2008b;19: 251–258. pmid:18216697
  27. 27. Bao B, Yu S, Tan J, Cai Y, Tian W, Ye X, et al. The analysis of frontal facial soft tissue of normal native adult of han race of Guangdong province by using the computer assisted photogrammetric-system. Hua Xi Kou Qiang Yi Xue Za Zhi. 1997;15: 266–268. pmid:11480018
  28. 28. Chiu C, Clark R. The facial soft tissue profile of the southern Chinese: prosthodontic considerations. J Prosthet Dent. 1992;68: 839–850. pmid:1432812
  29. 29. Choe K, Sclafani A, Litner J, Yu G, Romo TI. The Korean American woman's face: anthropometric measurements and quantitative analysis of facial aesthetics. Arch Facial Plast Surg. 2004;6: 244–252. pmid:15262719
  30. 30. Eliakim-Ikechukwu C, Ekpo A, Etika M, Ihentuge C, Mesembe O. Facial aesthetic angles of the Ibo and Yoruba ethnic groups of Nigeria. IOSR J Pharm Biol Sci. 2013;5: 14–17.
  31. 31. Etöz B, Etöz A, Ercan I. Nasal shapes and related differences in nostril forms: a morphometric analysis in young adults. J Craniofac Surg. 2008;19: 1402–1408. pmid:18812872
  32. 32. Ferdousi M, Mamun A, Banu L, Paul S. Angular Photogrammetric Analysis of the Facial Profile of the Adult Bangladeshi Garo. Adv Anthropol. 2013;3: 188–192.
  33. 33. Fernández-Riveiro P, Suárez-Quintanilla D, Smyth-Chamosa E, Suárez-Cunqueiro M. Linear photogrammetric analysis of the soft tissue facial profile. Am J Orthod Dentofacial Orthop. 2002;122: 59–66. pmid:12142898
  34. 34. Fernández-Riveiro P, Smyth-Chamosa E, Suárez-Quintanilla D, Susrez-Cunqueiro M: Angular photogrammetric analysis of the soft tissue facial profile. Eur J Orthod. 2003;25: 393–399. pmid:12938846
  35. 35. Gode S, Tiris F, Akyildiz S, Apaydin F. Photogrammetric analysis of soft tissue facial profile in Turkish rhinoplasty population. Aesthetic Plast Surg. 2011;35: 1016–1021. pmid:21487908
  36. 36. He Z, Jian X, Wu X, Gao X, Zhou S, Zhong X. Anthropometric measurement and analysis of the external nasal soft tissue in 119 young Han Chinese adults. J Craniofac Surg. 2009;20: 1347–1351. pmid:19816253
  37. 37. Husein OF, Sepehr A, Garg R, Sina-Khadiv M, Gattu S, Waltzman J, et al. Anthropometric and aesthetic analysis of the Indian American woman's face. J Plast Reconstr Aesthet Surg. 2010;63: 1825–1831. pmid:19962360
  38. 38. Kale-Varlk S. Angular photogrammetric analysis of the soft tissue facial profile of Anatolian Turkish adults. J Craniofac Surg. 2008;19: 1481–1486. pmid:19098536
  39. 39. Lee D, Kim W, Chung C, Kim S, Baek S. Photogrammetric study on the face of adult Korean female. J Korean Soc Plast Reconstr Surg. 1989;16: 423–432.
  40. 40. Lin C, Shaari R, Alam M, Rahman S. Photogrammetric Analysis of Nasolabial Angle and Mentolabial Angle norm in Malaysian Adults. Bangladesh J Med Sci. 2013;12: 209–214.
  41. 41. Loveday O, Babatunde F, Isobo U, Sunday O, Ijeoma O. Photogrammetric analysis of soft tissue profile of the face of Igbos in Port Harcourt. Asian J Med Sci. 2011;3: 228–233.
  42. 42. Malkoc S, Demir A, Uysal T, Canbuldu N. Angular photogrammetric analysis of the soft tissue facial profile of Turkish adults. Eur J Orthod. 2009;31: 174–179. pmid:19064675
  43. 43. Mostafa A, Banu L, Sultana A. Lower Jaw and Orolabial Analysis in Adult Bangladeshi Buddhist Chakma Females. Chattagram Maa-O-Shishu Hospital Med College J. 2013;12: 5–8.
  44. 44. Oghenemavwe E, Osunwoke A, Ordu S, Omovigho O. Photometric analysis of soft tissue facial profile of adult Urhobos. Asian J Med Sci. 2010;2: 248–252.
  45. 45. Osunwoke E, Omin E. Photometric facial analysis of soft tissue profile of Okrika adults. Annu Res Rev Biol. 2014;4: 1980–1987.
  46. 46. Ozdemir ST, Sigirli D, Ercan I, Cankur NS. Photographic facial soft tissue analysis of healthy Turkish young adults: anthropometric measurements. Aesthetic Plast Surg. 2009;33: 175–184. pmid:19089493
  47. 47. Porter J, Olson K. Anthropometric facial analysis of the African American woman. Arch Facial Plast Surg. 2001;3: 191–197. pmid:11497505
  48. 48. Porter J. The average African American male face: an anthropometric analysis. Arch Facial Plast Surg. 2004;6: 78–81. pmid:15023793
  49. 49. Reddy M, Ahuja N, Raghav P, Kundu V, Mishra V. Computer-assisted angular photogrammetric analysis of the soft tissue facial profile of North Indian adults. J Indian Orthod Soc. 2011;45: 119–123.
  50. 50. Sepehr A, Mathew PJ, Pepper JP, Karimi K, Devcic Z, Karam AM. The Persian woman's face: a photogrammetric analysis. Aesthetic Plast Surg. 2012;36: 687–691. pmid:22350308
  51. 51. Sim R, Smith J, Chan A. Comparison of the aesthetic facial proportions of southern Chinese and white women. Arch Facial Plast Surg. 2000;2: 113–120. pmid:10925436
  52. 52. Song W, Koh K, Kim S, Hu K, Kim H, Park J, et al. Horizontal angular asymmetry of the face in korean young adults with reference to the eye and mouth. J Oral Maxillofac Surg. 2007;65: 2164–2168. pmid:17954309
  53. 53. Ukoha U, Udemezue O, Oranusi C, Asomugha A, Dimkpa U, Nzeukwu L. Photometric facial analysis of the Igbo Nigerian adult male. Niger Med J. 2012;53: 240–244. pmid:23661886
  54. 54. Wamalwa P, Amisi SK, Wang Y, Chen S. Angular photogrammetric comparison of the soft-tissue facial profile of Kenyans and Chinese. J Craniofac Surg. 2011;22: 1064–1072. pmid:21586946
  55. 55. Wang J, Jang Y, Park S, Lee B. Measurement of aesthetic proportions in the profile view of Koreans. Ann Plast Surg. 2009;62: 109–113. pmid:19158515
  56. 56. Yoo J, Kim J, Shin K, Kim S, Choi H, Jeon H, et al. Centralization or decentralization of facial structures in Korean young adults. J Craniofac Surg. 2013;24: 1007–1010. pmid:23714934
  57. 57. Risch N, Burchard E, Ziv E, Tang H. Categorization of humans in biomedical research: genes, race and disease. Genome Biol. 2002;3: comment2007.2001–2007.2012.
  58. 58. Fang F, Clapham P, Chung K. A systematic review of interethnic variability in facial dimensions. Plast Reconstr Surg. 2011;127: 874–881. pmid:21285791
  59. 59. Farkas L, Forrest C, Litsas L. Revision of neoclassical facial canons in young adult Afro-Americans. Aesthetic Plast Surg. 2000;24: 179–184. pmid:10890944
  60. 60. Race E and Genetics Working Group. The Use of Racial, Ethnic, and Ancestral Categories in Human Genetics Research. Am J Hum Genet. 2005;77: 519–532. pmid:16175499
  61. 61. Lorusso L. The justification of race in biological explanation. J Med Ethics. 2011;37: 535–539. pmid:21546520
  62. 62. Schwartz R. Racial profiling in medical research. N Engl J Med. 2001;344: 1392–1393. pmid:11333999
  63. 63. Editorial: Genes, drugs and race. Nat Genet. 2001;29: 239–240. pmid:11687784
  64. 64. Aiach G, Laxenaire A, Vendroux J. Deepening the nasofrontal angle. Aesthetic Plast Surg 2002;26(suppl 1): S5. pmid:12454715
  65. 65. Brown M, Guyuron B. Redefining the ideal nasolabial angle: Part 2. Expert analysis. Plast Reconstr Surg. 2013;132: 221e–225e. pmid:23897350
  66. 66. Sinno H, Markarian M, Ibrahim A, Lin S. The ideal nasolabial angle in rhinoplasty: a preference analysis of the general population. Plast Reconstr Surg. 2014;134: 201–210. pmid:25068320
  67. 67. Yuan Y, Little R. Meta-analysis of studies with missing data. Biometrics. 2009;65: 487–496. pmid:18565168
  68. 68. Gelman A, Hill J. Data analysis using regression and multilevel/hierarchical models. Cambridge, UK: Cambridge University Press; 2007.
  69. 69. Wong W, Su X, Li X, Cheung C, Klein R, Cheng C, et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob Health. 2014;2: e106–e116. pmid:25104651
  70. 70. Curran P, Bauer D. Building path diagrams for multilevel models. Psychol Methods. 2007;12: 283–297. pmid:17784795