Background
Since the proportion and number of population residing in urban areas are increasing worldwide [
1], influence of urban features on human health has greater importance [
2]. This is especially true for developing countries where the urbanization rate is higher but relevant data is insufficient [
3]. Impact of urbanicity on health is complex and can be both positive and negative [
4]. Sufficiently detailed evidence is therefore needed for proper public health planning to maximize benefits while simultaneously minimizing the detrimental impacts of urbanization on residents’ health [
5]. Although evidence is available on the association of urbanicity and urbanization with human health in developing countries, most of the studies have relied on the urban–rural dichotomy in the exposure assessment. This procedure of urban exposure assessment is inadequate since it is not supportive for a detailed investigation of the nuance pattern of urbanization and health association [
6,
7].
A number of researchers have developed urbanicity scales for quantitative assessment of the “static” feature of urbanization [
7‐
16]. These scales have enabled the investigation of delicate patterns of urbanization and urbanicity impacts on population health [
17]. An example is the study by Gordon-Larsen et. al, in which the urbanicity scale was able to demonstrate delicate patterns of simultaneous impacts of urbanicity and urbanization on adult overweightness in China during 1991-2009 [
18]. Another example is Riha et al’s study in which the urbanicity scale developed by Novak et al. was used to reveal that even small increases in urbanicity in rural areas was associated with higher prevalence of chronic disease risk factors in Subsaharan Africa [
19]. However, the majority of the studies did not report the properties of the urbanicity scales [
17].
Some of these scales have been formally validated, including those developed by Dahly and Adair in the Phillippines context [
7], Van de Poel et al. and Jones-Smith and Popkin in a China context [
10,
14], and Novak et al. in a multi-country context (Ethiopia India Peru) [
15]. Van de Poel et al’s score is derived from the factor analysis of a set of 26 community level characteristics that reflect a community’s level of urbanicity [
10]. The scoring system of the other three scales is based on the equal-weight 10-point score for each component, with the number of components being 7 for Dahly and Adair’s [
7] and Novak et al’s [
15] and 12 for Jones-Smith and Popkin’s scales [
14]. The authors reported that validity of these scales was satisfactory in terms of unidimensionality, internal consistency, temporal stability, criterion–related validity when compared to both the official urban–rural dichotomy and four-category urban classification, and construct validity for various health and social outcomes. However, their robustness when utilized in other study settings is unknown. In addition, since these scales used different indicators, or variables, in their development (probably depending on local availability of related data), their comparability is still unknown [
17]. These deficiencies limit international comparison and generalization of study findings about urbanization, urbanicity, and health.
In this study, the potential utility of the previously validated urbanicity scales was further investigated, especially those based on the equal-weight 10-point score for each component. Specifically, this study’s objectives were: (a) to examine the comparability of the urban scales proposed by Dahly and Adair [
7], Jones-Smith and Popkin [
14], and Novak et al. [
15] in classifying the urbanicity level of villages and communities in Thailand; (b) to evaluate the validity and reliability of these scales in the Thailand context.
Discussion
Validity and reliability of an urbanicity scale is of utmost importance in the development of effective strategies to minimize adverse social and health consequences of increased urbanization [
17]. This study evaluated the comparability and robustness of the previously validated urbanicity scales proposed by Dahly and Adair [
7], Novak et al. [
15], and Jones-Smith and Popkin [
14] when utilized in a Thailand context. Results showed that while correlation among the three scales was high, those proposed by Dahly and Adair and Jones-Smith and Popkin were more comparable. As for the properties of the scales, all three scales had good criterion-related validity (as demonstrated by the significant differences in the mean urbanicity scores across the official urban–rural dichotomy and four-category urbanicity classification) and construct validity (as demonstrated by their significant association with the mean per capita monthly income and body mass index). The unidimensionality assumption was, however, attained only for Dahly and Adair’s scale, and the internal consistency was satisfactory only for Jones-Smith and Popkin’s. Overall, Jones-Smith and Popkin’s scale had the highest validity and reliability among the three scales.
This evidence ensures the generalizability of the study’s findings about the association of urbanicity/urbanization with social and health impacts from one area to another in developing countries. However, when the urbanicity level is the main interest, caution is required when comparing different studies since the urbanicity scales used in the studies might not be comparable.
Although the urbanicity scores and existing official urban–rural classifications were highly correlated, the quantitative nature of the formers render their superiority over the latters in facilitating the detection of more delicate patterns of the health and social impacts of urbanicity/urbanization (such as nonlinearity pattern, differential impacts among communities within the same category of urban–rural classification) [
43]. Since communities in the same category of the official urban–rural classifications are actually heterogeneous in terms of development level, the qualitative nature of the official urban–rural classifications may obscure nuance, or significant details of urbanicity/urbanization impacts. This issue has already been demonstrated in a number of previous studies [
7,
14,
18,
19].
Since the proposed components in these three scales - based on the existing literature - were all associated with urbanicity, unidiemnsionality was therefore assumed for these scales [
9,
44‐
46]. However, when applying these scales to this study’s context, two out of three of the scales did not comply with this assumption. The only scale with unidimensionality actually had relatively low internal consistency, which may cause a misleading conclusion about its dimensionality [
47]. In addition, the magnitude of internal consistency and criterion-related validity was also less when compared to the original validation results. However, items with low inter-correlations and/or no unidimensionality can yield an interpretable scale provided that a large proportion of the test variance is attributable to the first principal factor, as is the case for these scales [
47].
This study’s variant findings about dimensionality and internal consistency of the urbanicity scales might be due to many possibilities. The most likely explanation is the differences in the data sources, the definition of variables, and their measurement methods used in the scale composition. This study relied solely on existing secondary data on village, sub-district, and even district levels. Definition and scoring procedure of the urbanicity related variables, therefore, had to be made to accommodate the available data. The application of Dahly and Adair’s and Jones-Smith and Popkin’s scales was largely affected by these issues, since a signification modification had been done. One example is that stricter definitions were given for sewage treatment system and bus station; another was relying on data at district level for housing related variables, which resulted in less variability of these two components in the Jones-Smith and Popkin’s scale. Relying on primary data collection may minimize these differences and improve the validity and reliability of the scales; however, this will require a higher budget.
In addition, the differences in the boundary of the study community can also be another explanation, especially for Novak et al’s scale. Based on the information on the average number of population in the community of Novak et al’s scale (8,538 and 3,855 for mean and median, respectively) and this study’s (621 and 508) [
15], their community was comparable to this study’s sub-district rather than village. This affected this study’s differential scoring results of many urbanicity components, particularly population size and educational facilities in a locality, which had very low correlation with other urbanicity components and diverged from the main factor in the factor analysis.
Since a significant proportion of data were imputed in this study, inaccurate data and bias from improper imputation might also be another possible explanation. The most concern was for the imputation of some data of the municipal communities by data from the most comparable villages (with possibly lower urbanized level). Some component scores (such as population size and density and paved road density) of these communities might therefore have been underestimated, resulting in lower correlation and failure to achieve unidimensionality among the component scores of the municipal communities. This possibility was examined by reanalyzing the data without these communities in the dataset. The results were however not significantly changed (details not shown).
Lastly, in real world data, the assumption on strict unidimentionality may not be practical [
48]. In this case, the “essential” unidimensionality — which may reflect several traits but one that very clearly dominates - may be more appropriate [
48]. Since urbanicity and urbanization have multiple determinants and their stage and pattern are heterogeneous in different areas, both among and within countries, this supposition may be relevant [
49,
50]. However, due to low communality (<0.20) and the skew of some study variables, this study was unable to examine this issue as a larger sample size is required. This issue needs further investigation.
The Jones-Smith and Popkin’s scale seemed to be slightly superior to the other two scales in terms of internal consistency as well as criterion-related and construct validities. In addition to its higher Pearson’s Spearman’s correlation coefficients and Kappa statistic (Table
3), its scores were well distributed in the stepwise manner according to the official four-category urban classification (Fig.
1). Furthermore, per capita monthly income and body mass index did significantly increase across the quintiles of the urbanicity score (Fig.
2c and (
f) ). This was not always the case for the other two scales. The Jones-Smith and Popkin’s scale differed from the other two scales in many ways, including its larger number of components and finer gradation in the scoring of transportation, health, and modern market components [
14]. In the scoring of these components, size, number, and proximity of the institutions/facilities/services were taken into consideration, resulting in higher variability of the urbanicity component scores. However, it must be weighed against higher requirements for more detailed data that might not be available/exist in certain countries. These observations can be useful for the future development or improvement of urbanicity scales.
Notwithstanding the above defect, all scales worked well in terms of criterion-related validity and construct validity. This was quite consistent with existing evidence on the relationship of urbanicity with health and social parameters, including per capita income and body mass index. For body mass index, this study provides firmer evidence by minimizing the potential confounding effect of age and gender in the analysis of urbanicity level and body mass index relationship. This means that the study’s findings about urbanicity and health and social impacts by using these urbanicity scales can be generalized internationally.
Although the sample size was quite large (537 villages and communities) compared to previous studies (118–270 villages/communities) and represented the whole country, some limitations need mentioning. First, communities in Bangkok, the capital city of Thailand, were not included in this study, since detailed community-specific data were not available. The extent of applicability and robustness of the urbanicity scales when utilizing in highly urbanized areas is still unconfirmed. Second, the validity of Van de Poel et al’s scale (of which the scoring system is based on factor analysis) was not able to be verified in this study due to a lack of relevant data as mentioned previously. Last, since a number of modifications on the original scales had been made in this study, it is still uncertain that some altered validation results are due to these modifications or the properties of the original urbanicity scales. Future studies that rely on primary data collection could make issues resulting from these limitations clearer.
Authors’ contributions
WJ conceived of the study, obtained the data, carried out the analyses and drafted the manuscript. WA obtained the data and reviewed the manuscript. TW conceived of the study and reviewed the manuscript. All authors read and approved the final manuscript.