Development of a territory-wide household-based composite index for measuring relative distribution of households by economic status in individual small areas throughout Hong Kong

Development of a territory-wide household-based composite index for measuring relative distribution of households by economic status in individual small areas throughout Hong Kong | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development of a territory-wide household-based composite index for measuring relative distribution of households by economic status in individual small areas throughout Hong Kong Eva L.H. Tsui, Philip L.H. Yu, K. F. Lam, Kelvin K.Y. Poon, Adam C.M. Ng, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3977343/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Dec, 2024 Read the published version in BMC Public Health → Version 1 posted 11 You are reading this latest preprint version Abstract Background Many countries have developed their country/nation-wide multidimensional area-based index on deprivation or socioeconomic status for resource allocation, service planning and research. However, whether each geographical unit proxied by a single index is sufficiently small to contain a relatively homogeneous population remains questionable. Globally, this is the first study that presents the distribution of domestic households by the territory-wide economic status index decile groups within each of the 2,252 small subunit groups (SSUGs) throughout Hong Kong, with a median study population of 1,300 and a median area of 42,400 m 2 . Methods The index development involved 248,000 anonymized sampled household-based data collected from the population census, representing 2·66 million domestic households and 6·93 million population in mid-2021. Our composite index comprises seven variables under income-/wealth-related and housing-related domains with weights determined using the analytic hierarchy process. After ranking all households from the most to the least well-off according to the numeric/ordinal value of each variable and then calculating their weighted rank scores, they were segregated into ten deciles from D1 (top 10% most well-off) to D10 (bottom 10%). Their relative distribution was summarized in a three-dimensional ternary plot to distinguish patterns across the 2,252 SSUGs within the 18 administrative districts. Results In Hong Kong, of the 2,252 SSUGs, only one-quarter contain a homogeneous composition of households with similar economic status, while the other three-quarters are heterogeneous to varying extents. Of the 18 administrative districts, only two are concentrated with more homogeneously well-off SSUGs. Conclusions Small-sized geographical units may contain a heterogeneous composition of households with diverse economic statuses, underlying the need for more precise information to quantify their relative distribution. Results of this study will be disseminated via an online interactive map dashboard which can serve as a versatile planning tool capable of performing analysis at different varying geographic scales for community-based resource prioritization, service planning and research across different domains. Socioeconomic status index SES deprivation small-area analysis Hong Kong Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background The World Health Organisation (WHO) highlights the importance of social, environmental and economic determinants of health and how they impact health equity. 1 Prior research has shown inverse relationships of an individual’s socioeconomic status (SES) with health problems including chronic kidney disease, cardiovascular disease, stroke, breast cancer, hypertension, and type 2 diabetes. 2–7 That said, individual SES cannot fully explain the observed spatial variation in health outcomes such as disease prevalence rate or mortality rate. Area-based studies illustrate that the socioeconomic conditions of the neighbourhood where one lives also play an important role. 8–10 Back in 1979, Townsend’s formulation of multiple deprivation, both social and material, was the first theoretical framework for the model of small-area multiple deprivation as a composite index of different domains of deprivation in the United Kingdom (UK). 11 Across the world, countries propelled either by government initiatives or academic efforts have developed their own multidimensional measure on deprivation or SES at a small-area level. These measures, augmented by visualization on maps to show the resultant geographical variations over small areas, are applied to resource allocation, service planning and research. 12 UK was amongst the pioneering countries to develop indices for regularly monitoring health inequality and applying them to policy making. The developed indices have evolved in phases to today’s English Indices of Deprivation (IoD) in line with Townsend’s definition of relative poverty. 11 The current English IoD comprise seven domains of deprivation, with each measured independently using the best indicators available to generate a domain score. The seven scores are then combined with explicit weightings to generate a multiple-deprivation measure. 11 More than 15 countries have developed similar indices with varying numbers of dimensions, domains and variables. 11,13–27 (Additional file 1) In this study, a similar but household-based index was developed for Hong Kong, one of the most densely populated areas in the world with a population of 7·4 million in mid-2021 on a total land area of 1,117 square kilometres (km 2 ), of which 40% are country parks and areas designated for nature conservation. 28,29 There are 18 administrative districts, each with a district council. For town planning purposes, the territory is also demarcated into 292 tertiary planning units (TPUs) and 4,916 subunits (SUs). Three prior local studies have produced small-area-based indices, including a deprivation index at both street block (currently replaced by SU) and TPU levels for exploring their association with suicide rate, 30 and two other indices at TPU level for investigating their association with cancer mortality and air pollution. 31,32 Literature review showed that the majority of area-based indexes used mainly population census data, with some complemented by administrative data. The common domains included are income, wealth, housing, employment, and education, while crime, health, and crowding are domains selectively adopted according to the local context. Most indicators for individual domains were chosen according to available information from the census, theoretical framework, previous research studies, particularly the English IoD and correlation with other indicators. Owing to data availability and limitations, only aggregate statistics pertaining to each geographical area were compiled in the process; hence, existing indexes worldwide have only a single index value (either a decile/quintile/quartile, score or rank) derived for each geographical unit. The suitability of using a single index value to proxy an area has been much discussed. The issue in question is whether the defined geographical unit is sufficiently small to contain a relatively homogeneous population while being sufficiently large to provide robust data for statistical analysis. 23 For instance, in New Zealand’s Deprivation Index, a particular geographical unit with a sparse population in a large geographic locality may hide tiny pockets of deprivation. In such context, a small local survey measuring individuals’ deprivation may be a better alternative. 21 Although the English IoD has been implemented to resemble an ‘ideal’ geographical unit, an area measured as relatively deprived may contain a large number of people who are not deprived and vice versa, rendering more comprehensive individual-level analyses of multiple deprivation necessary. 12 This study aims to explore whether Hong Kong, given its high population density, has the above issue related to the extent of homogeneity of a defined small area. A novel approach was adopted to construct the “General Household Economic Status Index” (GHESI) for Hong Kong. Instead of deriving a single index value to proxy each geographical unit, this study generates the distribution of domestic households by the GHESI’s decile groups within each geographical unit defined at different geographical scales from small to large. Moreover, their differences in pattern within and between geographical scales are summarized and visualized on maps and ternary plots. Methods Data source, study population and scale of geographical unit The main data source of this study was anonymized granular household-based data collected by the 2021 Population Census. In addition to basic demographic information of all households and live-in persons, a broader range of socioeconomic characteristics were collected through a long-form questionnaire randomly administered to 10% of territory-wide households. In light of the relevance of GHESI variables to the study population, we excluded around 0·49 million individuals from the 2021 Census data, including persons living in non-domestic households such as homes for the aged as well as non-domestic buildings, unsheltered accommodation or vessels and foreign domestic helpers who are often regarded as a separate economic entity from employer’s household. The final 248,000 sampled household-based data represent 2·66 million domestic households and 6·93 million population in mid-2021. The study population resided in 3,102 SUs. Consistent with the standard practice in publishing Census results, SUs with population size below 400 were aggregated, yielding a total of 2,252 small SU groups (SSUGs), with a median study population of 1,300 (inter-quartile range = 2,300) over a median area of 42,400 square metres (m 2 ). To identify the geographical pattern of larger administrative units, these SSUGs were segregated into 18 district council districts (DCD), with a median study population of 381,300 over a median area of 31 km 2 . GHESI development Area-based socioeconomic domains commonly included in overseas indexes (Additional file 1) are income, wealth, housing, education and employment. Pathways between these socioeconomic determinants of health have been previously identified. For instance, education is associated with occupation and in turn both are associated with income. 33 Contrary to overseas indexes using unemployment-related indicators in the employment domain, this study included under the household income variable both employed-related cash income and unemployment-related government subsidies and social security allowances. 28 Education, occupation and employment were deliberately not included as variables due to their strong correlation with household income and their counting units inappropriate for the present household-based index. The GHESI thus developed comprises seven variables selected for the income-/wealth-related domain and housing-related domain. (Table 1 ) To determine the weights for combining domains/indicators into an overall composite index, this study adopted the analytic hierarchy process (AHP), which is a multi-criteria decision analysis method involving both qualitative and quantitative assessments. 34 AHP generates a set of weights through a series of pairwise comparisons of variables at different hierarchical levels performed by decision-makers or stakeholders, quantifying their personal preference on an intensity scale of importance (1 = equal importance to 9 = extreme importance) with reciprocals. 35 Twelve experts from different disciplines were invited to judge, via pairwise comparisons, the relative importance among the seven variables with respect to the goal of ranking territory-wide households in terms of economic status during a focus group discussion. Each expert scored individually, and the geometric mean of their intensity scores was calculated to represent the overall view of the expert panel. 36 Our study has refined the AHP by sequencing the pairwise comparisons into first the within-domain stage, followed by the between-domain stage. The consistency ratios of pairwise comparisons for income-/wealth-related and housing-related domains are 5·26% and 2·37%, respectively, both less than the pre-specified 10% threshold requirement. 35 The final weights for the seven variables are shown in Table 1 . Additional file 2 provides a detailed account of the logistic arrangements and how these weights were derived from the aggregated pairwise comparison matrix. Table 1 Seven variables included in GHESI Domain Variable Description Weights derived using Analytic Hierarchy Process Income-/wealth- related ( 1 ) Household income per household member The household income includes total cash income from all jobs and other sources such as rent, interest and government subsidies. 37 30·4% ( 2 ) Number of live-in workers For example, foreign domestic helpers/ chauffeurs/ gardeners. 9·6% ( 3 ) Accommodation 100% owned Whether the accommodation is owner-occupied without mortgage and loans. (Yes/No) 7·3% Housing-related ( 4 ) Type of housing Four categories in ordinal order: Private permanent housing, Home of subsidised housing, Public rental housing, Temporary housing 10·1% ( 5 ) Subdivided Units (SDUs) Subdivided units are formed by splitting a unit of quarters into two or more “internally connected” and “externally accessible” units. 37 (Yes/No) 10·8% ( 6 ) Saleable area per household member Saleable area is the floor area exclusively allocated to a residential unit including balconies, verandahs, utility platforms and other similar features but excluding common areas, bay windows, roofs, other similar features and car parking spaces. 38 The saleable area of each sampled household was divided by the respective number of study household member(s) collected from the population census to compute the value of this variable. 37 16·8% ( 7 ) Rateable value per saleable area# The rateable value is an estimated annual rental value of a property at a designated valuation reference date, assuming that the property was then vacant and to let from year to year, with due adjustments made during the assessment to reflect any differences in size, location, facilities, standard of finishes and management, etc. of the property. 38 15·0% # based on the rateable value for 2021–22 at the designated valuation reference date of 1 October 2020 provided by the Rating and Valuation Department Standardizing the variables in Table 1 is necessary as they have different scales. As in prior studies, 11,14,15,23, the data values of households were first transformed to ranks from the most to the least well-off, for both continuous and categorical variables with intrinsic ordinal values, taking into account the sampling gross-up factor of each sampled household. For ties in ranking, a shared rank was used. The ranks of the seven variables assigned to each household were then summed up with the AHP-derived weights to calculate the weighted rank score. The study households were then segregated into ten deciles according to the weighted rank scores, ranging from decile 1 (D1) representing the top 10% of households (most well-off) to D10 representing the bottom 10% (least well-off). The frequency distribution of households by GHESI deciles as well as mean and standard deviation (SD) were output for each geographical unit. For visualization purposes, each frequency distribution was further amalgamated into three classes, namely D1–D3, D4–D7 and D8–D10, and plotted as a dot to indicate its relative distribution among these three classes on a ternary plot. 39 This graphical approach helped distinguish patterns of frequency distributions across the 2,252 SSUGs, and within each of the18 DCDs in Hong Kong. On the three-dimensional ternary plot with the scale from 0–100% that indicates for each geographical unit its relative distribution on D1–D3 (top 30%), D4–D7 (middle 40%) and D8–D10 (bottom 30%), every intersecting point of the three scale lines is summed to 100%. The entire triangular area in Fig. 1 is stratified into seven coloured categories, each representing a different form of distribution after taking into account the statistics on four moments (mean, SD, skewness and kurtosis) as listed in Additional file 3. The categories in darker/lighter green on the left are (A) strongly positive-skewed and (B) weakly positive-skewed; being a mirror image in darker/lighter red on the right is (G) strongly negative-skewed and (F) weakly negative-skewed; and in the middle from orange-yellow to yellow is (C) with a modal peak in the middle class, (D) widely dispersed distribution with a central tendency and (E) bi-modal distribution which peaks on two opposite ends. Figure 1: Rules to classify ternary plot into seven coloured categories GHESI validation GHESI was validated using primarily the Census’ rich database. The methodology and results are detailed in Additional file 4. For construct validity, GHESI results were correlated against a common attribute of poverty defined in terms of income below a threshold (locally set at 50%) with respect to median household income by household sizes. 40,41 The external criterion for validation was selected with reference to the Hong Kong Commission on Poverty, which has identified subdivided units, single-parent households and elderly households as target groups of poverty alleviation. 42 GHESI was also externally validated against a specified public healthcare user group. In addition, the association of the three excluded social-related variables (education, occupation and employment) with GHESI was examined. In brief, GHESI demonstrated satisfactory construct and criterion validity. Statistical analyses were performed and thematic maps were compiled using SAS 9·4, Python 3·9·11, ArcGIS Pro 3·0·1. Results The economic status of 2,252 SSUGs in Hong Kong is summarised on the three-dimensional ternary plot shown in Fig. 2, with the red cross representing the territory-wide figure and grey dots denoting SSUGs in size proportional to their respective number of study households. Of the seven categories, Categories A and G are the most homogeneous SSUGs in terms of economic status profile as reflected by the lowest SD (Additional file 3). The 411 SSUGs (18·3%) in Category A have the majority of households which are relatively more well-off, with at least 70% being classified into D1–D3 representing the top 30% households. On the contrary, the 113 (5·0%) SSUGs in Category G have the majority of households which are relatively less well-off, with at least 70% being classified into D8–D10 representing the bottom 30% households. There are only 68 SSUGs (3·0%) in Category C, with at least 70% of households belonging to the middle class (D4–D7). Category B accounts for the largest share of SSUGs (33·8%), characterized as relatively less homogeneous but skewed towards more well-off. Conversely, Category F contains 14·5% of SSUGs with a reversed economic status pattern compared with Category B’s. For the remaining two Categories which are most heterogeneous in composition as indicated by their higher SDs, Category D contains 25·1% of SSUGs with a widely diverse economic status, while Category E comprises very few SSUGs (n = 6 or 0·3%) having a bi-modal distribution with households clustered in two opposite ends. In summary, around one-quarter of SSUGs (in Categories A, C and G) are more homogeneous while another one-quarter of SSUGs (in Categories D and E) are more heterogeneous. The ordering from Category A to Category G also reveals an economic status gradient from high to low, with the lowest mean decile value among SSUGs in Category A (= 2·3) rising to the highest in Category G (= 8·6). In addition, the less well-off Categories F and G account for a disproportionately larger percentage share of total households and population compared with their share of SSUGs, and vice versa for the more well-off Categories A, B as well as the middle Category D. Figure 2: Classification results of SSUGs into seven coloured categories on ternary plot Note Red cross represents the territory-wide figure. Each grey dot represents a SSUG in size proportional to its number of study households. Table 2 provides a two-way distribution of the 2,252 SSUGs by nine mean decile value groups from 1.0 to 10.0 and seven Categories on a ternary plot. Most SSUGs with the lowest and highest mean decile value are concentrated in Categories A and G, respectively. As the mean decile value increases from 2.0 to 4.0 or decreases from 9.0 to 7.0, the concentration shifts gradually towards Categories B and F, respectively. The middle three groups with a mean decile value of 4.0–7.0 account for 45% of SSUGs which predominantly spread over Categories B and D, D only or D and F, respectively. The present findings indicate the absence of a one-to-one relationship between mean decile value and Category, as evidenced by SSUGs in the mean decile group of 5.0–<6.0 spreading across five different Categories. Table 2 Distribution of SSUGs by households’ mean decile value and Category on ternary plot Mean decile value of households in SSUG Category labelled for SSUG Total A B C D E F G 1.0–<2.0 174 0 0 0 0 0 0 174 2.0–<3.0 233 92 0 0 0 0 0 325 3.0–<4.0 4 393 0 1 0 0 0 398 4.0–<5.0 0 271 28 131 0 0 0 430 5.0–<6.0 0 6 34 295 6 6 0 347 6.0–<7.0 0 0 6 135 0 103 0 244 7.0–<8.0 0 0 0 4 0 172 1 177 8.0–<9.0 0 0 0 0 0 45 100 145 9.0–≤10.0 0 0 0 0 0 0 12 12 Notes: For mean decile, the lower the value, the higher the economic status and vice versa. For Categories A to G, refer to the classification rules with respect to ternary plot’s triangular area shown in Fig. 1. For each of the mean decile groups, the predominant Category/ies is/are bolded. Figure 3 shows ternary plots with all SSUGs geographically demarcated into 18 DCDs. For Central & Western/Wanchai, 46%/45% of their SSUGs are classified into Category A and 48%/49% into Category B, indicating that these two DCDs are concentrated with more homogeneously well-off SSUGs. Similarly, Kowloon City/Southern have 35%/40% of SSUGs falling in Category A and 26%/32% in Category B. The respective share of SSUGs in the remaining 14 DCDs ranges from 0–27% in Category A (homogeneously more well-off); 0–7% in Category C (homogeneously middle class) and 0–15% in Category G (homogeneously less well-off). In other words, none of the DCDs have SSUGs exceeding 27% in any of these three Categories. Instead, 68–89% of the SSUGs spread across Categories B, D, E and F, revealing dispersion in economic status distribution by varying extents. Figure 3: Display of SSUGs in each of the 18 DCDs on the ternary plot Note Red cross represents the overall district figure. Each grey dot represents a SSUG in size proportional to its number of study households. SUs, the smallest geographical units for town planning, can be aggregated to TPUs at the next hierarchical level. In this study, the frequency distribution of domestic households by ten GHESI deciles, its mean and SD together with the study variables’ distribution by geographical units at different aggregated SU/TPU groups and DCD levels are open for access in an online interactive map dashboard (website link to be provided upon the study publication). Figures 4 and 5 provide two thematic maps with a gradient colouring scheme to respectively indicate the Category and mean households’ decile value of SUs throughout Hong Kong. These illustrations provide a spatial visualization on the distribution pattern and central tendency of households by economic status, with their inter-relationships as outlined above. Figure 4: Thematic map of SUs coloured by category on ternary plot Figure 5: Thematic map of SUs coloured by mean GHESI decile value of households Discussion Compared with existing indexes, the GHESI comprises unique indicators, namely the rateable value per saleable area and saleable area per household member (Table 1 ). Both are representative measures reflecting the estimated annual rental value of a property and degree of crowding, which generally exhibit an economic status gradient. To combine different domains and associated indicators into an overall index, most overseas/local indexes adopted the statistical modelling approach using principal component analysis (PCA) or factor analysis for dimension reduction and then derivation of their weights. However, the classical PCA, which usually requires a specific structure on the input data, cannot be applied to our study data made up of continuous and categorical variables in different scales and distributions. 44 In contrast, other indexes use either equal or differential weights through experts’ subjective judgement, which can be arbitrary. In this study, the AHP method, a hybrid approach combining both qualitative and quantitative assessments, was adopted. AHP had been applied to the reuse selection of historic buildings in Taiwan and was found to encompass the interdependencies among various criteria and enable decision-makers to better understand their complex inter-relationships, hence improving the decision’s acceptability. 45 We have refined the AHP method by including all 21 pairwise comparisons among the seven variables, starting from the nine within-domain, followed by the 12 between-domain comparisons. We have devised a formula (Additional file 2) to derive the relative weighting between the two domains, replacing the conventional approach of judging the two domains at the first hierarchical level, followed by judging the criteria/sub-criteria at the lower level(s). Although more pairwise comparisons (21 versus 10) were required under our refined AHP approach, they could meet the pre-set 10% consistency ratio threshold. Methodologically, the final weights for the seven variables were still generated from the pairwise comparison matrix. The GHESI demonstrated satisfactory construct validity on both household and area bases, suggesting that it is generally consistent with the definition of poverty according to income. In fact, the income variable/indicator accounts for the largest weight among all variables/indicators in most overseas SES/deprivation indexes. 11,21,22 In addition, the GHESI was found to be strongly associated with education, occupation and employment, supporting our deliberate decision to exclude them from the GHESI in view of the identified socioeconomic pathways. 33 Likewise, this study finding challenges the necessity of including all socioeconomic variables in SES/deprivation indexes. Even when included, the three excluded variables highly correlated with the GHESI could only marginally improve the Index’s accuracy, and there is uncertainty whether some domains might be over-weighted particularly when the AHP method is adopted. Criterion validation of the GHESI using the three locally defined target groups of poverty alleviation and one public general outpatients group found supporting evidence that the GHESI developed in this study is a valid measure of household economic status in Hong Kong. Population size may vary widely among geographical units. When ranking areas on individual indicators, for areas with very low population, technical approaches like exclusion from study population, smoothing method or shrinkage method to borrow strength from the neighbouring areas were adopted in overseas indexes to reduce the variability of indicators. 11,14 This study aggregated the results into small/large groups with minimum size requirements to ensure data reliability. Overseas studies consider combining variables or summing the raw rank of all variables undesirable because non-deprivation in one variable might in effect cancel out deprivation in another variable. 11,14,23 To address the problem, they adopted an exponential transformation on the rank for each variable such that the transformed score of less well-off areas is spread out and less likely to be offset by a higher rank on other variables. However, this technical approach is not necessary for our study which can output the relative distribution of households in each geographical unit by GHESI deciles from economically most to least well-off, instead of merely identifying deprived areas. In the past, most local studies could only resort to using a single indicator of household income to proxy SES. Take for example Sham Shui Po (SSP). Among the 18 DCDs, SSP ranked lowest in median monthly domestic household income according to the Census results, but ranked 12th in mean GHESI decile value. While Census’ income distribution results showed that SSP had a higher proportion of households with monthly household income exceeding HK $ 100,000 (6·3% versus 3·0%–3·7%), its proportion of households with monthly household income below HK $ 10,000 (23·0% versus 21·0%–23·1%) was comparable to that of six DCDs (M–R in Fig. 3), which ranked 13th to 18th in mean GHESI decile value. In addition, when compared with these six DCDs on the other six variables included in the GHESI, SSP had a relatively higher average rateable value per saleable area and a larger proportion of subdivided units, but fell predominantly within mid-range on the other four variables. As shown in the ternary plot, small areas in SSP (L in Fig. 3) are widely dispersed, with 23·6% of households falling in D1–D3, 37·5% in D4–D7, and 38·9% in D8–D10, as indicated by the red cross with the overall DCD being classified into Category D. The example of SSP clearly underlines the limitation of adopting a single indicator or a single index value to proxy the economic status of a small area/DCD in Hong Kong. In an extreme case with half of the population being very rich while the other half being very poor, problems arise if resource planning is made solely on the basis of median household income. In the ternary plots of the above example, frequency distribution of households’ economic status was reduced from ten dimensions (i.e., deciles) to three dimensions to visualize, summarise and differentiate the patterns across geographical units. If this study follows overseas indexes in using a single SES index value to proxy individual small areas, the majority of SSUGs would fall in Categories A, C or G. However, of the 2,252 SSUGs in Hong Kong with a median area as small as 42,400 m 2 , only one-quarter are found in these three Categories having a homogeneous composition of households with similar economic status, whereas the remaining three-quarters demonstrate heterogeneity to varying extents. Such refined small-area-based information would add value to planning community-based services or programmes for defined target groups. What contributes to the diverse patterns observed across geographical areas is outside the scope of this study. Since the early 1990s, the Hong Kong Hospital Authority has implemented a single electronic health record system for patients utilizing services in all 43 public hospitals in the territory. 46 Then in 2016, the government launched the Electronic Health Record Sharing System (eHealth), providing an electronic platform with the goal of consolidating free and lifelong electronic health records for all members of the public. 47 Though both systems contain clinical/health data and detailed personal information, they lack essential socioeconomic characteristics of patients required for studying the association between SES and health status or outcome. On the other hand, health data are not available in the population census. Therefore, the GHESI output, detailing relative distribution of household economic status across areas at varying geographic scales, may potentially serve as a surrogate predictor for health outcomes in future research. In Hong Kong, the Institute of Health Equity has recently been established to study health equity issues and advise the government on policies and intervention programmes for improving health equity. One of its strategic suggestions is to create/enhance data collection. 48 The main goal of this study is to construct an index that can serve as a planning tool with flexibility on the choice of geographic scale for varied uses and applications. The GHESI is a generic tool, not only applicable in the health context, but also in other domains such as social welfare, and housing. With rapid developments across the local territory, this version of GHESI would eventually be outdated, given the time lag in the output of five-yearly census data. Nonetheless, this is a pioneer study for Hong Kong. There are areas for improvement and refinement in the methodological aspects, along with the need for regular updates in alignment with the subsequent rounds of population census to ensure its timely release for applications. Conclusion In addition to developing a composite territory-wide index for measuring economic status of domestic households, this is the first study that made use of the census household-based records to output the relative distribution of households in individual small areas by the GHESI decile groups. We have also pioneered in adopting the analytic hierarchy process method to determine the weights of the selected variables. Among the 2,252 individual small geographical units in Hong Kong, only one-quarter exhibit a highly homogeneous economic status profile, with the other one-half demonstrating lower homogeneity, while the remaining one-quarter can be characterized as heterogeneous. In addition, detailed results of this study will be disseminated for public access via an online interactive map dashboard to enable flexible analysis and visualization at different geographic scales to meet varied uses and applications, by policymakers and researchers, not only in health but also in other domains, in support of community-based resource prioritization, service planning and research. Nonetheless, in view of rapid developments in the economy, this Index needs to be updated regularly in line with the five-yearly population census. Abbreviations AHP Analytic hierarchy process DCD District council districts eHealth Electronic Health Record Sharing System GHESI General Household Economic Status Index IoD Indices of Deprivation Km 2 Square kilometre M 2 Square metre SES Socioeconomic status SSP Sham Shui Po SSUGs Small subunit groups SUs Subunits TPUs Tertiary planning units UK United Kingdom WHO World Health Organisation Declarations Ethics approval and consent to participate The ethics approval for the conduct of this study is not required because (i) the 248,000 anonymized sampled household-based data for the Index development were collected from the population census which was conducted by the Census and Statistics Department of the Hong Kong Special Administrative Region Government according to the Census and Statistics Ordinance (Cap 316); 49 and (ii) the presentation of this study’s findings is consistent with the standard practice in publishing Census results. Consent for publication Not applicable. Availability of data and materials The anonymized granular household-based records sourced from the 2021 Population Census of Hong Kong are not publicly available according to the Census and Statistics Ordinance (Cap 316). 49 The aggregate data for geographical units at different levels, ranging from small to large SU groups, small to large TPU groups, and then to DCDs will be available for online public access via an interactive map dashboard (website link will be provided upon the study publication). Competing interests The authors declare that they have no competing interests. Funding This study received no external funding. Author’s contributions ELHT, PLHY, KFL, KKYP, ACMN and KYC conceptualized the study and designed the methodology. KYC and WL conducted literature review. Data curation was performed by KKYP, ACMN, KYC, DHYL and JLYC. Formal analysis was conducted by ELHT, KKYP, ACMN and KYC. KYC, MLHL and DHYL were responsible for data visualisation. ELHT, PLHY, KFL, KKYP, ACMN, KYC, and SPWN validated the results. ELHT, PLHY, KFL, KKYP and ACMN supervised the study. ELHT, KYC and WL wrote the first draft of the manuscript. All authors critically reviewed and edited the manuscript and agreed with the decision to submit for publication. Acknowledgements This research was supported by the Health Bureau, the Census and Statistics Department and the Rating and Valuation Department of the Hong Kong Special Administrative Region Government. We would like to acknowledge the valuable support from the expert group towards the research process to determine the weights of the Index variables. The members include Mr Andrew SH Au, Prof Kara KW Chan, Ms Marion SY Chan, Ms Ivy WH Cheung, Prof Alfred TK Ho, Prof WK Li, Mr Duncan TY Ma, Mr Tim HC Pang, Ms KL Pang, Mr Kenneth LK To, Prof Wilson WH Wong and Prof Samuel YS Wong. References Social Determinants of Health – World Health Organization [Internet]. 2021 [cited 2023 Dec 6]. 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Available from: https://www.oecd.org/statistics/data-collection/Income%20distribution_guidelines.pdf . Statistics Department HKSAR. 2021 Nov [cited 2024 Jan 2]. Available from: https://www.censtatd.gov.hk/en/data/stat_report/product/B9XX0005/att/B9XX0005E2020AN20E0100.pdf . The Chief Executive’s 2023 Policy Address . HKSAR.2023 Oct 25[cited 2024 Jan 2]. Available from: https://www.policyaddress.gov.hk/2023/public/pdf/policy/policy-full_en.pdf . Introduction of General Out-patient Clinic Services . Hospital Authority, Hong Kong. [cited 2024 Jan 2]. Available from: https://www.ha.org.hk/visitor/ha_visitor_index.asp?Content_ID=10052 . Kolenikov S, Angeles G. Socioeconomic status Measurement with Discrete Proxy Variables: Is Principal Component Analysis a Reliable Answer? Rev Income Wealth. 2009;55(1):128–65. Wang HJ, Zeng ZT. A multi-objective decision-making process for reuse selection of historic buildings. Expert Syst Appl. 2010;37(2):1241–9. Past P. & Future Clinical Management System (CMS) for Hospital Authority in Hong Kong – A Journey of 20 + years[Internet]. Hospital Authority, HKSAR; 2016 [cited 2023 Dec 13]. Available from: https://www.ha.org.hk/haconvention/hac2016/proceedings/downloads/IHF1.4.pdf . What’s eHealth [Internet]. eHealth Record Office, HKSAR; [cited 2023 Dec 13]. Available from: https://www.ehealth.gov.hk/en/whats-ehealth/index.html . Assess Health Equity and Identify Social Determinants of Health. CUHK Institute of Health Equity, The Chinese University of Hong Kong; 2022 [cited 2023 Dec 13]. Available from: https://www.ihe.cuhk.edu.hk/assess-health-equity-and-identify-social-determinants-of-health/ . Census and Statistics Ordinance. Cap.316. H.K.; 2022. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additionalfile2.docx Additionalfile3.docx Additionalfile4.docx Cite Share Download PDF Status: Published Journal Publication published 21 Dec, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 02 Jul, 2024 Reviewers agreed at journal 10 Jun, 2024 Reviews received at journal 10 Jun, 2024 Reviews received at journal 04 Jun, 2024 Reviewers agreed at journal 28 May, 2024 Reviewers agreed at journal 28 May, 2024 Reviewers invited by journal 28 May, 2024 Editor invited by journal 27 Mar, 2024 Submission checks completed at journal 22 Mar, 2024 Editor assigned by journal 22 Mar, 2024 First submitted to journal 21 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3977343","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282901433,"identity":"01faca7d-6adc-4399-9b22-e032b859ef76","order_by":0,"name":"Eva L.H. 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Each grey dot represents a SSUG in size proportional to its number of study households.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3977343/v1/6941e39cba121f48592a0b98.png"},{"id":53661356,"identity":"1db12e15-8e7b-422f-8895-33aeb00938eb","added_by":"auto","created_at":"2024-03-28 16:13:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":784087,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDisplay of SSUGs in each of the 18 DCDs on the ternary plot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Red cross represents the overall district figure. Each grey dot represents a SSUG in size proportional to its number of study households.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3977343/v1/a7c24b40b1fbd9eef8f4a8e9.png"},{"id":53661357,"identity":"f519fee9-8327-4315-b9c7-cb372aaf0e6f","added_by":"auto","created_at":"2024-03-28 16:13:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":352343,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThematic map of SUs coloured by category on ternary plot\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3977343/v1/6a04a6621f2dd49108121f4c.png"},{"id":53661351,"identity":"1c659192-ce11-440f-85fe-8dd2465a5182","added_by":"auto","created_at":"2024-03-28 16:13:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":355541,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThematic map of SUs coloured by mean GHESI decile value of households\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3977343/v1/ecf6c441e34e25efd7854b1e.png"},{"id":72201697,"identity":"6fc673e5-7e5f-49b1-adfd-07fc7c0c70f4","added_by":"auto","created_at":"2024-12-23 16:09:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2344147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3977343/v1/61c221eb-2364-401e-8e6d-6580b41354e7.pdf"},{"id":53662624,"identity":"877307b6-0b40-4034-8653-032ae64d1279","added_by":"auto","created_at":"2024-03-28 16:21:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19425,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3977343/v1/980f860367b16d8db3020c6d.docx"},{"id":53661352,"identity":"93575ad3-9db6-4456-878e-ac0d6b35171a","added_by":"auto","created_at":"2024-03-28 16:13:34","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":21079,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3977343/v1/5adc64fd10ce1bbe341ef3af.docx"},{"id":53662625,"identity":"3dfbff0e-d383-4ec3-911a-e9ad5b0693fd","added_by":"auto","created_at":"2024-03-28 16:21:34","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":119878,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-3977343/v1/81a202cca782a18a9abc4fcf.docx"},{"id":53661354,"identity":"5fa6fbc2-a1a7-45a0-bb73-314679d615f0","added_by":"auto","created_at":"2024-03-28 16:13:34","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":456544,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-3977343/v1/dbfd848f3fa1a588ef295af3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a territory-wide household-based composite index for measuring relative distribution of households by economic status in individual small areas throughout Hong Kong","fulltext":[{"header":"Background","content":"\u003cp\u003eThe World Health Organisation (WHO) highlights the importance of social, environmental and economic determinants of health and how they impact health equity.\u003csup\u003e1\u003c/sup\u003e Prior research has shown inverse relationships of an individual\u0026rsquo;s socioeconomic status (SES) with health problems including chronic kidney disease, cardiovascular disease, stroke, breast cancer, hypertension, and type 2 diabetes.\u003csup\u003e2\u0026ndash;7\u003c/sup\u003e That said, individual SES cannot fully explain the observed spatial variation in health outcomes such as disease prevalence rate or mortality rate. Area-based studies illustrate that the socioeconomic conditions of the neighbourhood where one lives also play an important role.\u003csup\u003e8\u0026ndash;10\u003c/sup\u003e Back in 1979, Townsend\u0026rsquo;s formulation of multiple deprivation, both social and material, was the first theoretical framework for the model of small-area multiple deprivation as a composite index of different domains of deprivation in the United Kingdom (UK).\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAcross the world, countries propelled either by government initiatives or academic efforts have developed their own multidimensional measure on deprivation or SES at a small-area level. These measures, augmented by visualization on maps to show the resultant geographical variations over small areas, are applied to resource allocation, service planning and research.\u003csup\u003e12\u003c/sup\u003e UK was amongst the pioneering countries to develop indices for regularly monitoring health inequality and applying them to policy making. The developed indices have evolved in phases to today\u0026rsquo;s English Indices of Deprivation (IoD) in line with Townsend\u0026rsquo;s definition of relative poverty.\u003csup\u003e11\u003c/sup\u003e The current English IoD comprise seven domains of deprivation, with each measured independently using the best indicators available to generate a domain score. The seven scores are then combined with explicit weightings to generate a multiple-deprivation measure.\u003csup\u003e11\u003c/sup\u003e More than 15 countries have developed similar indices with varying numbers of dimensions, domains and variables.\u003csup\u003e11,13\u0026ndash;27\u003c/sup\u003e (Additional file 1)\u003c/p\u003e \u003cp\u003eIn this study, a similar but household-based index was developed for Hong Kong, one of the most densely populated areas in the world with a population of 7\u0026middot;4\u0026nbsp;million in mid-2021 on a total land area of 1,117 square kilometres (km\u003csup\u003e2\u003c/sup\u003e), of which 40% are country parks and areas designated for nature conservation.\u003csup\u003e28,29\u003c/sup\u003e There are 18 administrative districts, each with a district council. For town planning purposes, the territory is also demarcated into 292 tertiary planning units (TPUs) and 4,916 subunits (SUs). Three prior local studies have produced small-area-based indices, including a deprivation index at both street block (currently replaced by SU) and TPU levels for exploring their association with suicide rate,\u003csup\u003e30\u003c/sup\u003e and two other indices at TPU level for investigating their association with cancer mortality and air pollution.\u003csup\u003e31,32\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eLiterature review showed that the majority of area-based indexes used mainly population census data, with some complemented by administrative data. The common domains included are income, wealth, housing, employment, and education, while crime, health, and crowding are domains selectively adopted according to the local context. Most indicators for individual domains were chosen according to available information from the census, theoretical framework, previous research studies, particularly the English IoD and correlation with other indicators. Owing to data availability and limitations, only aggregate statistics pertaining to each geographical area were compiled in the process; hence, existing indexes worldwide have only a single index value (either a decile/quintile/quartile, score or rank) derived for each geographical unit.\u003c/p\u003e \u003cp\u003eThe suitability of using a single index value to proxy an area has been much discussed. The issue in question is whether the defined geographical unit is sufficiently small to contain a relatively homogeneous population while being sufficiently large to provide robust data for statistical analysis.\u003csup\u003e23\u003c/sup\u003e For instance, in New Zealand\u0026rsquo;s Deprivation Index, a particular geographical unit with a sparse population in a large geographic locality may hide tiny pockets of deprivation. In such context, a small local survey measuring individuals\u0026rsquo; deprivation may be a better alternative.\u003csup\u003e21\u003c/sup\u003e Although the English IoD has been implemented to resemble an \u0026lsquo;ideal\u0026rsquo; geographical unit, an area measured as relatively deprived may contain a large number of people who are not deprived and vice versa, rendering more comprehensive individual-level analyses of multiple deprivation necessary.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study aims to explore whether Hong Kong, given its high population density, has the above issue related to the extent of homogeneity of a defined small area. A novel approach was adopted to construct the \u0026ldquo;General Household Economic Status Index\u0026rdquo; (GHESI) for Hong Kong. Instead of deriving a single index value to proxy each geographical unit, this study generates the distribution of domestic households by the GHESI\u0026rsquo;s decile groups within each geographical unit defined at different geographical scales from small to large. Moreover, their differences in pattern within and between geographical scales are summarized and visualized on maps and ternary plots.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source, study population and scale of geographical unit\u003c/h2\u003e \u003cp\u003eThe main data source of this study was anonymized granular household-based data collected by the 2021 Population Census. In addition to basic demographic information of all households and live-in persons, a broader range of socioeconomic characteristics were collected through a long-form questionnaire randomly administered to 10% of territory-wide households. In light of the relevance of GHESI variables to the study population, we excluded around 0\u0026middot;49\u0026nbsp;million individuals from the 2021 Census data, including persons living in non-domestic households such as homes for the aged as well as non-domestic buildings, unsheltered accommodation or vessels and foreign domestic helpers who are often regarded as a separate economic entity from employer\u0026rsquo;s household. The final 248,000 sampled household-based data represent 2\u0026middot;66\u0026nbsp;million domestic households and 6\u0026middot;93\u0026nbsp;million population in mid-2021.\u003c/p\u003e \u003cp\u003eThe study population resided in 3,102 SUs. Consistent with the standard practice in publishing Census results, SUs with population size below 400 were aggregated, yielding a total of 2,252 small SU groups (SSUGs), with a median study population of 1,300 (inter-quartile range\u0026thinsp;=\u0026thinsp;2,300) over a median area of 42,400 square metres (m\u003csup\u003e2\u003c/sup\u003e). To identify the geographical pattern of larger administrative units, these SSUGs were segregated into 18 district council districts (DCD), with a median study population of 381,300 over a median area of 31 km\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGHESI development\u003c/h2\u003e \u003cp\u003eArea-based socioeconomic domains commonly included in overseas indexes (Additional file 1) are income, wealth, housing, education and employment. Pathways between these socioeconomic determinants of health have been previously identified. For instance, education is associated with occupation and in turn both are associated with income.\u003csup\u003e33\u003c/sup\u003e Contrary to overseas indexes using unemployment-related indicators in the employment domain, this study included under the household income variable both employed-related cash income and unemployment-related government subsidies and social security allowances.\u003csup\u003e28\u003c/sup\u003e Education, occupation and employment were deliberately not included as variables due to their strong correlation with household income and their counting units inappropriate for the present household-based index. The GHESI thus developed comprises seven variables selected for the income-/wealth-related domain and housing-related domain. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eTo determine the weights for combining domains/indicators into an overall composite index, this study adopted the analytic hierarchy process (AHP), which is a multi-criteria decision analysis method involving both qualitative and quantitative assessments.\u003csup\u003e34\u003c/sup\u003e AHP generates a set of weights through a series of pairwise comparisons of variables at different hierarchical levels performed by decision-makers or stakeholders, quantifying their personal preference on an intensity scale of importance (1\u0026thinsp;=\u0026thinsp;equal importance to 9\u0026thinsp;=\u0026thinsp;extreme importance) with reciprocals.\u003csup\u003e35\u003c/sup\u003e Twelve experts from different disciplines were invited to judge, via pairwise comparisons, the relative importance among the seven variables with respect to the goal of ranking territory-wide households in terms of economic status during a focus group discussion. Each expert scored individually, and the geometric mean of their intensity scores was calculated to represent the overall view of the expert panel.\u003csup\u003e36\u003c/sup\u003e Our study has refined the AHP by sequencing the pairwise comparisons into first the within-domain stage, followed by the between-domain stage. The consistency ratios of pairwise comparisons for income-/wealth-related and housing-related domains are 5\u0026middot;26% and 2\u0026middot;37%, respectively, both less than the pre-specified 10% threshold requirement.\u003csup\u003e35\u003c/sup\u003e The final weights for the seven variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Additional file 2 provides a detailed account of the logistic arrangements and how these weights were derived from the aggregated pairwise comparison matrix.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSeven variables included in GHESI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeights derived using Analytic Hierarchy Process\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIncome-/wealth- related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Household income per household member\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe household income includes total cash income from all jobs and other sources such as rent, interest and government subsidies.\u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u0026middot;4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Number of live-in workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFor example, foreign domestic helpers/ chauffeurs/ gardeners.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u0026middot;6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Accommodation 100% owned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhether the accommodation is owner-occupied without mortgage and loans. (Yes/No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u0026middot;3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eHousing-related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Type of housing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFour categories in ordinal order:\u003c/p\u003e \u003cp\u003ePrivate permanent housing,\u003c/p\u003e \u003cp\u003eHome of subsidised housing,\u003c/p\u003e \u003cp\u003ePublic rental housing,\u003c/p\u003e \u003cp\u003eTemporary housing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u0026middot;1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Subdivided Units (SDUs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSubdivided units are formed by splitting a unit of quarters into two or more \u0026ldquo;internally connected\u0026rdquo; and \u0026ldquo;externally accessible\u0026rdquo; units.\u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(Yes/No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u0026middot;8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) Saleable area per household member\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaleable area is the floor area exclusively allocated to a residential unit including balconies, verandahs, utility platforms and other similar features but excluding common areas, bay windows, roofs, other similar features and car parking spaces.\u003csup\u003e38\u003c/sup\u003e The saleable area of each sampled household was divided by the respective number of study household member(s) collected from the population census to compute the value of this variable.\u003csup\u003e37\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u0026middot;8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Rateable value per saleable area#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe rateable value is an estimated annual rental value of a property at a designated valuation reference date, assuming that the property was then vacant and to let from year to year, with due adjustments made during the assessment to reflect any differences in size, location, facilities, standard of finishes and management, etc. of the property.\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u0026middot;0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e# based on the rateable value for 2021\u0026ndash;22 at the designated valuation reference date of 1 October 2020 provided by the Rating and Valuation Department\u003c/p\u003e \u003cp\u003eStandardizing the variables in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is necessary as they have different scales. As in prior studies, \u003csup\u003e11,14,15,23,\u003c/sup\u003e the data values of households were first transformed to ranks from the most to the least well-off, for both continuous and categorical variables with intrinsic ordinal values, taking into account the sampling gross-up factor of each sampled household. For ties in ranking, a shared rank was used. The ranks of the seven variables assigned to each household were then summed up with the AHP-derived weights to calculate the weighted rank score. The study households were then segregated into ten deciles according to the weighted rank scores, ranging from decile 1 (D1) representing the top 10% of households (most well-off) to D10 representing the bottom 10% (least well-off). The frequency distribution of households by GHESI deciles as well as mean and standard deviation (SD) were output for each geographical unit. For visualization purposes, each frequency distribution was further amalgamated into three classes, namely D1\u0026ndash;D3, D4\u0026ndash;D7 and D8\u0026ndash;D10, and plotted as a dot to indicate its relative distribution among these three classes on a ternary plot.\u003csup\u003e39\u003c/sup\u003e This graphical approach helped distinguish patterns of frequency distributions across the 2,252 SSUGs, and within each of the18 DCDs in Hong Kong.\u003c/p\u003e \u003cp\u003eOn the three-dimensional ternary plot with the scale from 0\u0026ndash;100% that indicates for each geographical unit its relative distribution on D1\u0026ndash;D3 (top 30%), D4\u0026ndash;D7 (middle 40%) and D8\u0026ndash;D10 (bottom 30%), every intersecting point of the three scale lines is summed to 100%. The entire triangular area in Fig.\u0026nbsp;1 is stratified into seven coloured categories, each representing a different form of distribution after taking into account the statistics on four moments (mean, SD, skewness and kurtosis) as listed in Additional file 3. The categories in darker/lighter green on the left are (A) strongly positive-skewed and (B) weakly positive-skewed; being a mirror image in darker/lighter red on the right is (G) strongly negative-skewed and (F) weakly negative-skewed; and in the middle from orange-yellow to yellow is (C) with a modal peak in the middle class, (D) widely dispersed distribution with a central tendency and (E) bi-modal distribution which peaks on two opposite ends.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1: Rules to classify ternary plot into seven coloured categories\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGHESI validation\u003c/h2\u003e \u003cp\u003eGHESI was validated using primarily the Census\u0026rsquo; rich database. The methodology and results are detailed in Additional file 4. For construct validity, GHESI results were correlated against a common attribute of poverty defined in terms of income below a threshold (locally set at 50%) with respect to median household income by household sizes.\u003csup\u003e40,41\u003c/sup\u003e The external criterion for validation was selected with reference to the Hong Kong Commission on Poverty, which has identified subdivided units, single-parent households and elderly households as target groups of poverty alleviation.\u003csup\u003e42\u003c/sup\u003e GHESI was also externally validated against a specified public healthcare user group. In addition, the association of the three excluded social-related variables (education, occupation and employment) with GHESI was examined. In brief, GHESI demonstrated satisfactory construct and criterion validity.\u003c/p\u003e \u003cp\u003eStatistical analyses were performed and thematic maps were compiled using SAS 9\u0026middot;4, Python 3\u0026middot;9\u0026middot;11, ArcGIS Pro 3\u0026middot;0\u0026middot;1.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe economic status of 2,252 SSUGs in Hong Kong is summarised on the three-dimensional ternary plot shown in Fig.\u0026nbsp;2, with the red cross representing the territory-wide figure and grey dots denoting SSUGs in size proportional to their respective number of study households. Of the seven categories, Categories A and G are the most homogeneous SSUGs in terms of economic status profile as reflected by the lowest SD (Additional file 3). The 411 SSUGs (18\u0026middot;3%) in Category A have the majority of households which are relatively more well-off, with at least 70% being classified into D1\u0026ndash;D3 representing the top 30% households. On the contrary, the 113 (5\u0026middot;0%) SSUGs in Category G have the majority of households which are relatively less well-off, with at least 70% being classified into D8\u0026ndash;D10 representing the bottom 30% households. There are only 68 SSUGs (3\u0026middot;0%) in Category C, with at least 70% of households belonging to the middle class (D4\u0026ndash;D7). Category B accounts for the largest share of SSUGs (33\u0026middot;8%), characterized as relatively less homogeneous but skewed towards more well-off. Conversely, Category F contains 14\u0026middot;5% of SSUGs with a reversed economic status pattern compared with Category B\u0026rsquo;s. For the remaining two Categories which are most heterogeneous in composition as indicated by their higher SDs, Category D contains 25\u0026middot;1% of SSUGs with a widely diverse economic status, while Category E comprises very few SSUGs (n\u0026thinsp;=\u0026thinsp;6 or 0\u0026middot;3%) having a bi-modal distribution with households clustered in two opposite ends. In summary, around one-quarter of SSUGs (in Categories A, C and G) are more homogeneous while another one-quarter of SSUGs (in Categories D and E) are more heterogeneous. The ordering from Category A to Category G also reveals an economic status gradient from high to low, with the lowest mean decile value among SSUGs in Category A (=\u0026thinsp;2\u0026middot;3) rising to the highest in Category G (=\u0026thinsp;8\u0026middot;6). In addition, the less well-off Categories F and G account for a disproportionately larger percentage share of total households and population compared with their share of SSUGs, and vice versa for the more well-off Categories A, B as well as the middle Category D.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 2: Classification results of SSUGs into seven coloured categories on ternary plot\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eRed cross represents the territory-wide figure. Each grey dot represents a SSUG in size proportional to its number of study households.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a two-way distribution of the 2,252 SSUGs by nine mean decile value groups from 1.0 to 10.0 and seven Categories on a ternary plot. Most SSUGs with the lowest and highest mean decile value are concentrated in Categories A and G, respectively. As the mean decile value increases from 2.0 to 4.0 or decreases from 9.0 to 7.0, the concentration shifts gradually towards Categories B and F, respectively. The middle three groups with a mean decile value of 4.0\u0026ndash;7.0 account for 45% of SSUGs which predominantly spread over Categories B and D, D only or D and F, respectively. The present findings indicate the absence of a one-to-one relationship between mean decile value and Category, as evidenced by SSUGs in the mean decile group of 5.0\u0026ndash;\u0026lt;6.0 spreading across five different Categories.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of SSUGs by households\u0026rsquo; mean decile value and Category on ternary plot\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean decile value of households in SSUG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eCategory labelled for SSUG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.0\u0026ndash;\u0026lt;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e174\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e174\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.0\u0026ndash;\u0026lt;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e233\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e325\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.0\u0026ndash;\u0026lt;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e393\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e398\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.0\u0026ndash;\u0026lt;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e271\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e131\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e430\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.0\u0026ndash;\u0026lt;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e295\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e347\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.0\u0026ndash;\u0026lt;7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e135\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e103\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e244\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.0\u0026ndash;\u0026lt;8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e172\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e177\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.0\u0026ndash;\u0026lt;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e145\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.0\u0026ndash;\u0026le;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNotes: For mean decile, the lower the value, the higher the economic status and vice versa. For Categories A to G, refer to the classification rules with respect to ternary plot\u0026rsquo;s triangular area shown in Fig.\u0026nbsp;1. For each of the mean decile groups, the predominant Category/ies is/are bolded.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure 3 shows ternary plots with all SSUGs geographically demarcated into 18 DCDs. For Central \u0026amp; Western/Wanchai, 46%/45% of their SSUGs are classified into Category A and 48%/49% into Category B, indicating that these two DCDs are concentrated with more homogeneously well-off SSUGs. Similarly, Kowloon City/Southern have 35%/40% of SSUGs falling in Category A and 26%/32% in Category B. The respective share of SSUGs in the remaining 14 DCDs ranges from 0\u0026ndash;27% in Category A (homogeneously more well-off); 0\u0026ndash;7% in Category C (homogeneously middle class) and 0\u0026ndash;15% in Category G (homogeneously less well-off). In other words, none of the DCDs have SSUGs exceeding 27% in any of these three Categories. Instead, 68\u0026ndash;89% of the SSUGs spread across Categories B, D, E and F, revealing dispersion in economic status distribution by varying extents.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 3: Display of SSUGs in each of the 18 DCDs on the ternary plot\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eRed cross represents the overall district figure. Each grey dot represents a SSUG in size proportional to its number of study households.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eSUs, the smallest geographical units for town planning, can be aggregated to TPUs at the next hierarchical level. In this study, the frequency distribution of domestic households by ten GHESI deciles, its mean and SD together with the study variables\u0026rsquo; distribution by geographical units at different aggregated SU/TPU groups and DCD levels are open for access in an online interactive map dashboard (website link to be provided upon the study publication). Figures\u0026nbsp;4 and 5 provide two thematic maps with a gradient colouring scheme to respectively indicate the Category and mean households\u0026rsquo; decile value of SUs throughout Hong Kong. These illustrations provide a spatial visualization on the distribution pattern and central tendency of households by economic status, with their inter-relationships as outlined above.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 4: Thematic map of SUs coloured by category on ternary plot\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 5: Thematic map of SUs coloured by mean GHESI decile value of households\u003c/b\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCompared with existing indexes, the GHESI comprises unique indicators, namely the rateable value per saleable area and saleable area per household member (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Both are representative measures reflecting the estimated annual rental value of a property and degree of crowding, which generally exhibit an economic status gradient. To combine different domains and associated indicators into an overall index, most overseas/local indexes adopted the statistical modelling approach using principal component analysis (PCA) or factor analysis for dimension reduction and then derivation of their weights. However, the classical PCA, which usually requires a specific structure on the input data, cannot be applied to our study data made up of continuous and categorical variables in different scales and distributions.\u003csup\u003e44\u003c/sup\u003e In contrast, other indexes use either equal or differential weights through experts\u0026rsquo; subjective judgement, which can be arbitrary. In this study, the AHP method, a hybrid approach combining both qualitative and quantitative assessments, was adopted. AHP had been applied to the reuse selection of historic buildings in Taiwan and was found to encompass the interdependencies among various criteria and enable decision-makers to better understand their complex inter-relationships, hence improving the decision\u0026rsquo;s acceptability.\u003csup\u003e45\u003c/sup\u003e We have refined the AHP method by including all 21 pairwise comparisons among the seven variables, starting from the nine within-domain, followed by the 12 between-domain comparisons. We have devised a formula (Additional file 2) to derive the relative weighting between the two domains, replacing the conventional approach of judging the two domains at the first hierarchical level, followed by judging the criteria/sub-criteria at the lower level(s). Although more pairwise comparisons (21 versus 10) were required under our refined AHP approach, they could meet the pre-set 10% consistency ratio threshold. Methodologically, the final weights for the seven variables were still generated from the pairwise comparison matrix.\u003c/p\u003e \u003cp\u003eThe GHESI demonstrated satisfactory construct validity on both household and area bases, suggesting that it is generally consistent with the definition of poverty according to income. In fact, the income variable/indicator accounts for the largest weight among all variables/indicators in most overseas SES/deprivation indexes.\u003csup\u003e11,21,22\u003c/sup\u003e In addition, the GHESI was found to be strongly associated with education, occupation and employment, supporting our deliberate decision to exclude them from the GHESI in view of the identified socioeconomic pathways.\u003csup\u003e33\u003c/sup\u003e Likewise, this study finding challenges the necessity of including all socioeconomic variables in SES/deprivation indexes. Even when included, the three excluded variables highly correlated with the GHESI could only marginally improve the Index\u0026rsquo;s accuracy, and there is uncertainty whether some domains might be over-weighted particularly when the AHP method is adopted. Criterion validation of the GHESI using the three locally defined target groups of poverty alleviation and one public general outpatients group found supporting evidence that the GHESI developed in this study is a valid measure of household economic status in Hong Kong.\u003c/p\u003e \u003cp\u003ePopulation size may vary widely among geographical units. When ranking areas on individual indicators, for areas with very low population, technical approaches like exclusion from study population, smoothing method or shrinkage method to borrow strength from the neighbouring areas were adopted in overseas indexes to reduce the variability of indicators.\u003csup\u003e11,14\u003c/sup\u003e This study aggregated the results into small/large groups with minimum size requirements to ensure data reliability. Overseas studies consider combining variables or summing the raw rank of all variables undesirable because non-deprivation in one variable might in effect cancel out deprivation in another variable.\u003csup\u003e11,14,23\u003c/sup\u003e To address the problem, they adopted an exponential transformation on the rank for each variable such that the transformed score of less well-off areas is spread out and less likely to be offset by a higher rank on other variables. However, this technical approach is not necessary for our study which can output the relative distribution of households in each geographical unit by GHESI deciles from economically most to least well-off, instead of merely identifying deprived areas.\u003c/p\u003e \u003cp\u003eIn the past, most local studies could only resort to using a single indicator of household income to proxy SES. Take for example Sham Shui Po (SSP). Among the 18 DCDs, SSP ranked lowest in median monthly domestic household income according to the Census results, but ranked 12th in mean GHESI decile value. While Census\u0026rsquo; income distribution results showed that SSP had a higher proportion of households with monthly household income exceeding HK\u003cspan\u003e$\u003c/span\u003e100,000 (6\u0026middot;3% versus 3\u0026middot;0%\u0026ndash;3\u0026middot;7%), its proportion of households with monthly household income below HK\u003cspan\u003e$\u003c/span\u003e10,000 (23\u0026middot;0% versus 21\u0026middot;0%\u0026ndash;23\u0026middot;1%) was comparable to that of six DCDs (M\u0026ndash;R in Fig.\u0026nbsp;3), which ranked 13th to 18th in mean GHESI decile value. In addition, when compared with these six DCDs on the other six variables included in the GHESI, SSP had a relatively higher average rateable value per saleable area and a larger proportion of subdivided units, but fell predominantly within mid-range on the other four variables. As shown in the ternary plot, small areas in SSP (L in Fig.\u0026nbsp;3) are widely dispersed, with 23\u0026middot;6% of households falling in D1\u0026ndash;D3, 37\u0026middot;5% in D4\u0026ndash;D7, and 38\u0026middot;9% in D8\u0026ndash;D10, as indicated by the red cross with the overall DCD being classified into Category D. The example of SSP clearly underlines the limitation of adopting a single indicator or a single index value to proxy the economic status of a small area/DCD in Hong Kong. In an extreme case with half of the population being very rich while the other half being very poor, problems arise if resource planning is made solely on the basis of median household income.\u003c/p\u003e \u003cp\u003eIn the ternary plots of the above example, frequency distribution of households\u0026rsquo; economic status was reduced from ten dimensions (i.e., deciles) to three dimensions to visualize, summarise and differentiate the patterns across geographical units. If this study follows overseas indexes in using a single SES index value to proxy individual small areas, the majority of SSUGs would fall in Categories A, C or G. However, of the 2,252 SSUGs in Hong Kong with a median area as small as 42,400 m\u003csup\u003e2\u003c/sup\u003e, only one-quarter are found in these three Categories having a homogeneous composition of households with similar economic status, whereas the remaining three-quarters demonstrate heterogeneity to varying extents. Such refined small-area-based information would add value to planning community-based services or programmes for defined target groups. What contributes to the diverse patterns observed across geographical areas is outside the scope of this study.\u003c/p\u003e \u003cp\u003eSince the early 1990s, the Hong Kong Hospital Authority has implemented a single electronic health record system for patients utilizing services in all 43 public hospitals in the territory.\u003csup\u003e46\u003c/sup\u003e Then in 2016, the government launched the Electronic Health Record Sharing System (eHealth), providing an electronic platform with the goal of consolidating free and lifelong electronic health records for all members of the public.\u003csup\u003e47\u003c/sup\u003e Though both systems contain clinical/health data and detailed personal information, they lack essential socioeconomic characteristics of patients required for studying the association between SES and health status or outcome. On the other hand, health data are not available in the population census. Therefore, the GHESI output, detailing relative distribution of household economic status across areas at varying geographic scales, may potentially serve as a surrogate predictor for health outcomes in future research.\u003c/p\u003e \u003cp\u003eIn Hong Kong, the Institute of Health Equity has recently been established to study health equity issues and advise the government on policies and intervention programmes for improving health equity. One of its strategic suggestions is to create/enhance data collection.\u003csup\u003e48\u003c/sup\u003e The main goal of this study is to construct an index that can serve as a planning tool with flexibility on the choice of geographic scale for varied uses and applications. The GHESI is a generic tool, not only applicable in the health context, but also in other domains such as social welfare, and housing. With rapid developments across the local territory, this version of GHESI would eventually be outdated, given the time lag in the output of five-yearly census data. Nonetheless, this is a pioneer study for Hong Kong. There are areas for improvement and refinement in the methodological aspects, along with the need for regular updates in alignment with the subsequent rounds of population census to ensure its timely release for applications.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn addition to developing a composite territory-wide index for measuring economic status of domestic households, this is the first study that made use of the census household-based records to output the relative distribution of households in individual small areas by the GHESI decile groups. We have also pioneered in adopting the analytic hierarchy process method to determine the weights of the selected variables. Among the 2,252 individual small geographical units in Hong Kong, only one-quarter exhibit a highly homogeneous economic status profile, with the other one-half demonstrating lower homogeneity, while the remaining one-quarter can be characterized as heterogeneous. In addition, detailed results of this study will be disseminated for public access via an online interactive map dashboard to enable flexible analysis and visualization at different geographic scales to meet varied uses and applications, by policymakers and researchers, not only in health but also in other domains, in support of community-based resource prioritization, service planning and research. Nonetheless, in view of rapid developments in the economy, this Index needs to be updated regularly in line with the five-yearly population census.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAHP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalytic hierarchy process\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDCD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDistrict council districts\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eeHealth\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic Health Record Sharing System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGHESI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral Household Economic Status Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIoD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndices of Deprivation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eKm\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSquare kilometre\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eM\u003csup\u003e2\u003c/sup\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSquare metre\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSES\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSSP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSham Shui Po\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSSUGs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSmall subunit groups\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSUs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSubunits\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTPUs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTertiary planning units\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eUK\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organisation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethics approval for the conduct of this study is not required because (i) the 248,000 anonymized sampled household-based data for the Index development were collected from the population census which was conducted by the Census and Statistics Department of the Hong Kong Special Administrative Region Government according to the Census and Statistics Ordinance (Cap 316);\u003csup\u003e49\u003c/sup\u003e and (ii) the presentation of this study\u0026rsquo;s findings is consistent with the standard practice in publishing Census results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe anonymized granular household-based records sourced from the 2021 Population Census of Hong Kong are not publicly available according to the\u0026nbsp;Census and Statistics Ordinance\u0026nbsp;(Cap 316).\u003csup\u003e49\u003c/sup\u003e The\u0026nbsp;aggregate data for geographical units at different levels, ranging from small to large SU\u0026nbsp;groups, small to large TPU\u0026nbsp;groups, and then to\u0026nbsp;DCDs\u0026nbsp;will be available for online public access via an interactive map dashboard (website link will be provided upon the study publication).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eELHT, PLHY, KFL, KKYP, ACMN and KYC conceptualized the study and designed the methodology. KYC and WL conducted literature review. Data curation was performed by KKYP, ACMN, KYC, DHYL and JLYC. Formal analysis was conducted by ELHT, KKYP, ACMN and KYC. KYC, MLHL and DHYL were responsible for data visualisation. ELHT, PLHY, KFL, KKYP, ACMN, KYC, and SPWN validated the results. ELHT, PLHY, KFL, KKYP and ACMN supervised the study. ELHT, KYC and WL wrote the first draft of the manuscript. All authors critically reviewed and edited the manuscript and agreed with the decision to submit for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Health Bureau, the Census and Statistics Department and the Rating and Valuation Department of the Hong Kong Special Administrative Region Government. We would like to acknowledge the valuable support from the expert group towards the research process to determine the weights of the Index variables. The members include Mr Andrew SH Au, Prof Kara KW Chan, Ms Marion SY Chan, Ms Ivy WH Cheung, Prof Alfred TK Ho, Prof WK Li, Mr Duncan TY Ma, Mr Tim HC Pang, Ms KL Pang, Mr Kenneth LK To, Prof Wilson WH Wong and Prof Samuel YS Wong.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSocial Determinants of Health \u0026ndash; World Health Organization [Internet]. 2021 [cited 2023 Dec 6]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.who.int/gb/ebwha/pdf_files/EB148/B148_24-en.pdf\u003c/span\u003e\u003cspan address=\"https://apps.who.int/gb/ebwha/pdf_files/EB148/B148_24-en.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng X, Liu J, Tao S, Hong HG, Li Y, Fu P. Associations between socioeconomic status and chronic kidney disease: a meta-analysis. 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H.K.; 2022.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Socioeconomic status index, SES, deprivation, small-area analysis, Hong Kong","lastPublishedDoi":"10.21203/rs.3.rs-3977343/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3977343/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMany countries have developed their country/nation-wide multidimensional area-based index on deprivation or socioeconomic status for resource allocation, service planning and research. However, whether each geographical unit proxied by a single index is sufficiently small to contain a relatively homogeneous population remains questionable. Globally, this is the first study that presents the distribution of domestic households by the territory-wide economic status index decile groups within each of the 2,252 small subunit groups (SSUGs) throughout Hong Kong, with a median study population of 1,300 and a median area of 42,400 m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe index development involved 248,000 anonymized sampled household-based data collected from the population census, representing 2\u0026middot;66\u0026nbsp;million domestic households and 6\u0026middot;93\u0026nbsp;million population in mid-2021. Our composite index comprises seven variables under income-/wealth-related and housing-related domains with weights determined using the analytic hierarchy process. After ranking all households from the most to the least well-off according to the numeric/ordinal value of each variable and then calculating their weighted rank scores, they were segregated into ten deciles from D1 (top 10% most well-off) to D10 (bottom 10%). Their relative distribution was summarized in a three-dimensional ternary plot to distinguish patterns across the 2,252 SSUGs within the 18 administrative districts.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn Hong Kong, of the 2,252 SSUGs, only one-quarter contain a homogeneous composition of households with similar economic status, while the other three-quarters are heterogeneous to varying extents. Of the 18 administrative districts, only two are concentrated with more homogeneously well-off SSUGs.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eSmall-sized geographical units may contain a heterogeneous composition of households with diverse economic statuses, underlying the need for more precise information to quantify their relative distribution. Results of this study will be disseminated via an online interactive map dashboard which can serve as a versatile planning tool capable of performing analysis at different varying geographic scales for community-based resource prioritization, service planning and research across different domains.\u003c/p\u003e","manuscriptTitle":"Development of a territory-wide household-based composite index for measuring relative distribution of households by economic status in individual small areas throughout Hong Kong","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 16:13:28","doi":"10.21203/rs.3.rs-3977343/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-02T05:20:53+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"270685310311959095664145970506644201321","date":"2024-06-10T19:19:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-10T13:48:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-04T18:22:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59595533183448017138735855655296139422","date":"2024-05-28T23:02:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332351244442936606175103279939736428264","date":"2024-05-28T18:22:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-28T16:08:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-27T04:31:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-23T02:52:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-23T02:52:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-02-22T02:36:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38164e30-3785-49cc-ad4a-59cdb9f2ac7a","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:01:23+00:00","versionOfRecord":{"articleIdentity":"rs-3977343","link":"https://doi.org/10.1186/s12889-024-21067-7","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-12-21 15:57:24","publishedOnDateReadable":"December 21st, 2024"},"versionCreatedAt":"2024-03-28 16:13:28","video":"","vorDoi":"10.1186/s12889-024-21067-7","vorDoiUrl":"https://doi.org/10.1186/s12889-024-21067-7","workflowStages":[]},"version":"v1","identity":"rs-3977343","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3977343","identity":"rs-3977343","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}