The effect of population distribution measures on evaluating spatial accessibility of primary health-care institutions: A case study from China

Abstract

Improvement of the equality of geographical allocation of limited health-care resources requires an accurate evaluation of spatial accessibility of the facilities. The adoption of appropriate population distribution measures is one of the leading factors affecting such an evaluation. Using primary health-care institutions in Hainan, China as an example, this study aimed to explore the disparities embedded in spatial accessibility evaluations based on six common measures of population distribution, namely community/ village population (VillagePop), average population distribution (AveragePop), population distribution by night-time light intensity (NighttimelightPop) together with the public population databases LandScan, WorldPop and PoiPop for construction of the weights. The enhanced two-step floating catchment area method, two-way analysis of variance (ANOVA), Dunnett test, root mean square error and the mean absolute error were employed to assess and compare spatial accessibilities based on these different population distribution measures. The spatial accessibility of primary health-care institutions in Hainan was found to vary when plotted using the various population distribution measures mentioned. As indicated by the statistical outcomes of both ANOVA and the Dunnett test, using the spatial accessibility calculated by VillagePop as reference, those calculated by AveragePop and PoiPop were found to be significantly different. In addition, the spatial accessibilities calculated by AveragePop and PoiPop demonstrated higher error rates in the identification of underserved areas compared with the reference. Considering the limitations of public population databases, the adoption of night-time light data is highly recommended for estimating population distribution in the absence of high-resolution data.

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References

Aday LA, Andersen R, 1974. A framework for the study of access to medical care. Health Serv Res 9:280-20.

Bagan H, Yamagata Y, 2015. Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data. GISci Remote Sens 52:765-80. DOI: https://doi.org/10.1080/15481603.2015.1072400

Calka B, Bielecka E, 2019. Reliability analysis of landscan gridded population data. The Case Study of Poland. IJGI 8:222. DOI: https://doi.org/10.3390/ijgi8050222

Chen J, Fan W, Li K, Liu X, Song M, 2019. Fitting Chinese cities’ population distributions using remote sensing satellite data. Ecol Indic 98:327-33. DOI: https://doi.org/10.1016/j.ecolind.2018.11.013

Chen X, Jia P, 2019. A comparative analysis of accessibility measures by the two-step floating catchment area (2SFCA) method. Int J Geogr Inf Sci 33:1739-58. DOI: https://doi.org/10.1080/13658816.2019.1591415

Delamater PL, 2013. Spatial accessibility in suboptimally configured health care systems: a modified two-step floating catchment area (M2SFCA) metric. Health Place 24:30-43. DOI: https://doi.org/10.1016/j.healthplace.2013.07.012

Delamater PL, Messina JP, Shortridge AM, Grady SC, 2012. Measuring geographic access to health care: raster and network-based methods. Int J Health Geogr 11:15. DOI: https://doi.org/10.1186/1476-072X-11-15

Delamater PL, Shortridge AM, Kilcoyne RC, 2019. Using floating catchment area (FCA) metrics to predict health care utilization patterns. BMC Health Serv Res 19:144. DOI: https://doi.org/10.1186/s12913-019-3969-5

Dessau RB, Pipper CB, 2008. [‘‘R"--project for statistical computing]. Ugeskr Laeger 170:328-30.

Dobson JE, Bright EA, Coleman PR, Durfee RC, Worley BA, 2000. LandScan: a global population database for estimating populations at risk. Photogramm Eng Remote Sens 66:849-57.

Dong Y, Liu C, Zhou P, Zhu Y, Tang Q, Wang S, Wang X, 2019. How serious is the economic burden of diabetes mellitus in Hainan province? a study based on “System of Health Account 2011”. Diabetes Ther 10:2251-63. DOI: https://doi.org/10.1007/s13300-019-00712-0

Gao N, Li F, Zeng H, Bilsen D van, De Jong M, 2019. Can more accurate night-time remote sensing data simulate a more detailed population distribution? Sustainability 11:4488. DOI: https://doi.org/10.3390/su11164488

Gaughan AE, Stevens FR, Huang Z, Nieves JJ, Sorichetta A, Lai S, Ye X, Linard C, Hornby GM, Hay SI, Yu H, Tatem AJ, 2016. Spatiotemporal patterns of population in mainland China, 1990 to 2010. Sci Data 3:160005. DOI: https://doi.org/10.1038/sdata.2016.5

General Office of the State Council, PRC, 2015. The Outline of the National Health Service System Plan (2015-2020). Available from: http://www.gov.cn/zhengce/content/2015-03/30/content_9560.htm Accessed: 22 November 2019.

Gregory IN, Marti-Henneberg J, Tapiador FJ, 2010. Modelling long-term pan-European population change from 1870 to 2000 by using geographical information systems. J R Stat Soc Ser A 173:31-50. DOI: https://doi.org/10.1111/j.1467-985X.2009.00598.x

Gu X, Zhang L, Tao S, Xie B, 2019. Spatial Accessibility to healthcare services in metropolitan suburbs: the case of Qingpu, Shanghai. Int J Environ Res Public Health 16:225. DOI: https://doi.org/10.3390/ijerph16020225

Guagliardo MF, 2004. Spatial accessibility of primary care: concepts, methods and challenges. Int J Health Geogr 3:3. DOI: https://doi.org/10.1186/1476-072X-3-3

Hu R, Dong S, Zhao Y, Hu H, Li Z, 2013. Assessing potential spatial accessibility of health services in rural China: a case study of Donghai County. Int J Equity Health 12:35. DOI: https://doi.org/10.1186/1475-9276-12-35

Huang Q, Yang Y, Li Y, Gao B, 2016. A simulation study on the urban population of China based on nighttime light data acquired from DMSP/OLS. Sustainability 8:521. DOI: https://doi.org/10.3390/su8060521

Izumi K, Kawatsu L, Ohkado A, Uchimura K, Kato S, 2016. Evaluating the impact of health resource reconstruction on improving spatial accessibility of tuberculosis care. Int J Tuberc Lung Dis 20:1501-8. DOI: https://doi.org/10.5588/ijtld.16.0047

Jiang S, Li J, Duan P, Wei Y, 2019. An image layer difference index method to extract light area from NPP/VIIRS nighttime light monthly data. Int J Remote Sens 40:4839-55. DOI: https://doi.org/10.1080/01431161.2019.1574993

Joseph AE, Bantock PR, 1982. Measuring potential physical accessibility to general practitioners in rural areas: A method and case study. Social Sci Med 16:85-90. DOI: https://doi.org/10.1016/0277-9536(82)90428-2

Joseph AE, Phillips DR, 1984. Accessibility and utilization: geographical perspectives on health care delivery. Harper & Row, New York, NY, USA.

Kaur Khakh AK, Fast V, Shahid R, 2019. Spatial accessibility to primary healthcare services by multimodal means of travel: synthesis and case study in the City of Calgary. IJERPH 16:170. DOI: https://doi.org/10.3390/ijerph16020170

Khan AA, 1992. An integrated approach to measuring potential spatial access to health care services. Socioecon Plann Sci 26:275-87. DOI: https://doi.org/10.1016/0038-0121(92)90004-O

Kim Y, Byon Y-J, Yeo H, 2018. Enhancing healthcare accessibility measurements using GIS: A case study in Seoul, Korea. PLoS One 13:e0193013. DOI: https://doi.org/10.1371/journal.pone.0193013

Kong W, Cheng J, Liu X, Zhang F, Fei T, 2019. Incorporating nocturnal UAV side-view images with VIIRS data for accurate population estimation: a test at the urban administrative district scale. Int J Remote Sens 40:8528-46. DOI: https://doi.org/10.1080/01431161.2019.1615653

Langford M, Higgs G, Fry R, 2016. Multi-modal two-step floating catchment area analysis of primary health care accessibility. Health Place 38:70-81. DOI: https://doi.org/10.1016/j.healthplace.2015.11.007

Lee S, Lee DK, 2018. What is the proper way to apply the multiple comparison test? Korean J Anesthesiol 71:353-60. DOI: https://doi.org/10.4097/kja.d.18.00242

Li K, Chen Y, Li Y, 2018. The random forest-based method of fine-resolution population spatialization by using the international space station nighttime photography and social sensing data. Remote Sens 10:1650. DOI: https://doi.org/10.3390/rs10101650

Li X, Lu J, Hu S, Cheng K, De Maeseneer J, Meng Q, Mossialos E, Xu DR, Yip W, Zhang H, Krumholz HM, Jiang L, Hu S, 2017. The primary health-care system in China. Lancet 390:2584-94. DOI: https://doi.org/10.1016/S0140-6736(17)33109-4

Li X, Zhou W, 2018. Dasymetric mapping of urban population in China based on radiance corrected DMSP-OLS nighttime light and land cover data. Sci Total Environ 643:1248-56. DOI: https://doi.org/10.1016/j.scitotenv.2018.06.244

Lu C, Zhang Z, Lan X, 2019. Impact of China’s referral reform on the equity and spatial accessibility of healthcare resources: a case study of Beijing. Soc Sci Med 235:112386. DOI: https://doi.org/10.1016/j.socscimed.2019.112386

Luo J, Chen G, Li C, Xia B, Sun X, Chen S, 2018. Use of an E2SFCA method to measure and analyse spatial accessibility to medical services for elderly people in Wuhan, China. IJERPH 15:1503. DOI: https://doi.org/10.3390/ijerph15071503

Luo W, Qi Y, 2009. An enhanced two-step floating catchment area (E2SFCA) method for measuring spatial accessibility to primary care physicians. Health Place 15:1100-7. DOI: https://doi.org/10.1016/j.healthplace.2009.06.002

Luo W, Wang FH, 2003. Measures of spatial accessibility to health care in a GIS environment: synthesis and a case study in the Chicago region. Environ Plan B-Plan Des 30:865-84. DOI: https://doi.org/10.1068/b29120

Luo P, Zhang X, Cheng J, Sun Q, 2019. Modeling population density using a new index derived from multi-sensor image data. Remote Sens 11:2620. DOI: https://doi.org/10.3390/rs11222620

Ma L, Luo N, Wan T, Hu C, Peng M, 2018. An improved healthcare accessibility measure considering the temporal dimension and population demand of different ages. IJERPH 15:2421. DOI: https://doi.org/10.3390/ijerph15112421

Mao L, Nekorchuk D, 2013. Measuring spatial accessibility to healthcare for populations with multiple transportation modes. Health Place 24:115-22. DOI: https://doi.org/10.1016/j.healthplace.2013.08.008

Mishra P, Singh U, Pandey CM, Mishra P, Pandey G, 2019. Application of student’s t-test, analysis of variance, and covariance. Ann Card Anaesth 22:407-11. DOI: https://doi.org/10.4103/aca.ACA_94_19

Nakamura T, Nakamura A, Mukuda K, Harada M, Kotani K, 2017. Potential accessibility scores for hospital care in a province of Japan: GIS-based ecological study of the two-step floating catchment area method and the number of neighborhood hospitals. BMC Health Serv Res 17:438. DOI: https://doi.org/10.1186/s12913-017-2367-0

Ngui AN, Apparicio P, 2011. Optimizing the two-step floating catchment area method for measuring spatial accessibility to medical clinics in Montreal. BMC Health Serv Res 11:166. DOI: https://doi.org/10.1186/1472-6963-11-166

Ni J, Liang M, Lin Y, Wu Y, Wang C, 2019. Multi-mode two-step floating catchment area (2SFCA) method to measure the potential spatial accessibility of healthcare services. ISPRS Int J Geo-Inf 8:236. DOI: https://doi.org/10.3390/ijgi8050236

Pan J, Zhao H, Wang X, Shi X, 2016. Assessing spatial access to public and private hospitals in Sichuan, China: The influence of the private sector on the healthcare geography in China. Social Sci Med 170:35-45. DOI: https://doi.org/10.1016/j.socscimed.2016.09.042

Penchansky R, Thomas JW, 1981. The concept of access: definition and relationship to consumer satisfaction. Med Care 19 2:127-40. DOI: https://doi.org/10.1097/00005650-198102000-00001

Shah TI, Bell S, Wilson K, 2016. Spatial accessibility to health care services: identifying under-serviced neighbourhoods in Canadian urban areas. PLoS One 11:e0168208. DOI: https://doi.org/10.1371/journal.pone.0168208

Shen Q, 1998. Location characteristics of inner-city neighborhoods and employment accessibility of low-wage workers. Environ Plann B Plann Des 25:345-65. DOI: https://doi.org/10.1068/b250345

Smith CM, Fry H, Anderson C, Maguire H, Hayward AC, 2017. Optimising spatial accessibility to inform rationalisation of specialist health services. Int J Health Geogr 16:15. DOI: https://doi.org/10.1186/s12942-017-0088-6

Song J, Tong X, Wang L, Zhao C, Prishchepov AV, 2019. Monitoring finer-scale population density in urban functional zones: a remote sensing data fusion approach. Landsc Urban Plan 190:UNSP 103580. DOI: https://doi.org/10.1016/j.landurbplan.2019.05.011

Stevens FR, Gaughan AE, Linard C, Tatem AJ, 2015. Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PLoS One 10:UNSP e0107042. DOI: https://doi.org/10.1371/journal.pone.0107042

Tao Z, Cheng Y, Zheng Q, Li G, 2018a. Measuring spatial accessibility to healthcare services with constraint of administrative boundary: a case study of Yanqing District, Beijing, China. Int J Equity Health 2018;17:7. DOI: https://doi.org/10.1186/s12939-018-0720-5

Tao Z, Yao Z, Kong H, Duan F, Li G, 2018b. Spatial accessibility to healthcare services in Shenzhen, China: improving the multi-modal two-step floating catchment area method by estimating travel time via online map APIs. BMC Health Serv Res 18:345. DOI: https://doi.org/10.1186/s12913-018-3132-8

The People’s Government of Hainan Province, 2018. Notice of the People’s Government of Hainan Province on the issuance of action plan for standardization construction of primary health-care institution in Hainan Province. Available from: http://www.hainan.gov.cn/hainan/szfbgtwj/201811/37479119e1d34c03934b8c0ce54dbf67.shtml Accessed: 22 November 2019.

Voigtländer S, Deiters T, 2015. Minimum standards for the spatial accessibility of primary care: a systematic review. Gesundheitswesen 77:949-57. DOI: https://doi.org/10.1055/s-0035-1548805

Wan N, Zhan FB, Zou B, Chow E, 2012. A relative spatial access assessment approach for analyzing potential spatial access to colorectal cancer services in Texas. Appl Geogr 32:291-9. DOI: https://doi.org/10.1016/j.apgeog.2011.05.001

Wang F, 2012. Measurement, optimization, and impact of health care accessibility: a methodological review. Ann Assoc Am Geogr 102:1104-12. DOI: https://doi.org/10.1080/00045608.2012.657146

Wang L, Fan H, Wang Y, 2019. Fine-resolution population mapping from international space station nighttime photography and multisource social sensing data based on similarity matching. Remote Sens 11:1900. DOI: https://doi.org/10.3390/rs11161900

Wang X, Pan J, 2016. Assessing the disparity in spatial access to hospital care in ethnic minority region in Sichuan Province, China. BMC Health Serv Res 16:399. DOI: https://doi.org/10.1186/s12913-016-1643-8

Wang X, Yang H, Duan Z, Pan J, 2018. Spatial accessibility of primary health care in China: A case study in Sichuan Province. Social Sci Med 209:14-24. DOI: https://doi.org/10.1016/j.socscimed.2018.05.023

Yang J, Mao L, 2018. Understanding temporal change of spatial accessibility to healthcare: An analytic framework for local factor impacts. Health Place 51:118-24. DOI: https://doi.org/10.1016/j.healthplace.2018.03.005

Yang X, Ye T, Zhao N, Chen Q, Yue W, Qi J, Zeng B, Jia P, 2019. Population mapping with multisensor remote sensing images and point-of-interest data. Remote Sens 11:574. DOI: https://doi.org/10.3390/rs11050574

Ye T, Zhao N, Yang X, Ouyang Z, Liu X, Chen Q, Hu K, Yue W, Qi J, Li Z, Jia P, 2019. Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model. Sci Total Environ 658:936-46. DOI: https://doi.org/10.1016/j.scitotenv.2018.12.276

Yip W, Fu H, Chen AT, Zhai T, Jian W, Xu R, Pan J, Hu M, Zhou Z, Chen Q, Mao W, Sun Q, Chen W, 2019. 10 years of health-care reform in China: progress and gaps in Universal Health Coverage. Lancet 394:1192-204. DOI: https://doi.org/10.1016/S0140-6736(19)32136-1

Zhang F, Li D, Ahrentzen S, Zhang J, 2019a. Assessing spatial disparities of accessibility to community-based service resources for Chinese older adults based on travel behavior: a city-wide study of Nanjing, China. Habitat Int 88:101984. DOI: https://doi.org/10.1016/j.habitatint.2019.05.003

Zhang S, Song X, Wei Y, Deng W, 2019b. Spatial equity of multilevel healthcare in the metropolis of Chengdu, China: a new assessment approach. IJERPH 16:493. DOI: https://doi.org/10.3390/ijerph16030493

Zhu L, Zhong S, Tu W, Zheng J, He S, Bao J, Huang C, 2019. Assessing spatial accessibility to medical resources at the community level in Shenzhen, China. Int J Environ Res Public Health 16:242. DOI: https://doi.org/10.3390/ijerph16020242

Published
2021-03-11
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Original Articles
Keywords:
Spatial accessibility, enhanced two-step floating catchment area, population distribution, night-time light, primary health care, China.
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Tan, J., Wang, X., & Pan, J. (2021). The effect of population distribution measures on evaluating spatial accessibility of primary health-care institutions: A case study from China. Geospatial Health, 16(1). https://doi.org/10.4081/gh.2021.936