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UID:pretalx-foss4g-2026-VKAKGR@talks.osgeo.org
DTSTART;TZID=JST:20260901T133000
DTEND;TZID=JST:20260901T140000
DESCRIPTION:Background\nPublic health nursing uses routinely collected data
  to characterize population needs\, plan targeted outreach\, and coordinat
 e prevention activities with clinical providers. Two operational questions
  are central to these tasks: where health risks are geographically concent
 rated and where residents actually access preventive services. Spatial clu
 ster detection and provider catchment analysis address these questions fro
 m different perspectives\, but they are often reported separately\, limiti
 ng their interpretability for local planning.\n\nObjective\nTo describe an
 d apply a geospatial workflow that combines provider catchment analysis an
 d spatial scan statistics using preventive checkup data\, and to report ca
 tchment patterns and chome level clustering of elevated HbA1c within a sha
 red geographic framework relevant to public health nursing practice.\n\nMe
 thods\nWe analyzed 3\,200 records from Japan’s Specific Health Checkups 
 program for adults aged 40–74 years in a single local government area in
  Japan. The study protocol was approved by the institutional ethics review
  board of the author’s university (approval number 2024 053). Analyses w
 ere conducted at two spatial scales. Service use patterns were summarized 
 across seven residential elementary school districts. Spatial clustering w
 as assessed at the chome level\, using Japanese neighborhood scale address
  units and polygon boundary data.\nThe clustering outcome was elevated gly
 cated hemoglobin\, defined as HbA1c ≥ 5.6%\, treated as an indicator of 
 increased diabetes risk. Spatial processing\, polygon handling\, and centr
 oid derivation were performed in QGIS. Statistical tabulation and testing 
 were performed in R. Spatial scan statistics were computed using SaTScan.\
 nGeographic boundary data for elementary school districts and chome units 
 were obtained from e Stat\, the Portal Site of Official Statistics of Japa
 n. Population denominators for residents aged 40–74 years were also obta
 ined from e Stat. No individual locations were mapped or reported. Spatial
  outputs were based on aggregated chome level tables linked to polygon cen
 troids.\nProvider catchment patterns were quantified using an origin desti
 nation contingency table crossing residential elementary school district\,
  with seven origins\, and provider location category\, with nine destinati
 ons: seven in area districts\, out of area providers\, and group screening
 . The association between residence and provider location was evaluated us
 ing Pearson’s chi square test\, and adjusted standardized residuals were
  examined to characterize origin destination pairs used more or less often
  than expected under independence. District specific proportions of within
  district use\, out of area use\, and group screening were summarized.\nFo
 r spatial clustering\, SaTScan input files were prepared by linking chome 
 level case counts\, population denominators for residents aged 40–74 yea
 rs\, and chome centroid coordinates. A Poisson spatial scan statistic was 
 applied to identify circular windows with elevated risk relative to the po
 pulation denominator. Cluster reporting included population\, observed and
  expected case counts\, observed to expected ratios\, relative risk\, and 
 statistical significance based on Monte Carlo testing.\n\nResults\nProvide
 r catchment analysis indicated strong geographic structuring of preventive
  checkup utilization. Residential district and provider location category 
 were strongly associated (Pearson’s χ² = 2864.8\, df = 48\, p < 0.01).
  Adjusted standardized residuals were positive for all seven within distri
 ct origin destination cells\, indicating higher than expected utilization 
 of providers located within the same district across all residential distr
 icts. However\, the magnitude of within district utilization differed subs
 tantially by district. Within district use ranged from 6.8% to 72.5%\, out
  of area use ranged from 11.2% to 37.3%\, and group screening ranged from 
 3.8% to 7.1%\, demonstrating heterogeneous catchment patterns within the s
 tudy area.\nSpatial scan analysis identified one statistically significant
  spatial concentration of elevated HbA1c. The most likely high risk cluste
 r comprised 40 chome units and had a population of 9\,577 adults aged 40
 –74 years. This cluster included 400 observed cases and 315.80 expected 
 cases\, corresponding to an observed to expected ratio of 1.27 and a relat
 ive risk of 1.37 (p < 0.01). Two additional areas had relative risks great
 er than 1 but were not statistically significant. The second cluster compr
 ised 10 chome units\, with a population of 4\,986 adults\, 209 observed ca
 ses\, 164.41 expected cases\, an observed to expected ratio of 1.27\, and 
 a relative risk of 1.32 (p > 0.05). The third cluster comprised three chom
 e units\, with a population of 177 adults\, 13 observed cases\, 5.84 expec
 ted cases\, an observed to expected ratio of 2.23\, and a relative risk of
  2.24 (p > 0.05).\n\nConclusions\nProvider catchment analysis and spatial 
 scan statistics provide complementary geographic evidence relevant to publ
 ic health nursing practice. Catchment heterogeneity indicates that pattern
 s of preventive service utilization differ by residential district\, inclu
 ding substantial variation in out of area utilization\, while spatial scan
  statistics identify chome level areas where elevated HbA1c is concentrate
 d beyond random variation. Presenting these results within a single geospa
 tial workflow supports interpretation of local risk concentration together
  with district level service use patterns relevant to planning outreach\, 
 communication strategies\, and provider collaboration.\nA limitation is th
 at the Poisson scan used census based population denominators for resident
 s aged 40–74 years rather than chome specific counts of checkup particip
 ants. Spatial variation in checkup participation may therefore influence c
 luster detection and interpretation. The use of open source software provi
 des a practical pathway to strengthen evidence informed public health nurs
 ing activities through transparent\, reproducible\, and adaptable geospati
 al analyses.
DTSTAMP:20260717T225743Z
LOCATION:Cosmos2
SUMMARY:Linking provider catchment and SaTScan risk clusters for evidence-b
 ased public health nursing in Japan - Ryo Horiike
URL:https://talks.osgeo.org/foss4g-2026/talk/VKAKGR/
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