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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Population Segmentation Using a Novel Socio-Demographic Dataset.

Elisabeth L Scheufele1, Brandi Hodor2, George Popa3

  • 1Boston Children's Hospital, Boston, MA.

Online Journal of Public Health Informatics
|September 19, 2022
PubMed
Summary
This summary is machine-generated.

Combining market segmentation with healthcare data reveals hyperlocal population insights. This approach identifies at-risk groups, like the New Melting Point segment, for targeted health improvement strategies, particularly for depression management.

Keywords:
Health Care SurveyMarketing SegmentationPublic HealthSocial Determinants of HealthSocial Marketing

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Area of Science:

  • Health Services Research
  • Population Health Management
  • Data Analytics in Healthcare

Background:

  • Healthcare data often lacks granular population insights.
  • Understanding social determinants of health is crucial for effective interventions.
  • Market segmentation data can bridge gaps in traditional healthcare datasets.

Purpose of the Study:

  • To integrate market segmentation data with healthcare surveys and claims.
  • To identify at-risk populations at a hyperlocal level.
  • To develop cohort-specific strategies for improving health outcomes, focusing on depression.

Main Methods:

  • Geocoded market segmentation data was appended to national healthcare surveys and medical claims.
  • Survey scores were normalized and compared to identify at-risk segments using the Nonparametric Mann-Whitney U test.
  • The New Melting Point (NMP) marketing segment was analyzed for specific risk factors.

Main Results:

  • The NMP segment showed significantly higher risk factors compared to comparable non-at-risk segments.
  • Key differences included inability to pay for basic needs, lack of transportation, and delayed medical care.
  • NMP exhibited higher rates of "Depressed: All/Most Time" and lower utilization of virtual visits.

Conclusions:

  • Appending market segmentation data provides actionable insights for providers, payers, and public health entities.
  • Hyperlocal data enables the development of tailored strategies for population health improvement.
  • Interventions for the NMP segment could include virtual visits and pharmacy incentives to manage depression.