Are remote area candidates
for virtual health ironically
less tech-conducive?
T
Jon M. Martin, PhD, MM
143rd American Public Health Association
(APHA) Annual Meeting and Exposition
October 31 - November 4, 2015 - Chicago
Health Informatics Information Technology: Session 4133.0, Abstract 329229
www.pfeiffer.edu
Background
 Tele-based delivery increasingly considered in HC
•
•
•
Lower cost; optimal use of on-call staff/physicians
Reduces commuting; provides remote accessibility
Experience, logistical/technical viability, and application is increasing

•
•
•
Tele-based delivery/connections used for:
a. Monitoring chronic patients remotely
b. Providing diagnoses/opinions from remote staff/physicians
c. Filling in on-call for odd shift staffing and cross-over, ER, ICU, etc.
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 More remote geographies are obvious candidates
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Less proximity/connection to major medical centers/corridors
Socio-economics - age, income, education less advantageous(?)
 Little research has examined if more remote areas’ are
relatively tech-conducive and/or if a proximity - tech
conducive relationship exists
 Tech-conduciveness could have implications re: teledelivery’s ease-of-infusion, design, implementation.
 Study examines relationship between proximity to
major medical and tech-conduciveness of NC counties
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Data Sources and Methods
1.
NC County
Demographics
(Census
Quickfacts)
2.
Internet
usage %’s
for US by
demographics (US
Census)
3.
NC Death
Statistics by
county (NC
SCHS)
ID NC
Major
Medical
> 250 beds
6.
County tiers
1-3 by
proximity to
major
medical
(see map)
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4.
Calc. of
county
tech
factors
weighted
by demographic
profiles
profile
Tech factors
normalized
5.
County
normalized
scores 1100 for
tech
conducive
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Regression
analysis
and
correlation
Sources of Secondary Data and Calculations
1. NC County Demographics: Age, Income, Ethnicity, Education
• http://quickfacts.census.gov/qfd/states/37000.html
2. Internet Usage % (used in Tech Factor Est.)
• http://www.census.gov/hhes/computer/publications/2012.html
Table 1. Reported Internet Usage for Individuals 3 Years and Older,
by Selected Characteristics: 2012
• % house w/ Internet; access internet; access home Internet
3. County Death Rates: T
• www.schs.state.nc.us/schs/data/databook/ (state center, health statistics)
2008-2012 Race-Specific and Sex Specific Age-Adjusted Death
Rates By County
• 3 Categories: a) All Causes; b) Disease of Heart; c) Cancer
4. County Tech-conducive Factors (Calculated):
• County level tech census data not available.
• County tech factors estimated by applying demographic-specific
national internet %’s (#2 above) to county demographic profiles.
5. County Tech-conduciveness Scores
• Tech factors are normalized into a 1-100 range/score
6. County Proximity/Access Tiers
• Assigned (1-3) based upon proximity to medical facilities/corridors
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County Proximity/Access Tiers
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Results
 Regressions
• H0: There is no significant relationship between county access tier
(CAT) levels and county tech-conducive scores (TCS).
• H1: There is a significant relationship between county access tier
(CAT) levels and tech-conducive scores (TCS).
•
Regression results supported H1….significant but weak relationship.
o
o
County Access Tier (CAT)TCS: stdb = .252**; adjR2 = .054**; DW= 2.057
CATTCS (Piedmont Region) : stdb = .517**; adjR2 = .245**; DW= 2.587
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• Exploratory regressions on Death:
o
o
o
TCSDeath Rates: stdb = -.538**; adjR2 = .282**; DW = 2.048
TCSHeart Deaths: stdb = -.400**; adjR2 = .152**; DW = 2.075
TCSCancer Deaths: stdb = -.433**; adjR2 = .179**; DW = 2.297
o
EducationDeath Rates: stdb = -.662**; adjR2 = .433**; DW = 2.216
EducationHeart Deaths: stdb = -.501**; adjR2 = .243**; DW = 1.978
EducationCancer Deaths: stdb = -.476**; adjR2 = .218**; DW = 1.927
o
o
o
TCS’ and Education’s relationships with overall death rates deserves further
respective understanding and examination.
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Results (cont.)
Significant Correlations
Var 1
Var 2
Pearson’s
Kendall’
s
Spearman’s
TCS
CAT
.252**
.218**
.278**
TCS
(Piedmont)
CAT
(Piedmont)
.513**
.599**
.710**
TCS
Death
-.538**
-.433**
-.602**
TCS
Heart Death
-.400**
-.315**
-.455**
TCS
Cancer Death
-.433**
-.388**
-.510**
CAT
Age
.423**
H
.347**
.420**
CAT
Education
.347**
.260**
.324**
Th
Implications
• TCS w/ CAT supports regression findings – sig. but weak correlation
• Piedmont (only) regression shows sig., moderately strong correlation
• TCS w/ Deaths support regressions – sig,. moderate relationship
• CAT w/ Age, Educ. implies slightly older, more educated in outer Tiers
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Conclusions & Implications
•
Piedmont region of NC reflects a strong TCS-Proximity relationship
(Less proximal = less tech conducive; more proximal = more “t-c”).
•
Eastern NC has relatively low TCS scores but good major medical;
tele-medicine may need additional infrastructure/development
•
Western NC has high tech scores but limited major medical, and
may be a good candidate for tele-based expansions.
•
Knowing, considering, understanding an area’s tech-conduciveness
could alter the application/configuration of tele-based delivery.
•
Major medical investments can
T be balanced against tech
infrastructure for optimizing LT investments/ROI in regional health.
•
Area’s TCS can indicate changes that need to occur through tech
design, configuration, education, socio-economic development, etc.
•
Death rates inverse relationship with TCS may have a socioeconomic basis deserving further understanding/research.
•
The basic methods/techniques employed in this study can be
similarly replicated for any country, state, region, county, or city to
examine proximity vs. tech conducive patterns/relationships.
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Limitations
Conditions
Results
Limitations
Normal data /
Linearity
K-S and plots indicate nonnormal for tech factors &
scores
Probability of nonlinear relationship(s)
Heteroskedasticity
Levene’s test
None
No perfect
multicollinearity
No perfect/high correlations
between any variables
None
Non-zero
variances
All variances (std. dev.) are
non-zero
None
Variable Types
County Access Tiers are
ordinal, discrete
T 1-3
Discrete nature could
cause some error in
regressions
Independent
errors
None
Predictors
uncorrelated with
external variables
Regression scattergrams OK
for dependent vs.
independent variables
None
County Tech
Factor
Calculations
Estimated from census
national averages/%’s
Actual county tech #’s
could be sig. different
from estimated
Proximity Tier
Assignments
Assigned based on hospital
beds >250
Assignments could
have error/variance
Census Tech
Questions/%’s
3 census-based responses
re: Internet use/availability
Questions may or may
not best reflect techconduciveness
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Future Research
•
Repeat study using AMOS/SEM
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Repeat study using spacial regression
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Repeat using different tech factor basis beyond Internet
access/usage (e.g. smartphone access and/or usage).
•
Repeat using applications on a national or regional basis and/or in
other states or provinces.
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Further explore the demographic basis of tech-conduciveness (TCS)
and death rates in counties.
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Further explore the relationship of Education to Death Rates
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Explore other models and analyses of tele-based considerations
regarding application, design, patient readiness, training, etc.
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