Population-based health promotion perspective for older driver safety: Conceptual framework to intervention plan

Figures & data

Figure 1 Conceptualization of the precede-proceed model of health promotion (Green and Kreuter 1999) indicating the five assessments of the PRECEDE phase (eg, social, epidemiological, etc.) and the four phases of the PROCEED phase (eg, implementation, process evaluation, etc), as well as the main domains (eg, health, behavior and lifestyle, environment, etc.). Re-printed with permission obtained from McGraw-Hill Companies; permission granted on August 03, 2007.

Figure 2 Algorithm for the mixed methods expanded coverage design used in “Public Health Model to Promote Safe Elderly Driving” study. We used the PRECEDE phase of the Precede-Proceed Model of Health Promotion^a and a systematic literature review on older driver safety (Classen et al 2006) to develop the structural model^b (Classen and Lopez 2006).

Table 1 Multi-method meta-matrix template used to compare and integrate significant findings from the logistic regression model with the comparable qualitative finding(s)

	PPMHP Domain
Quantitative	Significant finding from the logistic regression model
	Description and interpretation of the significant finding
	Referent category	Level 1 Significant protective or risk factor	Level 2 Significant protective or risk factor
Qualitative	Description and interpretation of a comparable theme from the meta-synthesis, corresponding to the above mentioned finding from the logistic regression model Relevant data (quotes) from the stakeholders with citations	Description and interpretation of a comparable theme from the meta-synthesis, corresponding to the above mentioned finding from the logistic. regression model. Relevant data (quotes) from the stakeholders with citations	Description and interpretation of a comparable theme from the meta-synthesis, corresponding to the above mentioned finding from the logistic regression model. Relevant data (quotes) from the stakeholders with citations
Comparison or Integration	Synopsis and interpretation of the compared data.
Discussion	Compared or integrated findings discussed in terms of the existing literature, the PPMHP and implications for intervention planning.

Table 2 Binary logistic regression model showing the significant age interactions and explanatory variables, from the five domains of the PPMHP, with the dependent variable (injury: yes/no)

Dependent variable: Injury (yes/no)	p	OR	Lower CI	Upper CI
Health domain
Gender
Male	(Referent)
Female	<0.01*	1.51	1.29	1.73
Behavior domain
Driver license compliance
Valid	(Referent)
Not Valid	0.04*	1.39	1.02	1.90
Driver drinking
Not drinking (Referent)	(Referent)
Drinking	<0.01*	2.00	1.57	2.54
System restraint use
Yes	(Referent)
None	<0.01*	6.20	5.03	7.63
Environment domain
Day of week
Sunday	(Referent)
Monday	0.18	1.17	0.93	1.48
Tuesday	0.02	1.33	1.05	1.69
Wednesday	<0.01*	1.64	1.27	2.13
Thursday	<0.01*	1.46	1.14	1.86
Friday	0.01*	1.36	1.07	1.74
Saturday	0.03*	1.29	1.02	1.62
Hour of day
9PM-7AM	(Referent)
8AM-1PM	0.01*	0.72	0.57	0.90
2PM-8PM	<0.01*	0.63	0.53	0.76
Registered vehicle owner*Age
Driver was registered owner*Age	(Referent)
Driver was not owner*Age	0.05*	0.69	0.48	1.00
Number of lanes
Two	(Referent)
One	0.03*	0.32	0.12	0.87
Three	0.41	0.32	0.68	1.17
Four-seven	0.29	0.89	0.75	1.09
Road surface condition
Dry	(Referent)
Adverse	<0.01*	1.50	1.16	1.95
Rural vs. urban
Rural	(Referent)
Urban	<0.01*	0.61	0.52	0.71
Body type
SUVs	(Referent)
Auto and auto derivatives	<0.01*	2.00	1.64	2.44
Vans, trucks, and light pick-ups	0.01*	0.77	0.64	0.94
Most harmful event
Collision w/object not fixed	(Referent)
Collision w/fixed object	<0.01*	249.55	152.61	408.03
Motor vehicle in transport	<0.01*	30.99	23.82	40.31
Non-collision	<0.01*	265.68	155.37	454.32
Relation to junction
Non-junction	(Referent)
Intersection-related	<0.01*	0.59	0.48	0.72
Interchange-related	0.94	0.98	0.64	1.51
Principal impact*Age
12 o’clock	(Referent)
1– 3 o’clock	0.03*	1.61	1.05	2.47
4 – 6 o’clock	0.50	1.20	0.71	2.05
7 – 9 o’clock	<0.01*	4.75	2.87	7.86
10 −11 o’clock	0.15	1.47	0.87	2.48
Bottom or roof top	0.95	0.96	0.28	3.33
Traffic control device
Functioning	(Referent)
Not present	0.01*	0.79	0.65	0.95
Number of occupants*Age
Driver only*Age	(Referent)
One passenger*Age	0.34	1.18	0.84	1.64
≥ Two passengers*Age	0.05*	0.60	0.36	1.01
Airbag deployment
Deployed	(Referent)
Did not deployed	<0.01*	0.25	0.21	0.29
National highway system
On NHS	(Referent)
Not on NHS	<0.01*	0.77	0.65	0.91
Predisposing domain
Straight	(Referent)
Lane-related	<0.01*	0.64	0.50	0.81
Maneuvers	0.02*	0.59	0.38	0.92
Making a left	<0.01*	0.66	0.51	0.87
Negotiating a curve/changing	0.35	1.15	0.86	1.54
Reinforcing domain
Number previous other MV convictions*Age	0.03*	0.65	0.44	0.97

* p < 0.05; In the case of each of the four significant age interactions, we did not include the values of the main effects.

Figure 3 Health promotion intervention on empirically determined modifiable determinants of motor vehicle crashes with anticipated intermediate and long-term outcomes for older drivers.

GreenLWKreuterMW1999Health Program Planning: An educational and ecological approach3rd edMountain View, CAMayfield Publishing

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ClassenSGarvanCWAwadziK2006Systematic literature review and model for older driver safetyTop Ger Rehabil22879

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