Mandatory Physician Reporting of At-Risk Drivers: The Older Driver Example

Mandatory Physician Reporting of At-Risk Drivers: The Older Driver Example Abstract Purpose of the Study In a number of states, physicians are mandated by state law to report at-risk drivers to licensing authorities. Often these patients are older adult drivers who may exhibit unsafe driving behaviors, have functional/cognitive impairments, or are diagnosed with conditions such as Alzheimer’s disease and/or seizure disorders. The hypothesis that mandatory physician reporting laws reduce the rate of crash-related hospitalizations among older adult drivers was tested. Design and Methods Using retrospective data (2004–2009), this study identified 176,066 older driver crash-related hospitalizations, from the State Inpatient Databases. Three age-specific negative binomial generalized estimating equation models were used to estimate the effect of physician reporting laws on state’s incidence rate of crash-related hospitalizations among older drivers. Results No evidence was found for an independent association between mandatory physician reporting laws and a lower crash hospitalization rate among any of the age groups examined. The main predictor of interest, mandatory physician reporting, failed to explain any significant variation in crash hospitalization rates, when adjusting for other state-specific laws and characteristics. Vision testing at in-person license renewal was a significant predictor of lower crash hospitalization rate, ranging from incidence rate ratio of 0.77 (95% confidence interval 0.62–0.94) among 60- to 64-year olds to 0.83 (95% confidence interval 0.67–0.97) among 80- to 84-year olds. Implications Physician reporting laws and age-based licensing requirements are often at odds with older driver’s need to maintain independence. This study examines this balance and finds no evidence of the benefits of mandatory physician reporting requirements on driver crash hospitalizations, suggesting that physician mandates do not yet yield significant older driver safety benefits, possibly to the detriment of older driver’s well-being and independence. Mandatory physician reporting, Licensing, Mobility, Independence, Older drivers Mobility is essential to optimal aging, with physical activity and transportation being key to one’s health and community participation (Kochtitzky, Freeland, & Yen, 2011; Satariano et al., 2012; Shah et al., 2012; Warburton, Nicol, & Bredin, 2006). Driving is essential for the independence of some 48 million adults 60 and older, yet driving involves risks, often exacerbated by age-related conditions, making the balance between mobility and safety a delicate one (Brown & Ott, 2004; Federal Highway Administration [FHWA], 2012; Owsley et al., 1998). Since 2005, more than 2.5 million older adults have received emergency department treatment and another 60,000 have died as drivers in transportation crashes (Centers for Disease Control and Prevention, 2015; National Highway Traffic Safety Administration [NHTSA], 2014). Sensational reporting of older driver crashes has further skewed the public perception of the elderly driver (ABC, 2015; NBC, 2015) prompting some states to tighten driving requirements for older adults, including the requirement that physicians report at-risk drivers (AAA, 2013; Collette, 2009). The American Medical Association (AMA) expects physicians to recognize patient’s driving impairments, advises safe driving, and encourages doctors to demand patient driving assessment by state licensing agencies when necessary (AMA, 2011; Jang et al., 2007). Although controversial, a few studies support the use of reporting requirements and licensing restrictions (Caragata Nasvadi & Wister, 2009; Meuser, Carr, & Ulfarsson, 2009) with Redelmeier and colleagues (2012) linking physician warnings to at-risk drivers with a decrease in road crashes (Redelmeier, Yarnell, Thiruchelvam, & Tibshirani, 2012). Furthermore, those patients disregarding these warnings may be reported to driving authorities and lose their driving privileges permanently (Meuser, Carr, & Ulfarsson, 2009; Snyder & Ganzini, 2009). Others, using examples from mandatory reporting of cardiac and seizure disorder patients, argue that physician reporting has demonstrated no safety benefits and is likely to yield similar results with reporting of other conditions (McLachlan, Starreveld, & Lee, 2007; Simpson et al., 2000). Adding to this debate, some argue physician reporting laws and other age-based licensing requirements are agist, at odds with older adults’ need for mobility and independence (Eby & Molnar, 2010; Grabowski, Campbell, & Morrisey, 2004; Unsworth, Wells, Browning, Thomas, & Kendig, 2007). Many argue that physicians lack the training needed to recognize driving impairments, may overestimate Dementia and stroke patient’s fitness to drive when compared with on-road assessors, and argue that patient–doctor confidentiality and a healthy rapport are their main responsibilities (Berger, Rosner, Kark, & Bennett, 2000; Eby & Molnar, 2010; Jang et al., 2007; Martin, Marottoli, & O’Neill, 2009, 2013; Ranchet et al., 2016; Ranchet, Tant, Akinwuntan, Morgan, & Devos, 2016). Additionally, mental health harm, especially depression, is often cited as an issue to consider when limiting one’s driving (Chihuri et al., 2016; Edwards, Perkins, Ross, & Reynolds, 2009; Redelmeier et al., 2012). The contentious nature of this issue, its implications on the mobility and independence of older adults, their relationship with their doctors, and its safety consequences signify its importance. This study examines the role of state physician reporting requirements on crash-related hospitalizations in 37 states, over 6 years, and provides a comprehensive account of their impact on older drivers. Design and Methods Study Population This study used data on older adult driver’s crash-related hospital admissions from the State Inpatient Databases (SIDs) of the Agency for Healthcare Quality and Research (AHRQ) from 2004 to 2009. The SIDs contain inpatient discharge abstracts, representing approximately 97% of all annual discharges in the United States (AHRQ, 2016). Crash hospitalizations, counted in 3-month intervals, resulted in 916 state quarters from the 37 states reporting data in 2004-2005, 39 in 2006, 40 in 2007, 32 in 2008, and 44 in 2009. A total of 176,066 hospitalized drivers aged 55 and older were identified, excluding those admitted directly from another hospital or long-term care center, as to avoid double counting. Drivers were identified using the International Classification of Diseases, 9th Edition, Clinical Modification (ICD9-CM) external causes of injury codes (E-codes) E810 through E819, with a fourth digit of 0.0, denoting driver, in the data fields E-code1 through E-code4. For more information on E-codes refer the AHRQ report on completeness of ECODE data (Barrett, Steiner, & Coben, 2005). Data Sources Data on state requirements for physician reporting of at-risk drivers and legal protection of reporting physicians were obtained from a number of sources (AAA, 2013; AMA, 2004 & 2010; PADOT, 2010). Table 1 contains a complete listing. Data were accessed through an approved collaboration with AHRQ and the Healthcare Cost and Utilization Project. The University of Pittsburgh Institutional Review Board classified this study as exempt. Table 1. Description of Constructed Model Predictors and Covariates, Rationale and Source Variables and rationale Models States (State quarters) Data source Physician reporting is required by law. 1–3 3/48 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) Physician reporting is voluntary, physicians are granted legal immunity. 1–3 27/540 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) In-person drivers’ license renewal at least once in three renewal cycles. To account for other age-based state licensing requirement. 1–3 32/824 (IIHS, 2011a) Required vision acuity testing at in-person renewal. To account for other age-based state licensing requirement. 1–3 36/756 (IIHS, 2011a) Road test for license renewal. To account for other age-based state licensing requirement. 1–3 2/48 (IIHS, 2011a) State’s primary seatbelt requirements. An approximate measure for state police enforcement (Grabowski et al., 2004). 1–3 All (IIHS, 2011c) State annual rainfall. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to rain (Bhattacharyya & Millham, 2001). 1–3 All (NOAA, 2011) Urban area speed limits equal to or higher than 60 mph, or lower than 60 mph. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to speed limits (Boufous, Finch, Hayen, & Williamson, 2008). 1–3 All (IIHS, 2011b) Patient’s sex. To adjust for any state-specific differences in the sex of the driving population and differences in crash hospitalizations. 1–3 All (AHRQ, 2016) Patient’s rural/urban location. To adjust for any state-specific differences in urban/rural crash hospitalization trends. 1–3 All (AHRQ, 2016) Proportion of the state’s population that has access to Trauma I and II centers within 45 minutes. To account for any state-specific differences in crash-related hospital admissions (Branas et al., 2005). 1–3 All (Branas et al., 2005) Annual state unemployment rates. To account for any state differences in road infrastructure and other factors that may differentially impact crash- related hospital admissions (Evans & Graham, 1991; Ruhm, 1996). 1–3 All (BLS, 2011) State annual fuel consumption per capita. Used as an approximate measure of driving exposure, to account for differences in risk of a crash-related hospital admission (FHWA, 2004–2009; Masten, Foss, & Marshall, 2011). 1–3 All (FHWA, 2012) Natural log transformed variable for each age cohort denoting the number of licensed drivers. To account for the difference in state’s driving population within age group of interest. 1–3 All (FHWA, 2012) State Census regions. To account for any region-wide characteristics in hospitalizations and crashes. 2–3 All (U.S. Census, 2009) Hospitalized counts of drivers aged 55 to 59 years. To distinguish the differential age-cohort effect on the hospitalization rates of groups, under the assumption that rate differences should remain constant if examined laws have no age-specific effect (Grabowski et al., 2004; Ruhm, 1996). 3 All (AHRQ, 2016) Variables and rationale Models States (State quarters) Data source Physician reporting is required by law. 1–3 3/48 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) Physician reporting is voluntary, physicians are granted legal immunity. 1–3 27/540 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) In-person drivers’ license renewal at least once in three renewal cycles. To account for other age-based state licensing requirement. 1–3 32/824 (IIHS, 2011a) Required vision acuity testing at in-person renewal. To account for other age-based state licensing requirement. 1–3 36/756 (IIHS, 2011a) Road test for license renewal. To account for other age-based state licensing requirement. 1–3 2/48 (IIHS, 2011a) State’s primary seatbelt requirements. An approximate measure for state police enforcement (Grabowski et al., 2004). 1–3 All (IIHS, 2011c) State annual rainfall. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to rain (Bhattacharyya & Millham, 2001). 1–3 All (NOAA, 2011) Urban area speed limits equal to or higher than 60 mph, or lower than 60 mph. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to speed limits (Boufous, Finch, Hayen, & Williamson, 2008). 1–3 All (IIHS, 2011b) Patient’s sex. To adjust for any state-specific differences in the sex of the driving population and differences in crash hospitalizations. 1–3 All (AHRQ, 2016) Patient’s rural/urban location. To adjust for any state-specific differences in urban/rural crash hospitalization trends. 1–3 All (AHRQ, 2016) Proportion of the state’s population that has access to Trauma I and II centers within 45 minutes. To account for any state-specific differences in crash-related hospital admissions (Branas et al., 2005). 1–3 All (Branas et al., 2005) Annual state unemployment rates. To account for any state differences in road infrastructure and other factors that may differentially impact crash- related hospital admissions (Evans & Graham, 1991; Ruhm, 1996). 1–3 All (BLS, 2011) State annual fuel consumption per capita. Used as an approximate measure of driving exposure, to account for differences in risk of a crash-related hospital admission (FHWA, 2004–2009; Masten, Foss, & Marshall, 2011). 1–3 All (FHWA, 2012) Natural log transformed variable for each age cohort denoting the number of licensed drivers. To account for the difference in state’s driving population within age group of interest. 1–3 All (FHWA, 2012) State Census regions. To account for any region-wide characteristics in hospitalizations and crashes. 2–3 All (U.S. Census, 2009) Hospitalized counts of drivers aged 55 to 59 years. To distinguish the differential age-cohort effect on the hospitalization rates of groups, under the assumption that rate differences should remain constant if examined laws have no age-specific effect (Grabowski et al., 2004; Ruhm, 1996). 3 All (AHRQ, 2016) View Large Table 1. Description of Constructed Model Predictors and Covariates, Rationale and Source Variables and rationale Models States (State quarters) Data source Physician reporting is required by law. 1–3 3/48 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) Physician reporting is voluntary, physicians are granted legal immunity. 1–3 27/540 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) In-person drivers’ license renewal at least once in three renewal cycles. To account for other age-based state licensing requirement. 1–3 32/824 (IIHS, 2011a) Required vision acuity testing at in-person renewal. To account for other age-based state licensing requirement. 1–3 36/756 (IIHS, 2011a) Road test for license renewal. To account for other age-based state licensing requirement. 1–3 2/48 (IIHS, 2011a) State’s primary seatbelt requirements. An approximate measure for state police enforcement (Grabowski et al., 2004). 1–3 All (IIHS, 2011c) State annual rainfall. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to rain (Bhattacharyya & Millham, 2001). 1–3 All (NOAA, 2011) Urban area speed limits equal to or higher than 60 mph, or lower than 60 mph. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to speed limits (Boufous, Finch, Hayen, & Williamson, 2008). 1–3 All (IIHS, 2011b) Patient’s sex. To adjust for any state-specific differences in the sex of the driving population and differences in crash hospitalizations. 1–3 All (AHRQ, 2016) Patient’s rural/urban location. To adjust for any state-specific differences in urban/rural crash hospitalization trends. 1–3 All (AHRQ, 2016) Proportion of the state’s population that has access to Trauma I and II centers within 45 minutes. To account for any state-specific differences in crash-related hospital admissions (Branas et al., 2005). 1–3 All (Branas et al., 2005) Annual state unemployment rates. To account for any state differences in road infrastructure and other factors that may differentially impact crash- related hospital admissions (Evans & Graham, 1991; Ruhm, 1996). 1–3 All (BLS, 2011) State annual fuel consumption per capita. Used as an approximate measure of driving exposure, to account for differences in risk of a crash-related hospital admission (FHWA, 2004–2009; Masten, Foss, & Marshall, 2011). 1–3 All (FHWA, 2012) Natural log transformed variable for each age cohort denoting the number of licensed drivers. To account for the difference in state’s driving population within age group of interest. 1–3 All (FHWA, 2012) State Census regions. To account for any region-wide characteristics in hospitalizations and crashes. 2–3 All (U.S. Census, 2009) Hospitalized counts of drivers aged 55 to 59 years. To distinguish the differential age-cohort effect on the hospitalization rates of groups, under the assumption that rate differences should remain constant if examined laws have no age-specific effect (Grabowski et al., 2004; Ruhm, 1996). 3 All (AHRQ, 2016) Variables and rationale Models States (State quarters) Data source Physician reporting is required by law. 1–3 3/48 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) Physician reporting is voluntary, physicians are granted legal immunity. 1–3 27/540 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) In-person drivers’ license renewal at least once in three renewal cycles. To account for other age-based state licensing requirement. 1–3 32/824 (IIHS, 2011a) Required vision acuity testing at in-person renewal. To account for other age-based state licensing requirement. 1–3 36/756 (IIHS, 2011a) Road test for license renewal. To account for other age-based state licensing requirement. 1–3 2/48 (IIHS, 2011a) State’s primary seatbelt requirements. An approximate measure for state police enforcement (Grabowski et al., 2004). 1–3 All (IIHS, 2011c) State annual rainfall. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to rain (Bhattacharyya & Millham, 2001). 1–3 All (NOAA, 2011) Urban area speed limits equal to or higher than 60 mph, or lower than 60 mph. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to speed limits (Boufous, Finch, Hayen, & Williamson, 2008). 1–3 All (IIHS, 2011b) Patient’s sex. To adjust for any state-specific differences in the sex of the driving population and differences in crash hospitalizations. 1–3 All (AHRQ, 2016) Patient’s rural/urban location. To adjust for any state-specific differences in urban/rural crash hospitalization trends. 1–3 All (AHRQ, 2016) Proportion of the state’s population that has access to Trauma I and II centers within 45 minutes. To account for any state-specific differences in crash-related hospital admissions (Branas et al., 2005). 1–3 All (Branas et al., 2005) Annual state unemployment rates. To account for any state differences in road infrastructure and other factors that may differentially impact crash- related hospital admissions (Evans & Graham, 1991; Ruhm, 1996). 1–3 All (BLS, 2011) State annual fuel consumption per capita. Used as an approximate measure of driving exposure, to account for differences in risk of a crash-related hospital admission (FHWA, 2004–2009; Masten, Foss, & Marshall, 2011). 1–3 All (FHWA, 2012) Natural log transformed variable for each age cohort denoting the number of licensed drivers. To account for the difference in state’s driving population within age group of interest. 1–3 All (FHWA, 2012) State Census regions. To account for any region-wide characteristics in hospitalizations and crashes. 2–3 All (U.S. Census, 2009) Hospitalized counts of drivers aged 55 to 59 years. To distinguish the differential age-cohort effect on the hospitalization rates of groups, under the assumption that rate differences should remain constant if examined laws have no age-specific effect (Grabowski et al., 2004; Ruhm, 1996). 3 All (AHRQ, 2016) View Large Statistical Analyses Counts of hospitalizations were grouped in 5-year age increments (60 to 85 and 85 and older) for each state. Three multivariate negative binomial regression models were constructed, for six age groups, that use autoregressive correlation-based generalized estimating equations (GEEs). Table 1 presents details of specified models. Exponentiated estimates are presented as incidence rate ratios (IRRs). Briefly, the first model examined the effect of physician reporting on the number of older driver hospitalizations, adjusting for a number of covariates. The second model adds state’s Census region, to account for any unobserved or seasonal characteristics of crash-related hospitalizations. The third model adds the hospitalized counts of drivers aged 55–59 years to distinguish the differential age-cohort effect of examined laws on the hospitalization rates of these groups, under the assumption that rate differences should remain constant if examined laws have no age-specific effect (Grabowski et al., 2004; Ruhm, 1996). Negative binomial was chosen as Poisson models lacked fit, resulting in significant overdispersion of the dependent variable (Hilbe, 2008). Using negative binomial models with GEE allows for estimation of population-averaged coefficients, which indicate the effect of selected predictors on the whole population; GEE was used to account for the nonindependence of within-state observations (Pan, 2001). Finite sample correction to standard errors was applied as our sample accounts for more than 5% of total population (Cohen, Cohen, West, & Aiken, 2002). Based on the quasi-likelihood independent criterion, a nonsignificant parameter (GDP per state capita) that decreased model fit was removed from the models. All analyses were performed using SAS 9.3 (SAS Institute Inc., Cary, NC). Results Descriptive Results There were more than 190 million hospital discharges during the study period from the included states. In total, 136,987 hospitalized drivers aged 60 and older were identified and 37,079 drivers were aged 55–59 years. Using the number of licensed state population person-years, Table 2 shows the pooled unadjusted driver hospitalization rates for 2004 to 2009. Drivers aged 55–59 years had 43.3 hospitalizations due to motor-vehicle crashes per 100,000 licensed driver person-years. The rate of hospitalizations increased with age, reaching a peak value of 98.89 crashes per 100,000 licensed driver person-years for patients aged 85 and older. Table 2. 2004–2009 Driver Crash Hospitalization Rates According to Age Group Age group (years) Hospitalized drivers in thousands Licensed person-years in millions (rate per 100k licensed person-years) Person-years in millions (rate per 100k person-years) 55–59 37.1 88.6 (43.3) 93.6 (41.1) 60–64 31.1 70.0 (44.6) 73.2 (42.5) 65–69 25.0 51.5 (48.3) 55.7 (44.6) 70–74 23.0 39.2 (58.5) 45.1 (51.0) 75–79 22.8 31.5 (72.6) 38.6 (59.2) 80–84 20.4 22.0 (92.5) 30.0 (68.3) 85+ 14.8 15.0 (98.8) 27.0 (55.0) Age group (years) Hospitalized drivers in thousands Licensed person-years in millions (rate per 100k licensed person-years) Person-years in millions (rate per 100k person-years) 55–59 37.1 88.6 (43.3) 93.6 (41.1) 60–64 31.1 70.0 (44.6) 73.2 (42.5) 65–69 25.0 51.5 (48.3) 55.7 (44.6) 70–74 23.0 39.2 (58.5) 45.1 (51.0) 75–79 22.8 31.5 (72.6) 38.6 (59.2) 80–84 20.4 22.0 (92.5) 30.0 (68.3) 85+ 14.8 15.0 (98.8) 27.0 (55.0) Notes: Data from the State Inpatient Data and Healthcare Cost and Utilization Project (AHRQ, 2016) and the Federal Highway Administration (FHWA, 2004–2009). View Large Table 2. 2004–2009 Driver Crash Hospitalization Rates According to Age Group Age group (years) Hospitalized drivers in thousands Licensed person-years in millions (rate per 100k licensed person-years) Person-years in millions (rate per 100k person-years) 55–59 37.1 88.6 (43.3) 93.6 (41.1) 60–64 31.1 70.0 (44.6) 73.2 (42.5) 65–69 25.0 51.5 (48.3) 55.7 (44.6) 70–74 23.0 39.2 (58.5) 45.1 (51.0) 75–79 22.8 31.5 (72.6) 38.6 (59.2) 80–84 20.4 22.0 (92.5) 30.0 (68.3) 85+ 14.8 15.0 (98.8) 27.0 (55.0) Age group (years) Hospitalized drivers in thousands Licensed person-years in millions (rate per 100k licensed person-years) Person-years in millions (rate per 100k person-years) 55–59 37.1 88.6 (43.3) 93.6 (41.1) 60–64 31.1 70.0 (44.6) 73.2 (42.5) 65–69 25.0 51.5 (48.3) 55.7 (44.6) 70–74 23.0 39.2 (58.5) 45.1 (51.0) 75–79 22.8 31.5 (72.6) 38.6 (59.2) 80–84 20.4 22.0 (92.5) 30.0 (68.3) 85+ 14.8 15.0 (98.8) 27.0 (55.0) Notes: Data from the State Inpatient Data and Healthcare Cost and Utilization Project (AHRQ, 2016) and the Federal Highway Administration (FHWA, 2004–2009). View Large Model Results No evidence was found of an independent association between mandatory physician reporting laws and a lower crash hospitalization rate among the age groups examined. The main predictor of interest, mandatory physician reporting, failed to explain any significant variation in crash hospitalization rates, when accounting for other state-specific laws and conditions. Voluntary and legally protected physician reporting also showed no association with crash hospitalization rates among older drivers, even when controlling for the crash hospitalizations among younger drivers during this period. There were mixed results for other state-specific requirements. Although in-person renewal requirements showed no difference in crash hospitalization rates, the requirement of undergoing vision testing at in-person license renewal was a significant predictor of a lower crash hospitalization rate. The association was consistent in at least one model specification for five of the six age groups and in at least two models for three age groups. For those aged 60–64 years, this requirement showed a significantly lower crash hospitalization rate, ranging from a base model IRR of 0.77 (95% confidence interval [CI] 0.62–0.94) to 0.88 (95% CI 0.78–0.98) controlling for the hospitalization rates of state’s 55- to 59-year olds. Older drivers, aged 65–69, 70–74, 75–79, and 80–84 years, had mixed results, with only the base model showing a safety benefit, IRRs of: 0.78 (95% CI 0.62–0.98), 0.76 (95% CI 0.62–0.93), 0.81 (95% CI 0.65–0.99), and 0.83 (95% CI 0.67–0.97), respectively. Road testing, not required for younger drivers, showed inconsistent results, shifting between a borderline protective association, α of 0.10, for those aged 70–74 years (IRR 0.88, 95% CI 0.77–1.01) to an elevated risk for those aged 85 and older (IRR 1.19, 95% CI 1.05–1.35). The three separate models are presented in Table 3. See Supplementary Appendix table for additional model details. Table 3. Negative Binomial Regression Models—Hospitalized Drivers According to State Laws Age group (years) Mandatory physician reporting Physician reporting (Protected) In-person renewal Vision testing when in-person Road test 60–64 States without law 25,757 10,448 3,816 6,875 Not applicable to age group States with law 4,258 19,567 26,199 23,140 Adjusted incident RR (95% CI) M1 0.96 (0.79–1.17) 1.02 (0.84–1.25) 0.87 (0.67–1.13) 0.77 (0.62–0.94)* Adjusted incident RR (95% CI) M2 0.96 (0.74–1.23) 1.01 (0.84–1.21) 0.87 (0.67–1.14) 0.77 (0.61–0.98)* Adjusted incident RR (95% CI) M3 0.88 (0.71–1.10) 0.99 (0.87–1.12) 0.92 (0.77–1.11) 0.88 (0.78–0.98)* 65–69 States without law 20,710 8,443 3,122 5,611 Not applicable to age group States with law 3,348 15,615 20,936 18,447 Adjusted incident RR (95% CI) M1 1.03 (0.83–1.29) 0.98 (0.81–1.20) 0.88 (0.66–1.16) 0.78 (0.62–0.98)* Adjusted incident RR (95% CI) M2 1.03 (0.79–1.35) 0.98 (0.82–1.17) 0.90 (0.68–1.19) 0.80 (0.61–1.03)** Adjusted incident RR (95% CI) M3 0.95 (0.75–1.20) 0.95 (0.84–1.07) 0.97 (0.82–1.14) 0.92 (0.78–1.08) 70–74 States without law 19,153 7,672 2,946 5,351 Not applicable to age group States with law 2,946 14,427 19,153 16,748 Adjusted incident RR (95% CI) M1 1.00 (0.82–1.23) 1.05 (0.86–1.27) 0.82 (0.65–1.05) 0.76 (0.62–0.93)* Adjusted incident RR (95% CI) M2 1.00 (0.78–1.27) 1.06 (0.88–1.27) 0.86 (0.67–1.09) 0.81 (0.63–1.03)** Adjusted incident RR (95% CI) M3 0.90 (0.73–1.12) 1.03 (0.91–1.15) 0.92 (0.81–1.06) 0.90 (0.79–1.03) 75–79 States without law 19,061 7,728 2,980 5,336 21,260 States with law 2,987 14,320 19,068 16,712 788 Adjusted incident RR (95% CI) M1 1.02 (0.81–1.28) 1.03 (0.85–1.26) 0.86 (0.69–1.08) 0.81 (0.65–0.99)* 0.87 (0.75–1.01) Adjusted incident RR (95% CI) M2 1.03 (0.77–1.38) 1.04 (0.86–1.26) 0.90 (0.72–1.12) 0.85 (0.66–1.08) 0.88 (0.77–1.01)** Adjusted incident RR (95% CI) M3 0.95 (0.72–1.25) 1.00 (0.87–1.16) 0.95 (0.82–1.1) 0.93 (0.79–1.09) 1.01 (0.9–1.13) 80–84 States without law 16,917 6,828 2,638 4,859 18,947 States with law 2,725 12,814 17,004 14,783 695 Adjusted incident RR (95% CI) M1 0.97 (0.77–1.21) 1.03 (0.85–1.24) 0.93 (0.72–1.20) 0.83 (0.67–0.97)* 0.94 (0.79–1.12) Adjusted incident RR (95% CI) M2 0.99 (0.74–1.31) 1.03 (0.86–1.24) 0.94 (0.72–1.23) 0.84 (0.66–1.06) 0.93 (0.78–1.11) Adjusted incident RR (95% CI) M3 0.90 (0.70–1.15) 0.99 (0.87–1.13) 1.02 (0.86–1.20) 0.92 (0.80–1.06) 1.12 (0.97–1.28) 85 + States without law 12,136 4,750 1,979 3,483 13,729 States with law 2,104 9,490 12,261 10,757 511 Adjusted incident RR (95% CI) M1 1.00 (0.84–1.20) 1.07 (0.9–1.27) 0.86 (0.67–1.09) 0.87 (0.72–1.04) 1.00 (0.81–1.24) Adjusted incident RR (95% CI) M2 0.99 (0.79–1.23) 1.10 (0.93–1.29) 0.86 (0.66–1.14) 0.90 (0.72–1.12) 1.02 (0.84–1.25) Adjusted incident RR (95% CI) M3 0.92 (0.76–1.11) 1.07 (0.96–1.19) 0.97 (0.82–1.15) 1.01 (0.91–1.11) 1.19 (1.05–1.35)* Age group (years) Mandatory physician reporting Physician reporting (Protected) In-person renewal Vision testing when in-person Road test 60–64 States without law 25,757 10,448 3,816 6,875 Not applicable to age group States with law 4,258 19,567 26,199 23,140 Adjusted incident RR (95% CI) M1 0.96 (0.79–1.17) 1.02 (0.84–1.25) 0.87 (0.67–1.13) 0.77 (0.62–0.94)* Adjusted incident RR (95% CI) M2 0.96 (0.74–1.23) 1.01 (0.84–1.21) 0.87 (0.67–1.14) 0.77 (0.61–0.98)* Adjusted incident RR (95% CI) M3 0.88 (0.71–1.10) 0.99 (0.87–1.12) 0.92 (0.77–1.11) 0.88 (0.78–0.98)* 65–69 States without law 20,710 8,443 3,122 5,611 Not applicable to age group States with law 3,348 15,615 20,936 18,447 Adjusted incident RR (95% CI) M1 1.03 (0.83–1.29) 0.98 (0.81–1.20) 0.88 (0.66–1.16) 0.78 (0.62–0.98)* Adjusted incident RR (95% CI) M2 1.03 (0.79–1.35) 0.98 (0.82–1.17) 0.90 (0.68–1.19) 0.80 (0.61–1.03)** Adjusted incident RR (95% CI) M3 0.95 (0.75–1.20) 0.95 (0.84–1.07) 0.97 (0.82–1.14) 0.92 (0.78–1.08) 70–74 States without law 19,153 7,672 2,946 5,351 Not applicable to age group States with law 2,946 14,427 19,153 16,748 Adjusted incident RR (95% CI) M1 1.00 (0.82–1.23) 1.05 (0.86–1.27) 0.82 (0.65–1.05) 0.76 (0.62–0.93)* Adjusted incident RR (95% CI) M2 1.00 (0.78–1.27) 1.06 (0.88–1.27) 0.86 (0.67–1.09) 0.81 (0.63–1.03)** Adjusted incident RR (95% CI) M3 0.90 (0.73–1.12) 1.03 (0.91–1.15) 0.92 (0.81–1.06) 0.90 (0.79–1.03) 75–79 States without law 19,061 7,728 2,980 5,336 21,260 States with law 2,987 14,320 19,068 16,712 788 Adjusted incident RR (95% CI) M1 1.02 (0.81–1.28) 1.03 (0.85–1.26) 0.86 (0.69–1.08) 0.81 (0.65–0.99)* 0.87 (0.75–1.01) Adjusted incident RR (95% CI) M2 1.03 (0.77–1.38) 1.04 (0.86–1.26) 0.90 (0.72–1.12) 0.85 (0.66–1.08) 0.88 (0.77–1.01)** Adjusted incident RR (95% CI) M3 0.95 (0.72–1.25) 1.00 (0.87–1.16) 0.95 (0.82–1.1) 0.93 (0.79–1.09) 1.01 (0.9–1.13) 80–84 States without law 16,917 6,828 2,638 4,859 18,947 States with law 2,725 12,814 17,004 14,783 695 Adjusted incident RR (95% CI) M1 0.97 (0.77–1.21) 1.03 (0.85–1.24) 0.93 (0.72–1.20) 0.83 (0.67–0.97)* 0.94 (0.79–1.12) Adjusted incident RR (95% CI) M2 0.99 (0.74–1.31) 1.03 (0.86–1.24) 0.94 (0.72–1.23) 0.84 (0.66–1.06) 0.93 (0.78–1.11) Adjusted incident RR (95% CI) M3 0.90 (0.70–1.15) 0.99 (0.87–1.13) 1.02 (0.86–1.20) 0.92 (0.80–1.06) 1.12 (0.97–1.28) 85 + States without law 12,136 4,750 1,979 3,483 13,729 States with law 2,104 9,490 12,261 10,757 511 Adjusted incident RR (95% CI) M1 1.00 (0.84–1.20) 1.07 (0.9–1.27) 0.86 (0.67–1.09) 0.87 (0.72–1.04) 1.00 (0.81–1.24) Adjusted incident RR (95% CI) M2 0.99 (0.79–1.23) 1.10 (0.93–1.29) 0.86 (0.66–1.14) 0.90 (0.72–1.12) 1.02 (0.84–1.25) Adjusted incident RR (95% CI) M3 0.92 (0.76–1.11) 1.07 (0.96–1.19) 0.97 (0.82–1.15) 1.01 (0.91–1.11) 1.19 (1.05–1.35)* Notes: M1–Adjusted for the natural log of licensed drivers in each specified age cohort, and patient’s sex and urban or rural location. Also adjusted for state’s primary seatbelt enforcement law, state unemployment rate, annual state total precipitation, state per capita fuel consumption, access to trauma centers, urban speed limits, and license renewal length. The dependent variable is the count of MV-crash hospitalizations of drivers per specified age cohort. CIs were estimated based on a GEE autoregressive first-order correlation structure at the state level and results reported are based on empirical standard error estimates. M2–Also adjusted for regional similarities. M3–Also adjusted for the State’s number of hospitalized drivers aged 55–59 years. CI = confidence interval; GEE = generalized estimating equation. *p < .05. **p < .10. View Large Table 3. Negative Binomial Regression Models—Hospitalized Drivers According to State Laws Age group (years) Mandatory physician reporting Physician reporting (Protected) In-person renewal Vision testing when in-person Road test 60–64 States without law 25,757 10,448 3,816 6,875 Not applicable to age group States with law 4,258 19,567 26,199 23,140 Adjusted incident RR (95% CI) M1 0.96 (0.79–1.17) 1.02 (0.84–1.25) 0.87 (0.67–1.13) 0.77 (0.62–0.94)* Adjusted incident RR (95% CI) M2 0.96 (0.74–1.23) 1.01 (0.84–1.21) 0.87 (0.67–1.14) 0.77 (0.61–0.98)* Adjusted incident RR (95% CI) M3 0.88 (0.71–1.10) 0.99 (0.87–1.12) 0.92 (0.77–1.11) 0.88 (0.78–0.98)* 65–69 States without law 20,710 8,443 3,122 5,611 Not applicable to age group States with law 3,348 15,615 20,936 18,447 Adjusted incident RR (95% CI) M1 1.03 (0.83–1.29) 0.98 (0.81–1.20) 0.88 (0.66–1.16) 0.78 (0.62–0.98)* Adjusted incident RR (95% CI) M2 1.03 (0.79–1.35) 0.98 (0.82–1.17) 0.90 (0.68–1.19) 0.80 (0.61–1.03)** Adjusted incident RR (95% CI) M3 0.95 (0.75–1.20) 0.95 (0.84–1.07) 0.97 (0.82–1.14) 0.92 (0.78–1.08) 70–74 States without law 19,153 7,672 2,946 5,351 Not applicable to age group States with law 2,946 14,427 19,153 16,748 Adjusted incident RR (95% CI) M1 1.00 (0.82–1.23) 1.05 (0.86–1.27) 0.82 (0.65–1.05) 0.76 (0.62–0.93)* Adjusted incident RR (95% CI) M2 1.00 (0.78–1.27) 1.06 (0.88–1.27) 0.86 (0.67–1.09) 0.81 (0.63–1.03)** Adjusted incident RR (95% CI) M3 0.90 (0.73–1.12) 1.03 (0.91–1.15) 0.92 (0.81–1.06) 0.90 (0.79–1.03) 75–79 States without law 19,061 7,728 2,980 5,336 21,260 States with law 2,987 14,320 19,068 16,712 788 Adjusted incident RR (95% CI) M1 1.02 (0.81–1.28) 1.03 (0.85–1.26) 0.86 (0.69–1.08) 0.81 (0.65–0.99)* 0.87 (0.75–1.01) Adjusted incident RR (95% CI) M2 1.03 (0.77–1.38) 1.04 (0.86–1.26) 0.90 (0.72–1.12) 0.85 (0.66–1.08) 0.88 (0.77–1.01)** Adjusted incident RR (95% CI) M3 0.95 (0.72–1.25) 1.00 (0.87–1.16) 0.95 (0.82–1.1) 0.93 (0.79–1.09) 1.01 (0.9–1.13) 80–84 States without law 16,917 6,828 2,638 4,859 18,947 States with law 2,725 12,814 17,004 14,783 695 Adjusted incident RR (95% CI) M1 0.97 (0.77–1.21) 1.03 (0.85–1.24) 0.93 (0.72–1.20) 0.83 (0.67–0.97)* 0.94 (0.79–1.12) Adjusted incident RR (95% CI) M2 0.99 (0.74–1.31) 1.03 (0.86–1.24) 0.94 (0.72–1.23) 0.84 (0.66–1.06) 0.93 (0.78–1.11) Adjusted incident RR (95% CI) M3 0.90 (0.70–1.15) 0.99 (0.87–1.13) 1.02 (0.86–1.20) 0.92 (0.80–1.06) 1.12 (0.97–1.28) 85 + States without law 12,136 4,750 1,979 3,483 13,729 States with law 2,104 9,490 12,261 10,757 511 Adjusted incident RR (95% CI) M1 1.00 (0.84–1.20) 1.07 (0.9–1.27) 0.86 (0.67–1.09) 0.87 (0.72–1.04) 1.00 (0.81–1.24) Adjusted incident RR (95% CI) M2 0.99 (0.79–1.23) 1.10 (0.93–1.29) 0.86 (0.66–1.14) 0.90 (0.72–1.12) 1.02 (0.84–1.25) Adjusted incident RR (95% CI) M3 0.92 (0.76–1.11) 1.07 (0.96–1.19) 0.97 (0.82–1.15) 1.01 (0.91–1.11) 1.19 (1.05–1.35)* Age group (years) Mandatory physician reporting Physician reporting (Protected) In-person renewal Vision testing when in-person Road test 60–64 States without law 25,757 10,448 3,816 6,875 Not applicable to age group States with law 4,258 19,567 26,199 23,140 Adjusted incident RR (95% CI) M1 0.96 (0.79–1.17) 1.02 (0.84–1.25) 0.87 (0.67–1.13) 0.77 (0.62–0.94)* Adjusted incident RR (95% CI) M2 0.96 (0.74–1.23) 1.01 (0.84–1.21) 0.87 (0.67–1.14) 0.77 (0.61–0.98)* Adjusted incident RR (95% CI) M3 0.88 (0.71–1.10) 0.99 (0.87–1.12) 0.92 (0.77–1.11) 0.88 (0.78–0.98)* 65–69 States without law 20,710 8,443 3,122 5,611 Not applicable to age group States with law 3,348 15,615 20,936 18,447 Adjusted incident RR (95% CI) M1 1.03 (0.83–1.29) 0.98 (0.81–1.20) 0.88 (0.66–1.16) 0.78 (0.62–0.98)* Adjusted incident RR (95% CI) M2 1.03 (0.79–1.35) 0.98 (0.82–1.17) 0.90 (0.68–1.19) 0.80 (0.61–1.03)** Adjusted incident RR (95% CI) M3 0.95 (0.75–1.20) 0.95 (0.84–1.07) 0.97 (0.82–1.14) 0.92 (0.78–1.08) 70–74 States without law 19,153 7,672 2,946 5,351 Not applicable to age group States with law 2,946 14,427 19,153 16,748 Adjusted incident RR (95% CI) M1 1.00 (0.82–1.23) 1.05 (0.86–1.27) 0.82 (0.65–1.05) 0.76 (0.62–0.93)* Adjusted incident RR (95% CI) M2 1.00 (0.78–1.27) 1.06 (0.88–1.27) 0.86 (0.67–1.09) 0.81 (0.63–1.03)** Adjusted incident RR (95% CI) M3 0.90 (0.73–1.12) 1.03 (0.91–1.15) 0.92 (0.81–1.06) 0.90 (0.79–1.03) 75–79 States without law 19,061 7,728 2,980 5,336 21,260 States with law 2,987 14,320 19,068 16,712 788 Adjusted incident RR (95% CI) M1 1.02 (0.81–1.28) 1.03 (0.85–1.26) 0.86 (0.69–1.08) 0.81 (0.65–0.99)* 0.87 (0.75–1.01) Adjusted incident RR (95% CI) M2 1.03 (0.77–1.38) 1.04 (0.86–1.26) 0.90 (0.72–1.12) 0.85 (0.66–1.08) 0.88 (0.77–1.01)** Adjusted incident RR (95% CI) M3 0.95 (0.72–1.25) 1.00 (0.87–1.16) 0.95 (0.82–1.1) 0.93 (0.79–1.09) 1.01 (0.9–1.13) 80–84 States without law 16,917 6,828 2,638 4,859 18,947 States with law 2,725 12,814 17,004 14,783 695 Adjusted incident RR (95% CI) M1 0.97 (0.77–1.21) 1.03 (0.85–1.24) 0.93 (0.72–1.20) 0.83 (0.67–0.97)* 0.94 (0.79–1.12) Adjusted incident RR (95% CI) M2 0.99 (0.74–1.31) 1.03 (0.86–1.24) 0.94 (0.72–1.23) 0.84 (0.66–1.06) 0.93 (0.78–1.11) Adjusted incident RR (95% CI) M3 0.90 (0.70–1.15) 0.99 (0.87–1.13) 1.02 (0.86–1.20) 0.92 (0.80–1.06) 1.12 (0.97–1.28) 85 + States without law 12,136 4,750 1,979 3,483 13,729 States with law 2,104 9,490 12,261 10,757 511 Adjusted incident RR (95% CI) M1 1.00 (0.84–1.20) 1.07 (0.9–1.27) 0.86 (0.67–1.09) 0.87 (0.72–1.04) 1.00 (0.81–1.24) Adjusted incident RR (95% CI) M2 0.99 (0.79–1.23) 1.10 (0.93–1.29) 0.86 (0.66–1.14) 0.90 (0.72–1.12) 1.02 (0.84–1.25) Adjusted incident RR (95% CI) M3 0.92 (0.76–1.11) 1.07 (0.96–1.19) 0.97 (0.82–1.15) 1.01 (0.91–1.11) 1.19 (1.05–1.35)* Notes: M1–Adjusted for the natural log of licensed drivers in each specified age cohort, and patient’s sex and urban or rural location. Also adjusted for state’s primary seatbelt enforcement law, state unemployment rate, annual state total precipitation, state per capita fuel consumption, access to trauma centers, urban speed limits, and license renewal length. The dependent variable is the count of MV-crash hospitalizations of drivers per specified age cohort. CIs were estimated based on a GEE autoregressive first-order correlation structure at the state level and results reported are based on empirical standard error estimates. M2–Also adjusted for regional similarities. M3–Also adjusted for the State’s number of hospitalized drivers aged 55–59 years. CI = confidence interval; GEE = generalized estimating equation. *p < .05. **p < .10. View Large Discussion This study, a nationwide analysis on the role of physician reporting on older driver crash hospitalizations, was based on more than 175,000 hospitalized drivers from 190 million hospital discharges examined. The hypothesis that laws requiring physician reporting of at-risk drivers would reduce hospitalization rates was not supported. Results suggest that these laws had no impact on older driver crash-related hospitalizations, even for the oldest age group over 85, and any protective effect may be attributed to other driving or licensing requirements. Similarly, the laws in 27 states that provide legal immunity to referring physicians showed no impact on older driver crash-related hospitalizations. Multiple models were specified purposely to ensure an exhaustive assessment of the impact of physician reporting. Models 1 and 2 assessed this impact under the assumption that medical reporting was done regardless of age of driver, with the latter adjusting for nonmeasured regional similarities. Model 3 assessed the differential impact of physician reporting on the specified age groups, when controlling for its impact on those 55–60 years of age, in the same state, under the assumption that mandatory physician reporting laws were more likely to apply to older adult drivers. Although similar to Canadian studies showing that physician reporting of cardiac illness and patients with seizure disorders had little impact on crashes, these results were nevertheless surprising (McLachlan et al., 2007; Simpson et al., 2000). Mandatory physician reporting laws in Oregon, Pennsylvania, and California are characterized by their broad reporting requirements, different from the condition-specific reporting requirements in the Canadian studies. The broader reach of the mandatory reporting laws was expected to have a greater safety impact. For example, Pennsylvania requires the reporting of any patient exhibiting unsafe driving behaviors, mostly older drivers (PENDOT, 2011). Oregon mandates the reporting of patients with functional or cognitive impairment that may impair driving, whereas California requires those with disorders characterized by lapses of consciousness, including Alzheimer’s and related disorders, to be reported (AMA, 2010). Additionally, physicians often discuss driving with their patients, and warnings to potentially unfit drivers were associated with a reduction in risk of road crashes, as patients report ceasing driving based on their physician’s recommendation (Drickamer & Marottoli, 1993; Persson, 1993; Redelmeier et al., 2012). Importantly, physicians in mandatory reporting states are shown to report at-risk drivers to licensing authorities (Cable, Reisner, Gerges, & Thirumavalavan, 2000). Moreover, authorities in Pennsylvania, a mandatory reporting state, receive more than 22,000 reports each year, with more than 40% of drivers facing driving restrictions or a revoked license (PENDOT, 2011). These observations led to the expectation for safety benefits of mandatory reporting; however, there are also a number of possible reasons for this lack of association. One is the possible differential degree of enforcement of reporting laws across states. In contrast to Pennsylvania, in Oregon only a small number of licensed drivers had their licenses revoked due to physician reporting, suggesting that reporting laws are not sufficiently similar across states (Snyder & Ganzini, 2009). Although our study attempts to control for general attitudes toward police enforcement, namely through the use of seatbelt enforcement, enforcement of mandatory physician reporting may be especially arduous. The study outcome of an impact on crash hospitalization rates in states with mandatory reporting is indeed contingent on such reporting, otherwise lack of enforcement of reporting may halt any benefits in crashes rates these laws may otherwise have. Other reasons include physicians being unaware of state reporting requirements, and possible attempts to avoid harming rapport with patient, or insufficient training in identifying at-risk drivers for reporting purposes (Aschkenasy, Drescher, & Ratzan, 2006; Eby & Molnar, 2010). Another model predictor, vision testing at in-person license renewals, was associated with significantly lower crash hospitalization rates. This relationship held for driver aged 60–74 years and less clearly among those aged 75–84 years. Reductions in crash-related hospitalizations ranged from 24%, for elderly drivers aged 70–74 years, to 12% for those aged 60–64 years, maintaining a high degree of consistency in the direction and significance across age groups and models. This finding is not entirely surprising as others have noted the benefits of vision screening on older driver safety (Levy, Vernick, & Howard, 1995; McGwin, Sarrels, Griffin, Owsley, & Rue, 2008). Levy and colleagues (1995) found that vision testing was associated with lower fatal crash risk for senior drivers and, similar to Model 1 of our study, McGwin and colleagues (2008) found that vision screening was associated with a reduction in crash fatalities among those aged 80 and older (McGwin et al., 2008). However, it is not entirely clear how vision testing at in-person visits impacts crash hospitalizations, as there is inconsistent evidence on the association between visual acuity, measured by vision testing, on crash involvement or driving performance. Possible relationships include one between vision declines in older age and poor driving-related performance, thus vision screening may reduce the number of risky drivers on the road (Owsley & McGwin, 1999; Zhang et al., 2007). The law’s deterrent effect is another possible, and likely, mechanism. Similar to Grabowski and colleagues (2004), who found that states with in-person license renewals had a lower fatal crash rate among older adult drivers, this study, with a somewhat different design, distinguishes vision testing at in-person renewals from remote renewals (remote renewals include mailed-in self-certification or physician’s certification) as to separate the visual acuity aspect of the requirement from the requirement’s possible deterrent effect via its in-person testing requirement. Without this important distinction, all state vision testing laws may be rendered the same, making self-certification of one’s visual acuity comparable with vision testing at in-person renewals, masking any deterrent effect of in-person testing. This possible explanation is supported by some that suggest that vision screening at renewal visits may cause older drivers to reduce or cease their driving, more likely to impact riskier drivers to surrender their driving privileges in the face of added licensing barriers (Bohensky, Charlton, Odell, & Keeffe, 2008; Dow, 2011; Kulikov, 2011). Other requirements, such as road testing, showed no consistent effect on crash hospitalization rates, likely due to the low number of states with road testing requirements (two states) making estimates unreliable. Studies in Australia and Illinois have shown that road testing assessments were not associated with crash rates (Langford, Fitzharris, Koppel, & Newstead, 2004; Rock, 1998). This study has a number of limitations. One drawback is the limited number of observations for on-road testing, making comparisons more difficult. However, comprehensive data from 40 states were obtained as an attempt to adjust for a number of important state-specific conditions. Another limitation is the lack of documentation on driver fault among crash hospitalizations. This is important as physician reporting is considered to target at-risk drivers, those more likely to cause crashes. Without such documentation, if significant differences between states exist in the distribution of driver fault in hospitalized crashes, study estimates may be biased; however, ascertaining fault was impossible. A separate limitation is the potential differences in crash severity between states. Although this study attempts to address state-based trends by including crash rates of those aged 55–59 years, not directly targeted by most licensing restrictions, some state differences may yet impact hospitalization rates. For example, potential differences in crash outcomes between states may impact the number of crashed older drivers that appear in hospitalization data, hence influencing the populations compared, as this study does not account for state-specific fatal crashes. Furthermore, if mandatory reporting laws and other requirements examined in this study differentially impact nonhospitalized crashes among older drives, namely those at low speeds, warranting a police crash reporting, records that do not appear in these hospitalization data nor in fatal driver crash data (Fatality Analysis Reporting System), then study conclusions must be interpreted with caution. As mentioned previously, this study used driver crash-related hospitalizations, possibly limiting its generalization to crashes resulting in hospitalizations, rather than fatal crashes or nonhospitalized survivable crashes. In conclusion, this study examined the complex driving environment and the policies and laws surrounding the older driver in their quest for mobility and independence. This study focuses on the role of mandatory physician reporting on older driver’s safety and finds little measurable impact of its purported beneficial role. This study shows that in the current driving environment for older adults these laws lack an independent association with a safety benefit, as measured by hospitalized driver crash rates. Other policies, namely vision testing at in-person renewals, may account for more significant safety benefits for older drivers. Although the study’s results may suggest support to those arguing against physician reporting requirements, it is important to note that it examines one dimension of the hypothesized impact of these requirements, namely its impact on hospitalized older drivers. Other studies examining the impact of these laws on nonhospitalized crashes among older adult drivers as well as their possible impact on fatal crashes should be conducted. Furthermore, mandatory physician reporting requirements must be followed by actual physician reporting of at-risk drivers for these requirements to yield measurable impact on older driver crashes. Enforcement of these laws must be further examined. Supplementary Material Supplementary data are available at The Gerontologist online. Funding This work was supported in part by the Association of Schools of Public Health/National Highway Traffic Safety Administration Fellowship awarded to Y. Agimi, and the study was performed while he was a Public Health Fellow at the U.S. Department of Transportation. Access to intramural data was obtained though collaboration with the Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality, of the U.S. Department of Health and Human Services. Conflict of Interest None to declare. Acknowledgments We thank Dr. Thomas J. Songer, from the Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, for his support and insights on this study as well as Dr. Maria Vegega, National Highway Traffic Safety Administration, for facilitating interagency cooperation making this study possible. We would like to thank the HCUP Partner States that voluntarily contribute their state data to the HCUP, without whom the project and data would not exist http://www.hcup-us.ahrq.gov/partners.jsp. References AAA . ( 2013 ). Driver licensing policies and practices . AAA Foundation for Traffic Safety . Retrieved January 13, 2013, from http://lpp.seniordrivers.org/lpp/index.cfm?selection=reportingdrs1 American Broadcasting Company (ABC) . ( 2015 ). Apple valley security officer killed after elderly driver mistakes gas pedal for brake . 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Mandatory Physician Reporting of At-Risk Drivers: The Older Driver Example

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© The Author(s) 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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Abstract

Abstract Purpose of the Study In a number of states, physicians are mandated by state law to report at-risk drivers to licensing authorities. Often these patients are older adult drivers who may exhibit unsafe driving behaviors, have functional/cognitive impairments, or are diagnosed with conditions such as Alzheimer’s disease and/or seizure disorders. The hypothesis that mandatory physician reporting laws reduce the rate of crash-related hospitalizations among older adult drivers was tested. Design and Methods Using retrospective data (2004–2009), this study identified 176,066 older driver crash-related hospitalizations, from the State Inpatient Databases. Three age-specific negative binomial generalized estimating equation models were used to estimate the effect of physician reporting laws on state’s incidence rate of crash-related hospitalizations among older drivers. Results No evidence was found for an independent association between mandatory physician reporting laws and a lower crash hospitalization rate among any of the age groups examined. The main predictor of interest, mandatory physician reporting, failed to explain any significant variation in crash hospitalization rates, when adjusting for other state-specific laws and characteristics. Vision testing at in-person license renewal was a significant predictor of lower crash hospitalization rate, ranging from incidence rate ratio of 0.77 (95% confidence interval 0.62–0.94) among 60- to 64-year olds to 0.83 (95% confidence interval 0.67–0.97) among 80- to 84-year olds. Implications Physician reporting laws and age-based licensing requirements are often at odds with older driver’s need to maintain independence. This study examines this balance and finds no evidence of the benefits of mandatory physician reporting requirements on driver crash hospitalizations, suggesting that physician mandates do not yet yield significant older driver safety benefits, possibly to the detriment of older driver’s well-being and independence. Mandatory physician reporting, Licensing, Mobility, Independence, Older drivers Mobility is essential to optimal aging, with physical activity and transportation being key to one’s health and community participation (Kochtitzky, Freeland, & Yen, 2011; Satariano et al., 2012; Shah et al., 2012; Warburton, Nicol, & Bredin, 2006). Driving is essential for the independence of some 48 million adults 60 and older, yet driving involves risks, often exacerbated by age-related conditions, making the balance between mobility and safety a delicate one (Brown & Ott, 2004; Federal Highway Administration [FHWA], 2012; Owsley et al., 1998). Since 2005, more than 2.5 million older adults have received emergency department treatment and another 60,000 have died as drivers in transportation crashes (Centers for Disease Control and Prevention, 2015; National Highway Traffic Safety Administration [NHTSA], 2014). Sensational reporting of older driver crashes has further skewed the public perception of the elderly driver (ABC, 2015; NBC, 2015) prompting some states to tighten driving requirements for older adults, including the requirement that physicians report at-risk drivers (AAA, 2013; Collette, 2009). The American Medical Association (AMA) expects physicians to recognize patient’s driving impairments, advises safe driving, and encourages doctors to demand patient driving assessment by state licensing agencies when necessary (AMA, 2011; Jang et al., 2007). Although controversial, a few studies support the use of reporting requirements and licensing restrictions (Caragata Nasvadi & Wister, 2009; Meuser, Carr, & Ulfarsson, 2009) with Redelmeier and colleagues (2012) linking physician warnings to at-risk drivers with a decrease in road crashes (Redelmeier, Yarnell, Thiruchelvam, & Tibshirani, 2012). Furthermore, those patients disregarding these warnings may be reported to driving authorities and lose their driving privileges permanently (Meuser, Carr, & Ulfarsson, 2009; Snyder & Ganzini, 2009). Others, using examples from mandatory reporting of cardiac and seizure disorder patients, argue that physician reporting has demonstrated no safety benefits and is likely to yield similar results with reporting of other conditions (McLachlan, Starreveld, & Lee, 2007; Simpson et al., 2000). Adding to this debate, some argue physician reporting laws and other age-based licensing requirements are agist, at odds with older adults’ need for mobility and independence (Eby & Molnar, 2010; Grabowski, Campbell, & Morrisey, 2004; Unsworth, Wells, Browning, Thomas, & Kendig, 2007). Many argue that physicians lack the training needed to recognize driving impairments, may overestimate Dementia and stroke patient’s fitness to drive when compared with on-road assessors, and argue that patient–doctor confidentiality and a healthy rapport are their main responsibilities (Berger, Rosner, Kark, & Bennett, 2000; Eby & Molnar, 2010; Jang et al., 2007; Martin, Marottoli, & O’Neill, 2009, 2013; Ranchet et al., 2016; Ranchet, Tant, Akinwuntan, Morgan, & Devos, 2016). Additionally, mental health harm, especially depression, is often cited as an issue to consider when limiting one’s driving (Chihuri et al., 2016; Edwards, Perkins, Ross, & Reynolds, 2009; Redelmeier et al., 2012). The contentious nature of this issue, its implications on the mobility and independence of older adults, their relationship with their doctors, and its safety consequences signify its importance. This study examines the role of state physician reporting requirements on crash-related hospitalizations in 37 states, over 6 years, and provides a comprehensive account of their impact on older drivers. Design and Methods Study Population This study used data on older adult driver’s crash-related hospital admissions from the State Inpatient Databases (SIDs) of the Agency for Healthcare Quality and Research (AHRQ) from 2004 to 2009. The SIDs contain inpatient discharge abstracts, representing approximately 97% of all annual discharges in the United States (AHRQ, 2016). Crash hospitalizations, counted in 3-month intervals, resulted in 916 state quarters from the 37 states reporting data in 2004-2005, 39 in 2006, 40 in 2007, 32 in 2008, and 44 in 2009. A total of 176,066 hospitalized drivers aged 55 and older were identified, excluding those admitted directly from another hospital or long-term care center, as to avoid double counting. Drivers were identified using the International Classification of Diseases, 9th Edition, Clinical Modification (ICD9-CM) external causes of injury codes (E-codes) E810 through E819, with a fourth digit of 0.0, denoting driver, in the data fields E-code1 through E-code4. For more information on E-codes refer the AHRQ report on completeness of ECODE data (Barrett, Steiner, & Coben, 2005). Data Sources Data on state requirements for physician reporting of at-risk drivers and legal protection of reporting physicians were obtained from a number of sources (AAA, 2013; AMA, 2004 & 2010; PADOT, 2010). Table 1 contains a complete listing. Data were accessed through an approved collaboration with AHRQ and the Healthcare Cost and Utilization Project. The University of Pittsburgh Institutional Review Board classified this study as exempt. Table 1. Description of Constructed Model Predictors and Covariates, Rationale and Source Variables and rationale Models States (State quarters) Data source Physician reporting is required by law. 1–3 3/48 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) Physician reporting is voluntary, physicians are granted legal immunity. 1–3 27/540 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) In-person drivers’ license renewal at least once in three renewal cycles. To account for other age-based state licensing requirement. 1–3 32/824 (IIHS, 2011a) Required vision acuity testing at in-person renewal. To account for other age-based state licensing requirement. 1–3 36/756 (IIHS, 2011a) Road test for license renewal. To account for other age-based state licensing requirement. 1–3 2/48 (IIHS, 2011a) State’s primary seatbelt requirements. An approximate measure for state police enforcement (Grabowski et al., 2004). 1–3 All (IIHS, 2011c) State annual rainfall. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to rain (Bhattacharyya & Millham, 2001). 1–3 All (NOAA, 2011) Urban area speed limits equal to or higher than 60 mph, or lower than 60 mph. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to speed limits (Boufous, Finch, Hayen, & Williamson, 2008). 1–3 All (IIHS, 2011b) Patient’s sex. To adjust for any state-specific differences in the sex of the driving population and differences in crash hospitalizations. 1–3 All (AHRQ, 2016) Patient’s rural/urban location. To adjust for any state-specific differences in urban/rural crash hospitalization trends. 1–3 All (AHRQ, 2016) Proportion of the state’s population that has access to Trauma I and II centers within 45 minutes. To account for any state-specific differences in crash-related hospital admissions (Branas et al., 2005). 1–3 All (Branas et al., 2005) Annual state unemployment rates. To account for any state differences in road infrastructure and other factors that may differentially impact crash- related hospital admissions (Evans & Graham, 1991; Ruhm, 1996). 1–3 All (BLS, 2011) State annual fuel consumption per capita. Used as an approximate measure of driving exposure, to account for differences in risk of a crash-related hospital admission (FHWA, 2004–2009; Masten, Foss, & Marshall, 2011). 1–3 All (FHWA, 2012) Natural log transformed variable for each age cohort denoting the number of licensed drivers. To account for the difference in state’s driving population within age group of interest. 1–3 All (FHWA, 2012) State Census regions. To account for any region-wide characteristics in hospitalizations and crashes. 2–3 All (U.S. Census, 2009) Hospitalized counts of drivers aged 55 to 59 years. To distinguish the differential age-cohort effect on the hospitalization rates of groups, under the assumption that rate differences should remain constant if examined laws have no age-specific effect (Grabowski et al., 2004; Ruhm, 1996). 3 All (AHRQ, 2016) Variables and rationale Models States (State quarters) Data source Physician reporting is required by law. 1–3 3/48 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) Physician reporting is voluntary, physicians are granted legal immunity. 1–3 27/540 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) In-person drivers’ license renewal at least once in three renewal cycles. To account for other age-based state licensing requirement. 1–3 32/824 (IIHS, 2011a) Required vision acuity testing at in-person renewal. To account for other age-based state licensing requirement. 1–3 36/756 (IIHS, 2011a) Road test for license renewal. To account for other age-based state licensing requirement. 1–3 2/48 (IIHS, 2011a) State’s primary seatbelt requirements. An approximate measure for state police enforcement (Grabowski et al., 2004). 1–3 All (IIHS, 2011c) State annual rainfall. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to rain (Bhattacharyya & Millham, 2001). 1–3 All (NOAA, 2011) Urban area speed limits equal to or higher than 60 mph, or lower than 60 mph. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to speed limits (Boufous, Finch, Hayen, & Williamson, 2008). 1–3 All (IIHS, 2011b) Patient’s sex. To adjust for any state-specific differences in the sex of the driving population and differences in crash hospitalizations. 1–3 All (AHRQ, 2016) Patient’s rural/urban location. To adjust for any state-specific differences in urban/rural crash hospitalization trends. 1–3 All (AHRQ, 2016) Proportion of the state’s population that has access to Trauma I and II centers within 45 minutes. To account for any state-specific differences in crash-related hospital admissions (Branas et al., 2005). 1–3 All (Branas et al., 2005) Annual state unemployment rates. To account for any state differences in road infrastructure and other factors that may differentially impact crash- related hospital admissions (Evans & Graham, 1991; Ruhm, 1996). 1–3 All (BLS, 2011) State annual fuel consumption per capita. Used as an approximate measure of driving exposure, to account for differences in risk of a crash-related hospital admission (FHWA, 2004–2009; Masten, Foss, & Marshall, 2011). 1–3 All (FHWA, 2012) Natural log transformed variable for each age cohort denoting the number of licensed drivers. To account for the difference in state’s driving population within age group of interest. 1–3 All (FHWA, 2012) State Census regions. To account for any region-wide characteristics in hospitalizations and crashes. 2–3 All (U.S. Census, 2009) Hospitalized counts of drivers aged 55 to 59 years. To distinguish the differential age-cohort effect on the hospitalization rates of groups, under the assumption that rate differences should remain constant if examined laws have no age-specific effect (Grabowski et al., 2004; Ruhm, 1996). 3 All (AHRQ, 2016) View Large Table 1. Description of Constructed Model Predictors and Covariates, Rationale and Source Variables and rationale Models States (State quarters) Data source Physician reporting is required by law. 1–3 3/48 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) Physician reporting is voluntary, physicians are granted legal immunity. 1–3 27/540 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) In-person drivers’ license renewal at least once in three renewal cycles. To account for other age-based state licensing requirement. 1–3 32/824 (IIHS, 2011a) Required vision acuity testing at in-person renewal. To account for other age-based state licensing requirement. 1–3 36/756 (IIHS, 2011a) Road test for license renewal. To account for other age-based state licensing requirement. 1–3 2/48 (IIHS, 2011a) State’s primary seatbelt requirements. An approximate measure for state police enforcement (Grabowski et al., 2004). 1–3 All (IIHS, 2011c) State annual rainfall. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to rain (Bhattacharyya & Millham, 2001). 1–3 All (NOAA, 2011) Urban area speed limits equal to or higher than 60 mph, or lower than 60 mph. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to speed limits (Boufous, Finch, Hayen, & Williamson, 2008). 1–3 All (IIHS, 2011b) Patient’s sex. To adjust for any state-specific differences in the sex of the driving population and differences in crash hospitalizations. 1–3 All (AHRQ, 2016) Patient’s rural/urban location. To adjust for any state-specific differences in urban/rural crash hospitalization trends. 1–3 All (AHRQ, 2016) Proportion of the state’s population that has access to Trauma I and II centers within 45 minutes. To account for any state-specific differences in crash-related hospital admissions (Branas et al., 2005). 1–3 All (Branas et al., 2005) Annual state unemployment rates. To account for any state differences in road infrastructure and other factors that may differentially impact crash- related hospital admissions (Evans & Graham, 1991; Ruhm, 1996). 1–3 All (BLS, 2011) State annual fuel consumption per capita. Used as an approximate measure of driving exposure, to account for differences in risk of a crash-related hospital admission (FHWA, 2004–2009; Masten, Foss, & Marshall, 2011). 1–3 All (FHWA, 2012) Natural log transformed variable for each age cohort denoting the number of licensed drivers. To account for the difference in state’s driving population within age group of interest. 1–3 All (FHWA, 2012) State Census regions. To account for any region-wide characteristics in hospitalizations and crashes. 2–3 All (U.S. Census, 2009) Hospitalized counts of drivers aged 55 to 59 years. To distinguish the differential age-cohort effect on the hospitalization rates of groups, under the assumption that rate differences should remain constant if examined laws have no age-specific effect (Grabowski et al., 2004; Ruhm, 1996). 3 All (AHRQ, 2016) Variables and rationale Models States (State quarters) Data source Physician reporting is required by law. 1–3 3/48 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) Physician reporting is voluntary, physicians are granted legal immunity. 1–3 27/540 (AAA, 2013; AMA, 2004 & 2010, 2010; PADOT, 2010) In-person drivers’ license renewal at least once in three renewal cycles. To account for other age-based state licensing requirement. 1–3 32/824 (IIHS, 2011a) Required vision acuity testing at in-person renewal. To account for other age-based state licensing requirement. 1–3 36/756 (IIHS, 2011a) Road test for license renewal. To account for other age-based state licensing requirement. 1–3 2/48 (IIHS, 2011a) State’s primary seatbelt requirements. An approximate measure for state police enforcement (Grabowski et al., 2004). 1–3 All (IIHS, 2011c) State annual rainfall. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to rain (Bhattacharyya & Millham, 2001). 1–3 All (NOAA, 2011) Urban area speed limits equal to or higher than 60 mph, or lower than 60 mph. To adjust for any state-specific differences in crash-related hospital admissions and older driving behavior due to speed limits (Boufous, Finch, Hayen, & Williamson, 2008). 1–3 All (IIHS, 2011b) Patient’s sex. To adjust for any state-specific differences in the sex of the driving population and differences in crash hospitalizations. 1–3 All (AHRQ, 2016) Patient’s rural/urban location. To adjust for any state-specific differences in urban/rural crash hospitalization trends. 1–3 All (AHRQ, 2016) Proportion of the state’s population that has access to Trauma I and II centers within 45 minutes. To account for any state-specific differences in crash-related hospital admissions (Branas et al., 2005). 1–3 All (Branas et al., 2005) Annual state unemployment rates. To account for any state differences in road infrastructure and other factors that may differentially impact crash- related hospital admissions (Evans & Graham, 1991; Ruhm, 1996). 1–3 All (BLS, 2011) State annual fuel consumption per capita. Used as an approximate measure of driving exposure, to account for differences in risk of a crash-related hospital admission (FHWA, 2004–2009; Masten, Foss, & Marshall, 2011). 1–3 All (FHWA, 2012) Natural log transformed variable for each age cohort denoting the number of licensed drivers. To account for the difference in state’s driving population within age group of interest. 1–3 All (FHWA, 2012) State Census regions. To account for any region-wide characteristics in hospitalizations and crashes. 2–3 All (U.S. Census, 2009) Hospitalized counts of drivers aged 55 to 59 years. To distinguish the differential age-cohort effect on the hospitalization rates of groups, under the assumption that rate differences should remain constant if examined laws have no age-specific effect (Grabowski et al., 2004; Ruhm, 1996). 3 All (AHRQ, 2016) View Large Statistical Analyses Counts of hospitalizations were grouped in 5-year age increments (60 to 85 and 85 and older) for each state. Three multivariate negative binomial regression models were constructed, for six age groups, that use autoregressive correlation-based generalized estimating equations (GEEs). Table 1 presents details of specified models. Exponentiated estimates are presented as incidence rate ratios (IRRs). Briefly, the first model examined the effect of physician reporting on the number of older driver hospitalizations, adjusting for a number of covariates. The second model adds state’s Census region, to account for any unobserved or seasonal characteristics of crash-related hospitalizations. The third model adds the hospitalized counts of drivers aged 55–59 years to distinguish the differential age-cohort effect of examined laws on the hospitalization rates of these groups, under the assumption that rate differences should remain constant if examined laws have no age-specific effect (Grabowski et al., 2004; Ruhm, 1996). Negative binomial was chosen as Poisson models lacked fit, resulting in significant overdispersion of the dependent variable (Hilbe, 2008). Using negative binomial models with GEE allows for estimation of population-averaged coefficients, which indicate the effect of selected predictors on the whole population; GEE was used to account for the nonindependence of within-state observations (Pan, 2001). Finite sample correction to standard errors was applied as our sample accounts for more than 5% of total population (Cohen, Cohen, West, & Aiken, 2002). Based on the quasi-likelihood independent criterion, a nonsignificant parameter (GDP per state capita) that decreased model fit was removed from the models. All analyses were performed using SAS 9.3 (SAS Institute Inc., Cary, NC). Results Descriptive Results There were more than 190 million hospital discharges during the study period from the included states. In total, 136,987 hospitalized drivers aged 60 and older were identified and 37,079 drivers were aged 55–59 years. Using the number of licensed state population person-years, Table 2 shows the pooled unadjusted driver hospitalization rates for 2004 to 2009. Drivers aged 55–59 years had 43.3 hospitalizations due to motor-vehicle crashes per 100,000 licensed driver person-years. The rate of hospitalizations increased with age, reaching a peak value of 98.89 crashes per 100,000 licensed driver person-years for patients aged 85 and older. Table 2. 2004–2009 Driver Crash Hospitalization Rates According to Age Group Age group (years) Hospitalized drivers in thousands Licensed person-years in millions (rate per 100k licensed person-years) Person-years in millions (rate per 100k person-years) 55–59 37.1 88.6 (43.3) 93.6 (41.1) 60–64 31.1 70.0 (44.6) 73.2 (42.5) 65–69 25.0 51.5 (48.3) 55.7 (44.6) 70–74 23.0 39.2 (58.5) 45.1 (51.0) 75–79 22.8 31.5 (72.6) 38.6 (59.2) 80–84 20.4 22.0 (92.5) 30.0 (68.3) 85+ 14.8 15.0 (98.8) 27.0 (55.0) Age group (years) Hospitalized drivers in thousands Licensed person-years in millions (rate per 100k licensed person-years) Person-years in millions (rate per 100k person-years) 55–59 37.1 88.6 (43.3) 93.6 (41.1) 60–64 31.1 70.0 (44.6) 73.2 (42.5) 65–69 25.0 51.5 (48.3) 55.7 (44.6) 70–74 23.0 39.2 (58.5) 45.1 (51.0) 75–79 22.8 31.5 (72.6) 38.6 (59.2) 80–84 20.4 22.0 (92.5) 30.0 (68.3) 85+ 14.8 15.0 (98.8) 27.0 (55.0) Notes: Data from the State Inpatient Data and Healthcare Cost and Utilization Project (AHRQ, 2016) and the Federal Highway Administration (FHWA, 2004–2009). View Large Table 2. 2004–2009 Driver Crash Hospitalization Rates According to Age Group Age group (years) Hospitalized drivers in thousands Licensed person-years in millions (rate per 100k licensed person-years) Person-years in millions (rate per 100k person-years) 55–59 37.1 88.6 (43.3) 93.6 (41.1) 60–64 31.1 70.0 (44.6) 73.2 (42.5) 65–69 25.0 51.5 (48.3) 55.7 (44.6) 70–74 23.0 39.2 (58.5) 45.1 (51.0) 75–79 22.8 31.5 (72.6) 38.6 (59.2) 80–84 20.4 22.0 (92.5) 30.0 (68.3) 85+ 14.8 15.0 (98.8) 27.0 (55.0) Age group (years) Hospitalized drivers in thousands Licensed person-years in millions (rate per 100k licensed person-years) Person-years in millions (rate per 100k person-years) 55–59 37.1 88.6 (43.3) 93.6 (41.1) 60–64 31.1 70.0 (44.6) 73.2 (42.5) 65–69 25.0 51.5 (48.3) 55.7 (44.6) 70–74 23.0 39.2 (58.5) 45.1 (51.0) 75–79 22.8 31.5 (72.6) 38.6 (59.2) 80–84 20.4 22.0 (92.5) 30.0 (68.3) 85+ 14.8 15.0 (98.8) 27.0 (55.0) Notes: Data from the State Inpatient Data and Healthcare Cost and Utilization Project (AHRQ, 2016) and the Federal Highway Administration (FHWA, 2004–2009). View Large Model Results No evidence was found of an independent association between mandatory physician reporting laws and a lower crash hospitalization rate among the age groups examined. The main predictor of interest, mandatory physician reporting, failed to explain any significant variation in crash hospitalization rates, when accounting for other state-specific laws and conditions. Voluntary and legally protected physician reporting also showed no association with crash hospitalization rates among older drivers, even when controlling for the crash hospitalizations among younger drivers during this period. There were mixed results for other state-specific requirements. Although in-person renewal requirements showed no difference in crash hospitalization rates, the requirement of undergoing vision testing at in-person license renewal was a significant predictor of a lower crash hospitalization rate. The association was consistent in at least one model specification for five of the six age groups and in at least two models for three age groups. For those aged 60–64 years, this requirement showed a significantly lower crash hospitalization rate, ranging from a base model IRR of 0.77 (95% confidence interval [CI] 0.62–0.94) to 0.88 (95% CI 0.78–0.98) controlling for the hospitalization rates of state’s 55- to 59-year olds. Older drivers, aged 65–69, 70–74, 75–79, and 80–84 years, had mixed results, with only the base model showing a safety benefit, IRRs of: 0.78 (95% CI 0.62–0.98), 0.76 (95% CI 0.62–0.93), 0.81 (95% CI 0.65–0.99), and 0.83 (95% CI 0.67–0.97), respectively. Road testing, not required for younger drivers, showed inconsistent results, shifting between a borderline protective association, α of 0.10, for those aged 70–74 years (IRR 0.88, 95% CI 0.77–1.01) to an elevated risk for those aged 85 and older (IRR 1.19, 95% CI 1.05–1.35). The three separate models are presented in Table 3. See Supplementary Appendix table for additional model details. Table 3. Negative Binomial Regression Models—Hospitalized Drivers According to State Laws Age group (years) Mandatory physician reporting Physician reporting (Protected) In-person renewal Vision testing when in-person Road test 60–64 States without law 25,757 10,448 3,816 6,875 Not applicable to age group States with law 4,258 19,567 26,199 23,140 Adjusted incident RR (95% CI) M1 0.96 (0.79–1.17) 1.02 (0.84–1.25) 0.87 (0.67–1.13) 0.77 (0.62–0.94)* Adjusted incident RR (95% CI) M2 0.96 (0.74–1.23) 1.01 (0.84–1.21) 0.87 (0.67–1.14) 0.77 (0.61–0.98)* Adjusted incident RR (95% CI) M3 0.88 (0.71–1.10) 0.99 (0.87–1.12) 0.92 (0.77–1.11) 0.88 (0.78–0.98)* 65–69 States without law 20,710 8,443 3,122 5,611 Not applicable to age group States with law 3,348 15,615 20,936 18,447 Adjusted incident RR (95% CI) M1 1.03 (0.83–1.29) 0.98 (0.81–1.20) 0.88 (0.66–1.16) 0.78 (0.62–0.98)* Adjusted incident RR (95% CI) M2 1.03 (0.79–1.35) 0.98 (0.82–1.17) 0.90 (0.68–1.19) 0.80 (0.61–1.03)** Adjusted incident RR (95% CI) M3 0.95 (0.75–1.20) 0.95 (0.84–1.07) 0.97 (0.82–1.14) 0.92 (0.78–1.08) 70–74 States without law 19,153 7,672 2,946 5,351 Not applicable to age group States with law 2,946 14,427 19,153 16,748 Adjusted incident RR (95% CI) M1 1.00 (0.82–1.23) 1.05 (0.86–1.27) 0.82 (0.65–1.05) 0.76 (0.62–0.93)* Adjusted incident RR (95% CI) M2 1.00 (0.78–1.27) 1.06 (0.88–1.27) 0.86 (0.67–1.09) 0.81 (0.63–1.03)** Adjusted incident RR (95% CI) M3 0.90 (0.73–1.12) 1.03 (0.91–1.15) 0.92 (0.81–1.06) 0.90 (0.79–1.03) 75–79 States without law 19,061 7,728 2,980 5,336 21,260 States with law 2,987 14,320 19,068 16,712 788 Adjusted incident RR (95% CI) M1 1.02 (0.81–1.28) 1.03 (0.85–1.26) 0.86 (0.69–1.08) 0.81 (0.65–0.99)* 0.87 (0.75–1.01) Adjusted incident RR (95% CI) M2 1.03 (0.77–1.38) 1.04 (0.86–1.26) 0.90 (0.72–1.12) 0.85 (0.66–1.08) 0.88 (0.77–1.01)** Adjusted incident RR (95% CI) M3 0.95 (0.72–1.25) 1.00 (0.87–1.16) 0.95 (0.82–1.1) 0.93 (0.79–1.09) 1.01 (0.9–1.13) 80–84 States without law 16,917 6,828 2,638 4,859 18,947 States with law 2,725 12,814 17,004 14,783 695 Adjusted incident RR (95% CI) M1 0.97 (0.77–1.21) 1.03 (0.85–1.24) 0.93 (0.72–1.20) 0.83 (0.67–0.97)* 0.94 (0.79–1.12) Adjusted incident RR (95% CI) M2 0.99 (0.74–1.31) 1.03 (0.86–1.24) 0.94 (0.72–1.23) 0.84 (0.66–1.06) 0.93 (0.78–1.11) Adjusted incident RR (95% CI) M3 0.90 (0.70–1.15) 0.99 (0.87–1.13) 1.02 (0.86–1.20) 0.92 (0.80–1.06) 1.12 (0.97–1.28) 85 + States without law 12,136 4,750 1,979 3,483 13,729 States with law 2,104 9,490 12,261 10,757 511 Adjusted incident RR (95% CI) M1 1.00 (0.84–1.20) 1.07 (0.9–1.27) 0.86 (0.67–1.09) 0.87 (0.72–1.04) 1.00 (0.81–1.24) Adjusted incident RR (95% CI) M2 0.99 (0.79–1.23) 1.10 (0.93–1.29) 0.86 (0.66–1.14) 0.90 (0.72–1.12) 1.02 (0.84–1.25) Adjusted incident RR (95% CI) M3 0.92 (0.76–1.11) 1.07 (0.96–1.19) 0.97 (0.82–1.15) 1.01 (0.91–1.11) 1.19 (1.05–1.35)* Age group (years) Mandatory physician reporting Physician reporting (Protected) In-person renewal Vision testing when in-person Road test 60–64 States without law 25,757 10,448 3,816 6,875 Not applicable to age group States with law 4,258 19,567 26,199 23,140 Adjusted incident RR (95% CI) M1 0.96 (0.79–1.17) 1.02 (0.84–1.25) 0.87 (0.67–1.13) 0.77 (0.62–0.94)* Adjusted incident RR (95% CI) M2 0.96 (0.74–1.23) 1.01 (0.84–1.21) 0.87 (0.67–1.14) 0.77 (0.61–0.98)* Adjusted incident RR (95% CI) M3 0.88 (0.71–1.10) 0.99 (0.87–1.12) 0.92 (0.77–1.11) 0.88 (0.78–0.98)* 65–69 States without law 20,710 8,443 3,122 5,611 Not applicable to age group States with law 3,348 15,615 20,936 18,447 Adjusted incident RR (95% CI) M1 1.03 (0.83–1.29) 0.98 (0.81–1.20) 0.88 (0.66–1.16) 0.78 (0.62–0.98)* Adjusted incident RR (95% CI) M2 1.03 (0.79–1.35) 0.98 (0.82–1.17) 0.90 (0.68–1.19) 0.80 (0.61–1.03)** Adjusted incident RR (95% CI) M3 0.95 (0.75–1.20) 0.95 (0.84–1.07) 0.97 (0.82–1.14) 0.92 (0.78–1.08) 70–74 States without law 19,153 7,672 2,946 5,351 Not applicable to age group States with law 2,946 14,427 19,153 16,748 Adjusted incident RR (95% CI) M1 1.00 (0.82–1.23) 1.05 (0.86–1.27) 0.82 (0.65–1.05) 0.76 (0.62–0.93)* Adjusted incident RR (95% CI) M2 1.00 (0.78–1.27) 1.06 (0.88–1.27) 0.86 (0.67–1.09) 0.81 (0.63–1.03)** Adjusted incident RR (95% CI) M3 0.90 (0.73–1.12) 1.03 (0.91–1.15) 0.92 (0.81–1.06) 0.90 (0.79–1.03) 75–79 States without law 19,061 7,728 2,980 5,336 21,260 States with law 2,987 14,320 19,068 16,712 788 Adjusted incident RR (95% CI) M1 1.02 (0.81–1.28) 1.03 (0.85–1.26) 0.86 (0.69–1.08) 0.81 (0.65–0.99)* 0.87 (0.75–1.01) Adjusted incident RR (95% CI) M2 1.03 (0.77–1.38) 1.04 (0.86–1.26) 0.90 (0.72–1.12) 0.85 (0.66–1.08) 0.88 (0.77–1.01)** Adjusted incident RR (95% CI) M3 0.95 (0.72–1.25) 1.00 (0.87–1.16) 0.95 (0.82–1.1) 0.93 (0.79–1.09) 1.01 (0.9–1.13) 80–84 States without law 16,917 6,828 2,638 4,859 18,947 States with law 2,725 12,814 17,004 14,783 695 Adjusted incident RR (95% CI) M1 0.97 (0.77–1.21) 1.03 (0.85–1.24) 0.93 (0.72–1.20) 0.83 (0.67–0.97)* 0.94 (0.79–1.12) Adjusted incident RR (95% CI) M2 0.99 (0.74–1.31) 1.03 (0.86–1.24) 0.94 (0.72–1.23) 0.84 (0.66–1.06) 0.93 (0.78–1.11) Adjusted incident RR (95% CI) M3 0.90 (0.70–1.15) 0.99 (0.87–1.13) 1.02 (0.86–1.20) 0.92 (0.80–1.06) 1.12 (0.97–1.28) 85 + States without law 12,136 4,750 1,979 3,483 13,729 States with law 2,104 9,490 12,261 10,757 511 Adjusted incident RR (95% CI) M1 1.00 (0.84–1.20) 1.07 (0.9–1.27) 0.86 (0.67–1.09) 0.87 (0.72–1.04) 1.00 (0.81–1.24) Adjusted incident RR (95% CI) M2 0.99 (0.79–1.23) 1.10 (0.93–1.29) 0.86 (0.66–1.14) 0.90 (0.72–1.12) 1.02 (0.84–1.25) Adjusted incident RR (95% CI) M3 0.92 (0.76–1.11) 1.07 (0.96–1.19) 0.97 (0.82–1.15) 1.01 (0.91–1.11) 1.19 (1.05–1.35)* Notes: M1–Adjusted for the natural log of licensed drivers in each specified age cohort, and patient’s sex and urban or rural location. Also adjusted for state’s primary seatbelt enforcement law, state unemployment rate, annual state total precipitation, state per capita fuel consumption, access to trauma centers, urban speed limits, and license renewal length. The dependent variable is the count of MV-crash hospitalizations of drivers per specified age cohort. CIs were estimated based on a GEE autoregressive first-order correlation structure at the state level and results reported are based on empirical standard error estimates. M2–Also adjusted for regional similarities. M3–Also adjusted for the State’s number of hospitalized drivers aged 55–59 years. CI = confidence interval; GEE = generalized estimating equation. *p < .05. **p < .10. View Large Table 3. Negative Binomial Regression Models—Hospitalized Drivers According to State Laws Age group (years) Mandatory physician reporting Physician reporting (Protected) In-person renewal Vision testing when in-person Road test 60–64 States without law 25,757 10,448 3,816 6,875 Not applicable to age group States with law 4,258 19,567 26,199 23,140 Adjusted incident RR (95% CI) M1 0.96 (0.79–1.17) 1.02 (0.84–1.25) 0.87 (0.67–1.13) 0.77 (0.62–0.94)* Adjusted incident RR (95% CI) M2 0.96 (0.74–1.23) 1.01 (0.84–1.21) 0.87 (0.67–1.14) 0.77 (0.61–0.98)* Adjusted incident RR (95% CI) M3 0.88 (0.71–1.10) 0.99 (0.87–1.12) 0.92 (0.77–1.11) 0.88 (0.78–0.98)* 65–69 States without law 20,710 8,443 3,122 5,611 Not applicable to age group States with law 3,348 15,615 20,936 18,447 Adjusted incident RR (95% CI) M1 1.03 (0.83–1.29) 0.98 (0.81–1.20) 0.88 (0.66–1.16) 0.78 (0.62–0.98)* Adjusted incident RR (95% CI) M2 1.03 (0.79–1.35) 0.98 (0.82–1.17) 0.90 (0.68–1.19) 0.80 (0.61–1.03)** Adjusted incident RR (95% CI) M3 0.95 (0.75–1.20) 0.95 (0.84–1.07) 0.97 (0.82–1.14) 0.92 (0.78–1.08) 70–74 States without law 19,153 7,672 2,946 5,351 Not applicable to age group States with law 2,946 14,427 19,153 16,748 Adjusted incident RR (95% CI) M1 1.00 (0.82–1.23) 1.05 (0.86–1.27) 0.82 (0.65–1.05) 0.76 (0.62–0.93)* Adjusted incident RR (95% CI) M2 1.00 (0.78–1.27) 1.06 (0.88–1.27) 0.86 (0.67–1.09) 0.81 (0.63–1.03)** Adjusted incident RR (95% CI) M3 0.90 (0.73–1.12) 1.03 (0.91–1.15) 0.92 (0.81–1.06) 0.90 (0.79–1.03) 75–79 States without law 19,061 7,728 2,980 5,336 21,260 States with law 2,987 14,320 19,068 16,712 788 Adjusted incident RR (95% CI) M1 1.02 (0.81–1.28) 1.03 (0.85–1.26) 0.86 (0.69–1.08) 0.81 (0.65–0.99)* 0.87 (0.75–1.01) Adjusted incident RR (95% CI) M2 1.03 (0.77–1.38) 1.04 (0.86–1.26) 0.90 (0.72–1.12) 0.85 (0.66–1.08) 0.88 (0.77–1.01)** Adjusted incident RR (95% CI) M3 0.95 (0.72–1.25) 1.00 (0.87–1.16) 0.95 (0.82–1.1) 0.93 (0.79–1.09) 1.01 (0.9–1.13) 80–84 States without law 16,917 6,828 2,638 4,859 18,947 States with law 2,725 12,814 17,004 14,783 695 Adjusted incident RR (95% CI) M1 0.97 (0.77–1.21) 1.03 (0.85–1.24) 0.93 (0.72–1.20) 0.83 (0.67–0.97)* 0.94 (0.79–1.12) Adjusted incident RR (95% CI) M2 0.99 (0.74–1.31) 1.03 (0.86–1.24) 0.94 (0.72–1.23) 0.84 (0.66–1.06) 0.93 (0.78–1.11) Adjusted incident RR (95% CI) M3 0.90 (0.70–1.15) 0.99 (0.87–1.13) 1.02 (0.86–1.20) 0.92 (0.80–1.06) 1.12 (0.97–1.28) 85 + States without law 12,136 4,750 1,979 3,483 13,729 States with law 2,104 9,490 12,261 10,757 511 Adjusted incident RR (95% CI) M1 1.00 (0.84–1.20) 1.07 (0.9–1.27) 0.86 (0.67–1.09) 0.87 (0.72–1.04) 1.00 (0.81–1.24) Adjusted incident RR (95% CI) M2 0.99 (0.79–1.23) 1.10 (0.93–1.29) 0.86 (0.66–1.14) 0.90 (0.72–1.12) 1.02 (0.84–1.25) Adjusted incident RR (95% CI) M3 0.92 (0.76–1.11) 1.07 (0.96–1.19) 0.97 (0.82–1.15) 1.01 (0.91–1.11) 1.19 (1.05–1.35)* Age group (years) Mandatory physician reporting Physician reporting (Protected) In-person renewal Vision testing when in-person Road test 60–64 States without law 25,757 10,448 3,816 6,875 Not applicable to age group States with law 4,258 19,567 26,199 23,140 Adjusted incident RR (95% CI) M1 0.96 (0.79–1.17) 1.02 (0.84–1.25) 0.87 (0.67–1.13) 0.77 (0.62–0.94)* Adjusted incident RR (95% CI) M2 0.96 (0.74–1.23) 1.01 (0.84–1.21) 0.87 (0.67–1.14) 0.77 (0.61–0.98)* Adjusted incident RR (95% CI) M3 0.88 (0.71–1.10) 0.99 (0.87–1.12) 0.92 (0.77–1.11) 0.88 (0.78–0.98)* 65–69 States without law 20,710 8,443 3,122 5,611 Not applicable to age group States with law 3,348 15,615 20,936 18,447 Adjusted incident RR (95% CI) M1 1.03 (0.83–1.29) 0.98 (0.81–1.20) 0.88 (0.66–1.16) 0.78 (0.62–0.98)* Adjusted incident RR (95% CI) M2 1.03 (0.79–1.35) 0.98 (0.82–1.17) 0.90 (0.68–1.19) 0.80 (0.61–1.03)** Adjusted incident RR (95% CI) M3 0.95 (0.75–1.20) 0.95 (0.84–1.07) 0.97 (0.82–1.14) 0.92 (0.78–1.08) 70–74 States without law 19,153 7,672 2,946 5,351 Not applicable to age group States with law 2,946 14,427 19,153 16,748 Adjusted incident RR (95% CI) M1 1.00 (0.82–1.23) 1.05 (0.86–1.27) 0.82 (0.65–1.05) 0.76 (0.62–0.93)* Adjusted incident RR (95% CI) M2 1.00 (0.78–1.27) 1.06 (0.88–1.27) 0.86 (0.67–1.09) 0.81 (0.63–1.03)** Adjusted incident RR (95% CI) M3 0.90 (0.73–1.12) 1.03 (0.91–1.15) 0.92 (0.81–1.06) 0.90 (0.79–1.03) 75–79 States without law 19,061 7,728 2,980 5,336 21,260 States with law 2,987 14,320 19,068 16,712 788 Adjusted incident RR (95% CI) M1 1.02 (0.81–1.28) 1.03 (0.85–1.26) 0.86 (0.69–1.08) 0.81 (0.65–0.99)* 0.87 (0.75–1.01) Adjusted incident RR (95% CI) M2 1.03 (0.77–1.38) 1.04 (0.86–1.26) 0.90 (0.72–1.12) 0.85 (0.66–1.08) 0.88 (0.77–1.01)** Adjusted incident RR (95% CI) M3 0.95 (0.72–1.25) 1.00 (0.87–1.16) 0.95 (0.82–1.1) 0.93 (0.79–1.09) 1.01 (0.9–1.13) 80–84 States without law 16,917 6,828 2,638 4,859 18,947 States with law 2,725 12,814 17,004 14,783 695 Adjusted incident RR (95% CI) M1 0.97 (0.77–1.21) 1.03 (0.85–1.24) 0.93 (0.72–1.20) 0.83 (0.67–0.97)* 0.94 (0.79–1.12) Adjusted incident RR (95% CI) M2 0.99 (0.74–1.31) 1.03 (0.86–1.24) 0.94 (0.72–1.23) 0.84 (0.66–1.06) 0.93 (0.78–1.11) Adjusted incident RR (95% CI) M3 0.90 (0.70–1.15) 0.99 (0.87–1.13) 1.02 (0.86–1.20) 0.92 (0.80–1.06) 1.12 (0.97–1.28) 85 + States without law 12,136 4,750 1,979 3,483 13,729 States with law 2,104 9,490 12,261 10,757 511 Adjusted incident RR (95% CI) M1 1.00 (0.84–1.20) 1.07 (0.9–1.27) 0.86 (0.67–1.09) 0.87 (0.72–1.04) 1.00 (0.81–1.24) Adjusted incident RR (95% CI) M2 0.99 (0.79–1.23) 1.10 (0.93–1.29) 0.86 (0.66–1.14) 0.90 (0.72–1.12) 1.02 (0.84–1.25) Adjusted incident RR (95% CI) M3 0.92 (0.76–1.11) 1.07 (0.96–1.19) 0.97 (0.82–1.15) 1.01 (0.91–1.11) 1.19 (1.05–1.35)* Notes: M1–Adjusted for the natural log of licensed drivers in each specified age cohort, and patient’s sex and urban or rural location. Also adjusted for state’s primary seatbelt enforcement law, state unemployment rate, annual state total precipitation, state per capita fuel consumption, access to trauma centers, urban speed limits, and license renewal length. The dependent variable is the count of MV-crash hospitalizations of drivers per specified age cohort. CIs were estimated based on a GEE autoregressive first-order correlation structure at the state level and results reported are based on empirical standard error estimates. M2–Also adjusted for regional similarities. M3–Also adjusted for the State’s number of hospitalized drivers aged 55–59 years. CI = confidence interval; GEE = generalized estimating equation. *p < .05. **p < .10. View Large Discussion This study, a nationwide analysis on the role of physician reporting on older driver crash hospitalizations, was based on more than 175,000 hospitalized drivers from 190 million hospital discharges examined. The hypothesis that laws requiring physician reporting of at-risk drivers would reduce hospitalization rates was not supported. Results suggest that these laws had no impact on older driver crash-related hospitalizations, even for the oldest age group over 85, and any protective effect may be attributed to other driving or licensing requirements. Similarly, the laws in 27 states that provide legal immunity to referring physicians showed no impact on older driver crash-related hospitalizations. Multiple models were specified purposely to ensure an exhaustive assessment of the impact of physician reporting. Models 1 and 2 assessed this impact under the assumption that medical reporting was done regardless of age of driver, with the latter adjusting for nonmeasured regional similarities. Model 3 assessed the differential impact of physician reporting on the specified age groups, when controlling for its impact on those 55–60 years of age, in the same state, under the assumption that mandatory physician reporting laws were more likely to apply to older adult drivers. Although similar to Canadian studies showing that physician reporting of cardiac illness and patients with seizure disorders had little impact on crashes, these results were nevertheless surprising (McLachlan et al., 2007; Simpson et al., 2000). Mandatory physician reporting laws in Oregon, Pennsylvania, and California are characterized by their broad reporting requirements, different from the condition-specific reporting requirements in the Canadian studies. The broader reach of the mandatory reporting laws was expected to have a greater safety impact. For example, Pennsylvania requires the reporting of any patient exhibiting unsafe driving behaviors, mostly older drivers (PENDOT, 2011). Oregon mandates the reporting of patients with functional or cognitive impairment that may impair driving, whereas California requires those with disorders characterized by lapses of consciousness, including Alzheimer’s and related disorders, to be reported (AMA, 2010). Additionally, physicians often discuss driving with their patients, and warnings to potentially unfit drivers were associated with a reduction in risk of road crashes, as patients report ceasing driving based on their physician’s recommendation (Drickamer & Marottoli, 1993; Persson, 1993; Redelmeier et al., 2012). Importantly, physicians in mandatory reporting states are shown to report at-risk drivers to licensing authorities (Cable, Reisner, Gerges, & Thirumavalavan, 2000). Moreover, authorities in Pennsylvania, a mandatory reporting state, receive more than 22,000 reports each year, with more than 40% of drivers facing driving restrictions or a revoked license (PENDOT, 2011). These observations led to the expectation for safety benefits of mandatory reporting; however, there are also a number of possible reasons for this lack of association. One is the possible differential degree of enforcement of reporting laws across states. In contrast to Pennsylvania, in Oregon only a small number of licensed drivers had their licenses revoked due to physician reporting, suggesting that reporting laws are not sufficiently similar across states (Snyder & Ganzini, 2009). Although our study attempts to control for general attitudes toward police enforcement, namely through the use of seatbelt enforcement, enforcement of mandatory physician reporting may be especially arduous. The study outcome of an impact on crash hospitalization rates in states with mandatory reporting is indeed contingent on such reporting, otherwise lack of enforcement of reporting may halt any benefits in crashes rates these laws may otherwise have. Other reasons include physicians being unaware of state reporting requirements, and possible attempts to avoid harming rapport with patient, or insufficient training in identifying at-risk drivers for reporting purposes (Aschkenasy, Drescher, & Ratzan, 2006; Eby & Molnar, 2010). Another model predictor, vision testing at in-person license renewals, was associated with significantly lower crash hospitalization rates. This relationship held for driver aged 60–74 years and less clearly among those aged 75–84 years. Reductions in crash-related hospitalizations ranged from 24%, for elderly drivers aged 70–74 years, to 12% for those aged 60–64 years, maintaining a high degree of consistency in the direction and significance across age groups and models. This finding is not entirely surprising as others have noted the benefits of vision screening on older driver safety (Levy, Vernick, & Howard, 1995; McGwin, Sarrels, Griffin, Owsley, & Rue, 2008). Levy and colleagues (1995) found that vision testing was associated with lower fatal crash risk for senior drivers and, similar to Model 1 of our study, McGwin and colleagues (2008) found that vision screening was associated with a reduction in crash fatalities among those aged 80 and older (McGwin et al., 2008). However, it is not entirely clear how vision testing at in-person visits impacts crash hospitalizations, as there is inconsistent evidence on the association between visual acuity, measured by vision testing, on crash involvement or driving performance. Possible relationships include one between vision declines in older age and poor driving-related performance, thus vision screening may reduce the number of risky drivers on the road (Owsley & McGwin, 1999; Zhang et al., 2007). The law’s deterrent effect is another possible, and likely, mechanism. Similar to Grabowski and colleagues (2004), who found that states with in-person license renewals had a lower fatal crash rate among older adult drivers, this study, with a somewhat different design, distinguishes vision testing at in-person renewals from remote renewals (remote renewals include mailed-in self-certification or physician’s certification) as to separate the visual acuity aspect of the requirement from the requirement’s possible deterrent effect via its in-person testing requirement. Without this important distinction, all state vision testing laws may be rendered the same, making self-certification of one’s visual acuity comparable with vision testing at in-person renewals, masking any deterrent effect of in-person testing. This possible explanation is supported by some that suggest that vision screening at renewal visits may cause older drivers to reduce or cease their driving, more likely to impact riskier drivers to surrender their driving privileges in the face of added licensing barriers (Bohensky, Charlton, Odell, & Keeffe, 2008; Dow, 2011; Kulikov, 2011). Other requirements, such as road testing, showed no consistent effect on crash hospitalization rates, likely due to the low number of states with road testing requirements (two states) making estimates unreliable. Studies in Australia and Illinois have shown that road testing assessments were not associated with crash rates (Langford, Fitzharris, Koppel, & Newstead, 2004; Rock, 1998). This study has a number of limitations. One drawback is the limited number of observations for on-road testing, making comparisons more difficult. However, comprehensive data from 40 states were obtained as an attempt to adjust for a number of important state-specific conditions. Another limitation is the lack of documentation on driver fault among crash hospitalizations. This is important as physician reporting is considered to target at-risk drivers, those more likely to cause crashes. Without such documentation, if significant differences between states exist in the distribution of driver fault in hospitalized crashes, study estimates may be biased; however, ascertaining fault was impossible. A separate limitation is the potential differences in crash severity between states. Although this study attempts to address state-based trends by including crash rates of those aged 55–59 years, not directly targeted by most licensing restrictions, some state differences may yet impact hospitalization rates. For example, potential differences in crash outcomes between states may impact the number of crashed older drivers that appear in hospitalization data, hence influencing the populations compared, as this study does not account for state-specific fatal crashes. Furthermore, if mandatory reporting laws and other requirements examined in this study differentially impact nonhospitalized crashes among older drives, namely those at low speeds, warranting a police crash reporting, records that do not appear in these hospitalization data nor in fatal driver crash data (Fatality Analysis Reporting System), then study conclusions must be interpreted with caution. As mentioned previously, this study used driver crash-related hospitalizations, possibly limiting its generalization to crashes resulting in hospitalizations, rather than fatal crashes or nonhospitalized survivable crashes. In conclusion, this study examined the complex driving environment and the policies and laws surrounding the older driver in their quest for mobility and independence. This study focuses on the role of mandatory physician reporting on older driver’s safety and finds little measurable impact of its purported beneficial role. This study shows that in the current driving environment for older adults these laws lack an independent association with a safety benefit, as measured by hospitalized driver crash rates. Other policies, namely vision testing at in-person renewals, may account for more significant safety benefits for older drivers. Although the study’s results may suggest support to those arguing against physician reporting requirements, it is important to note that it examines one dimension of the hypothesized impact of these requirements, namely its impact on hospitalized older drivers. Other studies examining the impact of these laws on nonhospitalized crashes among older adult drivers as well as their possible impact on fatal crashes should be conducted. Furthermore, mandatory physician reporting requirements must be followed by actual physician reporting of at-risk drivers for these requirements to yield measurable impact on older driver crashes. Enforcement of these laws must be further examined. Supplementary Material Supplementary data are available at The Gerontologist online. Funding This work was supported in part by the Association of Schools of Public Health/National Highway Traffic Safety Administration Fellowship awarded to Y. Agimi, and the study was performed while he was a Public Health Fellow at the U.S. Department of Transportation. Access to intramural data was obtained though collaboration with the Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality, of the U.S. Department of Health and Human Services. Conflict of Interest None to declare. Acknowledgments We thank Dr. Thomas J. Songer, from the Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, for his support and insights on this study as well as Dr. Maria Vegega, National Highway Traffic Safety Administration, for facilitating interagency cooperation making this study possible. We would like to thank the HCUP Partner States that voluntarily contribute their state data to the HCUP, without whom the project and data would not exist http://www.hcup-us.ahrq.gov/partners.jsp. References AAA . ( 2013 ). Driver licensing policies and practices . AAA Foundation for Traffic Safety . Retrieved January 13, 2013, from http://lpp.seniordrivers.org/lpp/index.cfm?selection=reportingdrs1 American Broadcasting Company (ABC) . ( 2015 ). Apple valley security officer killed after elderly driver mistakes gas pedal for brake . 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The GerontologistOxford University Press

Published: Jan 9, 2017

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