To the Editor: We appreciate the important issues detailed by O'Callaghan and O'Neill, although our reading of the evidence is that, despite the majority of safe older drivers, there is a proportion of older drivers who may be unsafe1-7 because of cognitive impairment. Given population aging, the size of this subgroup will increase. Persons classified as cognitively impaired (including those with a mean Mini-Mental State Examination (MMSE) score of 26.54 or MMSE scores of ≤265) have demonstrated poorer driving performance in on-road driving assessments, crashes, driving simulators, and other performance measures.4-8 Although many older adults may begin to modify their driving behaviors, reducing their exposure to higher-risk situations, such modifications may not be sufficient for drivers who are at greater risk of crashing.9 Our study identifies a large proportion of older drivers with MMSE scores of 26 or lower. Additionally, the sample is likely to be biased toward higher-functioning older adults because of selection effects at entry to the studies, hence underestimating the numbers with possible cognitive impairment. We concur with O'Callaghan and O'Neill that the MMSE results in misclassification errors, but this is a property of all screening instruments and not specific to the MMSE.10 The MMSE remains the most widely used screening measure for dementia and gives a broad indication at the population level of the expected number of adults with possible cognitive impairment. Comparison of the MMSE cutoff of 23/24 with clinical diagnoses of dementia from two of the studies contributing to the Dynamic Analyses to Optimise Ageing (DYNOPTA) project showed that the MMSE had sensitivity of 0.93% and specificity of 0.70%, which is similar to other published reports. Our results indicated that 9.4% of the sample had a MMSE score of 26 or lower and reported driving. This raises concern regarding safety issues for this subset of older drivers, especially given projections for the larger number of older drivers with dementia over coming years. Our study provides important information on the prevalence of older drivers with cognitive performance at levels that may indicate cognitive compromise, although again, our data may well underestimate the proportion in the entire population of older drivers. We have sought to strike a cautionary note in the interest of older drivers themselves and have suggested possible strategies for prolonging driving careers. Finally, we agree that the optimum balance between mobility and safety should be the goal of health professionals in seeking to enhance the quality of life of older drivers while maintaining overall road safety. We would like to thank Dr. Virginia Wadley for her valuable input. The data on which this research is based were drawn from nine Australian longitudinal studies: the Australian Longitudinal Study of Ageing; the Australian Longitudinal Study of Women's Health; the Australian Diabetes, Obesity and Lifestyle Study; the Blue Mountain Eye Study; the Canberra Longitudinal Study of Ageing; the Household, Income and Labour Dynamics in Australia study; the Melbourne Longitudinal Studies on Healthy Ageing; the Personality And Total Health Through Life Study; and the Sydney Older Persons Study. These studies were pooled and harmonized for the DYNOPTA project. DYNOPTA was funded by National Health and Medical Research Council Grant 410215. The authors would like to thank the participants for volunteering their time to be involved in the respective studies. Details of all studies contributing data to DYNOPTA, including individual study leaders and funding sources, are available on the DYNOPTA Web site (http://dynopta.anu.edu.au). Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other personal conflicts with this paper. Author Contributions: LR, KA, and KK: study concept and design, acquisition of data, analysis and interpretation of data, and preparation of manuscript. JB: analysis and interpretation of data, acquisition of data, and preparation of manuscript. ML: acquisition and interpretation of data and preparation of manuscript. PM: acquisition of data and preparation of manuscript. Sponsor's Role: The findings and views reported in this paper are those of the authors and not those of the original studies or their respective funding agencies.