The Economic Burden of Vision Loss and Blindness in the United States.

PURPOSE

To estimate the economic burden of vision loss (VL) in the United States and by state.

DESIGN

Analysis of secondary data sources (American Community Survey [ACS], American Time Use Survey, Bureau of Labor Statistics, Medical Expenditure Panel Survey [MEPS], National and State Health Expenditure Accounts, and National Health Interview Survey [NHIS]) using attributable fraction, regression, and other methods to estimate the incremental direct and indirect 2017 costs of VL.

PARTICIPANTS

People with a yes response to a question asking if they are blind or have serious difficulty seeing even when wearing glasses in the ACS, MEPS, or NHIS.

MAIN OUTCOME MEASURES

We estimated the direct costs of medical, nursing home (NH), and supportive services and the indirect costs of absenteeism, lost household production, reduced labor force participation, and informal care by age group, sex, and state in aggregate and per person with VL.

RESULTS

We estimated an economic burden of VL of $134.2 billion: $98.7 billion in direct costs and $35.5 billion in indirect costs. The largest burden components were NH ($41.8 billion), other medical care services ($30.9 billion), and reduced labor force participation ($16.2 billion), all of which accounted for 66% of the total. Those with VL incurred $16 838 per year in incremental burden. Informal care was the largest burden component for people 0 to 18 years of age, reduced labor force participation was the largest burden component for people 19 to 64 years of age, and NH costs were the largest burden component for people 65 years of age or older. New York, Connecticut, Massachusetts, Rhode Island, and Vermont experienced the highest costs per person with VL. Sensitivity analyses indicate total burden may range between $76 and $218 billion depending on the assumptions used in the model.

CONCLUSIONS

Self-reported VL imposes a substantial economic burden on the United States. Burden accrues in different ways at different ages, leading to state differences in the composition of per-person costs based on the age composition of the population with VL. Information on state variation can help local decision makers target resources better to address the burden of VL.