For a study, researchers sought to calculate the economic impact of vision loss (VL) in the United States, state by state. 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]) were analyzed using attributable fraction, regression, and other methods to estimate the incremental direct and indirect costs of VL in 2017. In the ACS, MEPS, or NHIS, people answered yes to the question “Are you blind or have severe difficulties seeing even with glasses?” In aggregate and per individual with VL, they calculated the direct costs of medical, nursing home (NH), and supporting services, as well as the indirect costs of absenteeism, missed household output, reduced labor force participation, and informal care.

They calculated a $134.2 billion economic burden for VL, including $98.7 billion in direct expenses and $35.5 billion in indirect costs. NH ($41.8 billion), other medical care services ($30.9 billion), and decreased labor force participation ($16.2 billion) contributed to 66% of the entire burden. Those with VL faced an additional burden of $16,838 per year. Informal care was the most significant burden component for individuals aged 0 to 18, lower labor force participation was the most significant burden component for people aged 19 to 64, and NH expenditures were the most significant burden component for those aged 65 and older. The states with the greatest VL expenses per person were New York, Connecticut, Massachusetts, Rhode Island, and Vermont. According to sensitivity evaluations, the overall cost might range between $76 and $218 billion, depending on the model assumptions.

The United States has a significant economic cost as a result of VL. Burden accumulates differently at various ages, resulting in state disparities in the composition of per-person expenditures dependent on the age composition of the VL population. Information on state variance could assist local decision-makers in effectively targeting resources to alleviate the burden of VL.