Mortality from breast carcinoma among US women: the role and implications of socio-economics, heterogeneous insurance, screening mammography, and geography.
Despite rapid advances in medicine and beneficial lifestyle changes, the incidence and mortality rate of gynecologic carcinoma remains high worldwide. This paper presents the econometric model findings of the major drivers of breast cancer mortality among US women. The results have implications for public health policy formulation on disease incidence and the drivers of mortality risks. The research methodology is a fixed-effects GLS regression model of breast cancer mortality in US females age 25 and above, using 1990-1997 time-series data pooled across 50 US states and DC. The covariates are age, years schooled, family income, 'screening' mammography, insurance coverage types, race, and US census region. The regressions have strong explanatory powers. Finding education and income to be significantly and positively correlated with mortality supports the 'life in the fast lanes' hypothesis of Phelps. The policy of raising a woman's education at a given income appears more beneficial than raising her income at a given education level. The relatively higher mortality rate for Blacks suggests implementing culturally appropriate set of disease prevention and health promotion programs and policies. Mortality differs across insurance types with Medicaid the worst suggesting need for program reform. Mortality is greater for women ages 25-44 years, females 40-49 years who have had screening mammography, smokers, and residents of some US states. These findings suggest imposing more effective tobacco use control policies (e.g., imposing a special tobacco tax on adult smokers), creating a more tractable screening mammography surveillance system, and designing region-specific programs to cut breast cancer mortality risks.
Okunade
Albert A
AA
Department of Economics, 450 BB (FCBE), University of Memphis, Memphis, TN 38152, USA. aokunade@memphis.edu
Karakus
Mustafa C
MC
D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000191:economics / Q000401:mortality*; D005260:Female; D006801:Humans; D015994:Incidence; D007348:Insurance, Health / Q000145:classification; D008327:Mammography / Q000191:economics* / Q000706:statistics & numerical data* / Q000639:trends; D008875:Middle Aged; D017059:Models, Econometric; D012044:Regression Analysis; D012307:Risk Factors; D012959:Socioeconomic Factors; D014481:United States / Q000453:epidemiology