Myocardial infarction and heart failure hospitalization rates in Maine, USA - variability along the urban-rural continuum

Rural Remote Health. 2008 Apr-Jun;8(2):980. Epub 2008 Jun 26.


Introduction: Cardiovascular disease, including myocardial infarction (MI) and heart failure (HF), remains the leading cause of death in wealthy countries and is of increasing concern in low- and middle-income countries as risk factors such as smoking and obesity become more common around the globe. Within each country the health burden of MI and HF generally falls more heavily on those who live in rural areas and on those who live in communities with lower average socioeconomic status (SES). Hospitalization rates are an important measure of community health because high rates may indicate a high burden of poor health, while inappropriately low rates (low hospitalization rates absent evidence of average good health) may indicate underutilization of health services. The objective of this study was to determine the predictors of MI and HF hospitalization rates at town level in the State of Maine, USA. Maine has large variations in wealth and along the urban-rural continuum at town level. Because our results shed light on variations in health and health-seeking behavior for different Maine populations (such as those living closer vs further from hospitals) they may be of interest to providers of healthcare to people who live in areas remote from healthcare, and to people who face other barriers to good cardiovascular health.

Methods: To determine predictors of HF and MI hospitalization in Maine, we constructed a geographic information system (GIS) for Maine's towns using publicly available electronic map layers, year 2000 census data, and electronic hospitalization records for all Maine hospitals. This GIS generated age-corrected MI and HF hospitalization rates for 1998-2002 as dependent variables and the following independent variables: poverty rate, unemployment rate, median income, educational attainment, rurality, physician density, and distance to the closest hospital. Univariable and multiple linear regression analysis were then performed to determine the significant predictors of MI and HF hospitalization rates.

Results: During the 5-year study period there were 24 452 hospitalizations of Maine residents to Maine hospitals for MI and 20 330 for HF. In multiple linear regression analysis, greater unemployment, a larger fraction of the population living in poverty, and proximity to a hospital predicted higher MI hospitalization rate (p = 0.000, r-sq = 19.1%) while greater unemployment and proximity to a hospital predicted higher HF hospitalization rate (p = 0.000, r-sq = 8.4%).

Conclusions: Our finding that higher MI and HF hospitalization rates were predicted for towns that had lower SES is in agreement with many previous studies and shows the importance of these variables to health, even in a setting such as Maine with large variability in rurality. The negative relationship between the distance to a hospital and hospitalization rates likely does not represent better health in those living remotely from healthcare. Rather, it may indicate that people who live in communities distant from hospitals are less likely to seek hospitalization. This suggests that patient behavior as well as socioeconomic status may impact heart-related hospitalization in Maine. It highlights the importance of patient and provider education to ensure that people who live remotely from health care are hospitalized appropriately.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Aged
  • Female
  • Health Services Accessibility / statistics & numerical data*
  • Health Status
  • Heart Failure / epidemiology*
  • Heart Failure / therapy
  • Hospitalization / statistics & numerical data*
  • Humans
  • Maine / epidemiology
  • Male
  • Middle Aged
  • Myocardial Infarction / epidemiology*
  • Myocardial Infarction / therapy
  • Patient Acceptance of Health Care / statistics & numerical data
  • Regression Analysis
  • Risk Factors
  • Rural Population / statistics & numerical data*
  • Socioeconomic Factors
  • Urban Population / statistics & numerical data*