Preventable Hospitalization Rates and Neighborhood Poverty among New York City Residents, 2008-2013.
Angelica BocourMaryellen TriaPublished in: Journal of urban health : bulletin of the New York Academy of Medicine (2018)
Knowing which demographic groups have higher rates of preventable hospitalizations can help identify geographic areas where improvements in primary care access and quality can be made. This study assessed whether preventable hospitalization rates by neighborhood poverty decreased from 2008 to 2013 and whether the gap between very high and low poverty neighborhoods changed. We examined trends in age-adjusted preventable hospitalization rates and rate ratios by neighborhood poverty overall and by sex using JoinPoint regression. Prevention Quality Indicators (PQIs) developed by the Agency for Healthcare Research and Quality were applied to inpatient hospitalization data from the New York State Department of Health's Statewide Planning and Research Cooperative System. PQIs were classified into composites. From 2008 to 2013, preventable hospitalization rates per 100,000 adults across each poverty group decreased. For very high poverty neighborhoods (ZIP codes with ≥30 % of persons living below the federal poverty level (FPL)), there were significant decreases overall (3430.56 to 2543.10, annual percent change [APC] = -5.91 %), for diabetes (676.15 to 500.83, APC = -5.75 %), respiratory (830.78 to 660.29, APC = -4.85 %), circulatory (995.69 to 701.81, APC = -7.24 %), and acute composites (928.18 to 680.17, APC = -5.62 %). The rate ratios also decreased over time; however, in 2013, the rates for very high poverty neighborhoods were two to four times higher than low poverty neighborhoods (ZIP codes with <10 % of persons below the FPL). While preventable hospitalization rates have decreased over time, disparities still exist. These findings underscore the need to ensure adequate access to quality and timely primary care among individuals living in high poverty neighborhoods.
Keyphrases
- primary care
- healthcare
- physical activity
- adverse drug
- public health
- cardiovascular disease
- emergency department
- machine learning
- risk assessment
- liver failure
- palliative care
- hepatitis b virus
- glycemic control
- intensive care unit
- big data
- artificial intelligence
- respiratory failure
- data analysis
- mechanical ventilation
- general practice