Public Assisted Households

Income is one of the strongest and most consistent predictors of health and disease in public health research. There are several safety nets administered by the federal government. The Public Assisted Household indicator accounts for these safety nets by measuring the proportion of neighborhood households that receive Supplemental Security Income (SSI), cash public assistance, and/or Supplemental Nutrition Assistance Program (SNAP) benefits , also commonly referred to as food stamps, to support their income. Studies show that households not earning a self-sufficient wage may be subject to increases in premature death from all causes for working adults, lower educational outcomes, and a higher risk of premature childbirth. Self-sufficiency is considered as having an income high enough to meet basic needs without public subsidies such as public housing, food stamps, Medicaid or childcare, or the private market, or informal networks. The higher the number of neighborhood households dependent on public assistance, the lower the likelihood household incomes reach a self-sufficient standard. Although posted under the Employment Opportunities domain, the Public Assisted Households indicator is also strongly tied to the Educational Opportunities, Economic Health, Neighborhood Characteristics, and Housing domains. Data for the Public Assisted Households indicator is available from the U.S. Census.

Neighborhood Indicator Value Ranksort descending
Overton 2.8% 1
Crestline 6.9% 2
Crestwood North 7.9% 3
Oxmoor 8.2% 4
Crestwood South 9.3% 5
Redmont Park 9.8% 6
Highland Park 10.9% 7
Roebuck Springs 11.1% 8
Five Points South 14.2% 9
Pine Knoll Vista 15.5% 10
Brummitt Heights 15.5% 10
Spring Lake 17.6% 12
Eastwood 20.0% 13
Huffman 21.4% 14
Glen Iris 21.8% 15
Forest Park 22.2% 16
South Titusville 23.6% 17
Killough Springs 25.2% 18
Smithfield Estates 26.4% 19
Echo Highlands 26.7% 20
Roebuck 26.8% 21
Zion City 27.3% 22
Thomas 28.1% 23
East Avondale 29.0% 24
Central City 29.2% 25
East Thomas 30.1% 26
Apple Valley 30.6% 27
Brownsville Heights 31.7% 28
Airport Highlands 31.8% 29
Penfield Park 32.2% 30
Maple Grove 32.2% 30
Jones Valley 32.4% 32
Woodland Park 32.7% 33
Dolomite 33.3% 34
Belview Heights 33.3% 34
Liberty Highlands 34.5% 36
Druid Hills 37.1% 37
East Birmingham 37.5% 38
Green Acres 38.8% 39
East Lake 38.9% 40
North Birmingham 39.8% 41
Oak Ridge 40.7% 42
Sun Valley 40.9% 43
Brown Springs 41.6% 44
South East Lake 41.9% 45
Roosevelt 42.0% 46
Bridlewood 42.1% 47
Grasselli Heights 42.1% 47
Acipco-Finley 43.1% 49
Oak Ridge Park 44.2% 50
West End Manor 44.2% 50
Sherman Heights 45.3% 52
Riley 45.4% 53
North East Lake 46.1% 54
Sandusky 46.5% 55
Germania Park 47.3% 56
Bush Hills 47.3% 56
Oakwood Place 47.5% 58
Wahouma 50.2% 59
Central Pratt 50.9% 60
Ensley Highlands 51.7% 61
Enon Ridge 52.3% 62
Evergreen 52.7% 63
South Pratt 53.1% 64
Central Park 53.3% 65
Powderly 55.8% 66
West Brownville 56.3% 67
Kingston 57.7% 68
Arlington - West End 58.2% 69
Fairview 58.9% 70
College Hills 59.3% 71
Fairmont 59.6% 72
Inglenook 60.0% 73
Woodlawn 60.2% 74
Ensley 61.9% 75
North Pratt 62.2% 76
Norwood 62.3% 77
Mason City 63.0% 78
Garden Highlands 63.3% 79
Rising - West Princeton 63.3% 79
Hooper City 63.6% 81
East Brownville 63.6% 81
Tarpley City 64.1% 83
West Goldwire 64.7% 84
Hillman 65.4% 85
Hillman Park 65.4% 85
Wylam 67.0% 87
Fountain Heights 67.5% 88
Harriman Park 68.4% 89
Industrial Center 71.0% 90
North Titusville 76.8% 91
North Avondale 79.9% 92
Smithfield 81.0% 93
Collegeville 84.8% 94
South Woodlawn 91.6% 95
Southside 91.9% 96
Graymont 103.1% 97
Tuxedo 105.3% 98
Gate City 116.7% 99

Key Citations:
1. Braveman, Paula, et al. “Issue Brief #4 Exploring the Social Determinants of Health – April 2011; Income, Wealth and Health” (2011). Robert Wood Johnson Foundation.
2. Bhatia, Rajiv and Mitchell Katz. “Estimation of Health Benefits from a Local Living Wage Ordinance” (2001). American Journal of Public Health.
3. Pickett, K.E. and M. Pearl. “Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review” (2001). Journal of Epidemiology and Community Health.
4. Pollack, C.E., et al. “Should Health Studies Measure Wealth?” (2007). American Journal of Preventive Medicine.
5. Subramanian, S.V. and Ichiro Kawachi. “Income Inequality and Health: What Have We Learned So Far?” (2004). Epidemiologic Reviews, Johns Hopkins Bloomberg School of Public Health.