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

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.