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.

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

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.