| Literature DB >> 24007840 |
Kacey C Ernst1, Beth S Phillips, Burris Duke Duncan.
Abstract
Entities:
Keywords: Children; Education; Health; Interventions; Poverty; Slums/informal settlements; Social
Mesh:
Year: 2013 PMID: 24007840 PMCID: PMC7112084 DOI: 10.1016/j.yapd.2013.04.005
Source DB: PubMed Journal: Adv Pediatr ISSN: 0065-3101
Trade-offs for migration from rural to urban areas
| Urban-poor advantage | Urban-poor penalty |
|---|---|
| Perceived improved opportunities for employment | Less food security |
Country-level indicators of slum populations and related exposures and health outcomes
| Indicators of slum populations | Health outcomes | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Region | Country | Year slum data collected | Percent urbanites, % | Percent urbanites in slums | Percent citizens in slums, % | MCV coverage rate, % | Low birth weight, % | Child malnutrition, % | Birth rate (newborns/pop) | Infant mortality rate | Under-5 mortality rate | Maternal mortality ratio |
| Asia | Bangladesh | 2009 | 25 | 61.6 | 15 | 96 | 22 | 40 | 23 | 49 | 46 | 240 |
| Cambodia | 2005 | 21 | 78.9 | 17 | 93 | 9 | 29 | 25 | 54 | 43 | 250 | |
| China | 2009 | 51 | 29.1 | 15 | 99 | 3 | 3 | 12 | 16 | 15 | 37 | |
| India | 2009 | 31 | 29.4 | 9 | 74 | 28 | 43 | 21 | 46 | 61 | 200 | |
| Indonesia | 2009 | 43 | 23 | 10 | 89 | 9 | 19 | 18 | 27 | 32 | 220 | |
| Iraq | 2009 | 67 | 52.8 | 35 | 76 | 15 | 7 | 28 | 40 | 38 | 63 | |
| Jordan | 2009 | 83 | 19.6 | 16 | 98 | 13 | 2 | 27 | 16 | 21 | 63 | |
| Laos | 2005 | 27 | 79.3 | 21 | 69 | 11 | 31 | 26 | 58 | 42 | 470 | |
| Lebanon | 2005 | 87 | 53.1 | 46 | 79 | N/A | N/A | 15 | 15 | 9 | 25 | |
| Mongolia | 2007 | 63 | 57.9 | 36 | 98 | 5 | 5 | 21 | 36 | 31 | 63 | |
| Myanmar (Burma) | 2005 | 31 | 45.6 | 14 | 99 | 9 | N/A | 19 | 48 | 62 | 200 | |
| Nepal | 2009 | 17 | 58.1 | 10 | 88 | 21 | 38 | 22 | 43 | 48 | 170 | |
| Pakistan | 2009 | 35 | 46.6 | 16 | 80 | 32 | N/A | 24 | 61 | 72 | 260 | |
| Philippines | 2009 | 63 | 40.9 | 26 | 79 | 21 | 21 | 25 | 19 | 25 | 99 | |
| Saudi Arabia | 2005 | 81 | 18 | 15 | 98 | N/A | 5 | 19 | 16 | 9 | 24 | |
| Syria | 2007 | 54 | 22.5 | 12 | 80 | 10 | 10 | 24 | 15 | 15 | 70 | |
| Thailand | 2009 | 34 | 27 | 9 | 98 | 7 | 7 | 13 | 16 | 12 | 48 | |
| Turkey | 2009 | 77 | 13 | 10 | 97 | 11 | N/A | 18 | 23 | 15 | 20 | |
| Vietnam | 2009 | 31 | 35.2 | 11 | 96 | 5 | 20 | 17 | 20 | 22 | 59 | |
| Yemen | 2007 | 34 | 76.8 | 26 | 71 | 10 | 26 | 37 | 77 | 168 | 540 | |
| Caribbean | Dominican Republic | 2009 | 66 | 14.8 | 10 | 79 | 11 | 3 | 19 | 21 | 25 | 150 |
| Haiti | 2009 | 47 | 70.1 | 33 | 59 | 25 | 17 | 24 | 52 | 70 | 350 | |
| Jamaica | 2005 | 52 | 60.5 | 31 | 88 | 12 | 2 | 19 | 14 | 18 | 110 | |
| St. Lucia | 2005 | 28 | 11.9 | 3 | 95 | 11 | N/A | 14 | 12 | 16 | 35 | |
| Trinidad and Tobago | 2005 | 13 | 24.7 | 3 | 92 | 19 | N/A | 14 | 27 | 28 | 46 | |
| Latin America | Argentina | 2009 | 91 | 20.8 | 19 | 93 | 7 | 2 | 17 | 11 | 14 | 77 |
| Belize | 2007 | 44 | 18.7 | 8 | 98 | 14 | 4 | 26 | 21 | 17 | 53 | |
| Bolivia | 2009 | 66 | 47.3 | 31 | 84 | 6 | 4 | 24 | 41 | 51 | 190 | |
| Brazil | 2009 | 84 | 26.9 | 23 | 97 | 8 | 2 | 17 | 21 | 16 | 56 | |
| Chile | 2005 | 87 | 9 | 8 | 91 | 6 | 1 | 14 | 7 | 9 | 25 | |
| Colombia | 2009 | 76 | 14.3 | 11 | 88 | 6 | 3 | 17 | 16 | 18 | 92 | |
| Costa Rica | 2005 | 62 | 10.9 | 7 | 83 | 7 | 1 | 16 | 9 | 10 | 40 | |
| Ecuador | 2005 | 66 | 21.5 | 14 | 98 | 10 | N/A | 20 | 19 | 23 | 110 | |
| El Salvador | 2005 | 63 | 28.9 | 18 | 89 | N/A | 7 | 17 | 20 | 15 | 81 | |
| French Guiana | 2005 | 81 | 10.5 | 9 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
| Guatemala | 2009 | 50 | 38.7 | 19 | 87 | 11 | 12 | 26 | 25 | 30 | 120 | |
| Guyana | 2009 | 29 | 33.2 | 10 | 98 | 19 | 10 | 17 | 36 | 36 | 280 | |
| Honduras | 2005 | 50 | 34.9 | 17 | 99 | 10 | 8 | 25 | 20 | 21 | 100 | |
| Mexico | 2007 | 77 | 14.4 | 11 | 98 | 7 | 3 | 19 | 17 | 16 | 50 | |
| Nicaragua | 2007 | 57 | 45.5 | 26 | 99 | 9 | 6 | 19 | 22 | 26 | 95 | |
| Panama | 2005 | 65 | 23 | 15 | 97 | N/A | N/A | 19 | 11 | 20 | 92 | |
| Paraguay | 2005 | 59 | 17.6 | 10 | 93 | 6 | 3 | 17 | 22 | 22 | 99 | |
| Peru | 2007 | 74 | 36.1 | 27 | 96 | 8 | 5 | 19 | 22 | 18 | 67 | |
| Suriname | 2005 | 67 | 3.9 | 3 | 85 | 11 | 7 | 17 | 29 | 30 | 130 | |
| North Africa | Egypt | 2009 | 43 | 13.1 | 6 | 96 | 13 | 5 | 24 | 24 | 21 | 66 |
| Morocco | 2009 | 58 | 13.1 | 8 | 95 | N/A | N/A | 19 | 26 | 33 | 100 | |
| Sub-Saharan Africa | Angola | 2009 | 59 | 65.8 | 39 | 88 | N/A | 15 | 39 | 84 | 158 | 450 |
| Benin | 2009 | 44 | 69.8 | 31 | 72 | 15 | 18 | 38 | 60 | 106 | 350 | |
| Burkina Faso | 2007 | 24 | 59.5 | 14 | 63 | 16 | 25 | 43 | 80 | 146 | 300 | |
| Burundi | 2005 | 10 | 64.3 | 6 | 92 | 11 | 35 | 41 | 60 | 139 | 800 | |
| Cameroon | 2009 | 49 | 46.1 | 23 | 76 | 11 | 14 | 32 | 60 | 127 | 690 | |
| Central African Republic | 2009 | 38 | 95.9 | 36 | 62 | 13 | N/A | 36 | 97 | 164 | 890 | |
| Chad | 2009 | 28 | 89.3 | 25 | 28 | N/A | N/A | 39 | 94 | 169 | 1100 | |
| Comoros | 2007 | 28 | 68.9 | 19 | 72 | N/A | N/A | 31 | 69 | 79 | 280 | |
| Congo | 2009 | 63 | 49.9 | 31 | 90 | 13 | 11 | 40 | 74 | 99 | 560 | |
| Equatorial Guinea | 2005 | 40 | 66.3 | 27 | 51 | N/A | N/A | 35 | 75 | 118 | 240 | |
| Ethiopia | 2009 | 17 | 76.4 | 13 | 57 | 20 | 33 | 43 | 75 | 77 | 350 | |
| Gabon | 2005 | 73 | 38.7 | 28 | 55 | N/A | N/A | 35 | 49 | 66 | 230 | |
| Gambia, The | 2007 | 59 | 34.8 | 21 | 91 | 11 | 15 | 33 | 70 | 101 | 360 | |
| Ghana | 2009 | 44 | 40.1 | 18 | 91 | 13 | 13 | 27 | 47 | 78 | 350 | |
| Guinea | 2007 | 28 | 45.7 | 13 | 58 | 12 | 20 | 37 | 59 | 126 | 610 | |
| Guinea-Bissau | 2005 | 43 | 83.1 | 36 | 61 | 11 | 17 | 35 | 94 | 161 | 790 | |
| Ivory Coast | 2009 | 50 | 57 | 29 | 49 | 17 | 28 | 30 | 63 | 115 | 400 | |
| Kenya | 2009 | 32 | 54.7 | 18 | 87 | 8 | 16 | 32 | 44 | 73 | 360 | |
| Lesotho | 2009 | 23 | 53.7 | 12 | 85 | N/A | 16 | 27 | 53 | 86 | 620 | |
| Liberia | 2009 | 47 | 68.3 | 32 | 40 | 14 | 19 | 36 | 73 | 78 | 770 | |
| Madagascar | 2009 | 31 | 76.2 | 24 | 70 | 16 | N/A | 37 | 50 | 62 | 240 | |
| Malawi | 2009 | 15 | 68.9 | 10 | 96 | 14 | 13 | 40 | 79 | 83 | 460 | |
| Mali | 2009 | 33 | 65.9 | 22 | 56 | 19 | 26 | 45 | 109 | 176 | 540 | |
| Mozambique | 2009 | 31 | 80.5 | 25 | 82 | 15 | 16 | 39 | 77 | 103 | 490 | |
| Namibia | 2009 | 39 | 33.5 | 13 | 74 | 16 | 17 | 21 | 46 | 42 | 200 | |
| Niger | 2009 | 20 | 81.7 | 16 | 76 | 27 | 38 | 50 | 110 | 125 | 590 | |
| Nigeria | 2009 | 51 | 62.7 | 32 | 71 | 12 | 25 | 39 | 74 | 124 | 630 | |
| Rwanda | 2009 | 17 | 65.1 | 11 | 95 | 6 | 17 | 36 | 63 | 54 | 340 | |
| Senegal | 2009 | 42 | 38.8 | 16 | 82 | 19 | 14 | 36 | 55 | 65 | 370 | |
| Sierra Leone | 2005 | 40 | 97 | 39 | 80 | 14 | 19 | 38 | 77 | 185 | 890 | |
| Somalia | 2009 | 34 | 73.6 | 25 | 46 | N/A | 31 | 42 | 104 | 180 | 1000 | |
| South Africa | 2009 | 62 | 23 | 14 | 78 | N/A | 9 | 19 | 43 | 47 | 300 | |
| Tanzania | 2009 | 26 | 63.5 | 17 | 93 | 10 | 15 | 32 | 66 | 68 | 460 | |
| Togo | 2005 | 37 | 62.1 | 23 | 67 | 11 | 21 | 35 | 50 | 110 | 300 | |
| Uganda | 2009 | 15 | 60.1 | 9 | 75 | 14 | 15 | 47 | 61 | 90 | 310 | |
| Zaire | 2007 | 29 | 51.7 | 15 | 71 | N/A | N/A | 33 | 54 | 77 | 200 | |
| Zambia | 2009 | 39 | 57.3 | 22 | 83 | 11 | 13 | 44 | 65 | 83 | 440 | |
| Zimbabwe | 2009 | 29 | 24.1 | 7 | 92 | 11 | 13 | 32 | 28 | 67 | 570 | |
Abbreviation: N/A, not available.
Fig. 1Distribution of urban slums worldwide.
Health outcomes in urban-nonslum, urban-slum, and rural areas in Kenya and Bangladesh
| Country | Health indicator | Urban nonslum | Urban slum | Rural | Slums worse than rural |
|---|---|---|---|---|---|
| Kenya | Infant mortality | 39 | 91 | 76 | 15 per 1000 |
| Under-5 mortality (per 1000) | 62 | 150 | 114 | 36 per 1000 | |
| Stunting | 24% | 47% | 32% | 15% | |
| Primary school attendance ages 6–13 | 97% | 91% | 90% | 1% | |
| Secondary attendance ages 14–17 | 66% | 65% | 80% | 15% | |
| Bangladesh | Under-5 mortality (per 1000) | 53 | 95 | 66 | 29% |
| Skilled birth attendant | 45 | 15 | 19 | 4% | |
| Improved sanitation | 54 | 9 | 54 | 43% | |
| Secondary school attendance | 53 | 18 | 48 | 30% | |
| Gender parity in school attendance | 1.08 | 1.26 | 1.18 |
Pearson’s coefficients (r) between proportion of urban populations residing in slums and health outcomes and exposure variables for countries in the developing world: an ecological analysis
| Correlation coefficient and | ||
|---|---|---|
| Exposure variables | ||
| Access to clean water (n = 66) | −0.765 | <.0001 |
| Access to sanitation (n = 66) | −0.756 | <.0001 |
| Proportion of population <$1.25 per day (n = 63) | 0.777 | <.0001 |
| Percentage of population that resides in urban areas (n = 69) | −0.529 | <.0001 |
| GDP of country (n = 66) | −0.469 | <.0001 |
| Birth rate (n = 83) | 0.718 | <.0001 |
| Unemployment rate (n = 51) | 0.238 | .09 |
| Health outcome variables | ||
| Low birth weight (n = 58) | 0.303 | .02 |
| Child malnutrition (n = 54) | 0.591 | <.0001 |
| MCV immunization coverage rate (n = 68) | −0.573 | <.0001 |
| Infant mortality rate (n = 83) | 0.816 | <.0001 |
| Under-5 mortality rate (n = 83) | 0.741 | <.0001 |
| Maternal mortality ratio (n = 82) | 0.711 | <.0001 |
Abbreviations: GDP, gross domestic product; MCV, measles-containing vaccine.
Number of countries (n) varies by availability of data.
Multiple linear regression of health indicators with percentage of urban population residing in a slum area as the primary predictor for developing countries
| Health outcome | β | |
|---|---|---|
| Low birth weight (n = 58) | 0.009 | .848 |
| Child malnutrition (n = 54) | 0.070 | .401 |
| MCV immunization coverage rate (n = 68) | −0.379 | .005 |
| Infant mortality rate (n = 83) | 0.556 | <.0001 |
| Under-5 mortality rate (n = 83) | 0.724 | .013 |
| Maternal mortality ratio (n = 82) | 3.73 | .015 |
All models adjusted for percentage of country residing in urban areas, gross domestic product per capita, percentage of population living on less than $1.25 per day and world region.
Abbreviation: MCV, measles-containing vaccine.
Fig. 2A startling contrast: slums adjacent to high-income apartment complexes. Favela de Paraisópolis. The favela (shanti town) on the left is ironically called Paraisópolis (Paradise City). Paraisópolis is situated in the center of metropolitan São Paulo, bordering on middle-income and upper-income residential areas. It lies in a large steep-sided ravine and has a physically challenging geography. It is an extremely densely populated area, with an estimated population of 80,000 to 100,000 people. Photo: Tuca Vieira.
Fig. 3Slum renovations in Brazil. A typical slum (left) … transformed in Brazil (right). Upgrading of unserviced settlements is justified as the centerpiece of a global strategy for improving the living conditions of the urban poor.
Taking action: recommendations for pediatricians to improve health equity in urban slums
| Realm of action | What you can do |
|---|---|
| Visit the depressed areas of cities in the countries you visit | If you have a close open relationship with your colleagues in countries you visit, discuss the situation with them. |
| Listen and observe | Apply the strategies of “positive deviants” and learn from the families who are doing well and invite them to teach those who are not. |
| Educate local community health workers | Discuss with these influential women and encourage them to empower the mothers in slum areas. It is they who will make a difference and your interest and encouragement will help. (Pediatricians know how to educate mothers and spend their lives empowering mothers to take charge of their lives and the lives of their children.) |
| Study the pertinent international conventions (Convention on the Rights of the Child) and the policies of the government | Add the appropriate articles of the conventions to your discussions with your colleagues and those in power. |
| Advocate while there | Encourage your local colleagues to discuss the inequalities with those in political position to improve the lives of children and their families. |
| Advocate while at home | Bring the plight of these children to the attention of your representatives and your friends: expose the injustices. |
| Investigate and learn the problems | Go beyond the direct medical problem at hand and inquire about health behaviors at home to address underlying issues if possible. |
| Connect with others | Maintain lists of local community programs and social services that patients in need can be connected to. |