CoChat
Sign Up
Cited by 225Dermatological diseases and infestationsDermatology and Skin DiseasesPublic Health and Social Inequalities

Skin disease and socioeconomic conditions in rural Africa: Tanzania

GIBBS, SAM·1996·International Journal of Dermatology
DOI 10.1111/j.1365-4362.1996.tb03687.x
Open this paper in CoChat — free account

Abstract

Background. Many skin diseases in developing countries are associated with socioeconomic factors. It is generally agreed that a public health approach to dermatology in this setting is particularly appropriate; but, there has been little epidemiologic research done to examine which particular socioeconomic factors are important determinants of the prevalence of skin disease. This is especially true in sub‐Saharan Africa. Methods. A survey of two villages in Ngara district, North West Tanzania, involved 254 randomly selected households using a questionnaire, measurements of houses and water quality, and examination of the skin of 1114 household members. Results. Significant skin disease was encountered in 300 individuals (26.9%). Transmissible diseases comprised the bulk of skin disease (73.9%) with younger age groups being affected most. Socioeconomic conditions were poor, with low quality, crowded housing, low levels of literacy, unsatisfactory water sources, and few households with a regular cash income. Household density was significantly associated with transmissible skin disease. Other indicators of poverty (e.g., no regular cash income and illiteracy) did not correlate with the prevalence of skin disease. Conclusions. Skin disease was highly prevalent in the villages surveyed, especially transmissible diseases in the younger age groups. Household density was the only socio‐economic factor significantly associated with skin disease. Reduction of household density is an attainable intervention that could reduce the prevalence of skin disease in rural African populations. Simple studies like this one should be the first step in community dermatology, assessing the burden of skin disease in communities and looking for particular factors with which public health interventions could effectively reduce the prevalence of skin disease.

Integrity signals

Registry

Crossref · 10.1111/j.1365-4362.1996.tb03687.x

Cites

18 of 18 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Prevalence studies from different continents have pointed to a high prevalence of skin diseases, especially in children, at an average of 30% (range 6 to 87%).

    “Prevalence studies from different continents have pointed to a high prevalence of SDs, especially in children, at an average of 30% (range 6 to 87%) [2,3,4,5,6,7,8,9,10].”

    From A Teledermatology Pilot Programme for the Management of Skin Diseases in Primary Health Care Centres: Experiences from a Resource-Limited Country (Mali, West Africa) (2018) · cites this paper as [10]

  • High household density is especially associated with transmissible skin disease, more important than salary, literacy, shoe use, distance to a water source, and quality of home construction in a Tanzanian study.

    “High household density, for example, is especially associated with transmissible skin disease—this factor was more important than salary, literacy, the use of shoes, distance to a water source, and quality of home construction in a Tanzanian study”

    From Global Burden of Skin Disease: Inequities and Innovations (2017) · cites this paper as [19]

  • The highly prevalent use of community-based traditional healers operating on a barter system is a significant barrier to comprehensive cost analysis.

    “The highly prevalent use of community-based traditional healers operating on a barter system in these settings is also a significant barrier to comprehensive cost analysis”

    From Global Burden of Skin Disease: Inequities and Innovations (2017) · cites this paper as [19]

  • Poverty, affluence, inequality, education, and health-care access are important social determinants in skin disease epidemiology.

    “Social factors such as poverty, affluence, inequality, education, and access to health care all have an important role in the epidemiology of skin disease (NHANES, 1978;Williams et al., 1994;Gibbs, 1996;Figueroa et al., 1998;Scholfield et al., 2009).”

    From The Global Burden of Skin Disease in 2010: An Analysis of the Prevalence and Impact of Skin Conditions (2014) · cites this paper as Gibbs, 1996

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
GIBBS, S.
Corresponding author
GIBBS, SAM
Venue
International Journal of Dermatology
Publisher
Wiley
Published
1996-09
Volume
35
Issue
9
Pages
633-639
ISSN
  • 0011-9059 (ISSN-L)
  • 1365-4632
DOI
10.1111/j.1365-4362.1996.tb03687.x
OpenAlex
W2087589809
Cited by
225
References
18
Citations by year
2026: 1 · 2025: 11 · 2024: 18 · 2023: 16 · 2022: 18 · 2021: 20 · 2020: 27 · 2019: 13 · 2018: 14 · 2017: 11 · 2016: 4 · 2015: 11 · 2014: 4 · 2013: 4 · 2012: 9
FWCI
1.31
Type
journal article
Language
en
Open access
closed
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 3924 d)
Locations
  • International Journal of Dermatology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Infectious Diseases › Dermatological diseases and infestations
  • Health Sciences › Medicine › Dermatology › Dermatology and Skin Diseases
  • Social Sciences › Health › Public Health and Social Inequalities
Keywords
skin diseases, socioeconomic factors, rural Africa, household density, poverty, household crowding, water quality, illiteracy, sub-Saharan Africa, Tanzania
MeSH terms
Africa, Data Collection, Developing Countries, Humans, Risk Factors, Rural Population, Skin Diseases, Socioeconomic Factors, Tanzania, Incidence

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review