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Record W4220763568 · doi:10.1186/s12889-022-12708-w

Disability and sexual and reproductive health service utilisation in Uganda: an intersectional analysis of demographic and health surveys between 2006 and 2016

2022· article· en· W4220763568 on OpenAlexafffund
Muriel Mac-Seing, Christina Zarowsky, Mengru Yuan, Kate Zinszer

Bibliographic record

VenueBMC Public Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsMcGill UniversityUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
FundersCanadian Institutes of Health Research
KeywordsBiostatisticsReproductive healthMedicinePublic healthResidenceGerontologyLogistic regressionSelf-rated healthDemographyEnvironmental healthPopulationNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The United Nations through universal health coverage, including sexual and reproductive health (SRH), pledges to include all people, leaving no one behind. However, people with disabilities continue to experience multiple barriers in accessing SRH services. Studies analysing the impacts of disability in conjunction with other social identities and health determinants reveal a complex pattern in SRH service use. Framed within a larger mixed methods study conducted in Uganda, we examined how disability, among other key social determinants of health (SDH), was associated with the use of SRH services. METHODS: We analysed data from repeated cross-sectional national surveys, the Uganda Demographic and Health Surveys (DHS) of 2006, 2011, and 2016. The three outcomes of interest were antenatal care visits, HIV testing, and modern contraception use. Our main exposure of interest was the type of disability, classified according to six functional dimensions: seeing, hearing, walking/climbing steps, remembering/concentrating, communicating, and self-care. We performed descriptive and multivariable logistic regression analyses, which controlled for covariates such as survey year, sex, age, place of residence, education, and wealth index. Interaction terms between disability and other factors such as sex, education, and wealth index were explored. Regression analyses were informed by an intersectionality framework to highlight social and health disparities within groups. RESULTS: From 2006 to 2016, 15.5-18.5% of study participants lived with some form of disability. Over the same period, the overall prevalence of at least four antenatal care visits increased from 48.3 to 61.0%, while overall HIV testing prevalence rose from 30.8 to 92.4% and the overall prevalence of modern contraception use increased from 18.6 to 34.2%. The DHS year, highest education level attained, and wealth index were the most consistent determinants of SRH service utilisation. People with different types of disabilities did not have the same SRH use patterns. Interactions between disability type and wealth index were associated with neither HIV testing nor the use of modern contraception. Women who were wealthy with hearing difficulty (Odds Ratio (OR) = 0.15, 95%CI 0.03 - 0.87) or with communication difficulty (OR = 0.17, 95%CI 0.03 - 0.82) had lower odds of having had optimal antenatal care visits compared to women without disabilities who were poorer. CONCLUSION: This study provided evidence that SRH service use prevalence increased over time in Uganda and highlights the importance of studying SRH and the different disability types when examining SDH. The SDH are pivotal to the attainment of universal health coverage, including SRH services, for all people irrespective of their social identities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.122
GPT teacher head0.400
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2022
Admission routes2
Has abstractyes

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