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P101 Estimating the quality of life and economic impact of arthritis in Tanzania

2022· article· en· W4224310676 on OpenAlexaff
Eleanor Grieve, Ping-Hsuan Hsieh, Emma McIntosh, Manuela Deidda

Bibliographic record

VenueLara D. Veeken · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsNational Defence Medical Centre
Fundersnot available
KeywordsTanzaniaMedicinePresenteeismAbsenteeismQuality of life (healthcare)Marital statusIndirect costsEconomic impact analysisEconomic costCross-sectional studyDisease burdenEnvironmental healthGerontologySocioeconomicsPopulationNursing

Abstract

fetched live from OpenAlex

Abstract Background/Aims Musculoskeletal (MSK) disorders are one of the major causes of non-traumatic disability within the global burden of disease. A 2010 Global Burden of Disease study reported that MSK diseases account for 20% of all Years Lived with Disability (YLDs) in low/middle income countries. Significantly contributing to this MSK burden is arthritis. In Tanzania, a lack of data exists on the prevalence, quality of life, economic and societal impact of arthritis. We aim to estimate the health, economic and societal burden of arthritis in Tanzania. Methods A community-based cross-sectional survey was undertaken between January 2021 to Sept 2021 in the Kilimanjaro region of Tanzania working in partnership with the Kilimanjaro Christian Research Institute and Kilimanjaro Christian Medical Centre. Clinical screening tools, including the Gait Arms Legs Spine (GALS) and Regional Examination of the Musculoskeletal system (REMS) were used to screen people with MSK diseases and possible arthritis through a tiered system approach. Economic and quality of life questionnaires were used for a representative sample of all residents (aged over 5 years old) in selected households. Ethiopia and Zimbabwe tariffs were used for the EuroQol EQ-5D. Resource use captured out-of-pocket costs, healthcare costs, absenteeism, presenteeism & work productivity loss. Regression based analysis were undertaken to estimate differences in utility scores and resources use / costs between those presenting as REMS+/- and GALS+/-. Other explanatory variables in the model included age, occupation, marital status, gender, religion, tribe, education. Results Preliminary results on data collected to-date show that for all QoL dimensions those with a positive diagnosis had lower utility scores. Whilst the population norms are in line with utility values for that age group from other countries (30-40 years, ∼0.9 utility), those presenting with a positive diagnosis had a significant reduction in utility of ∼0.12 to 0.15 depending on what country tariff was used. A higher proportion of participants in GALS/REMS +ve groups experienced work loss (6 x more likely) and had visited healthcare facilities/hospitalisation (2.5 x more likely) compared to GALS/REMS -ve. Health-related costs reported by household financial respondents did not show a significant statistical difference albeit a likely economically significant difference (Tanzanian Shillings 13,535 vs 6,683, p = 0.05), when adjusted for age and gender. Conclusion This is the first study to estimate burden and prevalence estimates of MSK in Tanzania using valid screening tools along with estimates of preference-based quality of life, disability impacts and quantification of the economic impacts of MSK. This study quantifies the significant burden of rheumatological conditions both in terms of health and poverty. The results will be used to guide clinical health practices, intervention design, service provision, and health promotion and awareness activities both at KCMC institutional level, Kilimanjaro region and national level. Disclosure E. Grieve: None. P. Hsieh: None. E. McIntosh: None. M. Deidda: None.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.044
GPT teacher head0.296
Teacher spread0.252 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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