Factors associated with poor glycemic control in patients with type 2 diabetes at the Bafoussam Regional Hospital: a preliminary cross-sectional study at the Bafoussam Regional Hospital (Cameroon)
Bibliographic record
Abstract
Background: Glycemic control remains suboptimal and is one of the main causes of an exponential increase in the occurrence of complications in patients with diabetes. A high rate of morbidity, mortality, disability and impaired quality of life of these patients is attributable to the complications of diabetes. This study aimed to determine the factors associated with poor glycemic control in a group of patients with type 2 diabetes (T2D) followed-up at the Bafoussam Regional Hospital. Methods: A cross-sectional study was carried out for 6 months (January to June 2022) at the Regional Hospital of Bafoussam. The data (sociodemographic, clinical and paraclinical) collected using a data sheet were entered using Microsoft Office Excel 2013 and analyzed by Epi info software version 7.2.2.16. The Khi squared statistical test and the Fisher exact test were used to investigate the association between qualitative variables with a significance threshold set at 5%. Multiple logistic regression was used to identify associated factors of poor glycemic control. Results: We recruited 70 (28 men) participants with T2D. The median age was 63.5 (45–87) years. The proportion of patients with a glycated hemoglobin (HbA1c) level greater than 7% was 31.4% [95% confidence interval (CI): 29.6–33.9%]. Poor glycemic control was significantly associated with irregular disease monitoring [adjusted odds ratio (aOR) =14; 95% CI: 1.16–169; P=0.04]. Meanwhile, patients using herbal therapy (aOR =0.06; 95% CI: 0.01–0.42; P=0.004), those with a monthly income of more than 100,000 FCFA (168.0 USD) (aOR =0.04; 95% CI: 0.002–0.82; P=0.03) and those observing treatment (aOR =0.07; 95% CI: 0.012–0.38; P=0.002) did not have poor glycemic control. Conclusions: Our study showed that the factors associated with poor glycemic control were low monthly income, lack of medical follow-up, use of herbal medicine and therapeutic non-adherence. We suggest urgent interventions to deal with the active management of people with T2D.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".