Impact of COVID-19 on the Noncommunicable Disease Programs Provided in the Primary Health Care Centers in Al-Rusafa Directorate of Health, 2020
Bibliographic record
Abstract
Background In recent years, noncommunicable diseases (NCDs) have globally shown an increasing impact on health status with disproportionately higher rates in developing countries. During the outbreak, health workers, equipment, and facilities have been reallocated to address the influx of patients with COVID-19. Restructuring of the health system could result in the closure of some health facilities. Objective The aim of this paper was to determine the impact of COVID-19 on the performance of NCD programs implemented in the primary health care centers in Al-Rusafa DOH through comparing the performance indicators of 2019 and 2020, and to identify the potential causes of the changes. Methods The study was conducted in Baghdad, Al-Rusafa, during the period from April to June 2021. A systematic sample was used to enroll 20 primary health care centers. The descriptive analysis focused on frequencies and percentages. Continuous variables are presented as mean (SD), and the independent t test was used to assess statistical significance. A P value of less than .05 is considered statistically significant. Results There was a decrease in the number of served patients, even reaching zero in some units. Most staff were partially or completely assigned to the COVID-19 pandemic. There was a decline in yearly need of education material and folders for programs in 2020 and awareness campaigns performed in 2019 and 2020. The main reasons in the decline of these services were the closure of outpatient services as per government directive, the closure of outpatient disease-specific consultation clinics, and the decrease in outpatient volume due to patients not presenting. Conclusions The COVID-19 pandemic affected the NCD services in Iraq, including the disruption of many aspects of these services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".