The shifting landscape of private healthcare providers before and during the COVID-19 pandemic: Lessons to strengthen the private sectors engagement for future pandemic and tuberculosis care
Bibliographic record
Abstract
INTRODUCTION: COVID-19 pandemic changed many aspects of healthcare services and deliveries, including among private healthcare providers (i.e., private healthcare facilities [HCFs] and private practitioners [PPs]). We aimed to compare the spatial distribution of private providers and describe changes in characteristics and services offered during and before the COVID-19 pandemic, and explore the tuberculosis (TB) and COVID-19-related services offered by the private sector in Bandung, Indonesia. METHODS: A cross-sectional study with historical comparison was conducted in 36 randomly selected community health centers areas (locally referred to as Puskesmas) in Bandung, Indonesia, during the COVID-19 pandemic from 5th April 2021 - 27th December 2021. Data pertaining to before the COVID-19 pandemic was abstracted from a similar survey conducted in 2017 (i.e., INSTEP study). We obtained latitude and longitude coordinates of private healthcare providers and then compared the geographical spread with data collected for INSTEP study. We also compared characteristics of, and services provided by private healthcare providers interviewed during the COVID-19 pandemic with those previously interviewed for INSTEP study. Differences were summarized using descriptive and bivariate analyses. RESULTS: From April-December 2021, we surveyed 367 private HCFs and interviewed 637 PPs. Compared to INSTEP study data, the number of operating HCFs was reduced by 3% during the COVID-19 pandemic (401 vs. 412 before COVID-19), although we observed increases in laboratory service (37.8% increase), x-ray service (66.7% increase), and pharmacy (18.1% increase). Among a subset of private HCFs managing patients with respiratory tract infection symptoms, a quarter (60/235, 25.3%) indicated that they had to close their facilities in response to the emerging situation during the COVID-19 pandemic. For PPs, the number of practicing PPs was reduced by 7% during the COVID-19 pandemic (872 vs. 936 before COVID-19). Interestingly, the number of practicing PPs encountering patients with TB disease increased during the COVID-19 pandemic (42.9% vs. 35.7% before COVID-19, p = 0.008). CONCLUSION: This study confirmed that the COVID-19 pandemic adversely impacted health care service deliveries in private sectors, largely marked by closures and shortened business hours. However, the increased service capacities (laboratory and pharmacy), as well as significant increase in the number of patients cared for TB disease by PPs during the COVID-19 pandemic, made a more compelling case to further the implementation of public-private mix model for TB care in Indonesia.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".