Epidemiological transition and the dual burden of communicable and noncommunicable diseases in Zimbabwe
Bibliographic record
Abstract
Background: The epidemiological transition model, coined by Abdel Omran in 1971, building on the demographic transition theory developed by Frank Notestein in 1945, has been largely credited for describing epidemiological situations both globally and nationally in many parts of the world. However, owing to its origins in the United States of America (USA), scholars critique the model's applicability to various geographical, socioeconomic, and epidemiological contexts, which are diversely different from the USA and vary from region to region. It was imperative to test the applicability of this model in sub-Saharan Africa (SSA), particularly Zimbabwe to ascertain versatility in describing epidemiological transitions, predicting population health status and whether the assumption of a shift from communicable diseases (CDs) to noncommunicable diseases (NCDs) could be confirmed in a low-income developing nation focusing on Zimbabwe. Methods: The study was a retrospective document review case study, using the existing framework of the epidemiological transition model, as a guiding principle, applying the model to describe the demographic and epidemiological circumstances prevailing within Zimbabwe. The researcher reviewed, compared, analyzed, and described the existing literature on population dynamics and epidemiological profile of the country for the period 1990–2020. Results: The epidemiological transition model attempts to describe the changes in epidemiological circumstances both at national and global scales. The model presumes a shift in CDs to NCDs. However, many scholars question the applicability of the model to diverse contexts, particularly within the SSA context. The Zimbabwean case was considered in light to its rising population growth, dual burden characterized by a high burden of communicable and rising NCDs. Findings from this study indicate that NCDs are on the rise in Zimbabwe. However, owing to a high burden of CDs, a dual disease burden model is the best fit to explain the epidemiological transition currently obtaining within Zimbabwe. Conclusions: Consequentially, funding streams targeting CDs should take heed of the currently obtaining epidemiological situation in the country and respond by challenging funding to public health interventions with a view to address the rising NCDs. Further, public health authorities should craft Public health policies that create supporting environments conducive for the populace to fight NCDs. Informed by the Ottawa charter, reorientation of health services to ensure more health systems responsiveness in the face of emerging NCDs is imperative. In addition, developing interpersonal skills for individuals to be able to act against NCD's risk behaviors and factors is key; at the same time, strengthening community action by capacitating community health workers to address risk behaviors and factors associated with NCDs at community level is imperative. Finally, the inadequacy of the epidemiological transition model inadvertently challenges epidemiologists to step up efforts to review, refine, and extend the model to suit SSA countries like Zimbabwe and elsewhere countries in similar circumstances.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".