A machine learning approach to evaluate the state of hypertension care coverage: From 2016 STEPs survey in Iran
<h4>Background</h4> The increasing burden of hypertension in low- to middle-income countries necessitates the assessment of care coverage to monitor progress and guide future policies. This study uses an ensemble learning approach to evaluate hypertension care coverage in a nationally re...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9491523/?tool=EBI |
| _version_ | 1798001283216441344 |
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| author | Hamed Tavolinejad Shahin Roshani Negar Rezaei Erfan Ghasemi Moein Yoosefi Nazila Rezaei Azin Ghamari Sarvenaz Shahin Sina Azadnajafabad Mohammad-Reza Malekpour Mohammad-Mahdi Rashidi Farshad Farzadfar |
| author_facet | Hamed Tavolinejad Shahin Roshani Negar Rezaei Erfan Ghasemi Moein Yoosefi Nazila Rezaei Azin Ghamari Sarvenaz Shahin Sina Azadnajafabad Mohammad-Reza Malekpour Mohammad-Mahdi Rashidi Farshad Farzadfar |
| author_sort | Hamed Tavolinejad |
| collection | DOAJ |
| description | <h4>Background</h4> The increasing burden of hypertension in low- to middle-income countries necessitates the assessment of care coverage to monitor progress and guide future policies. This study uses an ensemble learning approach to evaluate hypertension care coverage in a nationally representative Iranian survey. <h4>Methods</h4> The data source was the cross-sectional 2016 Iranian STEPwise approach to risk factor surveillance (STEPs). Hypertension was based on blood pressure ≥140/90 mmHg, reported use of anti-hypertensive medications, or a previous hypertension diagnosis. The four steps of care were screening (irrespective of blood pressure value), diagnosis, treatment, and control. The proportion of patients reaching each step was calculated, and a random forest model was used to identify features associated with progression to each step. After model optimization, the six most important variables at each step were considered to demonstrate population-based marginal effects. <h4>Results</h4> The total number of participants was 30541 (52.3% female, median age: 42 years). Overall, 9420 (30.8%) had hypertension, among which 89.7% had screening, 62.3% received diagnosis, 49.3% were treated, and 7.9% achieved control. The random forest model indicated that younger age, male sex, lower wealth, and being unmarried/divorced were consistently associated with a lower probability of receiving care in different levels. Dyslipidemia was associated with reaching diagnosis and treatment steps; however, patients with other cardiovascular comorbidities were not likely to receive more intensive blood pressure management. <h4>Conclusion</h4> Hypertension care was mostly missing the treatment and control stages. The random forest model identified features associated with receiving care, indicating opportunities to improve effective coverage. |
| first_indexed | 2024-04-11T11:34:57Z |
| format | Article |
| id | doaj.art-a846a383c4444617893071186d2674c0 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-11T11:34:57Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-a846a383c4444617893071186d2674c02022-12-22T04:26:01ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01179A machine learning approach to evaluate the state of hypertension care coverage: From 2016 STEPs survey in IranHamed TavolinejadShahin RoshaniNegar RezaeiErfan GhasemiMoein YoosefiNazila RezaeiAzin GhamariSarvenaz ShahinSina AzadnajafabadMohammad-Reza MalekpourMohammad-Mahdi RashidiFarshad Farzadfar<h4>Background</h4> The increasing burden of hypertension in low- to middle-income countries necessitates the assessment of care coverage to monitor progress and guide future policies. This study uses an ensemble learning approach to evaluate hypertension care coverage in a nationally representative Iranian survey. <h4>Methods</h4> The data source was the cross-sectional 2016 Iranian STEPwise approach to risk factor surveillance (STEPs). Hypertension was based on blood pressure ≥140/90 mmHg, reported use of anti-hypertensive medications, or a previous hypertension diagnosis. The four steps of care were screening (irrespective of blood pressure value), diagnosis, treatment, and control. The proportion of patients reaching each step was calculated, and a random forest model was used to identify features associated with progression to each step. After model optimization, the six most important variables at each step were considered to demonstrate population-based marginal effects. <h4>Results</h4> The total number of participants was 30541 (52.3% female, median age: 42 years). Overall, 9420 (30.8%) had hypertension, among which 89.7% had screening, 62.3% received diagnosis, 49.3% were treated, and 7.9% achieved control. The random forest model indicated that younger age, male sex, lower wealth, and being unmarried/divorced were consistently associated with a lower probability of receiving care in different levels. Dyslipidemia was associated with reaching diagnosis and treatment steps; however, patients with other cardiovascular comorbidities were not likely to receive more intensive blood pressure management. <h4>Conclusion</h4> Hypertension care was mostly missing the treatment and control stages. The random forest model identified features associated with receiving care, indicating opportunities to improve effective coverage.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9491523/?tool=EBI |
| spellingShingle | Hamed Tavolinejad Shahin Roshani Negar Rezaei Erfan Ghasemi Moein Yoosefi Nazila Rezaei Azin Ghamari Sarvenaz Shahin Sina Azadnajafabad Mohammad-Reza Malekpour Mohammad-Mahdi Rashidi Farshad Farzadfar A machine learning approach to evaluate the state of hypertension care coverage: From 2016 STEPs survey in Iran PLoS ONE |
| title | A machine learning approach to evaluate the state of hypertension care coverage: From 2016 STEPs survey in Iran |
| title_full | A machine learning approach to evaluate the state of hypertension care coverage: From 2016 STEPs survey in Iran |
| title_fullStr | A machine learning approach to evaluate the state of hypertension care coverage: From 2016 STEPs survey in Iran |
| title_full_unstemmed | A machine learning approach to evaluate the state of hypertension care coverage: From 2016 STEPs survey in Iran |
| title_short | A machine learning approach to evaluate the state of hypertension care coverage: From 2016 STEPs survey in Iran |
| title_sort | machine learning approach to evaluate the state of hypertension care coverage from 2016 steps survey in iran |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9491523/?tool=EBI |
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