Cargando…
Recalibrating the Non-Communicable Diseases risk prediction tools for the rural population of Western India
BACKGROUND: The aim of the present study was to recalibrate the effectiveness of Indian Diabetes Risk Score (IDRS) and Community-Based Assessment Checklist (CBAC) by opportunistic screening of Diabetes Mellitus (DM) and Hypertension (HT) among the people attending health centres, and estimating the...
Autores principales: | , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8862298/ https://www.ncbi.nlm.nih.gov/pubmed/35193546 http://dx.doi.org/10.1186/s12889-022-12783-z |
_version_ | 1784655028600963072 |
---|---|
author | Gupta, Manoj Kumar Raghav, Pankaja Tanvir, Tooba Gautam, Vaishali Mehto, Amit Choudhary, Yachana Mittal, Ankit Singh, Gyanendra Singh, Garima Baskaran, Pritish Rehana, V.R. Jabbar, Shaima Abdul Sridevi, S. Goel, Akhil Dhanesh Bhardwaj, Pankaj Saurabh, Suman Srikanth, S. Naveen, K.H. Prasanna, T. Rustagi, Neeti Sharma, Prem Prakash |
author_facet | Gupta, Manoj Kumar Raghav, Pankaja Tanvir, Tooba Gautam, Vaishali Mehto, Amit Choudhary, Yachana Mittal, Ankit Singh, Gyanendra Singh, Garima Baskaran, Pritish Rehana, V.R. Jabbar, Shaima Abdul Sridevi, S. Goel, Akhil Dhanesh Bhardwaj, Pankaj Saurabh, Suman Srikanth, S. Naveen, K.H. Prasanna, T. Rustagi, Neeti Sharma, Prem Prakash |
author_sort | Gupta, Manoj Kumar |
collection | PubMed |
description | BACKGROUND: The aim of the present study was to recalibrate the effectiveness of Indian Diabetes Risk Score (IDRS) and Community-Based Assessment Checklist (CBAC) by opportunistic screening of Diabetes Mellitus (DM) and Hypertension (HT) among the people attending health centres, and estimating the risk of fatal and non-fatal Cardio-Vascular Diseases (CVDs) among them using WHO/ISH charts. METHODS: All the people aged ≥ 30 years attending the health centers were screened for DM and HT. Weight, height, waist circumference, and hip circumferences were measured, and BMI and Waist-Hip Ratio (WHR) were calculated. Risk categorization of all participants was done using IDRS, CBAC, and WHO/ISH risk prediction charts. Individuals diagnosed with DM or HT were started on treatment. The data was recorded using Epicollect5 and was analyzed using SPSS v.23 and MedCalc v.19.8. ROC curves were plotted for DM and HT with the IDRS, CBAC score, and anthropometric parameters. Sensitivity (SN), specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy and Youden’s index were calculated for different cut-offs of IDRS and CBAC scores. RESULTS: A total of 942 participants were included for the screening, out of them, 9.2% (95% CI: 7.45–11.31) were diagnosed with DM for the first time. Hypertension was detected among 25.7% (95% CI: 22.9–28.5) of the participants. A total of 447 (47.3%) participants were found with IDRS score ≥ 60, and 276 (29.3%) with CBAC score > 4. As much as 26.1% were at moderate to higher risk (≥ 10%) of developing CVDs. Area Under the Curve (AUC) for IDRS in predicting DM was 0.64 (0.58–0.70), with 67.1% SN and 55.2% SP (Youden’s Index 0.22). While the AUC for CBAC was 0.59 (0.53–0.65). For hypertension both the AUCs were 0.66 (0.62–0.71) and 0.63 (0.59–0.67), respectively. CONCLUSIONS: IDRS was found to have the maximum AUC and sensitivity thereby demonstrating its usefulness as compared to other tools for screening of both diabetes and hypertension. It thus has the potential to expose the hidden NCD iceberg. Hence, we propose IDRS as a useful tool in screening of Diabetes and Hypertension in rural India. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-12783-z. |
format | Online Article Text |
id | pubmed-8862298 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88622982022-02-23 Recalibrating the Non-Communicable Diseases risk prediction tools for the rural population of Western India Gupta, Manoj Kumar Raghav, Pankaja Tanvir, Tooba Gautam, Vaishali Mehto, Amit Choudhary, Yachana Mittal, Ankit Singh, Gyanendra Singh, Garima Baskaran, Pritish Rehana, V.R. Jabbar, Shaima Abdul Sridevi, S. Goel, Akhil Dhanesh Bhardwaj, Pankaj Saurabh, Suman Srikanth, S. Naveen, K.H. Prasanna, T. Rustagi, Neeti Sharma, Prem Prakash BMC Public Health Research BACKGROUND: The aim of the present study was to recalibrate the effectiveness of Indian Diabetes Risk Score (IDRS) and Community-Based Assessment Checklist (CBAC) by opportunistic screening of Diabetes Mellitus (DM) and Hypertension (HT) among the people attending health centres, and estimating the risk of fatal and non-fatal Cardio-Vascular Diseases (CVDs) among them using WHO/ISH charts. METHODS: All the people aged ≥ 30 years attending the health centers were screened for DM and HT. Weight, height, waist circumference, and hip circumferences were measured, and BMI and Waist-Hip Ratio (WHR) were calculated. Risk categorization of all participants was done using IDRS, CBAC, and WHO/ISH risk prediction charts. Individuals diagnosed with DM or HT were started on treatment. The data was recorded using Epicollect5 and was analyzed using SPSS v.23 and MedCalc v.19.8. ROC curves were plotted for DM and HT with the IDRS, CBAC score, and anthropometric parameters. Sensitivity (SN), specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy and Youden’s index were calculated for different cut-offs of IDRS and CBAC scores. RESULTS: A total of 942 participants were included for the screening, out of them, 9.2% (95% CI: 7.45–11.31) were diagnosed with DM for the first time. Hypertension was detected among 25.7% (95% CI: 22.9–28.5) of the participants. A total of 447 (47.3%) participants were found with IDRS score ≥ 60, and 276 (29.3%) with CBAC score > 4. As much as 26.1% were at moderate to higher risk (≥ 10%) of developing CVDs. Area Under the Curve (AUC) for IDRS in predicting DM was 0.64 (0.58–0.70), with 67.1% SN and 55.2% SP (Youden’s Index 0.22). While the AUC for CBAC was 0.59 (0.53–0.65). For hypertension both the AUCs were 0.66 (0.62–0.71) and 0.63 (0.59–0.67), respectively. CONCLUSIONS: IDRS was found to have the maximum AUC and sensitivity thereby demonstrating its usefulness as compared to other tools for screening of both diabetes and hypertension. It thus has the potential to expose the hidden NCD iceberg. Hence, we propose IDRS as a useful tool in screening of Diabetes and Hypertension in rural India. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-12783-z. BioMed Central 2022-02-22 /pmc/articles/PMC8862298/ /pubmed/35193546 http://dx.doi.org/10.1186/s12889-022-12783-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Gupta, Manoj Kumar Raghav, Pankaja Tanvir, Tooba Gautam, Vaishali Mehto, Amit Choudhary, Yachana Mittal, Ankit Singh, Gyanendra Singh, Garima Baskaran, Pritish Rehana, V.R. Jabbar, Shaima Abdul Sridevi, S. Goel, Akhil Dhanesh Bhardwaj, Pankaj Saurabh, Suman Srikanth, S. Naveen, K.H. Prasanna, T. Rustagi, Neeti Sharma, Prem Prakash Recalibrating the Non-Communicable Diseases risk prediction tools for the rural population of Western India |
title | Recalibrating the Non-Communicable Diseases risk prediction tools for the rural population of Western India |
title_full | Recalibrating the Non-Communicable Diseases risk prediction tools for the rural population of Western India |
title_fullStr | Recalibrating the Non-Communicable Diseases risk prediction tools for the rural population of Western India |
title_full_unstemmed | Recalibrating the Non-Communicable Diseases risk prediction tools for the rural population of Western India |
title_short | Recalibrating the Non-Communicable Diseases risk prediction tools for the rural population of Western India |
title_sort | recalibrating the non-communicable diseases risk prediction tools for the rural population of western india |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8862298/ https://www.ncbi.nlm.nih.gov/pubmed/35193546 http://dx.doi.org/10.1186/s12889-022-12783-z |
work_keys_str_mv | AT guptamanojkumar recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT raghavpankaja recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT tanvirtooba recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT gautamvaishali recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT mehtoamit recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT choudharyyachana recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT mittalankit recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT singhgyanendra recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT singhgarima recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT baskaranpritish recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT rehanavr recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT jabbarshaimaabdul recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT sridevis recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT goelakhildhanesh recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT bhardwajpankaj recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT saurabhsuman recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT srikanths recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT naveenkh recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT prasannat recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT rustagineeti recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia AT sharmapremprakash recalibratingthenoncommunicablediseasesriskpredictiontoolsfortheruralpopulationofwesternindia |