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Shewade HD, Frederick A, Kalyanasundaram M, Chadwick J, Kiruthika G, Rajasekar TD, Gayathri K, Vijayaprabha R, Sabarinathan R, Shivakumar SVBY, Jeyashree K, Bhavani PK, Aarthi S, Suma KV, Pathinathan DP, Parthasarathy R, Nivetha MB, Thampi JG, Chidambaram D, Bhatnagar T, Lokesh S, Devika S, Laux TS, Viswanathan S, Sridhar R, Krishnamoorthy K, Sakthivel M, Karunakaran S, Rajkumar S, Ramachandran M, Kanagaraj KD, Kaleeswari M, Durai VP, Saravanan R, Sugantha A, Khan SZHM, Sangeetha P, Vasudevan R, Nedunchezhian R, Sankari M, Jeevanandam N, Ganapathy S, Rajasekaran V, Mathavi T, Rajaprakash AR, Murali L, Pugal U, Sundaralingam K, Savithri S, Vellasamy S, Dheenadayal D, Ashok P, Jayasree K, Sudhakar R, Rajan KP, Tharageshwari N, Chokkalingam D, Anandrajkumar SM, Selvavinayagam TS, Padmapriyadarsini C, Ramachandran R, Murhekar MV. --Eleven tips for operational researchers working with health programmes: our experience based on implementing differentiated tuberculosis care in south India. Glob Health Action 2023; 16:2161231. [PMID: 36621943 PMCID: PMC9833404 DOI: 10.1080/16549716.2022.2161231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Due to the workload and lack of a critical mass of trained operational researchers within their ranks, health systems and programmes may not be able to dedicate sufficient time to conducting operational research (OR). Hence, they may need the technical support of operational researchers from research/academic organisations. Additionally, there is a knowledge gap regarding implementing differentiated tuberculosis (TB) care in programme settings. In this 'how we did it' paper, we share our experience of implementing a differentiated TB care model along with an inbuilt OR component in Tamil Nadu, a southern state in India. This was a health system initiative through a collaboration of the State TB cell with the Indian Council of Medical Research institutes and the World Health Organisation country office in India. The learnings are in the form of eleven tips: four broad principles (OR on priority areas and make it a health system initiative, implement simple and holistic ideas, embed OR within routine programme settings, aim for long-term engagement), four related to strategic planning (big team of investigators, joint leadership, decentralised decision-making, working in advance) and three about implementation planning (conducting pilots, smart use of e-tools and operational research publications at frequent intervals). These may act as a guide for other Indian states, high TB burden countries that want to implement differentiated care, and for operational researchers in providing technical assistance for strengthening implementation and conducting OR in health systems and programmes (TB or other health programmes). Following these tips may increase the chances of i) an enriching engagement, ii) policy/practice change, and iii) sustainable implementation.
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Affiliation(s)
- Hemant Deepak Shewade
- ICMR – National Institute of Epidemiology, Chennai, India,CONTACT Hemant Deepak Shewade ; Department of Health Research, Government of India, ICMR-National Institute of Epidemiology, R-127, Second Main Road, TNHB, Ayapakkam, Chennai600077, India
| | | | | | | | - G. Kiruthika
- ICMR – National Institute of Epidemiology, Chennai, India
| | | | - K. Gayathri
- ICMR – National Institute of Epidemiology, Chennai, India
| | | | | | | | | | - P. K. Bhavani
- ICMR – National Institute for Research in Tuberculosis, Chennai, India
| | - S. Aarthi
- State TB Cell, Government of Tamil Nadu, Chennai, India
| | - K. V. Suma
- The WHO Country Office for India, New Delhi, India
| | | | | | | | | | | | | | - S. Lokesh
- ICMR – National Institute of Epidemiology, Chennai, India
| | | | | | - Stalin Viswanathan
- Department of Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, India
| | - R. Sridhar
- Government Hospital of Thoracic Medicine, Tambaram, India
| | - K. Krishnamoorthy
- Department of Respiratory Medicine, Tirunelveli Medical College Hospital, Tirunelveli, India
| | - M. Sakthivel
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - S. Karunakaran
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - S. Rajkumar
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - M. Ramachandran
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - K. D. Kanagaraj
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - M. Kaleeswari
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - V. P. Durai
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - R. Saravanan
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - A. Sugantha
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | | | - P. Sangeetha
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - R. Vasudevan
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - R. Nedunchezhian
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - M. Sankari
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - N. Jeevanandam
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - S. Ganapathy
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - V. Rajasekaran
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - T. Mathavi
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - A. R. Rajaprakash
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - Lakshmi Murali
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - U. Pugal
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - K. Sundaralingam
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - S. Savithri
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - S. Vellasamy
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - D. Dheenadayal
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - P. Ashok
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - K. Jayasree
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - R. Sudhakar
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - K. P. Rajan
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | | | | | | | - T. S. Selvavinayagam
- Directorate of Public Health and Preventive Medicine, Government of Tamil Nadu, Chennai, India
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Shewade HD, Frederick A, Kiruthika G, Kalyanasundaram M, Chadwick J, Rajasekar TD, Gayathri K, Vijayaprabha R, Sabarinathan R, Kathiresan J, Bhavani P, Aarthi S, Suma K, Pathinathan DP, Parthasarathy R, Nivetha MB, Thampi JG, Chidambaram D, Bhatnagar T, Lokesh S, Devika S, Laux TS, Viswanathan S, Sridhar R, Krishnamoorthy K, Sakthivel M, Karunakaran S, Rajkumar S, Ramachandran M, Kanagaraj K, Kaleeswari M, Durai V, Saravanan R, Sugantha A, Khan SZHM, Sangeetha P, Vasudevan R, Nedunchezhian R, Sankari M, Jeevanandam N, Ganapathy S, Rajasekaran V, Mathavi T, Rajaprakash A, Murali L, Pugal U, Sundaralingam K, Savithri S, Vellasamy S, Dheenadayal D, Ashok P, Jayasree K, Sudhakar R, Rajan K, Tharageshwari N, Chokkalingam D, Anandrajkumar S, Selvavinayagam T, Padmapriyadarshini C, Ramachandran R, Murhekar MV. The First Differentiated TB Care Model From India: Delays and Predictors of Losses in the Care Cascade. Glob Health Sci Pract 2023; 11:e2200505. [PMID: 37116929 PMCID: PMC10141439 DOI: 10.9745/ghsp-d-22-00505] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/07/2023] [Indexed: 04/03/2023]
Abstract
To reduce TB deaths in resource-limited settings, a differentiated care strategy can be used to triage patients with high risk of severe illness (i.e., those with very severe undernutrition, respiratory insufficiency, or inability to stand without support) at diagnosis and refer them for comprehensive assessment and inpatient care. Globally, there are few examples of implementing this type of strategy in routine program settings. Beginning in April 2022, the Indian state of Tamil Nadu implemented a differentiated care strategy called Tamil Nadu-Kasanoi Erappila Thittam (TN-KET) for all adults aged 15 years and older with drug-susceptible TB notified by public facilities. Before evaluating the impact on TB deaths, we sought to understand the retention and delays in the care cascade as well as predictors of losses. During April-June 2022, 14,961 TB patients were notified and 11,599 (78%) were triaged. Of those triaged, 1,509 (13%) were at high risk of severe illness; of these, 1,128 (75%) were comprehensively assessed at a nodal inpatient care facility. Of 993 confirmed as severely ill, 909 (92%) were admitted, with 8% unfavorable admission outcomes (4% deaths). Median admission duration was 4 days. From diagnosis, the median delay in triaging and admission of severely ill patients was 1 day each. Likelihood of triaging decreased for people with extrapulmonary TB, those diagnosed in high-notification districts or teaching hospitals, and those transferred out of district. Predictors of not being comprehensively assessed included: aged 25-34 years, able to stand without support, and diagnosis at a primary or secondary-level facility. Inability to stand without support was a predictor of unfavorable admission outcomes. To conclude, the first quarter of implementation suggests that TN-KET was feasible to implement but could be improved by addressing predictors of losses in the care cascade and increasing admission duration.
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Affiliation(s)
- Hemant Deepak Shewade
- Indian Council of Medical Research, National Institute of Epidemiology, Chennai, India
| | | | - G. Kiruthika
- Indian Council of Medical Research, National Institute of Epidemiology, Chennai, India
| | | | - Joshua Chadwick
- Indian Council of Medical Research, National Institute of Epidemiology, Chennai, India
| | - T. Daniel Rajasekar
- Indian Council of Medical Research, National Institute of Epidemiology, Chennai, India
| | - K. Gayathri
- Indian Council of Medical Research, National Institute of Epidemiology, Chennai, India
| | - R. Vijayaprabha
- Indian Council of Medical Research, National Institute of Epidemiology, Chennai, India
| | - R. Sabarinathan
- Indian Council of Medical Research, National Institute of Epidemiology, Chennai, India
| | - Jeyashree Kathiresan
- Indian Council of Medical Research, National Institute of Epidemiology, Chennai, India
| | - P.K. Bhavani
- Indian Council of Medical Research, National Institute for Research in Tuberculosis, Chennai, India
| | - S. Aarthi
- State TB Cell, Government of Tamil Nadu, Chennai, India
| | - K.V. Suma
- World Health Organization Country Office for India, New Delhi, India
| | | | | | | | - Jerome G. Thampi
- World Health Organization Country Office for India, New Delhi, India
| | | | - Tarun Bhatnagar
- Indian Council of Medical Research, National Institute of Epidemiology, Chennai, India
| | - S. Lokesh
- Indian Council of Medical Research, National Institute of Epidemiology, Chennai, India
| | | | | | - Stalin Viswanathan
- Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - R. Sridhar
- Government Hospital of Thoracic Medicine, Tambaram, India
| | | | - M. Sakthivel
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - S. Karunakaran
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - S. Rajkumar
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - M. Ramachandran
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - K.D. Kanagaraj
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - M. Kaleeswari
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - V.P. Durai
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - R. Saravanan
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - A. Sugantha
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | | | - P. Sangeetha
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - R. Vasudevan
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - R. Nedunchezhian
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - M. Sankari
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - N. Jeevanandam
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - S. Ganapathy
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - V. Rajasekaran
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - T. Mathavi
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - A.R. Rajaprakash
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - Lakshmi Murali
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - U. Pugal
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - K. Sundaralingam
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - S. Savithri
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - S. Vellasamy
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - D. Dheenadayal
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - P. Ashok
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - K. Jayasree
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - R. Sudhakar
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | - K.P. Rajan
- Directorate of Medical and Rural Health Services, Government of Tamil Nadu, Chennai, India
| | | | - D. Chokkalingam
- Indian Council of Medical Research, National Institute of Epidemiology, Chennai, India
| | | | - T.S. Selvavinayagam
- Directorate of Public Health and Preventive Medicine, Government of Tamil Nadu, Chennai, India
| | - C. Padmapriyadarshini
- Indian Council of Medical Research, National Institute for Research in Tuberculosis, Chennai, India
| | | | - Manoj V. Murhekar
- Indian Council of Medical Research, National Institute of Epidemiology, Chennai, India
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Abstract
Background & objectives In most of rural India, warfarin is the only oral anticoagulant available. Among patients taking warfarin, there is a strong association between poor control of the international normalized ratio (INR) and adverse events. This study was aimed to quantify INR control in a secondary healthcare system in rural Chhattisgarh, India. Methods The INR data were retrospectively obtained from all patients taking warfarin during 2014-2016 at a secondary healthcare system in rural Chhattisgarh, India. Patients attending the clinic had their INR checked at the hospital laboratory and their warfarin dose adjusted by a physician on the same day. The time in therapeutic range (TTR) was calculated for patients who had at least two INR visits. Results The 249 patients had 2839 INR visits. Their median age was 46 yr, and the median body mass index was 17.7 kg/m[2]. They lived a median distance of 78 km (2-3 h of travel) from the hospital. The median INR was 1.7 for a target INR of 2.0-3.0 (n=221) and 2.1 for a target of 2.5-3.5 (n=28). The median TTR was 13.0 per cent, and INR was subtherapeutic 66.0 per cent of the time. Distance from the hospital was not correlated with TTR. Interpretation & conclusions INR values were subtherapeutic two-thirds of the time, and TTR values were poor regardless of distance from the health centre. Future studies should be done to identify interventions to improve INR control.
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Affiliation(s)
- Puja Chebrolu
- Department of Medicine, Washington University in St. Louis, Missouri, USA
| | | | - Timothy S Laux
- Department of Hospital Medicine, Columbia University Medical Center, New York, USA
| | - Noor Al-Hammadi
- Division of Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Brian Gage
- Department of Medicine, Washington University in St. Louis, Missouri, USA
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Affiliation(s)
- Timothy S. Laux
- Jan Swasthya Sahyog/People’s Health Support Group, Bilaspur, India; Columbia University Medical Center, New York, New York
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Laux TS, Patil S. Predictors of tuberculosis treatment outcomes among a retrospective cohort in rural, Central India. J Clin Tuberc Other Mycobact Dis 2018; 12:41-47. [PMID: 31720398 PMCID: PMC6830133 DOI: 10.1016/j.jctube.2018.06.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2018] [Revised: 06/18/2018] [Accepted: 06/20/2018] [Indexed: 11/18/2022] Open
Abstract
Introduction Programmatic design affects access to healthcare and can influence tuberculosis treatment outcomes. Potential predictors of tuberculosis treatment outcomes in one rural Indian setting were examined to improve outcomes with a focus on access to care. Methods Routinely collected tuberculosis treatment data from Jan Swasthya Sahyog, a community based healthcare system in rural Chhattisgarh, India were examined from 2003–2015. Predictors were analyzed for associations with death, loss to follow-up or failure in multivariable logistic regression models. The effect of distance from treatment on outcomes was graphed and Pearson's correlation coefficients (r2) calculated. Descriptive time to event analyses were performed for all deaths and loss to follow-up from January 2010 to September 2015. Results 4979 patients with active TB were treated during the study period. Patients were mostly male, malnourished, diagnosed with pulmonary disease and many travelled lengthy distances. Positive treatment outcomes improved from 55% to 80% from 2003 to 2015 for all patients though positive treatment outcomes have been above 80% in the primary care setting since 2012. The annual case fatality rate was 4.4% with small yearly variation.Gender and site of treatment (primary versus secondary care facility) and also season of treatment initiation and travel time to care best predicted outcomes in both the complete model and model which included only patients with initial BMI data. No differences were found between primary and secondary care patients for initial BMI, percentage of sputum positivity among those with pulmonary disease and grade of sputum positivity among the sputum positive. Those who traveled the furthest to access care achieved the worst outcomes during the summer and, to a lesser degree, the monsoon. Distance from care was associated with treatment outcomes in a dose-response manner out to substantial distances. From 2010 to 2015, most patients who died or were lost to follow-up did so in the first week of treatment. Conclusions The provision of care through local facilities improves the treatment of tuberculosis in rural India. Interventions addressing death or loss to follow-up should focus on the newly diagnosed. Rural Indian physicians should be aware of how access issues affect TB treatment outcomes.
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Affiliation(s)
- Timothy S. Laux
- Jan Swasthya Sahyog (People's Health Support Group), Ganiyari, Bilaspur, Chhattisgarh 495112, India
- The HEAL Initiative, University of California San Francisco, San Francisco, CA, USA
| | - Sushil Patil
- Jan Swasthya Sahyog (People's Health Support Group), Ganiyari, Bilaspur, Chhattisgarh 495112, India
- The HEAL Initiative, University of California San Francisco, San Francisco, CA, USA
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Laux TS, Barnoya J, Cipriano E, Herrera E, Lopez N, Polo VS, Rothstein M. Prevalence of chronic kidney disease of non-traditional causes in patients on hemodialysis in southwest Guatemala. Rev Panam Salud Publica 2016; 39:186-193. [PMID: 27657183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2015] [Accepted: 01/14/2016] [Indexed: 06/06/2023] Open
Abstract
Objective To document the prevalence of patients on hemodialysis in southwestern Guatemala who have chronic kidney disease (CKD) of non-traditional causes (CKDnt). Methods This cross-sectional descriptive study interviewed patients on hemodialysis at the Instituto Guatemalteco de Seguridad Social on their health and occupational history. Laboratory serum, urine and vital sign data at the initiation of hemodialysis were obtained from chart reviews. Patients were classified according to whether they had hypertension or obesity or neither. The proportion of patients with and without these traditional CKD risk factors was recorded and the association between demographic and occupational factors and a lack of traditional CKD risk factors analyzed using multivariate logistic regression. Results Of 242 total patients (including 171 non-diabetics) enrolled in hemodialysis in southwestern Guatemala, 45 (18.6% of total patients and 26.3% of non-diabetics) lacked traditional CKD risk factors. While agricultural work history was common, only travel time greater than 30 minutes and age less than 50 years old were significantly associated with CKD in the absence of traditional risk factors. Individuals without such risk factors lived throughout southwestern Guatemala's five departments. Conclusions The prevalence of CKDnT appears to be much lower in this sample of patients receiving hemodialysis in Southwestern Guatemala than in hospitalized patients in El Salvador. It has yet to be determined whether the prevalence is higher in the general population and in patients on peritoneal dialysis.
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Laux TS. The art of letting go and the mandate of going further. Natl Med J India 2016; 29:30-31. [PMID: 27492037 DOI: 10.4103/0970-258x.186916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Affiliation(s)
- Timothy S Laux
- Jan Swasthya Sahyog, Ganiyari, Chhattisgarh, India; HEAL Initiative, University of California San Francisco, San Francisco, California, USA
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Laux TS, Barnoya J, Guerrero DR, Rothstein M. Dialysis enrollment patterns in Guatemala: evidence of the chronic kidney disease of non-traditional causes epidemic in Mesoamerica. BMC Nephrol 2015; 16:54. [PMID: 25881146 PMCID: PMC4406024 DOI: 10.1186/s12882-015-0049-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2014] [Accepted: 04/01/2015] [Indexed: 12/15/2022] Open
Abstract
Background In western Nicaragua and El Salvador, chronic kidney disease (CKD) is highly prevalent and generally affects young, male, agricultural (usually sugar cane) workers without the established CKD risk factors. It is yet unknown if the prevalence of this CKD of Non-Traditional causes (CKDnT) extends to the northernmost Central American country, Guatemala. Therefore, we sought to compare dialysis enrollment rates by region, municipality, sex, daily temperature, and agricultural production in Guatemala and assess if there is a similar CKDnT distribution pattern as in Nicaragua and El Salvador. Methods The National Center for Chronic Kidney Disease Treatment (Unidad Nacional de Atención al Enfermo Renal Crónico) is the largest provider of dialysis in Guatemala. We used population, Human Development Index, literacy, and agricultural databases to assess the geographic, economic, and educational correlations with the National Center for Chronic Kidney Disease Treatment’s hemodialysis and peritoneal dialysis enrollment database. Enrollment rates (per 100 000) inhabitants were compared by region and mapped for comparison to regional agricultural and daytime temperature data. The distribution of men and women enrolled in dialysis were compared by region using Fisher’s exact tests. Spearman’s rank correlation coefficients were calculated. Results Dialysis enrollment is higher in the Southwest compared to the rest of the country where enrollees are more likely (p < 0.01) to be male (57.8%) compared to the rest of the country (49.3%). Dialysis enrollment positively correlates with Human Development Index and literacy rates. These correlations are weaker in the agricultural regions (predominantly sugar cane) of Southwest Guatemala. Conclusions In Guatemala, CKDnT incidence may have a similar geographic distribution as Nicaragua and El Salvador (higher in the high temperature and sugar cane growing regions). Therefore, it is likely that the CKNnT epidemic extends throughout the Mesoamerican region.
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Affiliation(s)
- Timothy S Laux
- Barnes-Jewish Hospital Department of Internal Medicine, St. Louis, MO, USA.,Department of Medicine, Division of Medical Education, Washington University School of Medicine, 660 South Euclid Avenue, Box 8121, St Louis, MO, 63110, USA
| | - Joaquin Barnoya
- Washington University in St Louis Division of Public Health Sciences, 660 S. Euclid Avenue, Campus Box 8100, St. Louis, MO, 63110, USA.
| | - Douglas R Guerrero
- Unidad Nacional de Atención al Enfermo Renal Crónico, 9a. Avenida 3-40 Zona 1, Ciudad de Guatemala, 01001, Guatemala, Guatemala
| | - Marcos Rothstein
- Division of Renal Diseases, Washington University in St. Louis, St. Louis, MO, USA.,Washington University School of Medicine, Division of Renal Diseases, 660 South Euclid Avenue, Campus Box 8126, St. Louis, MO, 63110, USA
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Laux TS, Bert PJ, González M, Unruh M, Aragon A, Lacourt CT. Prevalence of obesity, tobacco use, and alcohol consumption by socioeconomic status among six communities in Nicaragua. Rev Panam Salud Publica 2013. [PMID: 23183562 DOI: 10.1590/s1020-49892012000900007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE To describe the prevalence of noncommunicable disease (NCD) risk factors (overweight/obesity, tobacco smoking, and alcohol consumption) and identify correlations between these and sociodemographic characteristics in western and central Nicaragua. METHODS This was a cross-sectional study of 1 355 participants from six communities in Nicaragua conducted in September 2007-July 2009. Demographic and NCD risk-related health behavior information was collected from each individual, and their body mass index (BMI), blood pressure, diabetes status, and renal function were assessed. Data were analyzed using descriptive statistics, bivariate analyses, and (non-stratified and stratified) logistic regression models. RESULTS Of the 1 355 study participants, 22.0% were obese and 55.1% were overweight/obese. Female sex, higher income, and increasing age were significantly associated with obesity. Among men, lifelong urban living correlated with obesity (Odds Ratio [OR] = 4.39, 1.18-16.31). Of the total participants, 31.3% reported ever smoking tobacco and 47.7% reported ever drinking alcohol. Both tobacco smoking and alcohol consumption were strikingly more common among men (OR = 13.0, 8.8-19.3 and 15.6, 10.7-22.6, respectively) and lifelong urban residents (OR = 2.42, 1.31-4.47 and 4.10, 2.33-7.21, respectively). CONCLUSIONS There was a high prevalence of obesity/overweight across all income levels. Women were much more likely to be obese, but men had higher rates of tobacco and alcohol use. The rising prevalence of NCD risk factors among even the poorest subjects suggests that an epidemiologic transition in underway in western and central Nicaragua whereby NCD prevalence is shifting to all segments of society. Raising awareness that health clinics can be used for chronic conditions needs to be priority.
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Affiliation(s)
- Timothy S Laux
- Barnes Jewish Hospital, Washington University in St. Louis, St. Louis, Missouri, United States of America.
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Laux TS, Bert PJ, González M, Unruh M, Aragon A, Lacourt CT. Prevalence of hypertension and associated risk factors in six Nicaraguan communities. Ethn Dis 2012; 22:129-135. [PMID: 22764632 PMCID: PMC4387575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2023] Open
Abstract
OBJECTIVE Describe the prevalence of hypertension. DESIGN Population based cross-sectional survey. SETTING Six Nicaraguan communities with varying economies. PARTICIPANTS 1,355 adults aged 20-60 years who completed both self-reported and quantitative measures of health. MAIN OUTCOME MEASURES Prevalence of hypertension (systolic > or = 140 mm Hg, diastolic > or = 90 mm Hg, or self-reported medical history with diagnosis by a health care professional), uncontrolled hypertension (systolic > or = 140 mm Hg or diastolic > or = 90 mm Hg), diabetes (urinary glucose excretion > or = 100 mg/ dL or self-reported medical history diagnosed by a health care professional), and uncontrolled diabetes (urinary glucose excretion > or =100 mg/dL only). RESULTS The prevalence of hypertension was 22.0% (19.2% in men, 24.2% in women). Blood pressure was controlled in 31.0% of male hypertensives and 55.1% of female hypertensives (odds ratio [OR] 2.86; 95% confidence interval [Cl] 1.74-4.69). Older age and higher body mass index were strongly associated with hypertension. Women who completed primary school had a lower risk of hypertension (OR .40; 95% Cl .19-.85) compared to those with no formal education. A history of living in both urban and rural settings was associated with lower prevalence of hypertension (OR .52; 95% CI .34-.79). Diabetes mellitus was found in 1.2% of men and 4.3% of women. Male sex was independently associated with decreased risk of diabetes (OR .31; 95% Cl .11-.86). CONCLUSIONS At least one cardiovascular risk factor was found in half of this Nicaraguan sample. Cardiovascular risk factors should be the target of educational efforts, screening, and treatment.
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Affiliation(s)
- Timothy S Laux
- University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA.
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