Abstract
Background:
Coronary artery diseases (CADs) require chronic treatment, and recurrent coronary events can be prevented by adhering to secondary prevention guidelines. This study was conducted to determine the adherence to secondary prevention strategies and its associated factors among adults with CAD in a rural cohort in South India.
Materials and Methods:
A community-based cross-sectional study was conducted within the ENDIRA (Epidemiology of Non-communicable Diseases In Rural Areas) Cohort in the rural part of Aluva municipality of Ernakulam district, Kerala, India, which comprises five adjacent panchayats with a population of approximately 100,000 individuals. CAD patients aged 35 to 80 years from this cohort who have had an event of myocardial infarction in the past decade as confirmed by medical records were included. The primary outcome measured was the adherence to secondary prevention strategies across six domains as per the guidelines of the American Heart Association. A multivariable logistic regression model was used to determine the independent predictors of inadequate adherence to secondary prevention strategies.
Results:
The study included 436 participants with a mean (± SD) age of 65.20 (±8.37) years, and 69% were males. The medication adherence among patients with CAD was 56.4% (95% CI 51.77–61.08), the blood pressure control was 77.3% (95% CI 73.36–81.23), the ideal body mass index was maintained by 48.9% (95% CI 44.16–53.54), the recommended physical activity was followed by 64.9% (95% CI 60.43–69.39), the smoking cessation rates were 61.8% (95% CI 52.33–71.19) and 72.5% (95% CI 68.29–76.67), and there was adequate mental health.
Conclusion:
The study reveals moderate adherence among CAD patients to the secondary prevention strategies in a resource-limited setting. Ensuring community access to high-quality follow-up care after CAD is crucial.
Keywords: Adherence, coronary artery disease, prevention, preventive strategies, secondary prevention
INTRODUCTION
The epidemiological transition is a complex and dynamic process marked by shifts in patterns of health and disease.[1] Over the past 3 decades, this transition has been characterized by the predominance of noncommunicable diseases (NCDs) over communicable diseases, with NCDs now posing a sustained threat to public health.[2] These chronic conditions necessitate continuous engagement with healthcare systems to prevent escalation to life-threatening states.[2]
Cardiovascular diseases (CVDs) have emerged as the leading cause of mortality and morbidity among NCDs.[3] Coronary artery diseases (CADs) account for the majority of CVD-related deaths.[3] Moreover, the survivors of myocardial infarction (MI) face an increased risk of recurrence, with a mortality rate significantly higher than those unaffected by CAD.[4] Secondary cardiovascular prevention, as defined by the World Heart Federation, encompasses strategies aimed at reducing the risk of subsequent cardiovascular events in patients with known atherosclerotic cardiovascular disease.[5] International guidelines for secondary prevention of CAD by organizations such as the American Heart Association (AHA) and European Society of Cardiology (ESC) underscore the importance of risk factor management combined with cardioprotective pharmacotherapy.[6] Adherence to these guidelines has been shown to significantly enhance patients’ quality of life by diminishing long-term morbidity, mortality, and the resultant socioeconomic burden.[6]
Despite their proven effectiveness, implementation of secondary prevention measures for CAD is suboptimal, particularly in low and middle-income countries (LMICs), including India, while their determinants and consequences are poorly defined.[6] Adequate medication adherence, defined as taking prescribed medications at least 80% of the time, is achieved by just 50% of the patients with chronic diseases after 1 year, and only 20% adhere strictly to their medication regimens.[6,7] As per the 2020 global report of the International Council of Cardiovascular Prevention and Rehabilitation (ICCPR), India has the most urgent requirement for cardiac rehabilitation.[8]
This community-based cross-sectional study was conducted to evaluate the adherence to secondary prevention strategies among CAD patients and to determine its independent predictors. Understanding these elements is critical for tailoring interventions to enhance the management of cardiovascular risks and improve patient outcomes.
MATERIALS AND METHODS
Study design, setting, and population
A community-based cross-sectional study was conducted in the rural area of Aluva municipality of Ernakulam district in Kerala, India, during January 2022 to March 2022 within the ENDIRA (Epidemiology of Non-communicable Diseases In Rural Areas) Cohort.[9] Access to healthcare professionals is restricted in the rural regions of India, including the rural areas of the state of Kerala, when compared to urban settings.[9] The list of patients with CAD from all the five panchayats of the ENDIRA Cohort (Kalady, Karukutty, Manjapra, Mukkanoor, and Thuravoor) consisting of 75 wards was available. CAD was defined as per Sheridan and Crossman review.[10]
Individuals from the ENDIRA Cohort aged 35 to 80 years who had been diagnosed with an event of myocardial infarction in the past 10 years were included in the study. The exclusion criteria included (1) stroke patients or patients in coma who are cognitively impaired and are unable to answer the questions of the interview, (2) patients with auditory impairment, (3) mentally ill patients, (4) bedridden patients, and (5) patients with incomplete medical records.
Sampling method
ENDIRA Cohort covers 75 wards in total, out of which 15 wards were selected by simple random sampling, and population proportion to size was used to identify the required number of participants from each ward. The complete list of patients with CAD within each ward was available to the principal investigator. From this list, the patients within each ward were selected via systematic random sampling using a sampling interval of three. Every third person from the list of that particular ward was taken into the study until the desired number was reached. The STROBE flowchart is given in Figure 1.
Figure 1.
STROBE Flowchart
Sample size
The sample size was calculated based on the study by Sudevan et al.,[11] where the prevalence of various domains for secondary prevention strategies among CAD patients varied from 39.22% to 93.86%. The lowest prevalence of 39.22% was taken and the formula used was n = Z21-α/2 PQ/d2 [Z1-α/2 = 1.96, P = 39.22, Q = 60.78, d (absolute precision) = 5%], and the minimum sample size came up to 436 with an 85% response rate.
Study procedure and study tool
Informed consent was obtained prior to the study, and participants were interviewed at their homes by the study personnel along with the ASHA (Accredited Social Health Activist) of that particular ward who were trained by the principal investigator.
A validated assessment tool applicable for measuring the utilization of healthcare services was unavailable. Hence, a tool was developed using a 4-step mini modified Delphi consensus method.[12] The initial version was prepared based on American Heart Association (AHA) and American College of Cardiology Foundation (ACCF) 2011 guidelines,[13] published literature, and a validated, translated Patient Health Questionnaire (PHQ-9).[14] An expert panel evaluated the relevance of each question using a Likert scale ranging from 1 (highly inapplicable) to 5 (highly applicable). The questions were evaluated based on two stages: preselection of questions using a median score ≥4, followed by the degree of consensus among expert panel members. Consensus was reached if ≥75% of members scored ≥4 for a particular question. The panel also provided written feedback which was used to refine the questions to ensure they fully addressed the study’s needs. An updated questionnaire, incorporating the panel’s feedback, was then reviewed in a consensus meeting, resulting in further approval, rejection, or modification of the questions.
The revised questionnaire was pilot-tested with 30 patients in the same study setting to evaluate its feasibility, usability, and acceptability. The patients on whom the pilot testing was conducted were excluded from the final analysis. Based on the pilot test, minor modifications were made before the questionnaire received final approval from the panel. The English version was then translated to the local language (Malayalam) by two different language experts. The internal consistency of the questionnaire was checked using Cronbach’s alpha, and a score of 0.67 was found to be acceptable, although it was slightly below the acceptable threshold.
The pretested, validated, structured questionnaire prepared was divided into seven sections: 1) sociodemographic details; 2) medical history including comorbidities; 3) tobacco and alcohol usage, type, and frequency; 4) details of CAD medications consumed including the medications prescribed by the doctor, the medications which are actually consumed by the patient along with the frequency of consumption per week; 5) diet history; 6) PHQ-9 questionnaire for depression screening; and 7) clinical examination details including height, weight, and blood pressure (BP). The socioeconomic status was classified into APL (Above Poverty Line) and BPL (Below Poverty Line) based on the colour of the ration card issued by the Government of Kerala.[15]
Criteria for adherence to secondary prevention strategies
The adherence to secondary prevention strategies for CAD was defined using the universally accepted 2011 guidelines of AHA (American Heart Association) and ACCF (American College of Cardiology Foundation)[13] as well as published literature [Table 1].
Table 1.
Criteria for defining adherence to secondary prevention strategies
| Domains | Criteria |
|---|---|
| CAD Medication | Adherence to prescription for at least 6 days a week [80%][16] |
| Blood Pressure* | <140/90 mm of Hg[13] |
| Body Mass Index† [WHO Classification][17] | 18.5-24.99 kg/m2 |
| Physical Activity (self-reported) | At least 150 minutes of moderate-intensity aerobic activity per week OR 75 minutes of vigorous aerobic activity per week OR a combination of both, preferably spread throughout the week[18] |
| Smoking Cessation (self-reported) | Complete cessation (only for previous smokers)[13] |
| Mental Health | Minimal depression as per PHQ-9[19] |
*Measured at the time of house visit using calibrated digital blood pressure monitor (Average of two readings taken 20 minutes apart). †Height and weight of participants were measured at the time of house visit
Statistical analysis
The data collected were then entered in Microsoft Excel (Microsoft Corporation, Washington, USA), numerically coded, and analyzed using IBM SPSS Statistics version 26 (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp). Descriptive analysis was done to characterize the study population and was expressed in frequencies and percentages. Regression model was used to find the independent predictors of inadequate adherence to secondary prevention strategies. A simple logistic regression model was used for all the variables to determine the unadjusted odds ratio. The variables with a P value < 0.05 in the simple logistic regression were taken for multivariable logistic regression analysis, and the final results were expressed using adjusted odds ratio and 95% confidence intervals. A P value < 0.05 was considered as statistically significant. The regression coefficients were tested using the Wald statistic.
Patient and public involvement
Patients and the general public were not directly involved in the study’s planning or design. However, ASHA workers from the respective wards assisted the principal investigator in data collection. Patient feedback was also incorporated into the study’s questionnaire during the preliminary pilot phase. The authors plan to share the study’s findings with ASHA workers and patient support groups to promote better adherence to secondary prevention strategies among patients with CAD in the region.
Ethical approval
This study conforms to widely accepted ethical principles guiding human research. Institutional Ethical Committee clearance was taken before the commencement of the study (ECASM-AIMS-2021-143). Written informed consent in the local language (Malayalam) was obtained from the study participants before collecting the data.
RESULTS
A total of 436 CAD patients were studied. The mean (± SD) age of the study participants was 65.20 (±8.37) years, and 69% were males. The median (IQR) monthly expenditure for CAD medication was Rs 2000 (1425,3000). The baseline details of the study participants are given in Table 2.
Table 2.
Sociodemographic characteristics and habits (n=436)
| Baseline Characteristics | Frequency (n) | Percentage (%) |
|---|---|---|
| Age (in years) | ||
| 35-45 | 7 | 1.6 |
| 46-55 | 54 | 12.4 |
| 56-65 | 146 | 33.5 |
| >65 | 229 | 52.5 |
| Gender | ||
| Male | 301 | 69 |
| Female | 135 | 31 |
| Marital Status | ||
| Married | 387 | 88.8 |
| Unmarried | 16 | 3.7 |
| Widow or Divorced | 33 | 7.5 |
| Education | ||
| No formal education | 29 | 6.7 |
| Primary (1-4 standards) and Middle (5-7 standards) | 195 | 44.7 |
| High (8-10 standards) and Higher Secondary (11-12 standards) | 181 | 41.5 |
| Graduation and Post Graduation | 31 | 7.1 |
| Current occupation | ||
| Professional | 12 | 2.8 |
| Skilled | 135 | 31 |
| Unskilled | 63 | 14.4 |
| Homemaker | 79 | 18.1 |
| Unemployed | 81 | 18.6 |
| Retired | 66 | 15.1 |
| Socioeconomic status | ||
| APL* | 319 | 73.2 |
| BPL† | 117 | 26.8 |
| Type of Family | ||
| Nuclear Family | 301 | 69 |
| Joint Family | 47 | 10.8 |
| Three Generation Family | 88 | 20.2 |
| Number of Family Members | ||
| ≤4 | 282 | 64.7 |
| >4 | 154 | 35.3 |
| Reported Tobacco use (in any form) at present | ||
| Yes | 39 | 8.9 |
| No | 397 | 91.1 |
| Reported Alcohol use at present | ||
| Yes | 66 | 15.1 |
| No | 370 | 84.9 |
*Above Poverty Line (based on the colour of Ration Card). †Below Poverty Line (based on the colour of Ration Card)
The distribution of CAD medications as per prescription is given in Table 3. Aspirin was prescribed to 375 (86%) patients, out of which 302 (69.3%) adhered to the prescription. Beta blocker prescriptions were given to 308 (70.6%) patients, but only 205 (47%) were taking them as directed. Medication adherence is generally higher among female patients as compared to male patients.
Table 3.
Distribution of CAD Medications (n=436)
| Medicines as per Guidelines | Listed in prescription n (%) | Consumption as per prescription n (%) | Males consuming as per prescription† n (%) | Females consuming as per prescription‡ n (%) |
|---|---|---|---|---|
| Aspirin | 375 (86) | 302 (69.3) | 206 (68.4) | 96 (71.1) |
| Clopidogrel | 370 (84.9) | 284 (65.1) | 194 (64.5) | 90 (66.7) |
| Statin | 369 (84.6) | 256 (58.7) | 171 (56.8) | 85 (63) |
| ACEi/ARB* | 307 (70.4) | 209 (47.9) | 132 (43.9) | 77 (57) |
| Beta Blocker | 308 (70.6) | 205 (47) | 134 (44.5) | 71 (52.6) |
*Angiotensin Converting Enzyme Inhibitor/Angiotensin Receptor Blocker. †Total number of males in the study population: 301. ‡Total number of females in the study population: 135
The adherence to the various domains of secondary prevention is given in Table 4. The majority of the patients (337 [77.3%]) had adequate blood pressure control, while adequate BMI was maintained only by 213 (48.9%) patients.
Table 4.
Adherence to secondary prevention domains (n=436)
| Domains | Adherence n (%) | 95% Confidence Interval |
|---|---|---|
| CAD Medications | ||
| Adherence to prescription for at least 6 days a week [80%] | 246 (56.4) | 51.77-61.08 |
| Blood Pressure Control | ||
| Controlled (<140/90 mm Hg) | 337 (77.3) | 73.36-81.23 |
| Body Mass Index (BMI) | ||
| Normal (18.5-24.99 kg/m2) | 213 (48.9) | 44.16-53.54 |
| Physical Activity | ||
| As recommended | 283 (64.9) | 60.43-69.39 |
| Smoking Cessation* | ||
| Complete cessation | 63 (61.8) | 52.33-71.19 |
| Mental Health | ||
| Minimal Depression | 316 (72.5) | 68.29-76.67 |
*Only 102 CAD patients with self-reported history of smoking were included
Independent predictors of inadequate adherence were determined under each of the six domains of secondary prevention, and only statistically significant predictors are given in Supplementary Figures. The regression model was deemed fit using Hosmer and Lemeshow test (χ2 = 9.55, P = 0.29). The logistic regression model was statistically significant (χ2 = 64.18, P value < 0.001).
-
Under CAD medication domain:
Patients with Non-ST-elevation myocardial infarction (NSTEMI) had 2.45 times odds of inadequate adherence compared to those with ST-elevation myocardial infarction (STEMI) [Supplementary Figure 1a (959.4KB, tif) ].
Patients initiated on medical management as the primary treatment had 0.49 times odds of inadequate adherence as compared to those who underwent an intervention [Supplementary Figure 1b (959.4KB, tif) ].
Patients who underwent coronary artery bypass graft (CABG) were 44% less likely to have inadequate adherence compared to those who did not [Supplementary Figure 1c (959.4KB, tif) ].
Patients who did not report any out-of-pocket expenditure (OOPE) for CAD medications had 5.16 times odds of inadequate adherence compared to those spending >Rs 4000 [Supplementary Figure 1d (959.4KB, tif) ].
Patients who were dissatisfied with the healthcare services had 7.68 times odds of inadequate adherence compared to those who were satisfied [Supplementary Figure 1e (959.4KB, tif) ].
-
Under BP control domain:
Patients spending <Rs 4000 per month for CAD medication had 2.98 times odds of inadequate adherence compared to those spending >Rs 4000 [Supplementary Figure 2a (337.3KB, tif) ].
Patients following self-medication had 9.77 times odds of inadequate adherence compared to those preferring public hospitals for follow-up [Supplementary Figure 2b (337.3KB, tif) ].
Under BMI domain [Supplementary Figure 3 (272.7KB, tif) ], those preferring private facilities for follow-up had 2.39 times odds of inadequate adherence compared to those preferring public facilities.
Under physical activity domain [Supplementary Figure 4 (272.7KB, tif) ], those with reported dyslipidemia were 38% less likely to have inadequate adherence compared to those without dyslipidemia.
Under smoking cessation domain [Supplementary Figure 5 (272.7KB, tif) ], those who reported alcohol consumption had 3.80 times odds of inadequate adherence compared to those without alcohol intake.
Under mental health domain [Supplementary Figure 6 (272.7KB, tif) ], uninsured CAD patients had 2.19 times odds of having poor mental health compared to insured patients.
DISCUSSION
This study offers insights into the multifactorial nature of adherence to secondary prevention for CAD in a rural cohort. It reported that the adherence spectrum across different domains of secondary prevention varied across the study population.
CAD medication domain
The overall adherence to CAD medications in this study was 56.4%. The profile of individual CAD medication adherence was different compared to the study by Sudevan et al.[11] This study reported a lower consumption of antiplatelets (67.2 vs 96%), statins (58.7% vs 89.4%), and beta blockers (47% vs 68.2%), while there was a higher consumption of ACEi/ARBs (47.9% vs 37.7%) in comparison to the report by a hospital-based study conducted by Sudevan et al.,[11] probably due to the community-based nature, the sampling technique, and the different guidelines for secondary prevention used in this study. Khatib et al.[20] reported that the rate of nonadherence to at least one medication used in the secondary prevention of CAD was 43.5%. Mathews et al.[21] link low medication adherence to increased risk of NSTEMI.
In this study, the patients initiated on medical management as the primary treatment had better adherence probably due to the targeted health education provided by the health care professionals. Also, patients who underwent CABG had less chance of inadequate adherence, perhaps spurred by the profound severity of their cardiac intervention. The relationship between OOPE and adherence is complex, and populations can behave differently regarding prescribed medication depending on the social and cultural context.[22] Recent evidence suggests that patient satisfaction correlates positively with medication adherence.[23]
Blood Pressure control domain
This study reported a 77.3% adherence to blood pressure control, which was comparable to 65.1% reported in a hospital-based study.[11] This study also reports a higher adherence compared to the data from REACH registry (58.1%) as well as EUROASPIRE V (58%).[24] This profile was higher in a recent multicentric Indian study which reported only 52.5% adherence.[25] A recent Indian study reported the catastrophic expenditure due to hypertension in the poor households as 41%.[26] Self-medication in this study had higher odds of inadequate BP control showcasing the importance of regular healthcare engagement for effective hypertension control.[27]
BMI, Physical activity, Smoking cessation, and Mental health domains
The adherence to BMI goal (48.9%) in this study was much higher compared to the EUROASPIRE series of surveys (2001–2018).[24] However, the adherence was lower compared to 63.76% reported in a hospital-based study.[11] The patients receiving care solely at private facilities had greater odds of inadequate adherence to BMI goal, which may be due to socioeconomic factors influencing dietary and lifestyle habits.[28]
Recommended physical activity was reported by 64.9% in this study compared to only 39.2% by Sudevan et al.[11] Patients with reported dyslipidemia in this study had higher adherence to recommended physical activity, potentially due to the targeted health education by the healthcare professionals.
The compliance to smoking cessation was only 61.8% in this study compared to 93.8% reported by Sudevan et al.[11] However, the results are comparable to the EUROASPIRE V data, which reported that 55% of the patients with CAD were persistent smokers.[24] The higher odds of nonadherence to complete smoking cessation in patients with alcohol consumption highlights the intertwined nature of these risk behaviors.[29] Health insurance can help meet huge and unforeseen healthcare expenses and hence reduces the stress levels among patients.[30]
This study attempted to evaluate the adherence to secondary prevention strategies, and the outcomes could enable the development of additional strategies to mitigate the shortcomings of cardiac rehabilitation in India.
Limitations of this study include a potential positive bias due to health education initiatives within the study cohort, which may not reflect broader rural populations in India. The disruption of routine healthcare services during the COVID-19 pandemic and a subjective assessment of healthcare satisfaction may also influence the findings. The possibility of a social desirability bias and the inability to establish causality due to the cross-sectional nature of the study are additional considerations.
CONCLUSION
This study concludes that the adherence to secondary prevention strategies in the target rural setting was moderate and the diverse determinants underscore the need for comprehensive interventions from the government, private organizations, healthcare providers, families, and the patients themselves to improve nationwide management of CAD and reduce the global burden of this disease. Furthermore, longitudinal studies and qualitative methods can be used to assess the deep-rooted issues in terms of adherence.
Conflicts of interest
There are no conflicts of interest.
(a) Independent predictors of inadequate adherence to secondary prevention strategies-CAD medication domain (CAD diagnosis). (b) Independent predictors of inadequate adherence to secondary prevention strategies-CAD medication domain (Primary treatment for CAD). (c) Independent predictors of inadequate adherence to secondary prevention strategies-CAD medication domain (CABG done). (d) Independent predictors of inadequate adherence to secondary prevention strategies-CAD medication domain (Expenditure for CAD medication [INR]). (e) Independent predictors of inadequate adherence to secondary prevention strategies-CAD medication domain (Healthcare satisfaction [self-reported])
(a) Independent predictors of inadequate adherence to secondary prevention strategies-Blood Pressure control domain (Expenditure for CAD medication [INR]). (b) Independent predictors of inadequate adherence to secondary prevention strategies- Blood Pressure control domain (Healthcare facility for follow up)
Independent predictors of inadequate adherence to secondary prevention strategies-BMI domain (Healthcare facility for follow up)
Independent predictors of inadequate adherence to secondary prevention strategies-Physical activity domain (Dyslipidemia [self-reported])
Independent predictors of inadequate adherence to secondary prevention strategies-Smoking cessation domain (Alcohol consumption)
Independent predictors of inadequate adherence to secondary prevention strategies-Mental health domain (Health Insurance)
Acknowledgement
The authors would like to thank Dr Mathews Numpeli, former DPMO, NHM, Ernakulam; Mrs Sajana P N, ASHA Co-ordinator, NHM Ernakulam; Mrs Rani Ramakrishnan, former BPRO, CHC Kalady; the Medical Officers of the PHCs, as well as the ASHA workers of all five rural panchayats and Dr Sambhu Ramesh, Senior Research Fellow, BALM, Chennai for their support during the course of the study. Lastly, the authors would like to extend their gratitude to all the CAD patients who cooperated and participated in this study. This research was conducted as part of the MD post graduate thesis of the principal investigator at Amrita Institute of Medical Sciences, Kochi, Kerala, India.
Funding Statement
Nil.
REFERENCES
- 1.McKeown RE. The epidemiologic transition: Changing patterns of mortality and population dynamics. Am J Lifestyle Med. 2009;3(1 Suppl):19S–26S. doi: 10.1177/1559827609335350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Shu J, Jin W. Prioritizing non-communicable diseases in the post-pandemic era based on a comprehensive analysis of the GBD 2019 from 1990 to 2019. Sci Rep. 2023;13:13325. doi: 10.1038/s41598-023-40595-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Roth GA, Huffman MD, Moran AE, Feigin V, Mensah GA, Naghavi M, et al. Global and regional patterns in cardiovascular mortality from 1990 to 2013. Circulation. 2015;132:1667–78. doi: 10.1161/CIRCULATIONAHA.114.008720. [DOI] [PubMed] [Google Scholar]
- 4.Mendis S, Abegunde D, Yusuf S, Ebrahim S, Shaper G, Ghannem H, et al. WHO study on Prevention of REcurrences of Myocardial Infarction and StrokE (WHO-PREMISE) Bull World Health Organ. 2005;83:820–9. [PMC free article] [PubMed] [Google Scholar]
- 5.World Heart Federation Secondary Prevention CVD Roadmaps. Roadmap for secondary prevention of cardiovascular disease. Available from: https://world-heart-federation.org/cvd-roadmaps/whf-global-roadmaps/secondary-prevention/ . [Last accessed on 2024 Feb 22]
- 6.Sigamani A, Gupta R. Revisiting secondary prevention in coronary heart disease. Indian Heart J. 2022;74:431–40. doi: 10.1016/j.ihj.2022.11.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Mathes T, Pieper D, Antoine SL, Eikermann M. 50% adherence of patients suffering chronic conditions--Where is the evidence? Ger Med Sci. 2012;10:Doc16. doi: 10.3205/000167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Babu AS, Turk-Adawi K, Supervia M, Jimenez FL, Contractor A, Grace SL. Cardiac rehabilitation in India: Results from the International Council of Cardiovascular Prevention and Rehabilitation’s global audit of cardiac rehabilitation. Glob Heart. 2020;15:28. doi: 10.5334/gh.783. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Menon J, Joseph J, Thachil A, Attacheril TV, Banerjee A. Surveillance of noncommunicable diseases by community health workers in Kerala: The epidemiology of noncommunicable diseases in rural areas (ENDIRA) study. Glob Heart. 2014;9:409–17. doi: 10.1016/j.gheart.2014.07.003. [DOI] [PubMed] [Google Scholar]
- 10.Sheridan PJ, Crossman DC. Critical review of unstable angina and non-ST elevation myocardial infarction. Postgrad Med J. 2002;78:717–26. doi: 10.1136/pmj.78.926.717. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Sudevan R, Vasudevan D, Raj M, Thachathodiyl R, Vijayakumar M, Abdullakutty J, et al. Compliance to secondary prevention strategies for coronary artery disease: A hospital-based cross-sectional survey from Ernakulam, South India. BMJ Open. 2020;10:e037618. doi: 10.1136/bmjopen-2020-037618. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Mohandas NV, Vijayakumar K, Sreedevi A, George N, Menon J, Dinesh A, et al. Healthcare service utilisation among adults with coronary artery disease in rural Aluva, South India: A community-based cross-sectional study. BMJ Open. 2024;14:e084468. doi: 10.1136/bmjopen-2024-084468. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Smith SC, Benjamin EJ, Bonow RO, Braun LT, Creager MA, Franklin BA, et al. AHA/ACCF Secondary Prevention and Risk Reduction Therapy for Patients with Coronary and other Atherosclerotic Vascular Disease: 2011 update: a guideline from the American Heart Association and American College of Cardiology Foundation. Circulation. 2011;124:2458–73. doi: 10.1161/CIR.0b013e318235eb4d. [DOI] [PubMed] [Google Scholar]
- 14.Indu PS, Anilkumar TV, Vijayakumar K, Kumar KA, Sarma PS, Remadevi S, et al. Reliability and validity of PHQ-9 when administered by health workers for depression screening among women in primary care. Asian J Psychiatr. 2018;37:10–4. doi: 10.1016/j.ajp.2018.07.021. [DOI] [PubMed] [Google Scholar]
- 15.PDS. Available from: https://civilsupplieskerala.gov.in/index.php/cards . [Last accessed on 2024 Feb 22]
- 16.Baumgartner PC, Haynes RB, Hersberger KE, Arnet I. A Systematic review of medication adherence thresholds dependent of clinical outcomes. Front Pharmacol. 2018;9:1290. doi: 10.3389/fphar.2018.01290. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.A healthy lifestyle-WHO recommendations. Available from: https://www.who.int/europe/news-room/fact-sheets/item/a-healthy-lifestyle---who-recommendations . [Last accessed on 2024 Feb 22]
- 18.American Heart Association American Heart Association recommendations for physical activity in adults and kids. Available from: https://www.heart.org/en/healthy-living/fitness/fitness-basics/aha-recs-for-physical-activity-in-adults . [Last accessed on 2024 Feb 22]
- 19.Kroenke K, Spitzer RL, Williams JBW. The PHQ-9. J Gen Intern Med. 2001;16:606–13. doi: 10.1046/j.1525-1497.2001.016009606.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Khatib R, Marshall K, Silcock J, Forrest C, Hall AS. Adherence to coronary artery disease secondary prevention medicines: Exploring modifiable barriers. Open Heart. 2019;6:e000997. doi: 10.1136/openhrt-2018-000997. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Mathews R, Wang W, Kaltenbach LA, Thomas L, Shah RU, Ali M, et al. Hospital variation in adherence rates to secondary prevention medications and the implications on quality. Circulation. 2018;137:2128–38. doi: 10.1161/CIRCULATIONAHA.117.029160. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Alves JC, Law MR, Luz TCB. Prevalence and factors associated with out-of-pocket pharmaceutical expenditure among primary healthcare patients: Evidence from the Prover project. Value Health Reg Issues. 2022;30:83–90. doi: 10.1016/j.vhri.2022.01.006. [DOI] [PubMed] [Google Scholar]
- 23.Barbosa CD, Balp MM, Kulich K, Germain N, Rofail D. A literature review to explore the link between treatment satisfaction and adherence, compliance, and persistence. Patient Prefer Adherence. 2012;6:39–48. doi: 10.2147/PPA.S24752. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Kotseva K, De Backer G, De Bacquer D, Rydén L, Hoes A, Grobbee D, et al. Lifestyle and impact on cardiovascular risk factor control in coronary patients across 27 countries: Results from the European Society of Cardiology ESC-EORP EUROASPIRE V registry. Eur J Prev Cardiol. 2019;26:824–35. doi: 10.1177/2047487318825350. [DOI] [PubMed] [Google Scholar]
- 25.Varghese JS, Venkateshmurthy NS, Sudharsanan N, Jeemon P, Patel SA, Thirumurthy H, et al. Hypertension diagnosis, treatment, and control in India. JAMA Netw Open. 2023;6:e2339098. doi: 10.1001/jamanetworkopen.2023.39098. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Rajasulochana SR, Parthibane S, Saravanan E, Kumar M, Jeyanthi E, Gola A, et al. Out-of-Pocket Expenditure in Hypertension Related Care in India: Estimates from National Sample Survey 2017-18. Asia Pacific Journal of Health Management. 2023:18. [Google Scholar]
- 27.Mahmood S, Jalal Z, Hadi MA, Shah KU. Association between attendance at outpatient follow-up appointments and blood pressure control among patients with hypertension. BMC Cardiovasc Disord. 2020;20:458. doi: 10.1186/s12872-020-01741-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Rengma MS, Sen J, Mondal N. Socio-economic, demographic and lifestyle determinants of overweight and obesity among adults of Northeast India. Ethiop J Health Sci. 2015;25:199–208. doi: 10.4314/ejhs.v25i3.2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Rautela YS, Reddy BV, Singh AK, Gupta A. Smoking and alcoholism among adult population and its association with outlet density in a hilly area of North India. J Prev Med Hyg. 2019;60:E361–7. doi: 10.15167/2421-4248/jpmh2019.60.4.1341. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Kundu S. Health Insurance—An alternative healthcare financing mechanism in Rural India. Indian J Hum Dev. 2009;3:381–94. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
(a) Independent predictors of inadequate adherence to secondary prevention strategies-CAD medication domain (CAD diagnosis). (b) Independent predictors of inadequate adherence to secondary prevention strategies-CAD medication domain (Primary treatment for CAD). (c) Independent predictors of inadequate adherence to secondary prevention strategies-CAD medication domain (CABG done). (d) Independent predictors of inadequate adherence to secondary prevention strategies-CAD medication domain (Expenditure for CAD medication [INR]). (e) Independent predictors of inadequate adherence to secondary prevention strategies-CAD medication domain (Healthcare satisfaction [self-reported])
(a) Independent predictors of inadequate adherence to secondary prevention strategies-Blood Pressure control domain (Expenditure for CAD medication [INR]). (b) Independent predictors of inadequate adherence to secondary prevention strategies- Blood Pressure control domain (Healthcare facility for follow up)
Independent predictors of inadequate adherence to secondary prevention strategies-BMI domain (Healthcare facility for follow up)
Independent predictors of inadequate adherence to secondary prevention strategies-Physical activity domain (Dyslipidemia [self-reported])
Independent predictors of inadequate adherence to secondary prevention strategies-Smoking cessation domain (Alcohol consumption)
Independent predictors of inadequate adherence to secondary prevention strategies-Mental health domain (Health Insurance)