Medication Adherence Clubs: A potential solution to managing large numbers of stable patients with multiple chronic diseases in informal settlements

Tropical Medicine and International Health, Volume 20, No. 10, Year 2015

Interpretation

Objectives: To assess the care of hypertension, diabetes mellitus and/or HIV patients enrolled into Medication Adherence Clubs (MACs). Methods: Retrospective descriptive study was carried out using routinely collected programme data from a primary healthcare clinic at informal settlement in Nairobi, Kenya. All patients enrolled into MACs were selected for the study. MACs are nurse-facilitated mixed groups of 25-35 stable hypertension, diabetes mellitus and/or HIV patients who met quarterly to confirm their clinical stability, have brief health discussions and receive medication. Clinical officer reviewed MACs yearly, when a patient developed complications or no longer met stable criteria. Results: A total of 1432 patients were enrolled into 47 clubs with 109 sessions conducted between August 2013 and August 2014. There were 1020 (71%) HIV and 412 (29%) non-communicable disease patients. Among those with NCD, 352 (85%) had hypertension and 60 (15%) had DM, while 12 had HIV concurrent with hypertension. A total of 2208 consultations were offloaded from regular clinic. During MAC attendance, blood pressure, weight and laboratory testing were completed correctly in 98-99% of consultations. Only 43 (2%) consultations required referral for clinical officer review before their routine yearly appointment. Loss to follow-up from the MACs was 3.5%. Conclusions: This study demonstrates the feasibility and early efficacy of MACs for mixed chronic disease in a resource-limited setting. It supports burden reduction and flexibility of regular clinical review for stable patients. Further assessment regarding long-term outcomes of this model should be completed to increase confidence for deployment in similar contexts. This was a retrospective, descriptive study using routinely collected programme data in Kenya, an East African country with an approximate population of 39 million (Kenya population and housing census 2009 12. Nairobi, where Kibera is located, has an estimated population of three million (Kenya population and housing census 2009 12. The country is experiencing a rapidly growing burden of non‐communicable diseases (UNAIDS report‐2011 (13). The prevalence of diabetes mellitus in urban areas is almost twice the national average of 3.3% (First Kenya National forum on non‐communicable disease 2011 13. Overall, non‐communicable diseases cause over 50% of all hospital deaths and admissions (First Kenya National forum on NCD 2011 14. Of these deaths, 13% are due to cardiovascular diseases and 4% are due to complications of diabetes mellitus (First Kenya National Forum on NCD 2011 14. Of the 600 000 HIV‐infected people on ART, 75.4% have achieved viral suppression (Kenya AIDS indicator survey 2012 6), adding to the pool of chronic, stable patients. Currently, Kenya Ministry of Health facilities offer hypertension and diabetes mellitus treatment only at the subcounty and county referral hospital levels and not at health centres or dispensaries. Most patients have to pay user fees to access non‐communicable disease services and medications. Despite decentralisation of HIV treatment where antiretroviral drugs are free, the majority of patients still pay out of pocket for treatment of opportunistic infections and laboratory tests other than those for routine HIV monitoring. The study site was Kibera South Health Centre with an estimated catchment population of 88 000 (MSF Belgium Kibera; Annual report 2013 15). MSF, in collaboration with the Ministry of Health, is providing a comprehensive integrated primary health package that includes HIV, TB, non‐communicable disease, general outpatient department, nutrition, maternal child health and sexual‐ and gender‐based violence care at no cost to the client. There are 5500 active HIV patients (4700 on antiretroviral drugs, 80% with suppressed viral loads), 2200 hypertension and diabetes mellitus patients and approximately 200 patients who are comorbid with HIV and hypertension or diabetes mellitus (MSF Belgium Kibera; Annual report 2013 15). The clinics are currently grappling with the challenge of large patient numbers requiring follow‐up, the majority of whom are stable. On average, there are approximately 3000 HIV and 1000 hypertension and diabetes mellitus combined consultations per month. There are approximately 100 new HIV and 70 new hypertension and diabetes mellitus patients per month. The same clinical officers and nurses who care for both HIV and non‐communicable disease patients also carry out outpatient department consultations averaging a total of 8000 consultations per month (MSF Belgium Kibera; Annual report 2013 15). This translates to approximately 45–50 consultations per day per clinician. The effect of this workload is an unacceptably long waiting time for patients at the clinics, averaging four to six hours (MSF Belgium Kibera; patient satisfaction survey 2012 16. The current loss to follow‐up rate for hypertension, diabetes mellitus and HIV cohorts, which is between 30 and 40% 7, (MSF Belgium Kibera; Annual report 2013 15), is possibly related to the long wait times and no available care on weekends. HIV and non‐communicable disease patients were informed about the option of joining MACs through daily health talks in waiting bays, patient empowerment meetings and posters in the clinic. Patients were screened by clinicians during routine follow–up and if they met the inclusion criteria (see below) were offered the option of attending a MAC. Some patients proposed to join a MAC independently of clinician inquiry after sensitisation. Patients were provided signed informed consent before enrolment into the groups, acknowledging that there would be hypertension, diabetes mellitus and HIV patients in the MACs, but that diagnosis disclosure was voluntary. Those declining to join MACs, yet met entrance criteria, were informed that they would continue to be seen as usual through spaced clinic appointments. The MACs were conducted on Wednesday, Thursday and Saturday afternoons between 3 p.m. and 5 p.m. Timing was structured to provide maximum flexibility for patients to attend. All patients who were enrolled into MACs between August 2013 and 31 August 2014 were included in the study. Inclusion criteria for the MACs for hypertension and diabetes mellitus patients were as follows: ≥25 years old, >6 months on medications, having blood pressure < 150/100 and/or HBA1c <8%; for HIV patients, the inclusion criteria were as follows: ≥25 years old, >1 year on ARVs, CD4 > 200, previous viral load was undetectable and not in WHO Stage 3 or 4 active disease. Outcome measures included the number and proportion of: (i) non‐communicable disease consultations where the patients’ blood pressure and/or blood sugar were below MACs threshold, (BP <150/100 mmHg, Hba1c <8.0%, respectively) whose weight was recorded and routine blood workup was requested as per the non‐communicable diseases protocol; (ii) HIV consultations where patients’ weight and blood pressure were recorded and routine blood workup was requested as per the HIV protocol; (iii) non‐communicable disease and HIV consultations where the patients were referred from MACs for clinical consultation for NCD, HIV or non‐NCD/HIV‐related problems (annual review by the clinical officer was not included as a referral back to clinic); and (iv) retention at months three, six and twelve. Lost to follow‐up was determined 3 months after a patient failed to attend a MAC session, pick up their medication and was not reported in any of the following outcomes: referred to regular clinic, transferred out or died. Patients’ demographic and baseline clinical characteristics were collected from the MACs register. Double data entry and validation was performed using EpiData Entry software (version 3.1, EpiData Association, Odense, Denmark). HIV variables were extracted from a FUCHIA database (version 1.7.1 Epicentre, Paris, France) and were merged with those from an EpiData database, which served as the non‐communicable diseases database. File reviews were performed for patients whose variables were missing on the electronic databases. Descriptive analysis was performed using EpiData Analysis software (version 2.2.2.182, EpiData Association, Odense, Denmark). Ethics approval was received from Kenya AMREF Ethics and Scientific Review Committee. The study met the Médecins Sans Frontières (MSF) Ethics Review Board (Geneva, Switzerland) approved criteria for studies of routinely collected data and was also approved by the Ethics Advisory Group of the International Union Against Tuberculosis and Lung Disease, Paris, France.

AI Digest

Study Justification:

– The study aimed to assess the care of patients with hypertension, diabetes mellitus, and/or HIV enrolled in Medication Adherence Clubs (MACs).
– The study was conducted in a resource-limited setting in Nairobi, Kenya, where there is a growing burden of non-communicable diseases and a large number of stable patients requiring follow-up.
– The study aimed to evaluate the feasibility and efficacy of MACs as a potential solution for managing large numbers of stable patients with multiple chronic diseases in informal settlements.

Study Highlights:

– A total of 1432 patients were enrolled in 47 MACs between August 2013 and August 2014.
– The MACs consisted of mixed groups of stable hypertension, diabetes mellitus, and/or HIV patients who met quarterly for clinical stability confirmation, health discussions, and medication distribution.
– During MAC attendance, blood pressure, weight, and laboratory testing were completed correctly in 98-99% of consultations.
– Only 43 (2%) consultations required referral for clinical officer review before their routine yearly appointment.
– Loss to follow-up from the MACs was 3.5%.

Recommendations for Lay Reader and Policy Maker:

– The study demonstrates the feasibility and early efficacy of MACs for managing mixed chronic diseases in resource-limited settings.
– MACs can help reduce the burden on regular clinics and provide flexibility for stable patients in terms of clinical review.
– Further assessment of the long-term outcomes of MACs is recommended to increase confidence in deploying this model in similar contexts.

Key Role Players Needed to Address Recommendations:

– Healthcare providers (nurses, clinical officers) to facilitate MACs and provide necessary care and support.
– Ministry of Health officials to support the implementation and scaling up of MACs in healthcare facilities.
– Community health workers to assist in patient education and engagement.
– Patient support groups and community organizations to promote awareness and participation in MACs.

Cost Items to Include in Planning Recommendations:

– Training and capacity building for healthcare providers on MAC implementation and management.
– Procurement and maintenance of necessary equipment and supplies for MACs.
– Patient education and awareness campaigns.
– Monitoring and evaluation activities to assess the impact and effectiveness of MACs.
– Integration of MACs into existing healthcare systems and infrastructure.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong, but there are some areas for improvement. The study design is retrospective and descriptive, which limits the ability to establish causality or make strong conclusions. However, the study uses routinely collected program data, which adds to the strength of the evidence. To improve the evidence, future studies could consider using a prospective design with a control group to compare outcomes between MACs and standard care. Additionally, including long-term follow-up data would provide more confidence in the efficacy of MACs. Overall, the study demonstrates the feasibility and early efficacy of MACs in a resource-limited setting, but further research is needed to fully assess the long-term outcomes and potential benefits of this model.

Abstract

This was a retrospective, descriptive study using routinely collected programme data in Kenya, an East African country with an approximate population of 39 million (Kenya population and housing census 2009 12. Nairobi, where Kibera is located, has an estimated population of three million (Kenya population and housing census 2009 12. The country is experiencing a rapidly growing burden of non‐communicable diseases (UNAIDS report‐2011 (13). The prevalence of diabetes mellitus in urban areas is almost twice the national average of 3.3% (First Kenya National forum on non‐communicable disease 2011 13. Overall, non‐communicable diseases cause over 50% of all hospital deaths and admissions (First Kenya National forum on NCD 2011 14. Of these deaths, 13% are due to cardiovascular diseases and 4% are due to complications of diabetes mellitus (First Kenya National Forum on NCD 2011 14. Of the 600 000 HIV‐infected people on ART, 75.4% have achieved viral suppression (Kenya AIDS indicator survey 2012 6), adding to the pool of chronic, stable patients. Currently, Kenya Ministry of Health facilities offer hypertension and diabetes mellitus treatment only at the subcounty and county referral hospital levels and not at health centres or dispensaries. Most patients have to pay user fees to access non‐communicable disease services and medications. Despite decentralisation of HIV treatment where antiretroviral drugs are free, the majority of patients still pay out of pocket for treatment of opportunistic infections and laboratory tests other than those for routine HIV monitoring. The study site was Kibera South Health Centre with an estimated catchment population of 88 000 (MSF Belgium Kibera; Annual report 2013 15). MSF, in collaboration with the Ministry of Health, is providing a comprehensive integrated primary health package that includes HIV, TB, non‐communicable disease, general outpatient department, nutrition, maternal child health and sexual‐ and gender‐based violence care at no cost to the client. There are 5500 active HIV patients (4700 on antiretroviral drugs, 80% with suppressed viral loads), 2200 hypertension and diabetes mellitus patients and approximately 200 patients who are comorbid with HIV and hypertension or diabetes mellitus (MSF Belgium Kibera; Annual report 2013 15). The clinics are currently grappling with the challenge of large patient numbers requiring follow‐up, the majority of whom are stable. On average, there are approximately 3000 HIV and 1000 hypertension and diabetes mellitus combined consultations per month. There are approximately 100 new HIV and 70 new hypertension and diabetes mellitus patients per month. The same clinical officers and nurses who care for both HIV and non‐communicable disease patients also carry out outpatient department consultations averaging a total of 8000 consultations per month (MSF Belgium Kibera; Annual report 2013 15). This translates to approximately 45–50 consultations per day per clinician. The effect of this workload is an unacceptably long waiting time for patients at the clinics, averaging four to six hours (MSF Belgium Kibera; patient satisfaction survey 2012 16. The current loss to follow‐up rate for hypertension, diabetes mellitus and HIV cohorts, which is between 30 and 40% 7, (MSF Belgium Kibera; Annual report 2013 15), is possibly related to the long wait times and no available care on weekends. HIV and non‐communicable disease patients were informed about the option of joining MACs through daily health talks in waiting bays, patient empowerment meetings and posters in the clinic. Patients were screened by clinicians during routine follow–up and if they met the inclusion criteria (see below) were offered the option of attending a MAC. Some patients proposed to join a MAC independently of clinician inquiry after sensitisation. Patients were provided signed informed consent before enrolment into the groups, acknowledging that there would be hypertension, diabetes mellitus and HIV patients in the MACs, but that diagnosis disclosure was voluntary. Those declining to join MACs, yet met entrance criteria, were informed that they would continue to be seen as usual through spaced clinic appointments. The MACs were conducted on Wednesday, Thursday and Saturday afternoons between 3 p.m. and 5 p.m. Timing was structured to provide maximum flexibility for patients to attend. All patients who were enrolled into MACs between August 2013 and 31 August 2014 were included in the study. Inclusion criteria for the MACs for hypertension and diabetes mellitus patients were as follows: ≥25 years old, >6 months on medications, having blood pressure 1 year on ARVs, CD4 > 200, previous viral load was undetectable and not in WHO Stage 3 or 4 active disease. Outcome measures included the number and proportion of: (i) non‐communicable disease consultations where the patients’ blood pressure and/or blood sugar were below MACs threshold, (BP <150/100 mmHg, Hba1c <8.0%, respectively) whose weight was recorded and routine blood workup was requested as per the non‐communicable diseases protocol; (ii) HIV consultations where patients’ weight and blood pressure were recorded and routine blood workup was requested as per the HIV protocol; (iii) non‐communicable disease and HIV consultations where the patients were referred from MACs for clinical consultation for NCD, HIV or non‐NCD/HIV‐related problems (annual review by the clinical officer was not included as a referral back to clinic); and (iv) retention at months three, six and twelve. Lost to follow‐up was determined 3 months after a patient failed to attend a MAC session, pick up their medication and was not reported in any of the following outcomes: referred to regular clinic, transferred out or died. Patients’ demographic and baseline clinical characteristics were collected from the MACs register. Double data entry and validation was performed using EpiData Entry software (version 3.1, EpiData Association, Odense, Denmark). HIV variables were extracted from a FUCHIA database (version 1.7.1 Epicentre, Paris, France) and were merged with those from an EpiData database, which served as the non‐communicable diseases database. File reviews were performed for patients whose variables were missing on the electronic databases. Descriptive analysis was performed using EpiData Analysis software (version 2.2.2.182, EpiData Association, Odense, Denmark). Ethics approval was received from Kenya AMREF Ethics and Scientific Review Committee. The study met the Médecins Sans Frontières (MSF) Ethics Review Board (Geneva, Switzerland) approved criteria for studies of routinely collected data and was also approved by the Ethics Advisory Group of the International Union Against Tuberculosis and Lung Disease, Paris, France.

Materials

N/A

Innovations

One potential innovation to improve access to maternal health is the implementation of Medication Adherence Clubs (MACs). MACs are nurse-facilitated mixed groups of stable patients with chronic diseases such as hypertension, diabetes mellitus, and HIV. These groups meet quarterly to confirm their clinical stability, have brief health discussions, and receive medication. This approach has been shown to be feasible and effective in a resource-limited setting, reducing the burden on healthcare facilities and improving patient outcomes. Further assessment is needed to determine the long-term outcomes of this model and its potential for deployment in similar contexts.

AI Innovations Description

The recommendation described in the study is the use of Medication Adherence Clubs (MACs) as a potential solution to managing large numbers of stable patients with multiple chronic diseases in informal settlements. MACs are nurse-facilitated mixed groups of 25-35 stable hypertension, diabetes mellitus, and/or HIV patients who meet quarterly to confirm their clinical stability, have brief health discussions, and receive medication. The study conducted in Nairobi, Kenya, demonstrated the feasibility and early efficacy of MACs for mixed chronic diseases in a resource-limited setting. The results showed that during MAC attendance, blood pressure, weight, and laboratory testing were completed correctly in 98-99% of consultations, and only 2% of consultations required referral for clinical officer review before their routine yearly appointment. The loss to follow-up from the MACs was 3.5%.

This innovation can improve access to maternal health by adapting the MAC model specifically for pregnant women. Pregnant women with chronic diseases such as hypertension, diabetes mellitus, or HIV can be enrolled in Maternal Medication Adherence Clubs (MMACs). Similar to MACs, MMACs can be nurse-facilitated groups where pregnant women meet regularly to confirm their clinical stability, receive medication, and have discussions about maternal health. MMACs can provide a supportive environment for pregnant women with chronic diseases, ensuring that they adhere to their medication regimen and receive necessary prenatal care. This innovation can help reduce the burden on healthcare facilities, improve patient outcomes, and increase access to maternal health services for women in resource-limited settings. Further assessment and research should be conducted to evaluate the long-term outcomes and effectiveness of MMACs in improving access to maternal health.

AI Innovations Methodology

Based on the provided information, the innovation of Medication Adherence Clubs (MACs) is being used to manage large numbers of stable patients with multiple chronic diseases in informal settlements. MACs are nurse-facilitated mixed groups of stable hypertension, diabetes mellitus, and/or HIV patients who meet quarterly to confirm their clinical stability, have brief health discussions, and receive medication. The study conducted in Nairobi, Kenya, demonstrated the feasibility and early efficacy of MACs for mixed chronic diseases in a resource-limited setting.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the objectives: Clearly state the specific goals of the simulation study, such as assessing the potential impact of MACs on improving access to maternal health services.

2. Identify the variables: Determine the key variables that will be measured and analyzed in the simulation study. These may include the number of maternal health consultations, wait times for patients, patient satisfaction, and retention rates.

3. Collect baseline data: Gather relevant data on the current state of maternal health services, including the number of consultations, wait times, and patient satisfaction levels. This data will serve as a baseline for comparison with the simulated scenarios.

4. Develop simulation scenarios: Create different scenarios that simulate the implementation of MACs in maternal health services. These scenarios should consider factors such as the number of MACs, the size of each club, and the frequency of meetings.

5. Define outcome measures: Determine the specific outcome measures that will be used to evaluate the impact of the simulation scenarios. For example, this could include the reduction in wait times for maternal health consultations or the increase in patient satisfaction.

6. Run the simulation: Use appropriate simulation software or tools to run the scenarios and collect data on the defined outcome measures. The simulation should take into account factors such as patient flow, resource allocation, and the impact of MACs on the overall healthcare system.

7. Analyze the results: Compare the outcomes of the different simulation scenarios with the baseline data to assess the impact of MACs on improving access to maternal health services. This analysis should consider both quantitative measures (e.g., wait times, number of consultations) and qualitative measures (e.g., patient satisfaction).

8. Draw conclusions and make recommendations: Based on the analysis of the simulation results, draw conclusions about the potential benefits of implementing MACs in maternal health services. Make recommendations for further implementation and improvement based on the findings.

It is important to note that this methodology is a general framework and may need to be adapted based on the specific context and available data. Additionally, the simulation results should be interpreted with caution and validated through real-world implementation and evaluation.

Author

Dima Health

Statistics:

Citations: 63

Identifiers:

DOI: 10.1111/tmi.12539

ISSN: 13602276

Research Areas:

Food Security, Health System and Policy, Infectious Diseases, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Violence and Injury

Study Countries:

Kenya

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Quantitative