Abstract
Background
The high incidence of Drug-Related Problems (DRPs), including medication adherence among patients with chronic diseases, especially during no direct monitoring by health workers, becomes a challenge to therapy success. Home Medication Review (HMR) is an alternative solution in primary healthcare services to avoid further complications.
Objective
This study aimed to analyze DRPs, their affecting factors, and DRP interventions for patients with chronic diseases in primary health centers (Puskesmas) through HMR.
Methods
A quantitative, cross-sectional observational study using purposive sampling was conducted in several Puskesmas in Yogyakarta from February to May 2023. The collaborative HMR involved healthcare providers at each Puskesmas. DRPs were assessed based on observations and semi-structured interviews. The DRP classification followed PCNE V9.1, and medication adherence was measured using both pill counts and self-report via the MARS-10 questionnaire. Data were analyzed using SPSS 25.0 to perform logistic regression with a 95% confidence level.
Results
A total of 544 patients, comprising 269 adults and 275 older patients, were involved. Older patients experienced more DRPs than the adults (94.2% vs 84.8%). The most DRP experienced by both age groups was ineffective therapies, with the two most frequent causes being related to patient factors. Among the adults, hypertension comorbidity, number of medications, and BMI factors were associated with DRPs (p < 0.05). In contrast, no factors correlated with DRPs in the older patients. There was fair agreement between pill count and MARS-10 regarding medication adherence for both adults and older patients (kappa coefficients of 0.298 and 0.355, respectively).
Conclusion
Patients in primary health facilities with hypertension and using at least three medications have over a three-fold increased risk of experiencing DRPs. Healthcare providers, including pharmacists, nurses, and physicians, should collaborate to identify medication-related issues and provide personalized advice and management plans to enhance medication adherence. This study highlights the need for a standardized, structured HMR program, not merely as a patient home visit but also to better control chronic diseases.
Keywords: medication reviews, chronic diseases, drug-related problems, primary health center, interprofessional collaboration, Indonesia
Background
The global prevalence of chronic diseases, including Indonesia, such as hypertension and type-2 diabetes mellitus (T2DM), continues to increase. In addition, low levels of medication adherence and high incidence rates of other Drug-Related Problems (DRPs) among patients with chronic diseases increase the risk of serious complications, thus deteriorating health conditions (Govindani et al., 2024; Kengne et al., 2024). Not only does the need for long-term treatment exist, but polypharmacy also becomes a risk factor for DRPs associated with drug interactions and adverse drug reactions (ADRs) (Carson & Kairuz, 2018; Davies et al., 2020).
Strategic efforts have been made by community pharmacists in Indonesia, such as Home Pharmacy Care, following the 2016 Pharmacy Service Standards in Primary Health Centers released by Indonesia’s Ministry of Health to minimize DRPs and optimize therapy. However, the standard operating procedures for such activities remain unvalidated and only follow a general national format. Meanwhile, the home medication review (HMR) service is a model of interprofessional collaborative practice that comprises various administrative and clinical stages, including patient involvement during treatment-related consultations and their health conditions at home (Patounas et al., 2021).
In developed countries, HMR has been widely implemented and has been proven to improve medication adherence and reduce DRP incidence (Bulajeva et al., 2014). Studies show that HMR contributes to improved chronic disease management by helping patients understand appropriate drug use, reducing side effects, and training how to store and dispose of medications safely to reduce environmental impact (Blenkinsopp et al., 2012; Chandrasekhar et al., 2019; Gillespie et al., 2009; Gudi et al., 2019). HMR plays a crucial role in optimizing the use of patients’ medications at home, aiming to minimize the incidence of Drug-Related Problems (DRPs) while optimizing treatment effectiveness. However, DRPs at primary healthcare facilities in Indonesia have not been thoroughly documented, leading some community-based programs to fail to address the actual medication issues (Rahmawati et al., 2017; Zairina et al., 2024).
HMR implementation in developing countries remains limited due to a lack of interprofessional collaboration and resources. Research on community-based health services in Puskesmas in Indonesia is primarily conducted by academic researchers, often without involving the healthcare providers. Consequently, these studies cannot lead to sustainable programs (Halisa et al., 2023; Padmasari et al., 2021; Patty & Parwati, 2025). The urgency of HMR is even more evident given the challenges faced by patients with chronic diseases, including low medication adherence, DRP incidence, increased complication risks, and inadequate home medication management (Shahrami et al., 2022; Yulistiani et al., 2023). Therefore, this research aimed to analyze DRPs, including medication non-adherence and its affecting factors, in patients with chronic diseases through HMR.
Methods
Study Design
A quantitative, cross-sectional observational study was used to measure independent variables comprising patients’ sociodemographic and clinical characteristics, and dependent variables comprising DRP incidence and medication adherence levels at a specific time point with no follow-up. The incidence of Drug-Related Problems (DRPs) identified by pharmacists during the Home Medication Review (HMR) program was classified using the Pharmaceutical Care Network Europe (PCNE) version 9.1 (Pharmaceutical Care Network Europe Association, 2020). This involved coding the DRPs as P1, P2, and P3, while the underlying causes were coded as C1-C9. In accordance with the PCNE guidelines, planned interventions for the identified DRPs were also determined. If the intervention involved medication adherence counseling or medication management at home, the pharmacist conducted it. However, if it required a decision from a General Practitioner (GP), such as changes in dosage, medication replacement, or discontinuation of a medication, the pharmacist communicated these needs to the GP.
Sample/Participants
Patients with such chronic diseases as hypertension, T2DM, or dyslipidemia were recruited from three Puskesmas located in the Special Region of Yogyakarta (DIY) Province, including Puskesmas Jetis in Yogyakarta City, Puskesmas Bambanglipuro in Bantul Regency, and Puskesmas Ngemplak I in Sleman Regency. Patient recruitment was conducted using a purposive sampling technique from February to May 2023, as patients visited the Primary Health Center. The inclusion criteria were patients aged at least 18 years who were receiving at least two types of medications, excluding vitamins/multivitamins. Patients referred to the hospital during their last visit to Puskesmas were excluded from the study.
Instruments
Three instruments were used: 1) a demographic questionnaire and semi-structured interviews about patients’ chronic diseases, medications, and drug storage and disposal; 2) DRP classification from PCNE version 9.1 (Pharmaceutical Care Network Europe Association, 2020). This latest version is unavailable in Indonesian but has been internationally validated by expert groups. Therefore, version 9.1 was used with translation from a trusted language institution by referring to the Indonesian version 9.0 for the same domain. PCNE classification version 9.1 includes three actual or potential domains of medication problems (code P1-P3). Achievement of therapy targets was assessed according to patients’ chronic disease diagnosis. The chronic diseases commonly found in Puskesmas include T2DM, hypertension, dyslipidemia, hyperuricemia, and asthma. The treatment effectiveness assessment was based on each patient’s chronic disease diagnosis, such as hypertension, T2DM, and dyslipidemia, following the national guidelines for disease management; and 3) MARS-10 (Medication Adherence Report Scale) (Thompson et al., 2000) for assessing medication adherence. This questionnaire includes 10 questions describing patients’ medication adherence.
This instrument has been translated into Indonesian using the forward-backward translation method. Translation was conducted by two nationally accredited English language institutions: the Language Development Center of the University of Muhammadiyah Purwokerto (LDC UMP) and English Language Training International (ELTI) Purwokerto.
The score is divided into two categories: patients with a perfect score (=10) and those with a score <10. The user questionnaire was tested for content validity using Gregory’s index of agreement (0.83), for internal validity using Pearson's Product-Moment Correlation (0.355), and for instrument reliability using Cronbach’s alpha (0.747) (Wibowo et al., 2021). The researcher has obtained permission from him through correspondence via email. The total questionnaire score was then used to assess medication adherence. Patients were deemed non-adherent if their score was <10 and adherent if it was 10. This study also used the pill count method to confirm patients’ medication adherence. This method calculates patients’ remaining home medications to obtain % adherence during therapy or within a certain period:
Patients were deemed adherent if the percentage was ≥80% and non-adherent if the score was <80%. The Kappa (k) coefficient was used to assess the level of agreement between the adherence measures. The strength of the agreement was evaluated using a commonly used classification scale for kappa coefficients (Wan et al., 2015).
Data Collection
The data collection began upon receiving ethical approval from the Ethics Committee. Patients were given explanations about the research, including its benefits, and those willing to participate completed and signed the informed consent form. The secondary data source was patients’ medical records from Puskesmas, while the primary data were from questionnaires and semi-structured interviews. The primary data comprised patients’ demographics recorded on ID cards, including name, place/date of birth, gender, address, marital status, and occupation. Education level, distance from the patients’ homes to the Puskesmas, use of non-prescription drugs, herbal medicines, or supplements, and drug disposal method were obtained through interviews. How patients/families stored drugs at home was also observed, and the suitability was assessed with the national guidelines from Indonesia’s Ministry of Health. The secondary data from medical records comprised the disease diagnosis, duration of chronic illness, drug types and doses, number of prescribed drugs, and comorbidities. For the medication adherence data, patients completed the MARS-10 form with the researchers present. The medication adherence data also used pill counts to calculate the remaining home medications based on patients’ last GP prescriptions in Puskesmas. The drug disposal method used by patients/families was identified through direct interviews, and its suitability was assessed against the literature from Indonesia’s Ministry of Health.
Data Analysis
The data analysis used IBM SPSS version 25.0. The chi-square test was used to analyze the factors associated with the medication adherence levels and DRP incidence. If the test requirement was not fulfilled, a Fisher’s test was employed. Additionally, a multivariate logistic regression was conducted to analyze all independent variables associated with DRPs in both adult and older patients. The test results were deemed significant if the p-value was <0.05.
Ethical Considerations
The participants were recruited when patients visited Puskesmas for follow-up. After receiving explanations about the research and agreeing to participate, they signed the informed consent. The research team then conducted HMR at patients’ homes on an agreed schedule. This research received ethics approval from the research ethics committee of Sekolah Tinggi Ilmu Kesehatan Akbidyo Yogyakarta No. e-KEPK/STIKesAkbidyo/22/III/2023.
Results
This research involved three Puskesmas (Figure 1) that represent primary health centers providing health services through community activities for patients living in the areas around the Puskesmas in Yogyakarta Province.
Figure 1.
Primary Health Centers in Yogyakarta Province as the research location for HMR
This first study involved 544 patients from primary health facilities in Indonesia, with nearly equal numbers of adults and older patients. The adult age groups were 26-35 years and 36-59 years, with 95.5% (257 out of 269) falling within these age ranges. In the older group, 230 patients (83.6%; 230 out of 275) were aged 60-74 years. The patients’ characteristics are described in Table 1.
Table 1.
Sociodemographic and clinical characteristics of participants
| Characteristics | Category | Adults (n = 269) | Older Individuals (n = 275) |
|---|---|---|---|
| n (%) | n (%) | ||
| Gender | Male | 65 (24) | 73 (27) |
| Female | 204 (76) | 202 (73) | |
| BMI (Body Mass Index) | ≥27 kg/m2 | 72 (27) | 62 (23) |
| <27 kg/m2 | 197 (73) | 213 (77) | |
| Residency status | Alone | 5 (2) | 34 (12) |
| With family | 264 (98) | 241 (88) | |
| Education level | Minimum of high school | 97 (36) | 50 (18) |
| No schooling–junior high school | 172 (64) | 225 (82) | |
| Occupation | Employed | 109 (41) | 78 (28) |
| Unemployed | 160 (59) | 197 (72) | |
| Marital status | Married | 258 (96) | 182 (66) |
| Single/Divorced | 11 (4) | 93 (34) | |
| Distance from home to Puskesmas (km) | ≤5 | 222 (83) | 240 (87) |
| >5 | 47 (17) | 35 (13) | |
| Type of diagnosis | Hypertension | 197 (73) | 191 (69) |
| Diabetes mellitus (DM) | 26 (10) | 9 (3) | |
| Dyslipidemia | 8 (3) | 6 (2) | |
| Hypertension + DM | 23 (9) | 32 (12) | |
| Hypertension + Dyslipidemia | 8 (3) | 19 (7) | |
| Hypertension + DM + Dyslipidemia | 3 (1) | 2 (1) | |
| Hypertension + Asthma | 3 (1) | 2 (1) | |
| Hypertension + DM + Hyperuricemia | 1 (0) | 14 (5) | |
| Duration of chronic illness (years) | ≤3 | 109 (41) | 89 (32) |
| >3 | 160 (59) | 186 (68) | |
| Number of drugs | 2 | 122 (45) | 129 (47) |
| >2 | 147 (55) | 146 (53) | |
| Use of OTC or herbal medicines/supplements | Yes | 29 (11) | 10 (4) |
| No | 240 (89) | 265 (96) |
Approximately three-quarters of the patients are female, with the body mass index outside an overweight or obese category, live with their families in a relatively close distance to Puskesmas, have hypertension comorbidity, and take no drugs other than those prescribed by GPs. Low education levels, being unemployed, disease duration, and use of more than two drug types were found in both age groups, particularly in older patients. Only the marital status factor was found to be higher among adult patients (95.9% vs 66%). HMR aims to optimize the use and management of drugs when patients are at home. When patients are not supervised and accompanied directly by health workers, they are frequently non-adherent to medications, forget, or even neglect how to manage, store, or dispose of medications. The DRPs identified during the HMR implementation are shown in Table 2.
Table 2.
Drug-related problems and classification by PCNE version 9.1 during HMR
| Code V9.1 | Secondary Domain | Adults(n = 269) | Older Individuals(n = 275) |
|---|---|---|---|
| P1.1 | No effect of drug treatment | 85 (31.6) | 71 (25.8) |
| P1.2 | Effect of drug treatment not optimal | 99 (36.8) | 81 (29.5) |
| C3.1 | Drug dose too low | 5 (1.9) | 10 (3.6) |
| C7.1 | Patients take less medications than prescribed or do not take medications at all | 114 (42.4) | 97 (35.3) |
| C7.2 | Patients use more medications than prescribed | 12 (4.5) | 7 (2.5) |
| C7.3 | Patients abuse drugs (unregulated overuse) | 0 (0) | 2 (0.7) |
| C7.6 | Patients store medications inappropriately | 166 (61.7) | 197 (71.6) |
Note. This study did not find DRPs related to untreated symptoms or indications (P1.3), possible adverse drug events (P2.1), unnecessary drug treatments (P3.1), or unclear problems or complaints (P3.2).
Most of the DRPs in patients at home were the non-optimal treatment effects (P1.2) among adult and older patients. This was relevant to the analysis of DRP causes, which was patients using less drugs than prescribed or taking no medications at all (C7.1), and this also occurred in all age groups. In addition, more than half of the patients stored drugs not according to the guidelines from Indonesia’s Ministry of Health. Patients’ demographic and clinical characteristics that affected DRP incidence were then further analyzed, with the results shown in Table 3 and Table 4 for adult and older patients, respectively.
Table 3.
Factors correlated with DRP incidence in adult patients
| Characteristics | Category | Total (%) | DRPs | p-value | |
|---|---|---|---|---|---|
| Yes | No | ||||
| Age (years) | 26 - 35 | 12 (4.5) | 11 | 1 | 0.699a |
| 36 - 59 | 257 (95.5) | 217 | 40 | ||
| Gender | Male | 65 (24.2) | 54 | 11 | 0.665 |
| Female | 204 (75.8) | 174 | 30 | ||
| BMI | Obese | 72 (26.8) | 68 | 4 | 0.008a |
| Not Obese | 197 (73.2) | 160 | 37 | ||
| Residency status | Alone | 5 (1.9) | 3 | 2 | 0.168a |
| With Family | 264 (98.1) | 225 | 39 | ||
| Education level | High | 97 (36.1) | 86 | 11 | 0.181 |
| Low | 172 (63.9) | 142 | 30 | ||
| Occupation | Employed | 109 (40.5) | 92 | 17 | 0.894 |
| Unemployed | 160 (59.5) | 136 | 24 | ||
| Marital status | Married | 258 (95.9) | 218 | 40 | > 0.999a |
| Single/Divorced | 11 (4.1) | 10 | 1 | ||
| Distance to Puskesmas (km) | ≤ 5 | 222 (82.5) | 188 | 34 | 0.942 |
| > 5 | 47 (17.5) | 40 | 7 | ||
| Diagnosed with hypertension | Yes | 235 (87.4) | 210 | 25 | <0.001 |
| No | 34 (12.6) | 18 | 16 | ||
| Duration of chronic illness (years) | ≤ 3 | 109 (40.5) | 95 | 14 | 0.367 |
| > 3 | 160 (59.5) | 133 | 27 | ||
| Number of drugs | 2 | 122 (45.4) | 97 | 25 | 0.029 |
| > 2 | 147 (54.6) | 131 | 16 | ||
| Use of OTC or Herbal Medicines/Supplements | Yes | 29 (10.8) | 27 | 2 | 0.274a |
| No | 240 (89.2) | 201 | 39 | ||
Note.
Fisher’s test
Table 4.
Factors correlated with DRP incidence in older patients
| Characteristic | Category | Total (%) | DRPs | p-value | |
|---|---|---|---|---|---|
| Yes | No | ||||
| Age (years) | 60-74 | 230 (84) | 215 | 15 | 0.484a |
| 75-90 | 45 (16) | 44 | 1 | ||
| Gender | Male | 73 (27) | 66 | 7 | 0.142 |
| Female | 202 (73) | 193 | 9 | ||
| BMI (Body Mass Index) | Obese | 62 (23) | 60 | 2 | 0.537a |
| Not Obese | 213 (77) | 199 | 14 | ||
| Living alone | Yes | 34 (12) | 33 | 1 | 0.702a |
| No | 241 (88) | 226 | 15 | ||
| Education level | High | 50 (18) | 47 | 3 | > 0.999a |
| Low | 225 (82) | 212 | 13 | ||
| Occupation | Employed | 78 (28) | 71 | 7 | 0.164 |
| Unemployed | 197 (72) | 188 | 9 | ||
| Marital status | Married | 182 (66) | 170 | 12 | 0.442a |
| Single/Divorced | 93 (34) | 89 | 4 | ||
| Distance to Puskesmas (km) | ≤ 5 | 240 (87) | 225 | 15 | 0.702a |
| > 5 | 35 (13) | 34 | 1 | ||
| Duration of chronic illness (years) | < 3 | 89 (32) | 81 | 8 | 0.120 |
| > 3 | 186 (68) | 178 | 8 | ||
| Number of drugs | 1-2 | 129 (47) | 123 | 6 | 0.437 |
| > 2 | 146 (53) | 136 | 10 | ||
Note.
Fisher’s test
There were differences in the factors correlated with DRP between the adult and older patient groups. Subsequently, a multivariate analysis was performed, with the results presented in Table 5.
Table 5.
Multivariate analysis of factors affecting DRPs
| Characteristics of Adult Patients | p-value | OR (95% CI) | Characteristics of Older Patients | p-value | OR (95% CI) |
|---|---|---|---|---|---|
| Diagnosed with hypertension | <0.001 | 3.83 (1.27-11.58) | Gender | 0.117 | 2.27 (0.82–6.35) |
| Number of drugs | 0.016 | 8.25 (3.53-19.25) | — | — | — |
| BMI (Body Mass Index) | 0.017 | 0.40 (0.19-0.85) | — | — | — |
Along with other independent variables, comorbidity with hypertension and taking more than two drugs are associated with an increased risk of developing DRPs, with 3.83-fold and 8.25-fold, respectively. Meanwhile, non-obese patients have a 2.5-fold risk of developing DRPs compared to obese patients. However, no factors correlated with DRPs were found in older patients. Since the findings showed that DRPs were caused by drug use at a lower frequency than GP’s prescription or pharmacist’s advice, it was likely that patients did not adhere to drug administration.
Table 6 and Table 7 show inconsistent findings on non-adherence proportions and affecting factors between MARS-10 and pill-count measurements. There was a fair level of agreement between pill counts and the MARS-10 on medication adherence in adults and older persons, with kappa coefficients of 0.298 and 0.355, respectively. Meanwhile, the tendency to underestimate medication adherence assessment using MARS-10 compared to pill-count was shown in both adult and older patients. This also occurred during further analysis of the factors affecting medication adherence. The gender factor correlated with adherence levels in adult patients as measured by MARS-10 but not by pill count. Similarly, BMI, comorbidities, and duration of chronic illness were related to adherence in pill-count but not in MARS-10. Meanwhile, no factors were associated with medication adherence in either the MARS-10 or the pill-count.
Table 6.
Medication adherence levels and affecting factors in adult patients
| Characteristics | Category | Total (%) | MARS-10 | p-value | Pill-count | p-value | ||
|---|---|---|---|---|---|---|---|---|
| Yes | No | Yes | No | |||||
| Gender | Male | 65 (24.2) | 15 | 50 | 0.042 | 39 | 26 | 0.691 |
| Female | 204 (75.8) | 75 | 129 | 128 | 76 | |||
| BMI | Obese | 72 (26.8) | 24 | 48 | 0.979 | 36 | 36 | 0.014 |
| Not Obese | 197 (73.2) | 66 | 131 | 131 | 66 | |||
| Residency status | Alone | 5 (1.9) | 1 | 4 | 0.667 | 3 | 2 | > 0.999a |
| With Family | 264 (98.1) | 89 | 175 | 164 | 100 | |||
| Education level | High | 97 (36.1) | 29 | 68 | 0.353 | 57 | 40 | 0.399 |
| Low | 172 (63.9) | 61 | 111 | 110 | 62 | |||
| Occupation | Employed | 109 (40.5) | 36 | 73 | 0.902 | 64 | 45 | 0.348 |
| Unemployed | 160 (59.5) | 54 | 106 | 103 | 57 | |||
| Marital status | Married | 258 (95.9) | 85 | 173 | 0.515 | 160 | 98 | > 0.999a |
| Single/Divorced | 11 (4.1) | 5 | 6 | 7 | 4 | |||
| Distance to Puskesmas (km) | ≤ 5 | 222 (82.5) | 77 | 145 | 0.354 | 138 | 84 | 0.953 |
| > 5 | 47 (17.5) | 13 | 34 | 29 | 18 | |||
| Duration of chronic illness (years) | ≤ 3 | 109 (40.5) | 40 | 69 | 0.353 | 59 | 50 | 0.026 |
| > 3 | 160 (59.5) | 50 | 110 | 108 | 52 | |||
| Number of drugs | 2 | 122 (45.4) | 34 | 88 | 0.077 | 80 | 42 | 0.282 |
| > 2 | 147 (54.6) | 56 | 91 | 87 | 60 | |||
| OTC or herbal medicines/supplements | Yes | 29 (10.8) | 8 | 21 | 0.478 | 17 | 12 | 0.684 |
| No | 240 (89.2) | 82 | 158 | 150 | 90 | |||
Note.
Fisher’s test
Table 7.
Medication adherence levels and affecting factors in older patients
| Characteristics | Category | Total (%) | MARS 10 | p-value | Pill-count | p-value | ||
|---|---|---|---|---|---|---|---|---|
| Yes | No | Yes | No | |||||
| Gender | Male | 73 (27) | 22 | 51 | 0.75 | 45 | 28 | 0.571 |
| Female | 202 (73) | 65 | 137 | 132 | 70 | |||
| BMI (Body Mass Index) | Obese | 62 (23) | 18 | 44 | 0.62 | 46 | 16 | 0.066 |
| Not Obese | 213 (77) | 69 | 144 | 131 | 82 | |||
| Living alone | Yes | 34 (12) | 12 | 22 | 0.62 | 26 | 8 | 0.115 |
| No | 241 (88) | 75 | 166 | 151 | 90 | |||
| Education level | High | 50 (18) | 16 | 34 | 0.95 | 32 | 18 | 0.953 |
| Low | 225 (82) | 71 | 154 | 145 | 80 | |||
| Occupation | Employed | 78 (28) | 24 | 54 | 0.85 | 53 | 25 | 0.435 |
| Unemployed | 197 (72) | 63 | 134 | 124 | 73 | |||
| Marital status | Married | 182 (66) | 57 | 125 | 0.87 | 114 | 68 | 0.403 |
| Singel/Divorced | 93 (34) | 30 | 63 | 63 | 30 | |||
| Distance to Puskesmas (km) | ≤ 5 | 240 (87) | 77 | 163 | 0.68 | 158 | 82 | 0.183 |
| > 5 | 35 (13) | 10 | 25 | 19 | 16 | |||
| Duration of chronic illness (years) | < 3 | 89 (32) | 35 | 54 | 0.06 | 57 | 32 | 0.939 |
| > 3 | 186 (68) | 52 | 134 | 120 | 66 | |||
| OTC or herbal medicines /supplements | Yes | 10 (4) | 3 | 7 | > 0.999a | 7 | 3 | > 0.999a |
| No | 265 (96) | 84 | 181 | 170 | 95 | |||
Note.
Fisher’s test
HMR is one of the well-structured health services at the community level; therefore, the identified DRPs were followed up with appropriate interventions. HMR was conducted by Puskesmas team, comprising a pharmacist, pharmacy technical personnel, and GPs. The direct interventions given during HMR are described in Table 8.
Table 8.
Forms of intervention for DRPs found during HMR
| Classification | Primary Domain | Forms of Intervention | Adults (n = 269) | Older Individuals (n = 275) |
|---|---|---|---|---|
| Planned Interventions | At prescriber level | Intervention discussed with prescriber | 13 (4.8) | 12 (4.4) |
| At patient level | Patient counselling: education to improve medication adherence | 106 (39.4) | 156 (56.7) | |
| Drug counselling: education on how to take medication | 109 (40.5) | 5 (1.8) | ||
| Spoken to family member/caregiver: education on how to store/dispose of drugs | 69 (25.7) | 197 (71.6) | ||
| No intervention | No drug-related problems | 19 (7.1) | 47 (17.1) |
As expected, pharmacist interventions related to DRP findings were mostly conducted at the patient level. This is in line with the findings described above, where the most frequent DRP was inappropriate drug use by patients. In adult patients, interventions on medication adherence and on how to take medications showed comparable effects. Meanwhile, older patients had the highest proportion of the need for education on home medication management and medication adherence. Meanwhile, less than 10% of the interventions required discussion with the GP as the prescribing doctor.
Discussion
HMR is a collaboration-based pharmaceutical service aiming to optimize drug use, prevent DRPs, and improve medication adherence and appropriate home medication management. In general, HMR is conducted by other health workers, such as a general practitioner (GP) and a nurse, in primary health facilities.
The sociodemographic characteristics of patients with chronic diseases in Yogyakarta Province show a profile consistent with the research findings in most primary health facilities in developing countries, such as South Africa (Kagura et al., 2023), and rural areas in China, with the most in the productive age group (Ding et al., 2021). The findings on chronic disease types differ from those in a study in Turkey, which is related to the Indonesian government’s policy that only 144 diseases are allowed to be managed by Puskesmas as primary health facilities, excluding Alzheimer’s disease. This relates to health workers’ competency and the capacity, completeness, and facilities in Puskesmas. The findings also show that health facility users were dominated by patients with a middle education level, and in the older group, this even reached more than 80%. This is in line with a secondary analysis of national data from the 2018 Basic Health Research Survey on the educational level of older adults who used Puskesmas (Laksono et al., 2024). Hypertension is most common in upper-middle-income countries, with good blood pressure control rates of <10% in low-income countries (Mishra et al., 2025). Although their research only involves patients with hypertension, an identical treatment problem of failure to meet therapy targets is also found in this research.
Our study shows that most of the DRP prevalence in both age groups was associated with failure to meet therapy targets (P1.1 and P1.2). Similar findings have been reported in many community-based studies, including those in India (Sreedevi et al., 2022), Ethiopia (Gebreziher et al., 2024), and Saudi Arabia (Riaz et al., 2021). This health problem is also found in a survey study of hypertension, T2DM, and dyslipidemia in China (Feng, 2018).
Further analysis revealed that the most common DRP causes in the two age groups were patients using fewer medications than prescribed or not taking medications at all. This is particularly related to patient adherence to the GP’s instructions. In patients with chronic diseases, long-term adherence and persistence are a significant challenge associated with not only disease control quality but also, and more importantly, complication prevention and death (Burnier, 2024). Some patients used medications more than the prescribed dose, including non-recommended overuse. All these causes were related to patient adherence to instructions for medication use, but the latter could increase adverse drug reactions (ADRs) or toxicity effects, especially in narrow therapeutic range drugs. Drug dose adjustments, whether omitting doses or taking more doses, were the most common cause in this study. Concerns and misconceptions about long-term medication are major barriers to medication adherence (Kvarnström et al., 2021). This study reinforces the findings of pharmacist intervention research during home visits, which shows that patient factors become DRP causes when receiving treatment upon returning from health facilities (Mayzel et al., 2020).
Our study shows the factors in DRP incidence in adult and older persons groups (84.8% vs 94.2%), respectively. Obesity, hypertension diagnosis, and use of >2 drug types were related to DRP incidence in adult patients, but not among older patients who had no factors for DRPs. Similarly, the same results were observed in the multivariate analysis conducted using logistic regression. The administration of at least three types of medications, hypertension comorbidity, and obesity can increase drug-related problems (DRPs) by up to 8.25 times, 3.83 times, and 2.5 times, respectively. The finding on hypertension aligns with a qualitative study exploring patient difficulties in managing hypertension comorbidity, including their perception that hypertension is not a priority to manage properly, as well as confusion and concern due to multiple drug administration (polypharmacy) (Fix et al., 2014). Many studies have linked several drugs to DRP incidence (Díez et al., 2022; Garedow et al., 2023; Ye et al., 2022), and this is supported by several review articles (Delara et al., 2022; Krustev et al., 2022; Pazan & Wehling, 2021). However, they mostly focus on the older adults as a group considered vulnerable to DRPs with clinically significant consequences. The finding, which also revealed DRPs in adult patients, is a strength of this study. Meanwhile, analyses of BMI associated with DRP incidence in adults remain very limited. Given that the DRP cause in this study was mostly medication adherence, it is likely that one of the disruptive factors is the high prevalence of depression among obese individuals, which may lead to decreased medication adherence (Chew et al., 2015). However, the study design limits further analysis of this association. Similarly, the absence of factors correlated with the incidence of DRPs in the older population may be due to the disproportionate number of patients in the subcategory of the group.
This study further analyzed factors affecting medication adherence using two approaches: self-report via MARS-10 and pill count. Unexpected differences were found when adult patients’ medication adherence was assessed by both approaches. Gender was associated with adherence levels by MARS-10 but not by pill-count. The latter showed that BMI and chronic illness duration were associated with adherence levels. Among the older group, both methods showed no covariates correlated with adherence levels (p>0.05). However, both adult and older groups reported similar impressions of misestimation by both methods. In MARS-10, all patients tended to be non-adherent, whereas the pill count indicated a higher proportion of adherent patients. MARS-10 is frequently questioned for its validity and precision due to its self-report nature (Stirratt et al., 2015), whereas pill-count is considered more objective because of its greater accuracy. For example, a study involving more than 250 hypertensive patients shows a strong correlation between adherence levels measured by the pill-count method and blood pressure control (Ernawati et al., 2022). The inter-rater reliability between the MARS-10 method and the pill-count was fair. This may be because MARS-10 tends to encompass all patient non-compliance experiences described in question no. 1 (Do you ever forget to take your medication?), while pill-count focuses more on short-term adherence since it is based on leftover medications from previous prescriptions. A larger sample involving a specific chronic disease and a measured clinical outcome yielded a more stable and reliable kappa value.
An essential measure in DRP management is the implementation of interventions to prevent and overcome problems. Since most DRP causes were related to patient factors, the interventions included education on improving adherence, how to take medications, and appropriate drug storage-disposal methods. Benefits for patients/families and improvements in interprofessional collaborative practice quality in primary health facilities can be achieved through HMR. Studies in Asian regions such as China and Jordan show that medication reviews significantly reduce DRP incidence (from 0.88 to 0.4 per patient) and improve patient adherence and health-related quality of life (HRQoL) (Basheti et al., 2018; Zhang et al., 2022). The study design lacked follow-up, making the extent of the reduction in DRPs unknown. Involving health workers in this study enabled immediate interventions that required GPs to adjust doses or substitute drugs, with a simpler, more convenient daily frequency to improve patient adherence. Not only for patients’ benefit, but such collaboration also strengthens communication between health workers (White et al., 2022).
An unexpected finding on methods of drug disposal at home by patients/families showed that they opened the main packaging and fed the drugs to pet chickens or used them as plant fertilizer. They perceived that such actions made their pets healthier and plants more fertile. Pharmacists then provided education on disposal techniques. In addition, one older patient with hypertension was found to take paracetamol for dizziness. The patient did not understand that medication non-adherence caused uncontrolled blood pressure, likely characterized by headache complaints. The direct interview revealed that the patient had been taking paracetamol for dizziness for the past 2 months, while the blood pressure checked during HMR was 190/90 mmHg. The patient was then escorted to the nearest Puskesmas to receive treatment.
The implementation of Home Medication Review (HMR) with over 500 patients across three primary healthcare facilities in the Yogyakarta province has uncovered medication issues that arise in the community after patients are no longer under healthcare providers’ supervision. The central government should consider a reward system as one of the drivers of program consistency.
Conclusion
The majority of DRPs found during HMR were failure to achieve therapy targets for each chronic disease, with the most common causes being patient factors due to medication non-adherence and non-conformity with drug disposal. Patients’ behavior toward home medication management and use potentially harms themselves and the environment. In-depth consideration should be given to interpreting and generalizing the results of this research, accounting for the study design limitations and the research location. A reliable medication adherence tool for primary health facilities, evaluated on specific clinical outcomes, warrants further investigation. Longitudinal studies and RCTs on the effectiveness of HMR should be conducted to reinforce the need for such services to optimize medication therapy.
Acknowledgment
The authors express their gratitude to all the healthcare providers who participated in this HMR Program.
Funding Statement
Funding This study was funded by the Directorate of Research and Community Service Universitas Islam Indonesia (Grant number 17 /Dir/DPPM/80/PPU/III/2023). Recipient: Vitarani Dwi Ananda Ningrum.
Declaration of Conflicting Interest
No conflict of interest to declare in this study.
Author Contribution
Conceptualization: Vitarani Dwi Ananda Ningrum, Yunilistianingsih, Muslimatul Khotimah, Nirma Atin Shintia, Rahma Yuantari.
Data curation: Vitarani Dwi Ananda Ningrum, Rahma Yuantari.
Formal analysis: Vitarani Dwi Ananda Ningrum, Septia Rahayu Efendi, Cindy Pramudyah Dewani, Hana Trisna Andini.
Funding acquisition: Vitarani Dwi Ananda Ningrum.
Investigation: Vitarani Dwi Ananda Ningrum, Yunilistianingsih, Muslimatul Khotimah, Nirma Atin Shintia.
Methodology: Vitarani Dwi Ananda Ningrum, Septia Rahayu Efendi, Cindy Pramudyah Dewani, Rahma Yuantari.
Project administration: Vitarani Dwi Ananda Ningrum.
Resources: Vitarani Dwi Ananda Ningrum.
Software: Septia Rahayu Efendi, Cindy Pramudyah Dewani.
Supervision: Vitarani Dwi Ananda Ningrum, Yunilistianingsih, Muslimatul Khotimah, Nirma Atin Shintia, Rahma Yuantari.
Validation: Vitarani Dwi Ananda Ningrum, Rahma Yuantari
Writing – original draft: Vitarani Dwi Ananda Ningrum, Septia Rahayu Efendi, Cindy Pramudyah Dewani.
Writing – review & editing: Vitarani Dwi Ananda Ningrum, Yunilistianingsih, Muslimatul Khotimah, Nirma Atin Shintia, Septia Rahayu Efendi, Cindy Pramudyah Dewani, Hana Trisna Andini, Rahma Yuantari.
All authors approved the final version of the article to be published.
Author Biography
Prof. Dr. apt. Vitarani D.A Ningrum is a Professor in Clinical Pharmacy and Pharmacotherapy at Department of Pharmacy, Universitas Islam Indonesia, Yogyakarta, Indonesia.
apt. Yunilistianingsih, M.Sc. is a Senior Community Pharmacist at Tegalrejo Primary Health Center, Yogyakarta, Indonesia.
apt. Muslimatul Khotimah, S.Si. is a Senior Community Pharmacist at Bambanglipuro Primary Health Center, Bantul Regency, Yogyakarta, Indonesia.
apt. Nirma Atin Shintia, M.Sc. is a Senior Community Pharmacist at Ngemplak 1 Primary Health Center, Sleman Regency, Yogyakarta, Indonesia.
apt. Septia Rahayu Efendi, S.Farm. is a researcher in community pharmacy.
apt Cindy Pramudyah Dewani, S.Farm. is a researcher in community pharmacy.
apt. Hana Trisna Andini, S.Farm. is a researcher in community pharmacy.
dr. Rahma Yuantari, M.Sc., Sp.PK. is a Doctor in clinical pathology and a community health researcher.
Data Availability
The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Declaration of Use of AI in Scientific Writing
Nothing to declare.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.