Forecasting the amount and cost of medicine to treat type 2 diabetes mellitus in Nepal using knowledge on medicine usage from a developed country

Prediction of pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II based on clinical risk

OBJECTIVE

To assess the effectiveness of different machine learning models in estimating the pharmaceutical and non-pharmaceutical expenditures associated with Diabetes Mellitus type II diagnosis, based on the clinical risk index determined by the analysis of comorbidities.

MATERIALS AND METHODS

In this cross-sectional study, we have used data from 11,028 anonymized records of patients admitted to a high-complexity hospital in Bogota, Colombia between 2017-2019 with a primary diagnosis of Diabetes. These cases were classified according to Charlson's comorbidity index in several risk categories. The main variables analyzed in this study are hospitalization costs (which include pharmaceutical and non-pharmaceutical expenditures), age, gender, length of stay, medicines and services consumed, and comorbidities assessed by the Charlson's index. The model's dependent variable is expenditure (composed of pharmaceutical and non-pharmaceutical expenditures). Based on these variables, different machine learning models (Multivariate linear regression, Lasso model, and Neural Networks) were used to estimate the pharmaceutical and non-pharmaceutical expenditures associated with the clinical risk classification. To evaluate the performance of these models, different metrics were used: Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2).

RESULTS

The results indicate that the Neural Networks model performed better in terms of accuracy in predicting pharmaceutical and non-pharmaceutical expenditures considering the clinical risk based on Charlson's comorbidity index. A deeper understanding and experimentation with Neural Networks can improve these preliminary results, therefore we can also conclude that the main variables used and those that were proposed can be used as predictors for the medical expenditures of patients with diabetes type-II.

CONCLUSIONS

With the increase of technology elements and tools, it is possible to build models that allow decision-makers in hospitals to improve the resource planning process given the accuracy obtained with the different models tested.

Analyzing the Implementation of Policies and Guidelines for the Prevention and Management of Type 2 Diabetes at Primary Health Care Level in Nepal

Background

Nepal, in recent years, is witnessing an increasing problem of type 2 diabetes that has resulted significant premature deaths and disability. Prevention and management of non-communicable diseases (NCDs) including diabetes have been prioritized in the national policies and guidelines of the Nepal Government. However, research looking at the overview of the implementation of the existing policies and guidelines for diabetes prevention and control is scarce. Hence, this study reviewed diabetes related existing policies and its implementation process at the primary health care level in Nepal.

Methods

This study involved two phases: Phase I: situation analyses through review of documents and Phase II: qualitative exploratory study. In phase I, four databases (Medline, Web of Science, Embase and PubMed) were systematically searched using key search terms related to diabetes care and policies between January 2000 and June 2021. Also, relevant gray literature was reviewed to understand the trajectory of policy development and its translation with regards to diabetes prevention and management at primary health care level in Nepal. Following the phase I, we conducted in-depth interviews (IDI) and key informant interviews (KII) with health care providers, policy makers, and managers (IDI = 13, and KII = 7) at peripheral and central levels in Kavrepalanchowk and Nuwakot districts of Nepal. The in-depth interviews were audio recorded, transcribed, and coded. The triangulation of data from document review and interviews was done and presented in themes.

Results

Four key themes were identified through triangulating findings from the document review and interviews including (i) limited implementation of policies into practices; (ii) lack of coordination among the different levels of service providers; (iii) lack of trained human resources for health and inadequate quality services at the primary health care level, and (iv) inadequate access and utilization of diabetes care services at primary health care level. Specifically, this study identified some key pertinent challenges to the implementation of policies and programs including inadequate resources, limited engagement of stakeholders in service design and delivery, lack of trained health care providers, lack of financial resources to strengthen peripheral health services, fragmented health governance, and weak reporting and monitoring systems.

Conclusion

This study revealed that the policies, plans, and strategies for prevention and management of NCDs in Nepal recognized the importance of diabetes prevention and control. However, a major gap remains with adequate and lack of clarity in terms of implementation of available policies, plans, strategies, and programs to address the problem of diabetes. We suggest the need for multisectoral approach (engaging both health and non-health sectors) at central as well as peripheral levels to strengthen the policies implementation process, building capacity of health care providers, ensuring adequate financial and non-financial resources, and improving quality of services at primary health care levels.

Translation, Cultural Adaptation and Validation of General Medication Adherence Scale (GMAS) into the Nepalese Language

BACKGROUND

The General Medication Adherence Scale (GMAS) evaluates intentional and unintentional behaviour of patients, disease and medication burden and cost-related burden associated with non-adherence. GMAS was developed and validated among Urdu-speaking patients with chronic diseases. However, validated tool in Nepalese language to measure medication adherence among chronic illness patients currently does not exist.

AIM

To translate, culturally adapt, and validate the English version of GMAS into the Nepalese language to measure medication adherence among chronic illness patients.

METHODS

The study was conducted among patients with chronic diseases in both hospital and community pharmacies of Nepal. The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Practice Guideline for linguistic translation and cultural adaptation was used to translate and culturally adapt the English version of GMAS into the Nepalese version. The translated version was validated amongst patients with chronic diseases in Nepal. Exploratory factor analysis was carried out using principal component analysis with varimax rotation. Test-retest reliability and internal consistency were analysed.

RESULTS

A total of 220 (53.6% females, and 51.4% of 51 to 70 aged patients) patients with chronic diseases participated in the study. The majority of patients took two medications (27.3%) from six months to five and half years (68.2%). Kaiser Meyer Olkin was found to be 0.83. A principal axis factor analysis was conducted on the 3 items of GMAS without and with orthogonal rotation (varimax). The scree plot showed an inflexion on the third item that meant three components were present. The overall Cronbach's alpha value of the full-phase study was 0.82.

CONCLUSION

The General Medication Adherence Scale was successfully translated into the Nepalese language, culturally adapted, and validated amongst chronic diseases patients of Nepal. Therefore, the GMAS-Nepalese version can be used to evaluate medication adherence among Nepalese-speaking patients with chronic disease.