Characteristics of Patients With Trigeminal Neuralgia Referred to the Indonesian National Brain Center Neurosurgery Clinic

Trigeminal neuralgia (TN) is a debilitating neuropathic pain involving the fifth cranial nerve. There has been no study investigating the clinical and socioeconomical characteristics of patients with TN in Indonesia. A total of 100 patients were included in this study. Symptoms indicating a later stage of the illness, namely, involvement of all the trigeminal nerve branches, numbness, and concomitant persistent pain, were the common presentations found in our cohort. Only one TN diagnosis was made by a general practitioner (GP). None were immediately referred to a neurosurgeon following their diagnosis. Access to our clinic took as long as 4.7 ± 5.1 years (mean ± SD) from the onset. Older age was a significant predictor of an increased likelihood of not knowing their illness upon the referral (21.9%, p = 0.008). Upon their first presentation, 25.5% of patients had experienced drug-related side effects due to prolonged medication. Only 50% of patients were compensated by the universal health coverage (UHC) system. Seven patients spent ≥ 50 million rupiahs and eight patients had already lost their jobs. In conclusion, early contact with a neurosurgeon contributes to better management of TN, both for the patients and healthcare system in Indonesia. A refined understanding of TN nature is still needed in this country.

Realizing the potential of routinely collected data for monitoring eye health services to help achieve universal health coverage

In the recent World Report on Vision, the World Health Organization (WHO) highlighted the need to strengthen health information systems (HIS) for eye health, including data from population-based surveys and facility-based sources such as service and resource data. The report also outlined the importance of strengthening eye health to enable Universal Health Coverage. In high-income countries, facility-based data are increasingly used to monitor eye services and answer research questions, including under the banner of big data. While there are some examples of comprehensive and robust information systems for eye care in low- and middle-income countries (LMICs), the potential of facility-based data is yet to be realized in many LMICs. Here, we discuss the potential of strengthening the collection and use of facility-based data for eye health in LMICs to monitor Universal Health Coverage relevant aspects of service access, quality, and equity.

Strengthening the Health System as a Strategy to Achieving a Universal Health Coverage in Underprivileged Communities in Africa: A Scoping Review

Universal health coverage (UHC) is defined as people having access to quality healthcare services (e.g., treatment, rehabilitation, and palliative care) they need, irrespective of their financial status. Access to quality healthcare services continues to be a challenge for many people in low- and middle-income countries (LMICs). The aim of this study was to conduct a scoping review to map out the health system strengthening strategies that can be used to attain universal health coverage in Africa. We conducted a scoping review and qualitatively synthesized existing evidence from studies carried out in Africa. We included studies that reported interventions to strengthen the health system, e.g., financial support, increasing work force, improving leadership capacity in health facilities, and developing and upgrading infrastructure of primary healthcare facilities. Outcome measures included health facility infrastructures, access to medicines, and sources of financial support. A total of 34 studies conducted met our inclusion criteria. Health financing and developing health infrastructure were the most reported interventions toward achieving UHC. Our results suggest that strengthening the health system, namely, through health financing, developing, and improving the health infrastructure, can play an important role in reaching UHC in the African context.

Strengthening eHealth Systems to Support Universal Health Coverage in sub-Saharan Africa

The aim of universal health coverage (UHC) is to ensure that all individuals in a country have access to quality healthcare services and do not suffer financial hardship in using these services. However, progress toward attaining UHC has been slow, particularly in sub-Saharan Africa. The use of information and communication technologies for healthcare, known as eHealth, can facilitate access to quality healthcare at minimal cost. eHealth systems also provide the information needed to monitor progress toward UHC. However, in most countries, eHealth systems are sometimes non-functional and do not serve programmatic purposes. Therefore, it is crucial to implement strategies to strengthen eHealth systems to support UHC. This perspective piece proposes a conceptual framework for strengthening eHealth systems to attain UHC goals and to help guide UHC and eHealth strategy development.

Government plans in the 2016 and 2021 Peruvian presidential elections: A natural language processing analysis of the health chapters

Background: While clinical medicine has exploded, electronic health records for Natural Language Processing (NLP) analyses, public health, and health policy research have not yet adopted these algorithms. We aimed to dissect the health chapters of the government plans of the 2016 and 2021 Peruvian presidential elections, and to compare different NLP algorithms. Methods: From the government plans (18 in 2016; 19 in 2021) we extracted each sentence from the health chapters. We used five NLP algorithms to extract keywords and phrases from each plan: Term Frequency–Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), TextRank, Keywords Bidirectional Encoder Representations from Transformers (KeyBERT), and Rapid Automatic Keywords Extraction (Rake). Results: In 2016 we analysed 630 sentences, whereas in 2021 there were 1,685 sentences. The TF-IDF algorithm showed that in 2016, 22 terms appeared with a frequency of 0.05 or greater, while in 2021 27 terms met this criterion. The LDA algorithm defined two groups. The first included terms related to things the population would receive (e.g., ’insurance’), while the second included terms about the health system (e.g., ’capacity’). In 2021, most of the government plans belonged to the second group. The TextRank analysis provided keywords showing that ’universal health coverage’ appeared frequently in 2016, while in 2021 keywords about the COVID-19 pandemic were often found. The KeyBERT algorithm provided keywords based on the context of the text. These keywords identified some underlying characteristics of the political party (e.g., political spectrum such as left-wing). The Rake algorithm delivered phrases, in which we found ’universal health coverage’ in 2016 and 2021. Conclusion: The NLP analysis could be used to inform on the underlying priorities in each government plan. NLP analysis could also be included in research of health policies and politics during general elections and provide informative summaries for the general population.