Patterns of psychotropic drug prescriptions and general practice consultations among community-dwelling older people with dementia during the first two years of the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic and subsequent lockdown measures had serious implications for community-dwelling older people with dementia. While the short-term impacts of the pandemic on this population have been well studied, there is limited research on its long-term impacts. Quantifying the long-term impacts may provide insights into whether healthcare adaptations are needed after the acute phase of the pandemic to balance infection prevention measures with healthcare provision. This study aims to examine patterns of psychotropic drug prescriptions and general practice consultations in community-dwelling older people with dementia during the first two years of the pandemic. METHODS: We utilised routine electronic health records from three Dutch academic general practice research networks located in the North, East, and South, between 2019 and 2021. We (1) compared the weekly prescription rates of five groups of psychotropic drugs and two groups of tracer drugs, and weekly general practice consultation rates per 1000 participants, between the first two years of the pandemic and the pre-pandemic phase, (2) calculated changes in these rates during three lockdowns and two relaxation phases relative to the corresponding weeks in 2019, and (3) employed interrupted time series analyses for the prescription rates. Analyses were performed for each region separately. RESULTS: The study population sizes in the North, East, and South between 2019 and 2021 were 1726 to 1916, 93 to 117, and 904 to 960, respectively. Data from the East was excluded from the statistical analyses due to the limited sample size. During the first two years of the pandemic, the prescription rates of psychotropic drugs were either lower or similar to those in the pre-pandemic phase, with differences varying from -2.6‰ to -10.2‰. In contrast, consultation rates during the pandemic were higher than in the pre-pandemic phase, increasing by around 38‰. CONCLUSIONS: This study demonstrates a decrease in psychotropic drug prescriptions, but an increase in general practice consultations among community-dwelling older people with dementia during the first two years of the pandemic. However, reasons for the decrease in psychotropic drug prescriptions are unclear due to limited information on the presence of neuropsychiatric symptoms and the appropriateness of prescribing.

Dutch GP healthcare consumption in COVID-19 heterogeneous regions: an interregional time-series approach in 2020-2021.

BACKGROUND: Many countries observed a sharp decline in the use of general practice services after the outbreak of the COVID-19 pandemic. However, research has not yet considered how changes in healthcare consumption varied among regions with the same restrictive measures but different COVID-19 prevalence. AIM: To investigate how the COVID-19 pandemic affected healthcare consumption in Dutch general practice during 2020 and 2021, among regions with known heterogeneity in COVID-19 prevalence, from a pre-pandemic baseline in 2019. DESIGN & SETTING: Population-based cohort study using electronic health records. The study was undertaken in Dutch general practices involved in regional research networks. METHOD: An interrupted time-series analysis of changes in healthcare consumption from before to during the pandemic was performed. Descriptive statistics were used on the number of potential COVID-19-related contacts, reason for contact, and type of contact. RESULTS: The study covered 3 595 802 contacts (425 639 patients), 3 506 637 contacts (433 340 patients), and 4 105 413 contacts (434 872 patients) in 2019, 2020, and 2021, respectively. Time-series analysis revealed a significant decrease in healthcare consumption after the outbreak of the pandemic. Despite interregional heterogeneity in COVID-19 prevalence, healthcare consumption decreased comparably over time in the three regions, before rebounding to a level significantly higher than baseline in 2021. Physical consultations transitioned to phone or digital over time. CONCLUSION: Healthcare consumption decreased irrespective of the regional prevalence of COVID-19 from the start of the pandemic, with the Delta variant triggering a further decrease. Overall, changes in care consumption appeared to reflect contextual factors and societal restrictions rather than infection rates.

A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers: Development and Validation Study.

BACKGROUND: Natural language processing (NLP) models such as bidirectional encoder representations from transformers (BERT) hold promise in revolutionizing disease identification from electronic health records (EHRs) by potentially enhancing efficiency and accuracy. However, their practical application in practice settings demands a comprehensive and multidisciplinary approach to development and validation. The COVID-19 pandemic highlighted challenges in disease identification due to limited testing availability and challenges in handling unstructured data. In the Netherlands, where general practitioners (GPs) serve as the first point of contact for health care, EHRs generated by these primary care providers contain a wealth of potentially valuable information. Nonetheless, the unstructured nature of free-text entries in EHRs poses challenges in identifying trends, detecting disease outbreaks, or accurately pinpointing COVID-19 cases. OBJECTIVE: This study aims to develop and validate a BERT model for detecting COVID-19 consultations in general practice EHRs in the Netherlands. METHODS: The BERT model was initially pretrained on Dutch language data and fine-tuned using a comprehensive EHR data set comprising confirmed COVID-19 GP consultations and non-COVID-19-related consultations. The data set was partitioned into a training and development set, and the model's performance was evaluated on an independent test set that served as the primary measure of its effectiveness in COVID-19 detection. To validate the final model, its performance was assessed through 3 approaches. First, external validation was applied on an EHR data set from a different geographic region in the Netherlands. Second, validation was conducted using results of polymerase chain reaction (PCR) test data obtained from municipal health services. Lastly, correlation between predicted outcomes and COVID-19-related hospitalizations in the Netherlands was assessed, encompassing the period around the outbreak of the pandemic in the Netherlands, that is, the period before widespread testing. RESULTS: The model development used 300,359 GP consultations. We developed a highly accurate model for COVID-19 consultations (accuracy 0.97, F1-score 0.90, precision 0.85, recall 0.85, specificity 0.99). External validations showed comparable high performance. Validation on PCR test data showed high recall but low precision and specificity. Validation using hospital data showed significant correlation between COVID-19 predictions of the model and COVID-19-related hospitalizations (F1-score 96.8; P<.001; R2=0.69). Most importantly, the model was able to predict COVID-19 cases weeks before the first confirmed case in the Netherlands. CONCLUSIONS: The developed BERT model was able to accurately identify COVID-19 cases among GP consultations even preceding confirmed cases. The validated efficacy of our BERT model highlights the potential of NLP models to identify disease outbreaks early, exemplifying the power of multidisciplinary efforts in harnessing technology for disease identification. Moreover, the implications of this study extend beyond COVID-19 and offer a blueprint for the early recognition of various illnesses, revealing that such models could revolutionize disease surveillance.