Healthcare avoidance during the early stages of the COVID-19 pandemic and all-cause mortality: a longitudinal community-based study

Background During the COVID-19 pandemic, global trends of reduced healthcare-seeking behaviour were observed. This raises concerns about the consequences of healthcare avoidance for population health. Aim To determine the association between healthcare avoidance during the early stages of the COVID-19 pandemic and all-cause mortality. Design and setting 32-month follow-up within the population-based Rotterdam Study, after sending a COVID-19 questionnaire at the onset of the pandemic in April 2020 to all non-institutionalised participants (response rate 73%). Method Cox proportional hazards models assessed the risk of all-cause mortality among respondents who avoided healthcare because of the COVID-19 pandemic. Mortality status was collected through municipality registries and medical records. Results Of 5656 respondents, one-fifth avoided healthcare due to the COVID-19 pandemic (N=1143). Compared to non-avoiders, those who avoided healthcare more often reported symptoms of depression (31.2% versus 12.3%) and anxiety (29.7% versus 12.2%), and more often valued their health as poor to fair (29.4% versus 10.1%). Healthcare avoiders had an increased adjusted risk of all-cause mortality (HR: 1.30; 95%CI 1.01-1.67), which remained nearly identical after adjustment for history of any non-communicable disease (1.20;0.93-1.54). However, this association attenuated after additional adjustment for mental and self-appreciated health factors (0.96;0.74-1.24). Conclusion We found an increased risk of all-cause mortality among individuals who avoided healthcare during COVID-19. These individuals were characterised by poor mental and physical self-appreciated health. Therefore, interventions should be targeted to these vulnerable individuals to safeguard their access to primary and specialist care in order to limit health disparities, inside and beyond healthcare crises.

Air pollution and childhood respiratory consultations in primary care: a systematic review

BACKGROUND

Outdoor air pollution is a known risk factor for respiratory morbidity worldwide. Compared with the adult population, there are fewer studies that analyse the association between short-term exposure to air pollution and respiratory morbidity in children in primary care.

OBJECTIVE

To evaluate whether children in a primary care setting exposed to outdoor air pollutants during short-term intervals are at increased risk of respiratory diagnoses.

METHODS

A search in Medline, the Cochrane Library, Web of Science and Embase databases throughout March 2023. Percentage change or risk ratios with corresponding 95% CI for the association between air pollutants and respiratory diseases were retrieved from individual studies. Risk of bias assessment was conducted with the Newcastle-Ottawa Scale (NOS) for cohort or case-control studies and an adjusted NOS for time series studies.

RESULTS

From 1366 studies, 14 were identified as meeting the inclusion criteria. Most studies had intermediate or high quality. A meta-analysis was not conducted due to heterogeneity in exposure and health outcome. Overall, studies on short-term exposure to air pollutants (carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2) and particulate matter ≤10 µm (PM10)) were associated with increased childhood respiratory consultations in primary care. In general, exposure to ozone was associated with a reduction in respiratory consultations.

CONCLUSIONS

The evidence suggests CO, SO2, NO2, PM10 and PM2.5 are risk factors for respiratory diseases in children in primary care in the short term. However, given the heterogeneity of the studies, interpretation of these findings must be done with caution.

PROSPERO REGISTRATION NUMBER

CRD42022259279.

Incidence and management of mallet finger in Dutch primary care: a cohort study

BACKGROUND

A mallet finger (MF) is diagnosed clinically and can be managed in primary care. The actual incidence of MF and how it is managed in primary care is unknown.

AIM

To determine the incidence of MF in primary care and to obtain estimates for the proportions of osseous and tendon MF. An additional aim was to gain insight into the management of patients diagnosed with MF in primary care.

DESIGN & SETTING

A cohort study using a healthcare registration database from general practice in the Netherlands.

METHOD

Patients aged ≥18 years with a new diagnosis of MF from 1 January 2015-31 December 2019 were selected using a search algorithm based on International Classification of Primary Care (ICPC) coding.

RESULTS

In total, 161 cases of MF were identified. The mean incidence was 0.58 per 1000 person-years. A radiograph was taken in 58% (n = 93) of cases; 23% (n = 37) of cases had an osseous MF. The most applied strategies were referral to secondary care (45%) or conservative treatment in GP practice (43%). Overall, 7% were referred to a paramedical professional.

CONCLUSION

On average, a Dutch GP assesses ≥1 patient with MF per year. Since only a minimal number of patients required surgical treatment and a limited number of GPs requested radiography, the recommendation in the guidelines to perform radiography in all patients with MF should potentially be reconsidered. The purpose of requesting radiographs should not be to distinguish between a tendinogenic or osseous MF, but to assess whether there is a possible indication for surgery.

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

Population-based cohort study using electronic health records.

SETTING

Dutch general practices involved in regional research networks.

METHODS

Interrupted time-series analysis of changes in healthcare consumption from before to during the pandemic. Descriptive statistics on the number of potential COVID-19 related contacts, reason for contact and type of contact.

RESULTS

The study covered 3 627 597 contacts (425 639 patients), 3 532 693 contacts (433 340 patients), and 4 134 636 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.

CONCLUSIONS

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.

Consensus for Statements Regarding a Definition for Spinal Osteoarthritis for Use in Research and Clinical Practice: A Delphi Study

OBJECTIVE

To determine consensus among an international, multidisciplinary group of experts regarding definitions of spinal osteoarthritis for research and for clinical practice.

METHODS

A 15-member, multidisciplinary steering committee generated 117 statements for a 3-round Delphi study. Experts in back pain and/or osteoarthritis were identified and invited to participate. In round 1, participants could propose additional statements for voting. All statements were rated on a 1-9 Likert scale, and consensus was set at ≥70% of respondents agreeing or disagreeing with the statement and <15% of respondents providing the opposite response.

RESULTS

In total, 255 experts from 11 different professional backgrounds were invited. From 173 available experts, 116 consented to participate. In round 1, 103 participants completed the survey, followed by 85 of 111 participants in round 2 (77%) and 87 of 101 participants in round 3 (86%). One-third of participants were from Europe (30%), most were male (58%), one-fifth were physical therapists (21%), and over one-third had been in their profession for 11-20 years (35%). Of 131 statements, consensus was achieved for 71 statements (54%): 53 in agreement (75%) and 18 in disagreement (25%).

CONCLUSION

Although there was consensus for statements for definitions of spinal osteoarthritis that were analogous to definitions of osteoarthritis in appendicular joints, a future definition still needs refinement. Importantly, this Delphi highlighted that a future definition should be considered across a spectrum of structural changes and patient symptoms and expressed on a progressive scale.

Clinical and radiographic features of spinal osteoarthritis predict long-term persistence and severity of back pain in older adults

BACKGROUND

Patients with back pain can show one or more features of spinal osteoarthritis (OA), such as morning stiffness, limited or painful range of motion (ROM), and lumbar disc degeneration (LDD). However, it has not been investigated whether these features are prognostic of long-term back pain.

OBJECTIVES

This study assessed whether spinal morning stiffness, ROM and LDD are prognostic factors for back pain after 1 year in older adults with back pain.

METHODS

This prospective observational study (BACE cohort) included patients aged>55 years visiting a general practitioner for a back-pain episode. Baseline patient-reported morning stiffness, physical examined ROM and radiographic LDD features (i.e., multilevel osteophytes and disc space narrowing) were analysed as potential prognostic factors in unadjusted and adjusted regression models with the outcomes of persistent back pain (yes/no) and back pain severity after 1-year follow-up.

RESULTS

This study included 543 patients with mean (SD) age 67 (8) years, 59% female, and 62% reporting back pain at 1-year follow-up. When studied in separate adjusted models, persistent back pain was associated with morning stiffness>30min (OR 3.0, 95%CI 1.3; 5.5), restricted lateroflexion (OR 1.8, 95%CI 1.0; 3.2), pain during rotation (OR=1.7, 95%CI 1.0; 2.9), multilevel osteophytes (OR 2.4, 95%CI 1.4; 4.1), and multilevel disc space narrowing (OR 1.5, 95%CI 0.9; 2.4). When investigated in the same adjusted model, persistent back pain remained associated with only morning stiffness>30min (OR 2.4, 95%CI 1.0; 3.9), pain during rotation (OR 1.6, 95%CI 0.9; 2.8), and multilevel osteophytes (OR 2.1, 95%CI 1.2; 3.7). The same spinal OA-related features were associated with back pain severity.

CONCLUSIONS

Spinal morning stiffness, painful rotation, and multilevel osteophytes are prognostic factors for persistent back pain and back pain severity after 1 year. Evaluating these clinical and radiographic features of spinal OA could help clinicians identify older patients who will experience long-term back pain.