BackgroundStrong primary health care (PHC) is the cornerstone for universal health coverage and a country’s health emergency response. PHC includes public health and first-contact primary care (PC). Internationally, the spread of COVID-19 and mortality rates vary widely. The authors hypothesised that countries perceived to have strong PHC have lower COVID-19 mortality rates.AimTo compare perceptions of PC experts on PC system strength, pandemic preparedness, and response with COVID-19 mortality rates in countries globally.Design & settingA convenience sample of international PHC experts (clinicians, researchers, and policymakers) completed an online survey (in English or Spanish) on country-level PC attributes and pandemic responses.MethodAnalyses of perceived PC strength, pandemic plan use, border controls, movement restriction, and testing against COVID-19 mortality were undertaken for 38 countries with ≥5 responses.ResultsIn total, 1035 responses were received from 111 countries, with 1 to 163 responders per country. The 38 countries with ≥5 responses were included in the analyses. All world regions and economic tiers were represented. No correlation was found between PC strength and mortality. Country-level mortality negatively correlated with perceived stringent border control, movement restriction, and testing regimes.ConclusionCountries perceived by expert participants as having a prepared pandemic plan and a strong PC system did not necessarily experience lower COVID-19 mortality rates. What appears to make a difference to containment is if and when the plan is implemented, and how PHC is mobilised to respond. Many factors contribute to spread and outcomes. Important responses are first to limit COVID-19 entry across borders, then to mobilise PHC, integrating the public health and PC sectors to mitigate spread and reduce burden on hospitals through hygiene, physical distancing, testing, triaging, and contract-tracing measures.

Primary health care (PHC) includes both primary care (PC) and essential public health (PH) functions. While much is written about the need to coordinate these two aspects, successful integration remains elusive in many countries. Furthermore, the current global pandemic has highlighted many gaps in a well-integrated PHC approach. Four key actions have been recognized as important for effective integration. A survey of PC stakeholders (clinicians, researchers, and policy-makers) from 111 countries revealed many of the challenges encountered when facing the pandemic without a coordinated effort between PC and PH functions. Participants’ responses to open-ended questions underscored how each of the key actions could have been strengthened in their country and are potential factors to why a strong PC system may not have contributed to reduced mortality. By integrating PC and PH greater capacity to respond to emergencies may be possible if the synergies gained by harmonizing the two are realized.

Objective To learn from primary health care experts’ experiences from the COVID-19 pandemic across countries. Methods We applied qualitative thematic analysis to open-text responses from a multinational rapid response survey of primary health care experts assessing response to the initial wave of the COVID-19 pandemic. Results Respondents’ comments focused on three main areas of primary health care response directly influenced by the pandemic: 1) impact on the primary care workforce, including task-shifting responsibilities outside clinician specialty and changes in scope of work, financial strains on practices, and the daily uncertainties and stress of a constantly evolving situation; 2) impact on patient care delivery, both essential care for COVID-19 cases and the non-essential care that was neglected or postponed; 3) and the shift to using new technologies. Conclusions Primary health care experiences with the COVID-19 pandemic across the globe were similar in their levels of workforce stress, rapid technologic adaptation, and need to pivot delivery strategies, often at the expense of routine care.

While the COVID-19 pandemic now affects the entire world, countries have had diverse responses. Some responded faster than others, with considerable variations in strategy. After securing border control, primary health care approaches (public health and primary care) attempt to mitigate spread through public education to reduce personto-person contact (hygiene and physical distancing measures, lockdown procedures), triaging of cases by severity, COVID-19 testing, and contact-tracing. An international survey of primary care experts' perspectives about their country's national responseswas conducted April to early May 2020. This mixed method paper reports on whether they perceived that their country's decision-making and pandemic response was primarily driven by medical facts, economic models, or political ideals; initially intended to develop herd immunity or flatten the curve, and the level of decision-making authority (federal, state, regional). Correlations with country-level death rates and implications of political forces and processes in shaping a country's pandemic response are presented and discussed, informed by our data and by the literature. The intersection of political decision-making, public health/ primary care policies and economic strategies is analysed to explore implications of COVID-19's impact on countries with different levels of social and economic development.

International Classification of Disease (ICD) coding plays a significant role in classify-ing morbidity and mortality rates. Currently, ICD codes are assigned to a patient’s medical record by hand by medical practitioners or specialist clinical coders. This practice is prone to errors, and training skilled clinical coders requires time and human resources. Automatic prediction of ICD codes can help alleviate this burden. In this paper, we propose a transformer-based architecture with label-wise attention for predicting ICD codes on a medical dataset. The transformer model is first pre-trained from scratch on a medical dataset. Once this is done, the pre-trained model is used to generate representations of the tokens in the clinical documents, which are fed into the label-wise attention layer. Finally, the outputs from the label-wise attention layer are fed into a feed-forward neural network to predict appropriate ICD codes for the input document. We evaluate our model using hospital discharge summaries and their corresponding ICD-9 codes from the MIMIC-III dataset. Our experimental results show that our transformer model outperforms all previous models in terms of micro-F1 for the full label set from the MIMIC-III dataset. This is also the first successful application of a pre-trained transformer architecture to the auto-coding problem on the full MIMIC-III dataset.