Background The coronavirus disease (COVID-19) pandemic has caused an unprecedented worldwide public health crisis that requires new management approaches. COVIDApp is a mobile app that was adapted for the management of institutionalized individuals in long-term care facilities. Objective The aim of this paper is to report the implementation of this innovative tool for the management of long-term care facility residents as a high-risk population, specifically for early identification and self-isolation of suspected cases, remote monitoring of mild cases, and real-time monitoring of the progression of the infection. Methods COVIDApp was implemented in 196 care centers in collaboration with 64 primary care teams. The following parameters of COVID-19 were reported daily: signs/symptoms; diagnosis by reverse transcriptase–polymerase chain reaction; absence of symptoms for ≥14 days; total deaths; and number of health care workers isolated with suspected COVID-19. The number of at-risk centers was also described. Results Data were recorded from 10,347 institutionalized individuals and up to 4000 health care workers between April 1 and 30, 2020. A rapid increase in suspected cases was seen until day 6 but decreased during the last two weeks (from 1084 to 282 cases). The number of confirmed cases increased from 419 (day 6) to 1293 (day 22) and remained stable during the last week. Of the 10,347 institutionalized individuals, 5,090 (49,2%) remained asymptomatic for ≥14 days. A total of 854/10,347 deaths (8.3%) were reported; 383 of these deaths (44.8%) were suspected/confirmed cases. The number of isolated health care workers remained high over the 30 days, while the number of suspected cases decreased during the last 2 weeks. The number of high-risk long-term care facilities decreased from 19/196 (9.5%) to 3/196 (1.5%). Conclusions COVIDApp can help clinicians rapidly detect and remotely monitor suspected and confirmed cases of COVID-19 among institutionalized individuals, thus limiting the risk of spreading the virus. The platform shows the progression of infection in real time and can aid in designing new monitoring strategies.

Background Covid-19 pandemic has particularly affected older people living in Long-term Care settings in terms of infection and mortality. Methods We carried out a cross-sectional analysis within a cohort of Long-term care nursing home residents between March first and June thirty, 2020, who were ≥ 65 years old and on whom at least one PCR test was performed. Socio-demographic, comorbidities, and clinical data were recorded. Facility size and community incidence of SARS-CoV-2 were also considered. The outcomes of interest were infection (PCR positive) and death. Results A total of 8021 residents were included from 168 facilities. Mean age was 86.4 years (SD = 7.4). Women represented 74.1%. SARS-CoV-2 infection was detected in 27.7% of participants, and the overall case fatality rate was 11.3% (24.9% among those with a positive PCR test). Epidemiological factors related to risk of infection were larger facility size (pooled aOR 1.73; P < .001), higher community incidence (pooled aOR 1.67, P = .04), leading to a higher risk than the clinical factor of low level of functional dependence (aOR 1.22, P = .03). Epidemiological risk factors associated with mortality were male gender (aOR 1.75; P < .001), age (pooled aOR 1.16; P < .001), and higher community incidence (pooled aOR 1.19, P = < 0.001) whereas clinical factors were low level of functional dependence (aOR 2.42, P < .001), Complex Chronic Condition (aOR 1.29, P < .001) and dementia (aOR 1.33, P <0.001). There was evidence of clustering for facility and health area when considering the risk of infection and mortality (P < .001). Conclusions Our results suggest a complex interplay between structural and individual factors regarding Covid-19 infection and its impact on mortality in nursing-home residents.

BackgroundDementia patients often show neuropsychiatric symptoms, known as behavioral and psychological symptoms of dementia (BPSD). These are a common motive for medical consultations, hospitalizations, and nursing home stays. Various studies have suggested that the high prevalence of psychotropic drug use to treat BPSD in institutionalized dementia patients may lead to impaired cognitive capacity, rigidity, somnolence, and other complications during the course of the illness. The aim of this study was to design a consensus-based intervention between care levels to optimize and potentially reduce prescription of psychotropic drugs in institutionalized patients with dementia and assess the changes occurring following its implementation.MethodsDesign: Prospective, quasi-experimental, pre/post intervention, multicenter study. Scope: 7 nursing homes associated with a single primary care team. Inclusion Criteria: Institutionalized patients diagnosed with dementia and under treatment with 1 or more psychotropic drugs for at least 3 months. Sample: 240 individuals; mean age, 87 years (SD: 6.795); 75% (180) women. Intervention: Creation of evidence-based therapeutic guidelines for psychotropic drug use in the treatment of BPSD by consensus between reference professionals. Joint review (primary care and geriatric care nursing home professionals) of the medication based on the guidelines and focusing on individual patient needs. Primary variable: Number of psychotropic drugs used per patient. Assessment: Preintervention, immediate postintervention, and at 1 and 6 months.ResultsOverall, the number of psychotropic drugs prescribed was reduced by 28% (from 636 before to 458 after the intervention). The mean number of psychotropic drugs prescribed per patient decreased from 2.71 at baseline to 1.95 at 1 month postintervention and 2.01 at 6 months (p < 0.001 for both time points). Antipsychotics were the drug class showing the highest reduction rate (49.66%). Reintroduction of discontinued psychotropic drugs was 2% at 1 month following the intervention and 12% at 6 months.ConclusionsA consensus guidelines-based therapeutic intervention with a patient-centered medication review by a multidisciplinary team led to a reduction in prescription of psychotropic drugs in institutionalized dementia patients.Electronic supplementary materialThe online version of this article (10.1186/s12877-018-1015-9) contains supplementary material, which is available to authorized users.

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Despite half a century of dedicated studies, medication adherence remains far from perfect, with many patients not taking their medications as prescribed. The magnitude of this problem is rising, jeopardizing the effectiveness of evidence-based therapies. An important reason for this is the unprecedented demographic change at the beginning of the 21st century. Aging leads to multimorbidity and complex therapeutic regimens that create a fertile ground for nonadherence. As this scenario is a global problem, it needs a worldwide answer. Could this answer be provided, given the new opportunities created by the digitization of health care? Daily, health-related information is being collected in electronic health records, pharmacy dispensing databases, health insurance systems, and national health system records. These big data repositories offer a unique chance to study adherence both retrospectively and prospectively at the population level, as well as its related factors. In order to make full use of this opportunity, there is a need to develop standardized measures of adherence, which can be applied globally to big data and will inform scientific research, clinical practice, and public health. These standardized measures may also enable a better understanding of the relationship between adherence and clinical outcomes, and allow for fair benchmarking of the effectiveness and cost-effectiveness of adherence-targeting interventions. Unfortunately, despite this obvious need, such standards are still lacking. Therefore, the aim of this paper is to call for a consensus on global standards for measuring adherence with big data. More specifically, sound standards of formatting and analyzing big data are needed in order to assess, uniformly present, and compare patterns of medication adherence across studies. Wide use of these standards may improve adherence and make health care systems more effective and sustainable.