Assessing the likelihood of contracting COVID-19 disease based on a predictive tree model: A retrospective cohort study

BACKGROUND

Primary care is the major point of access in most health systems in developed countries and therefore for the detection of coronavirus disease 2019 (COVID-19) cases. The quality of its IT systems, together with access to the results of mass screening with Polymerase chain reaction (PCR) tests, makes it possible to analyse the impact of various concurrent factors on the likelihood of contracting the disease.

METHODS AND FINDINGS

Through data mining techniques with the sociodemographic and clinical variables recorded in patient's medical histories, a decision tree-based logistic regression model has been proposed which analyses the significance of demographic and clinical variables in the probability of having a positive PCR in a sample of 7,314 individuals treated in the Primary Care service of the public health system of Catalonia. The statistical approach to decision tree modelling allows 66.2% of diagnoses of infection by COVID-19 to be classified with a sensitivity of 64.3% and a specificity of 62.5%, with prior contact with a positive case being the primary predictor variable.

CONCLUSIONS

The use of a classification tree model may be useful in screening for COVID-19 infection. Contact detection is the most reliable variable for detecting Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases. The model would support that, beyond a symptomatic diagnosis, the best way to detect cases would be to engage in contact tracing.

Risk of underdiagnosis of hypertension in schizophrenia patients

AIM

Arterial hypertension requires proper screening and management, and its underdiagnosis in patients with schizophrenia (SZ) and/or antipsychotic treatment has been postulated. The objective of the study is to assess whether there are differences in the proportion of screened patients with a blood pressure >140/90 mmHg that are undiagnosed or not confirmed later (risk of underdiagnosis).

METHODS

Cross-sectional study of clinical records from SIDIAPQ (Spain) during the 2006-2011 period. Three groups were studied: SZ, no SZ but under antipsychotic treatment, and control groups. Patients with established hypertension, cardiovascular disease, dementia, or diagnosis of SZ or starting with antipsychotic treatment during this period were excluded.

RESULTS

The SZ group had a lower risk of underdiagnosis than the control group (OR 0.91; 95% CI: 0.83-0.99; p < 0.05), at the expense of men (OR 0.8; 95% CI: 0.71-0.9; p < 0.001) and patients younger than 50 years of age (OR: 0.84; 95% CI: 0.74-0.93; p < 0.003). In the no SZ but under antipsychotic treatment group there were some differences, but not in the overall results.

CONCLUSION

Preventive management of hypertension seemed to be sufficient for SZ and antipsychotic treatment patients. The lower prevalence of hypertension found in these groups may be due to other factors (blood pressure-lowering effect of psychoactive drugs or smoking) but these hypotheses must be evaluated with specific studies.

Screening of cardiovascular risk factors in patients with schizophrenia and patients treated with antipsychotic drugs: are we equally exhaustive as with the general population?

Many studies have previously shown increased cardiovascular risk factors related to schizophrenia independently from the use of antipsychotic drugs. However, a poorer effort in clinical detection and management of cardiovascular risk in patients with severe mental illness could also explain these results. To test this hypothesis, we analyzed the differences in screening and incidence of cardiovascular risk factors between schizophrenia, non-schizophrenic patients on treatment with antipsychotic drugs (NS-TAD) and the general population. Data from adult subjects assessed by high-quality register general practitioners from 2006 to 2011 were extracted from the Catalonian SIDIAP database. The schizophrenia, NS-TAD, and control groups were compared in terms of measurements and incidence of diabetes, dyslipidemia, obesity, hypertension, and smoking. A total of 4911 patients in the schizophrenia group, 4157 in NS-TAD group, and 98644 in the control group were included. Schizophrenia patients were screened for dyslipidemia and diabetes more frequently than the control group, while for obesity or hypertension, they were screened equal to controls. Also, as compared to the control group, the NS-TAD group was more frequently screened for obesity with no differences in dyslipidemia and diabetes and less frequently for hypertension. Smoking was less frequently screened in both study groups. The incidence of all risk factors studied in both study groups was higher than or equal to the control group, except for hypertension, which had lower incidence. The lack of screening of risk factors does not appear decisive in the increased cardiovascular risk of patients diagnosed with schizophrenia seen in primary care. Studies evaluating the possible under diagnosis of the risk factors are required.

ABBREVIATIONS

Schizophrenia (SZ); Treatment with antipsychotic drugs (TAD); Cardiovascular risk factor/s (CVRF); Without schizophrenia but on therapy with antipsychotic drugs (NS-TAD); Defined Daily Dose (DDD).

Effectiveness of an intensive E-mail based intervention in smoking cessation (TABATIC study): study protocol for a randomized controlled trial

BACKGROUND

Intensive interventions on smoking cessation increase abstinence rates. However, few electronic mail (E-mail) based intensive interventions have been tested in smokers and none in primary care (PC) setting. The aim of the present study is to evaluate the effectiveness of an intensive E-mail based intervention in smokers attending PC services.

METHODS/DESIGN

Randomized Controlled Multicentric Trial.

STUDY POPULATION

1060 smokers aged between 18-70 years from Catalonia, Salamanca and Aragón (Spain) who have and check regularly an E-mail account. Patients will be randomly assigned to control or intervention group.

INTERVENTION

Six phase intensive intervention with two face to face interviews and four automatically created and personal E-mail patients tracking, if needed other E-mail contacts will be made. Control group will receive a brief advice on smoking cessation.

OUTCOME MEASURES

Will be measured at 6 and 12 months after intervention: self reported continuous abstinence (confirmed by cooximetry), point prevalence abstinence, tobacco consumption, evolution of stage according to Prochaska and DiClemente's Stages of Change Model, length of visit, costs for the patient to access Primary Care Center.

STATISTICAL ANALYSIS

Descriptive and logistic and Poisson regression analysis under the intention to treat basis using SPSS v.17.

DISCUSSION

The proposed intervention is an E-mail based intensive intervention in smokers attending primary care. Positive results could be useful to demonstrate a higher percentage of short and long-term abstinence among smokers attended in PC in Spain who regularly use E-mail. Furthermore, this intervention could be helpful in all health services to help smokers to quit.

TRIAL REGISTRATION

Clinical Trials.gov Identifier: NCT01494246.

Comparison of the information provided by electronic health records data and a population health survey to estimate prevalence of selected health conditions and multimorbidity

BACKGROUND

Health surveys (HS) are a well-established methodology for measuring the health status of a population. The relative merit of using information based on HS versus electronic health records (EHR) to measure multimorbidity has not been established. Our study had two objectives: 1) to measure and compare the prevalence and distribution of multimorbidity in HS and EHR data, and 2) to test specific hypotheses about potential differences between HS and EHR reporting of diseases with a symptoms-based diagnosis and those requiring diagnostic testing.

METHODS

Cross-sectional study using data from a periodic HS conducted by the Catalan government and from EHR covering 80% of the Catalan population aged 15 years and older. We determined the prevalence of 27 selected health conditions in both data sources, calculated the prevalence and distribution of multimorbidity (defined as the presence of ≥2 of the selected conditions), and determined multimorbidity patterns. We tested two hypotheses: a) health conditions requiring diagnostic tests for their diagnosis and management would be more prevalent in the EHR; and b) symptoms-based health problems would be more prevalent in the HS data.

RESULTS

We analysed 15,926 HS interviews and 1,597,258 EHRs. The profile of the EHR sample was 52% women, average age 47 years (standard deviation: 18.8), and 68% having at least one of the selected health conditions, the 3 most prevalent being hypertension (20%), depression or anxiety (16%) and mental disorders (15%). Multimorbidity was higher in HS than in EHR data (60% vs. 43%, respectively, for ages 15-75+, P <0.001, and 91% vs. 83% in participants aged ≥65 years, P <0.001). The most prevalent multimorbidity cluster was cardiovascular. Circulation disorders (other than varicose veins), chronic allergies, neck pain, haemorrhoids, migraine or frequent headaches and chronic constipation were more prevalent in the HS. Most symptomatic conditions (71%) had a higher prevalence in the HS, while less than a third of conditions requiring diagnostic tests were more prevalent in EHR.

CONCLUSIONS

Prevalence of multimorbidity varies depending on age and the source of information. The prevalence of self-reported multimorbidity was significantly higher in HS data among younger patients; prevalence was similar in both data sources for elderly patients. Self-report appears to be more sensitive to identifying symptoms-based conditions. A comprehensive approach to the study of multimorbidity should take into account the patient perspective.