Epidemiological studies are essential in medicine and public health as they help identify risk factors and causes of diseases. Additionally, they are key to planning, implementing, and evaluating health interventions aimed at preventing and controlling the spread of diseases. Among these studies, analytical observational studies, such as cross-sectional, case-control, and cohort studies, are the most used. The validity of their results largely depends on the robustness of the design, execution, and statistical analysis. Objective: The objective of this study is to examine the most common errors in the selection of methodological design and statistical tests in analytical observational studies and to provide recommendations to correct them. Methodology: A comprehensive review of the available literature on methodology in epidemiological observational studies was conducted, focusing on cross-sectional, case-control, and cohort studies. Common errors in the selection of designs and statistical tests were identified and analyzed. Results and Conclusions: Errors in the selection of methodological design and statistical tests are common in epidemiological observational studies. Based on the identified errors, a series of recommendations is provided to improve the selection of methodological design and statistical tests, thereby increasing the reliability of the results in cross-sectional, case-control, and cohort studies.

BACKGROUND: The matched case-control design, up until recently mostly pertinent to epidemiological studies, is becoming customary in biomedical applications as well. For instance, in omics studies, it is quite common to compare cancer and healthy tissue from the same patient. Furthermore, researchers today routinely collect data from various and variable sources that they wish to relate to the case-control status. This highlights the need to develop and implement statistical methods that can take these tendencies into account.
RESULTS: We present an R package penalizedclr, that provides an implementation of the penalized conditional logistic regression model for analyzing matched case-control studies. It allows for different penalties for different blocks of covariates, and it is therefore particularly useful in the presence of multi-source omics data. Both L1 and L2 penalties are implemented. Additionally, the package implements stability selection for variable selection in the considered regression model.
CONCLUSIONS: The proposed method fills a gap in the available software for fitting high-dimensional conditional logistic regression models accounting for the matched design and block structure of predictors/features. The output consists of a set of selected variables that are significantly associated with case-control status. These variables can then be investigated in terms of functional interpretation or validation in further, more targeted studies.

Objective: Previous studies have reported controversial results on the association between gout and the risk of cancer. This study aimed to investigate the relationship between gout and the incidence of head and neck cancer (HNC). Methods: The data of participants who underwent health checkups in 2009 were analyzed using the National Health Insurance Database in South Korea. A total of 14,348 HNC patients and 57,392 control participants were analyzed for a prior history of gout. Overlap weighting was applied, and odds ratios (ORs) of gout for HNC patients were analyzed. The overlap-weighted model adjusted for demographic, socioeconomic, and lifestyle factors and comorbidities. HNC sites were classified as oral cavity cancer, oropharyngeal cancer, nasopharyngeal cancer, hypopharyngeal cancer, nasal cavity/sinus cancer, larynx cancer, or salivary gland cancer, and the ORs of gout were estimated for each site. Results: Overall, patients with HNC had 1.12-fold greater odds of having gout (95% confidence intervals [CIs] = 1.04-1.20). According to the site of HNC, oral cavity cancer, oropharynx cancer, and larynx cancer demonstrated high odds of having gout (OR = 1.25, 95% CI = 1.16-1.34 for oral cavity cancer; OR = 1.08, 95% CI = 1.01-1.15 for oropharynx cancer; and OR = 1.12, 95% CI = 1.06-1.20 for larynx cancer). On the other hand, nasal cavity/sinus cancer, nasopharynx cancer, and salivary gland cancer presented low odds of having gout (OR = 0.78, 95% CI = 0.72-0.84 for nasal cavity/sinus cancer; OR = 0.89, 95% CI = 0.83-0.96 for nasopharynx cancer; and OR = 0.88, 95% CI = 0.81-0.96 for salivary gland cancer). Conclusions: A prior history of gout was associated with a high overall incidence of HNC. Oral cavity cancer, oropharynx cancer, and larynx cancer have a high incidence in gout patients. However, nasal cavity/sinus cancer, nasopharyngeal cancer, and salivary gland cancer have low incidences in gout patients. The impact of gout on HNC risk should be specifically considered according to the site of the HNC.

BACKGROUND: Fatigue is a common symptom that negatively affects the outcomes and functions of rheumatoid arthritis (RA) patients. This study aimed to assess the fatigue by two scales and validate their consistency, also to comprehensively evaluate fatigue-related risk factors in RA patients.
METHODS: In this case-control study, the fatigue of 160 RA patients and 60 healthy controls was evaluated by the Bristol Rheumatoid Arthritis Fatigue Multi-Dimensional Questionnaire (BRAF-MDQ) and the Chinese version of the Brief Fatigue Inventory (BFI-C). The 28-joint disease activity score using erythrocyte sedimentation rate of RA patients was assessed.
RESULTS: The BRAF-MDQ and BFI-C scores were elevated in RA patients versus healthy controls (all p < .001). Interestingly, BRAF-MDQ global fatigue score positively correlated with BFI-C global fatigue score in both RA patients (r = .669, p < .001) and healthy controls (r = .527, p < .001); meanwhile, Kendall\'s tau-b test showed a high consistency between BRAF-MDQ and BFI-C global fatigue scores in RA patients (W = 0.759, p < .001) and healthy controls (W = 0.933, p < .001). Notably, higher education level (В = -4.547; 95% confidence interval: -7.065, -2.029; p < .001) and swollen joint count (В = 1.965; 95% confidence interval: 1.375, 2.554; p < .001) independently related to BRAF-MDQ global fatigue score; higher education level (В = -0.613; 95% confidence interval: -0.956, -0.269; p = .001) and clinical disease activity index (В = 0.053; 95% confidence interval: 0.005, 0.102; p = .032) independently linked with BFI-C global fatigue score.
CONCLUSIONS: Fatigue commonly occurs in RA patients, which independently relates to education level and disease activity. Furthermore, BRAF-MDQ and BFI-C scales exhibit a high consistency in assessing fatigue.

In September 2023, France was one of the first countries that started a national immunisation campaign with nirsevimab, a new monoclonal antibody against respiratory syncytial virus (RSV). Using data from a network of paediatric intensive care units (PICUs), we aimed to estimate nirsevimab effectiveness against severe cases of RSV bronchiolitis in France. We conducted a case-control study based on the test-negative design and included 288 infants reported by 20 PICUs. We estimated nirsevimab effectiveness at 75.9% (48.5-88.7) in the main analysis and 80.6% (61.6-90.3) and 80.4% (61.7-89.9) in two sensitivity analyses. These real-world estimates confirmed the efficacy observed in clinical studies.

OBJECTIVE: The rate of readmission after hospitalisation for respiratory diseases has become a common and challenging clinical problem. Social and functional patient variables could help identify cases at high risk of readmission. The aim was to identify the nursing diagnoses that were associated with readmission after hospitalisation for respiratory disease in Spain.
METHODS: Case-control study within the cohort of patients admitted for respiratory disease during 2016-19 in a tertiary public hospital in Spain (n = 3781).
METHODS: Cases were patients who were readmitted within the first 30 days of discharge, and their controls were the remaining patients. All nursing diagnoses (n = 130) were collected from the electronic health record. They were then grouped into 29 informative diagnostic categories. Clinical confounder-adjusted odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated using logistic regression models.
RESULTS: The readmission rate was 13.1%. The nursing diagnoses categories \'knowledge deficit\' (OR: 1.61; 95%CI: 1.13-2.31), \'impaired skin integrity and risk of ulcer infection\' (OR: 1.45; 95%CI: 1.06-1.97) and \'activity intolerance associated with fatigue\' (OR: 1.56; 95%CI: 1.21-2.01) were associated with an increased risk of suffering an episode of hospital readmission rate at 30% after hospital discharge, and this was independent of sociodemographic background, care variables and comorbidity.
UNASSIGNED: The nursing diagnoses assigned as part of the care plan of patients during hospital admission may be useful for predicting readmissions.

Background/Introduction: Odontogenic infection is one of the main etiologies of deep neck infection (DNI). However, the relationship between chronic periodontitis (CP) and the incidence of DNI has not been examined. This study aimed to evaluate the incidence of DNI and peritonsillar abscess (PTA) after CP. Methods: The Korean National Health Insurance Service-National Sample Cohort 2002-2019 was used. In Study I, 4585 PTA patients were matched with 19,340 control I participants. A previous history of CP for 1 year was collected, and the odds ratios (ORs) of CP for PTA were analyzed using conditional logistic regression. In Study II, 46,293 DNI patients and 185,172 control II participants were matched. A previous history of CP for 1 year was collected, and conditional logistic regression was conducted for the ORs of CP for DNI. Secondary analyses were conducted in demographic, socioeconomic, and comorbidity subgroups. Results: In Study I, a history of CP was not related to the incidence of PTA (adjusted OR = 1.28, 95% confidence interval [CI] = 0.91-1.81). In Study II, the incidence of DNI was greater in participants with a history of CP (adjusted OR = 1.55, 95% CI = 1.41-1.71). The relationship between CP history and DNI was greater in groups with young, male, low-income, and rural residents. Conclusions: A prior history of CP was associated with a high incidence of DNI in the general population of Korea. Patients with CP need to be managed for the potential risk of DNI.

This study aimed to develop a predictive model for intensive care unit (ICU) admission by using heart rate variability (HRV) data. This retrospective case-control study used two datasets (emergency department [ED] patients admitted to the ICU, and patients in the operating room without ICU admission) from a single academic tertiary hospital. HRV metrics were measured every 5 min using R-peak-to-R-peak (R-R) intervals. We developed a generalized linear mixed model to predict ICU admission and assessed the area under the receiver operating characteristic curve (AUC). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from the coefficients. We analyzed 610 (ICU: 122; non-ICU: 488) patients, and the factors influencing the odds of ICU admission included a history of diabetes mellitus (OR [95% CI]: 3.33 [1.71-6.48]); a higher heart rate (OR [95% CI]: 3.40 [2.97-3.90] per 10-unit increase); a higher root mean square of successive R-R interval differences (RMSSD; OR [95% CI]: 1.36 [1.22-1.51] per 10-unit increase); and a lower standard deviation of R-R intervals (SDRR; OR [95% CI], 0.68 [0.60-0.78] per 10-unit increase). The final model achieved an AUC of 0.947 (95% CI: 0.906-0.987). The developed model effectively predicted ICU admission among a mixed population from the ED and operating room.

Acrylamide is a probable carcinogen. Its main sources are the diet and tobacco. The association between acrylamide intake from the diet and tobacco and prostate cancer (PCa) has not been previously evaluated. We aimed to evaluate the relationship between dietary acrylamide intake and exposure to acrylamide through cigarettes and PCa risk. A population-based case-control (CAPLIFE) study was conducted, including 428 incident PCa cases and 393 controls. Smoking and dietary information, with a validated food frequency questionnaire, was collected. We calculated the amount of acrylamide from both sources, and tertiles (Ts) were created. Multivariable logistic regression and restricted cubic spline models were applied to assess the association between exposure to acrylamide and PCa risk. The median was similar for acrylamide in both dietary and smoking acrylamide among PCa cases and controls. No association was observed between dietary acrylamide intake and overall PCa risk (adjusted ORT3vsT1 = 0.90 (95% CI 0.59, 1.37)). A risk trend was observed for acrylamide exposure from cigarette smoking (p-trend = 0.032), with the highest odds in those subjects with the high exposure to acrylamide through cigarettes (adjusted ORT3vsT1 = 1.67 (95% CI 0.92, 3.04)). The restricted cubic splines suggested a linear relationship. In conclusion, acrylamide from smoking could be positively associated with PCa risk, but no association was observed for dietary acrylamide.

BACKGROUND: Bipolar disorders are serious mental illnesses, yet evidence suggests that the diagnosis and treatment of bipolar disorder can be delayed by around 6 years.
OBJECTIVE: To identify signals of undiagnosed bipolar disorder using routinely collected electronic health records.
METHODS: A nested case-control study conducted using the UK Clinical Practice Research Datalink (CPRD) GOLD dataset, an anonymised electronic primary care patient database linked with hospital records. \'Cases\' were adult patients with incident bipolar disorder diagnoses between 1 January 2010 and 31 July 2017.
METHODS: The patients with bipolar disorder (the bipolar disorder group) were matched by age, sex, and registered general practice to 20 \'controls\' without recorded bipolar disorder (the control group). Annual episode incidence rates were estimated and odds ratios from conditional logistic regression models were reported for recorded health events before the index (diagnosis) date.
RESULTS: There were 2366 patients with incident bipolar disorder diagnoses and 47 138 matched control patients (median age 40 years and 60.4% female: n = 1430/2366 with bipolar disorder and n = 28 471/47 138 without). Compared with the control group, the bipolar disorder group had a higher incidence of diagnosed depressive, psychotic, anxiety, and personality disorders and escalating self-harm up to 10 years before a bipolar disorder diagnosis. Sleep disturbance, substance misuse, and mood swings were more frequent among the bipolar disorder group than the control group. The bipolar disorder group had more frequent face-to-face consultations, and were more likely to miss multiple scheduled appointments and to be prescribed ≥3 different psychotropic medication classes in a given year.
CONCLUSIONS: Psychiatric diagnoses, psychotropic prescriptions, and health service use patterns might be signals of unreported bipolar disorder. Recognising these signals could prompt further investigation for undiagnosed significant psychopathology, leading to timely referral, assessment, and initiation of appropriate treatments.