Primary care settings are the frontline of healthcare, addressing a wide array of patient needs. To effectively understand and manage healthcare delivery in these settings, identifying the most frequent diagnoses is crucial. This article outlines the methodology used to select studies focusing on the reasons for patient visits (RFVs) in primary care, a critical step in analyzing the landscape of common diagnoses. This rigorous approach ensures that the data analyzed is robust and representative of typical primary care practice.
Inclusion Criteria for Study Selection
The selection of relevant studies was conducted meticulously by three independent reviewers to minimize bias and ensure comprehensive data. Studies were included based on several key criteria to ensure they accurately reflected primary care practice. Firstly, the study setting had to be explicitly within general practice or primary care environments. Secondly, to ensure sufficient data depth, each study was required to report a minimum of ten Reasons For Visits (RFVs). Finally, to capture a substantial volume of primary care interactions, studies needed to include data from at least 20,000 patient visits or involve a minimum of 5 clinicians over at least one year, or alternatively, cover 7,500 patients over a year or more. These thresholds were established to represent a significant practice size, approximating a scenario with five clinicians each seeing 20 patients daily over 200 working days annually. Observational study designs were a prerequisite for inclusion.
Exclusion Criteria to Maintain Focus
To maintain the focus on general primary care and avoid skewing the data with specialized or limited scopes, specific exclusion criteria were applied. Studies were excluded if they concentrated on particular types of visits, such as routine health check-ups, or if they were limited to specific medical conditions, like acute illnesses only. Furthermore, studies focusing on select patient demographics, for example, adolescents exclusively, were also excluded to ensure a broad representation of the primary care patient population. Visits resulting from specialist referrals, such as to pediatrics or internal medicine, were not considered as they represent a different level of care. Lastly, to ensure contemporary relevance, studies published before 1996 were excluded. In instances where multiple publications drew data from the same source, the most recent and comprehensive datasets were prioritized. Duplicate publications were only considered if they presented distinct analyses, such as subgroup analyses, offering unique insights.
Data Extraction Process
To ensure accuracy and consistency, data extraction was performed independently by two reviewers. The primary focus of data extraction was the reported Reasons For Visits (RFVs). These RFVs were defined as the patient’s presenting complaints or the health problems managed by the primary care physicians. For each of the top RFVs, up to a maximum of 20 per study, the number, percentage, or rate of visits associated with each condition was recorded. Beyond RFVs, descriptive characteristics of each study were also collected. This included whether the RFV was reported by patients or clinicians, the total number of visits analyzed, the number of clinicians or practices involved, the geographical location and duration of data collection, the proportion of female patients, the percentage of patients aged 65 and older, and the coding system utilized (e.g., International Classification of Primary Care, ICD-9, ICD-10).
Bias Risk Assessment
To critically evaluate the quality and reliability of the included studies, a risk of bias assessment was conducted using a scoring system across five key characteristics. Each characteristic was scored from 0 (high risk of bias) to 1 (low risk of bias). The characteristics assessed were: representativeness of the clinician sample (judged by having at least two of these three features: inclusion of both male and female clinicians, no restrictions on years in practice, and no limitations based on practice size); representativeness of the patient sample (assessed by at least two of these criteria: inclusion of both male and female patients, a mix of urban and rural settings, and no age group restrictions); data collection method (prospective data collection scored 1, retrospective 0); specification of a coding system (yes = 1, no = 0); and the duration of data collection (one year or more = 1, less than one year = 0).
Data Categorization and Analysis
The reported RFVs were categorized into two levels: “general” and “specific”. General categories represented broader groupings, for example, “respiratory” issues. Specific categories were more precise diagnoses, such as “pneumonia”. Within each category type, a standardized coding scheme was applied to ensure consistency across studies. For instance, under specific RFVs, various descriptions like “back complaint,” “dorsopathies,” “back symptoms,” “dorsalgia,” “low back symptoms,” and “neck pain” were uniformly coded as “back pain/spinal pain.” To analyze the frequency of visits, RFVs from each study were ranked by their prevalence. Since studies reported frequency in different formats (number, percent, rate), the rank of each RFV was used as a standardized measure of relative frequency. For each study, the top 20 RFVs were ranked from 20 (most common) down to 1. RFVs outside the top 20 received a rank of zero. These ranks were then combined, and mean ranks were calculated for each RFV across all studies. The RFVs with the highest mean ranks were identified as the most common. RFVs present in only a single study were excluded from the combined analysis to ensure robust findings.
This rigorous methodology provides a strong foundation for understanding the most frequent diagnoses encountered in primary care, contributing valuable insights for healthcare planning and resource allocation.
