Primary care is the frontline of healthcare, where patients seek medical attention for a wide range of health concerns. Understanding the most common reasons people visit primary care physicians is crucial for healthcare providers, policymakers, and patients alike. By analyzing a collection of research studies, we can identify the top diagnoses encountered in primary care settings. This knowledge helps in resource allocation, medical training, and ultimately, in improving patient care.
To determine these top diagnoses, a comprehensive review of existing studies was conducted. This review focused on research performed in general practice or primary care settings. The studies included needed to report at least 10 Reasons For Visits (RFVs) and involve a significant number of patient encounters – specifically, a minimum of 20,000 visits or data from at least 5 clinicians over a year, or 7,500 patients over a year. This large scale approach ensured a robust and representative dataset for analysis. Observational studies were prioritized to reflect real-world primary care scenarios.
Studies were carefully selected to represent typical primary care practice. Research focusing on very specific types of visits, particular conditions (like only acute illnesses), or selected populations (such as just teenagers) were excluded to maintain a broad view of general primary care. Similarly, studies centered on visits resulting from specialist referrals or those published before 1996 were also not included to ensure contemporary and generalizable data. When multiple reports used the same data, the most recent and complete datasets were chosen, unless different analyses were performed in separate publications, in which case both could be considered.
Data from the included studies were meticulously extracted. The primary focus was on the reported RFVs – essentially, the reasons patients presented for care or the problems doctors managed. For each study, the top RFVs (up to 20) were noted, along with the frequency of visits associated with each condition, whether presented as a number, percentage, or rate. Details about each study’s design were also gathered, such as whether the RFV was reported by patients or clinicians, the total visits, the number of clinicians or practices involved, location, data collection period, patient demographics (gender and age), and the diagnostic coding system used (like the International Classification of Primary Care or ICD codes).
To ensure the quality of the included studies, a risk of bias assessment was performed. Five key characteristics were evaluated for each study. These included whether the study sample was representative of clinicians (considering gender, experience, and practice size) and patients (considering gender, urban/rural mix, and age range). Other factors assessed were whether data collection was prospective or retrospective, if a specific coding system was used, and the duration of data collection (at least one year being preferable). Each characteristic was scored to reflect the potential for bias in the study findings.
The reported RFVs were then categorized into “general” and “specific” types. General categories were broader groupings like “respiratory problems,” while specific categories were more precise diagnoses such as “pneumonia.” To standardize the data across different studies which might use varying terms, a uniform coding system was applied within each category. For example, various descriptions like “back complaint,” “dorsopathies,” “back symptoms,” “dorsalgia,” “low back symptoms,” and “neck pain” were all categorized under a single, more standardized term: “back pain/spinal pain.” This standardization process was essential for combining data across studies effectively.
To synthesize the findings and identify the most common RFVs, a ranking system was employed. Within each study, RFVs were ranked from most to least frequent. Since studies reported visit frequency in different ways (number, percent, rate, etc.), the rank of each RFV was used as a measure of its relative frequency. For each study, the top 20 ranked RFVs were assigned scores, with the most common getting a score of 20, the second most common a score of 19, and so on. RFVs outside the top 20 in a study received a rank of zero for that study. These ranks were then combined across all studies, and average ranks were calculated for each RFV. The RFVs with the highest average ranks were identified as the most commonly seen in primary care. Any RFV that appeared in only one study was excluded from the combined ranking to ensure the common diagnoses were consistently observed across multiple research settings.
Further analysis explored potential variations in RFV rankings. Countries were classified based on their economic development status (developed or developing, according to United Nations classifications), and the average ranks of clinician-reported RFVs were compared between these groups. Additionally, subgroup analyses from studies (e.g., based on clinician or patient gender, or practice setting) were also combined using the same ranking approach, provided that at least two studies offered data for each subgroup. This allowed for a deeper understanding of how common diagnoses might differ across various contexts within primary care.
By systematically reviewing and synthesizing data from multiple studies, this analysis provides a robust overview of the top 20 diagnoses in primary care. This information is invaluable for understanding the demands on primary care services and for guiding improvements in healthcare delivery, training, and research priorities within this essential field of medicine.
