A chronic metabolic condition, diabetes mellitus (DM) is characterized by consistently elevated blood sugar levels caused by abnormalities in either the action or secretion of insulin, or both¹. The International Diabetes Federation estimates that over 540 million persons worldwide had diabetes in 2023, and that figure is expected to increase significantly over the next several decades, making diabetes a major public health concern². Cardiovascular disease, nephropathy, neuropathy, and retinopathy are among the serious long-term sequelae linked to the condition that dramatically raise morbidity, death, and medical expenses³. To avoid these consequences and guarantee the best possible outcomes for patients, effective diabetes care is essential. This includes lifestyle changes, medication, and routine blood glucose testing⁴.

Despite advancements in diabetes care, disparities in management remain prevalent, particularly among low-income and rural populations⁵. Socioeconomic status profoundly influences health outcomes, with individuals from disadvantaged backgrounds often experiencing limited access to healthcare facilities, medications, and education regarding disease management⁶. Rural populations are particularly vulnerable due to geographic isolation, scarcity of healthcare providers, and lower availability of specialized diabetes care services⁷. These barriers contribute to delayed diagnosis, suboptimal glycemic control, and higher rates of diabetes-related complications, perpetuating a cycle of health inequity⁸.

These differences are caused by a number of variables, such as systemic healthcare inequality, cultural attitudes, health literacy, and social determinants of health⁹. Low-income individuals often face financial constraints that impede adherence to prescribed medications, regular monitoring, and dietary recommendations¹⁰. Additionally, limited transportation options and the long distances to healthcare facilities in rural areas exacerbate challenges in accessing routine care¹¹. Psychosocial factors, such as stress and limited social support, further complicate disease management, while healthcare system-level issues, including insufficient staffing and lack of community-based interventions, hinder effective delivery of diabetes care¹².

Studies have documented that patients in rural and low-income settings frequently exhibit poorer glycemic control, higher rates of hospitalization, and increased prevalence of diabetes-related complications compared to urban or higher-income populations¹³. Despite recognition of these disparities, targeted interventions addressing the unique challenges faced by these populations remain limited¹⁴. Community-based programs, telemedicine, and patient education initiatives have shown promise, yet their implementation is often inconsistent and insufficiently tailored to the specific needs of vulnerable populations¹⁵. Moreover, there is a lack of comprehensive understanding regarding the interplay between socioeconomic, cultural, and systemic factors that drive disparities in diabetes management¹⁶.

Despite growing evidence of disparities in diabetes care among low-income and rural populations, there is limited research exploring effective, context-specific interventions to improve outcomes; this study aims to investigate the factors contributing to diabetes management disparities in these populations and identify strategies to enhance equitable care.

Study Design and Setting: The Department of Endocrinology, Ayub Teaching Hospital Abbottabad, KP Pakistan, conducted this cross-sectional study. The research was conducted from January 2023 to December 2023, a 12-month timeframe.

Study Population: Adult patients (≥18 years old) from low-income or rural backgrounds who had been diagnosed with type 2 diabetes mellitus (T2DM) were included in the study population. Patients with gestational diabetes, type 1 diabetes, or severe comorbidities such as advanced renal, cardiac, or hepatic failure were excluded from the study.

Sample Size Calculation: The standard formula for cross-sectional research was used to get the sample size:

Where Z=1.96 (standard normal deviate at 95% confidence interval), p=0.6871 (prevalence of poor glycemic control among patients with diabetes in low- and middle-income countries) [17], and d=0.10 (margin of error). Substituting these values, the estimated sample size was 83 participants. To account for a potential 10% non-response rate, the final sample size was adjusted to 92 participants.

Sampling Technique: The method used was non-probability sequential sampling. All eligible patients who met the inclusion criteria and gave their agreement to participate were enrolled when they presented to the outpatient and inpatient departments during the study period.

Data Collection Tool: A systematic questionnaire created especially for this study was used to gather data. The questionnaire was divided into four sections: laboratory parameters (HbA1c, fasting blood glucose), clinical characteristics (duration of diabetes, comorbidities, family history), sociodemographic information (age, gender, residence, income level, and educational status), and diabetes management practices (treatment modality, adherence to medication, diet, and exercise). To guarantee clarity and reliability, the questionnaire was pretested on a small sample of patients, and modifications were made as necessary.

Operational Definitions and Standards: Poor glycemic control was defined as HbA1c ≥7.0%, in accordance with the American Diabetes Association (ADA) Standards of Care 2024 [18]. Laboratory measurements of HbA1c were performed in an NGSP-certified laboratory following standardized protocols [19].

Data Collection Procedure: Trained medical officers conducted face-to-face interviews using the questionnaire after obtaining informed consent. Blood samples were collected under aseptic conditions, and HbA1c was analyzed using high-performance liquid chromatography (HPLC). Data accuracy and completeness were cross-verified by the principal investigator.

Data Analysis: SPSS version 26 was used to code and analyze the data. The following descriptive statistics were computed: frequencies and percentages for categorical variables, and mean ± standard deviation for continuous data. Using logistic regression and chi-square tests, associations between glycemic control and clinical or sociodemographic factors were evaluated; a p-value of less than 0.05 was consider significant statistically.

Ethical Considerations: The study was conducted according to the guidelines set forth in the Declaration of Helsinki. The Institutional Review Board of the hospital gave its ethical approval. All participants gave their written, informed consent before beginning any activity. Patient information was kept confidential for the duration of the study.

The study included 92 participants in total. With a range of 28 to 75 years, the average age was 50.9 ± 11.2 years. Male participants made up slightly more than half (54.3%), while female participants made up 45.7%. A majority of the participants (55.4%) resided in rural areas, reflecting the targeted population of interest, whereas 44.6% were from urban regions. Socioeconomic disparities were evident, with 67.4% belonging to low-income households, compared to only 5.4% from higher-income backgrounds. Nearly three-fourths of the participants had only completed basic school or had no formal education, indicating a low level of educational attainment. Table 1 provides a summary of the specific sociodemographic features.

Table 1: Sociodemographic characteristics of study participants (N = 92)

Legend: The data are shown as n (%) for categorical variables and mean ± SD for continuous variables.

Participants' duration of diabetes ranged from 1 to 18 years, with a mean of 7.6 ± 4.1 years. Nearly one-third of patients (31.5%) had disease duration of less than 5 years, while 44.6% had been diagnosed for 5–10 years, and 23.9% had diabetes for more than a decade. Regarding treatment modalities, the majority were on oral hypoglycemic agents (56.5%), while 26.1% were managed exclusively with insulin. A smaller proportion (17.4%) required combined therapy. Comorbidities were frequently encountered; with hypertension (41.3%) and dyslipidemia (29.3%) being the most prevalent. 52.2% of individuals reported having a family history of diabetes, indicating a significant hereditary component. These findings are illustrated in Figure 1.

For continuous variables, data are shown as mean ± SD, and for categorical variables, n (%).

With respect to diabetes management practices, 62% of patients reported attending regular follow-up visits, while 38% had irregular follow-up patterns. Among the barriers to effective care, financial difficulties were the most frequently cited (46.7%), followed by distance to healthcare facilities (33.7%). A notable proportion of patients (20.7%) also reported a lack of social support, which negatively influenced adherence to management plans. Medication adherence was good in 69.6% of participants, while the remaining 30.4% reported missing or inconsistent medication use. Knowledge about diabetes self-care practices was limited, with only 31.5% of participants demonstrating adequate understanding. These observations highlight the interplay of socioeconomic and psychosocial barriers in effective diabetes care, as illustrated in Figure 2.

Data are presented as frequencies and percentages.

The mean HbA1c for the overall study population was 8.39% ± 1.57, with values ranging from 5.0% to 12.4%. The prevalence of poor glycemic control (defined as HbA1c ≥7.0%) was alarmingly high at 83.7% (77/92). Rural participants demonstrated significantly worse glycemic outcomes, with a mean HbA1c of 8.79 ± 1.25, compared to 7.89 ± 1.77 in urban residents. The independent samples t-test revealed this difference to be significant statistically (t = 2.75, p = 0.008). Similarly, poor glycemic control was more frequent among rural participants (92.2%) than among urban dwellers (73.2%), a difference that was statistically significant (χ² = 7.48, p = 0.006). These disparities are summarized in Table 2.

Table 2: Comparison of glycemic control by residence (N = 92)

For HbA1c, the data are shown as mean ± SD, and for poor glycemic control, n (%). The chi-squared test and the independent t-test were used. p less than 0.05 is regarded as significant.

To determine independent factors that indicate inadequate glycemic control An examination of multivariate logistic regression was conducted. Even after controlling for age, income, and length of diabetes, living in a rural area was a significant factor, with an odds ratio (OR) of 7.07 (95% CI: 1.76–28.5, p = 0.006). Duration of diabetes showed a borderline association (OR = 1.17, p = 0.085), suggesting that longer disease duration may contribute to poorer outcomes, although not reaching statistical significance in this sample. Income status and age did not demonstrate significant associations, highlighting that contextual factor such as residence may outweigh socioeconomic indicators in determining outcomes. These results highlight how important structural impediments are to the management of diabetes. Table 3 displays the specific regression results.

Table 3: Logistic regression predicting poor glycemic control (HbA1c ≥7.0)

Logistic regression coefficients (β), odds ratios (OR), 95% confidence intervals, and p-values are presented. p < 0.05 considered significant.

In this cross-sectional sample of 92 patients poor glycaemic control was highly prevalent (83.7%), and rural residence was strongly associated with worse outcomes (mean HbA1c 8.79% vs. 7.89% in urban patients; adjusted OR ≈ 7.1). Overall, participants were middle-aged, predominantly low-income, and had limited education factors that, together with structural barriers such as financial constraints and distance to facilities, appeared to undermine effective diabetes management. Medication adherence was reasonably good for most patients, yet knowledge of self-care was limited, suggesting that adherence alone was not sufficient to achieve glycaemic targets in this population. The multivariable model indicated that residence, as a proxy for access and structural conditions had a larger effect on control than individual-level socioeconomic variables in this sample. These results point to an urgent need for context-specific health-system interventions to reach rural and disadvantaged patients.

Given that two-thirds to three-quarters of patients typically fall short of the recommended HbA1c target in low- and middle-income countries, the high incidence of poor glycaemic control in our study is in line with those estimatess²⁰. Our figure (83.7%) falls at the higher end of this range and aligns with reports from Pakistan showing that poor control often affects 60–80% of clinic populations²¹. The rural–urban disparity observed also reflects wider evidence that rural patients experience worse outcomes due to lower access, higher costs, and reduced service availability²². Financial hardship, long travel distances, and limited self-care knowledge identified in this study have similarly been highlighted as major barriers in other regional studies²³. The discrepancy between self-reported adherence and actual glycaemic control suggests that taking medication on its own without ongoing monitoring, prompt dose adjustments, and organized instruction is not enough to produce the best results²⁴. In addition, the high comorbidity burden and the trend for longer duration of diabetes to worsen outcomes correspond with evidence that management in such populations requires integrated chronic disease care rather than isolated diabetes control²⁵. Overall, the findings reinforce the broader consensus that structural and health-system factors play a greater role than individual behaviour alone in explaining disparities, and without addressing these, diabetes outcomes are unlikely to improve in low-resource and rural contexts²⁶.

Limitations and Future suggestions: This study has several limitations. First, the results of this single-center study have limited generalizability to other geographic locations and healthcare environments due to its relatively small sample size. Second, its cross-sectional design restricts causal interpretation of the observed associations. Third, adherence and perceived barriers were self-reported variables that were influenced by social desirability bias and memory. In addition, we did not assess facility-level or health-system factors (e.g., medication stockouts, staffing levels, or laboratory availability) that may have influenced glycaemic outcomes.

Future research should involve larger, multi-center studies that include both primary and tertiary care settings to improve external validity. Longitudinal cohort designs would provide deeper insights into the causal relationship between socioeconomic, geographic, and clinical variables and long-term diabetes outcomes. Intervention-based studies, such as testing community health worker programs, telemedicine services, or subsidized medication schemes, are warranted to address the rural–urban gap. Developing sustainable and culturally appropriate ways to enhance diabetes management in low-resource communities will also require incorporating mixed methods approaches and health-system level assessments

This study found that a large percentage of diabetic patients had poor glycaemic control, and that living in a remote area, experiencing financial difficulties, and having restricted access to treatment were the main causes. Despite reasonable adherence, inadequate knowledge and structural barriers limited effective management. Addressing these disparities will require health-system level interventions, targeted patient education, and context-specific strategies to improve outcomes in low-income and rural populations.

1. “Diagnosis and classification of diabetes: standards of care in diabetes—2025.” Diabetes Care 48, no. Supplement_1 (2025): S27–S49. https://doi.org/10.2337/dc25-S002
2. International Diabetes Federation. IDF Diabetes Atlas, 10th edition. Brussels: International Diabetes Federation; 2023. Available from: https://idf.org/media/uploads/2024/06/IDF-Annual-Report-2023.pdf
3. Kulkarni A, Thool AR, Daigavane S. Understanding the clinical relationship between diabetic retinopathy, nephropathy, and neuropathy: a comprehensive review. 2024 Mar 21;16(3). DOI: 10.7759/cureus.56674
4. Centers for Disease Control and Prevention. Health and economic benefits of diabetes interventions. https://www.cdc.gov/nccdphp/priorities/diabetes-interventions.html
5. Steiger K, Herrin J, Swarna KS, Davis EM, McCoy RG. Disparities in acute and chronic complications of diabetes along the US rural-urban continuum. Diabetes Care. 2024 May 1;47(5):818–25. https://doi.org/10.2337/dc23-1552
6. Staloff JA, Morenz AM, Hayes SA, Bhatia-Lin AL, Liao JM. Area-Level socioeconomic disadvantage and access to primary care: A rapid review. Health Affairs Scholar. 2025 Apr 2:qxaf066. https://doi.org/10.1093/haschl/qxaf066
7. Javed A. Bridging the Healthcare Gap in Rural Populations: Challenges, Innovations, and Solutions. The American Journal of Medicine. 2025 Jan 21. https://www.amjmed.com/article/S0002-9343(25)00039-7/fulltext
8. Kurani SS, Lampman MA, Funni SA, Giblon RE, Inselman JW, Shah ND, Allen S, Rushlow D, McCoy RG. Association between area-level socioeconomic deprivation and diabetes care quality in US primary care practices. JAMA Network Open. 2021 Dec 1;4(12):e2138438. doi:10.1001/jamanetworkopen.2021.38438
9. Cooper ZW, Mowbray O, Johnson L. Social determinants of health and diabetes: using a nationally representative sample to determine which social determinant of health model best predicts diabetes risk. Clinical Diabetes and Endocrinology. 2024 Feb 25;10(1):4. https://doi.org/10.1186/s40842-023-00162-5
10. Alijanzadeh M, Hashemipour S, Attaran F, Saremi S, Modarresnia L, Ghafelehbashi H, Griffiths MD, Alimoardi Z. Availability and affordability of diabetes healthcare services associated with the frequency of diabetes-related complications. BMC Health Services Research. 2024 Dec 18;24(1):1586. doi.org/10.1186/s12913-024-12065-x
11. Biswas RK, Kabir E. Influence of distance between residence and health facilities on non-communicable diseases: An assessment over hypertension and diabetes in Bangladesh. PLoS One. 2017 May 18;12(5):e0177027. https://doi.org/10.1371/journal.pone.0177027
12. Jiakponna EC, Agbomola JO, Ipede O, Karakitie L, Ogunsina AJ, Adebayo KT, Tinuoye MO. Psychosocial factors in chronic disease management: implications for health psychology. Int J Sci Res Arch. 2024;12(2):117–28. https://doi.org/10.30574/ijsra.2024.12.2.1219
13. Flood D, Geldsetzer P, Agoudavi K, Aryal KK, Brant LC, Brian G, Dorobantu M, Farzadfar F, Gheorghe-Fronea O, Gurung MS, Guwatudde D. Rural-urban differences in diabetes care and control in 42 low-and middle-income countries: a cross-sectional study of nationally representative individual-level data. Diabetes Care. 2022 Sep 1;45(9):1961–70. https://doi.org/10.2337/dc21-2342
14. Khavjou O, Tayebali Z, Cho P, Myers K, Zhang P. Rural–urban disparities in state-level diabetes prevalence among US adults, 2021. Preventing Chronic Disease. 2025 Jan 16;22:E05. https://doi.org/10.5888/pcd22.240199
15. Kim KK, McGrath SP, Lindeman D. Improving Hypertension and Diabetes Outcomes with Digital Care Coordination and Remote Monitoring in Rural Health. arXiv preprint arXiv:2508.19378. 2025 Aug 26. https://arxiv.org/abs/2508.19378
16. Mankar A, Kawalkar U, Jadhao N, Joge U, Paldiwal A, Talapalliwar M, Patil MS, Jadhao Jr NR, Talapalliwar MR. Exploring the interplay of socioeconomic and behavioral factors: unraveling gender disparities in glycemic control among adult type 2 diabetic patients in outpatient care. 2024 Mar 19;16(3). DOI: 10.7759/cureus.56505
17. Azagew AW, Mekonnen CK, Lambie M, Shepherd T, Babatunde OO. Poor glycemic control and its predictors among people living with diabetes in low-and middle-income countries: a systematic review and meta-analysis. BMC Public Health. 2025 Dec;25(1):1–26. doi.org/10.1186/s12889-025-21828-y
18. American Diabetes Association Professional Practice Committee. Standards of Care in Diabetes—2024. Diabetes Care. 2024 Jan 1;47(Suppl 1):S1–S180. doi: 10.2337/dc24-SINT
19. National Glycohemoglobin Standardization Program (NGSP). NGSP Laboratory Network Protocol [Internet]. NGSP; 2010 [cited 2025 Sep 6]. Available from: https://ngsp.org/docs/Protocol.pdf
20. Fina Lubaki JP, Omole OB, Francis JM. Poor glycaemic control: prevalence, factors and implications for the care of patients with type 2 diabetes in Kinshasa, Democratic Republic of the Congo: A cross-sectional study. Frontiers in Clinical Diabetes and Healthcare. 2023 Nov 20;4:1241882. https://doi.org/10.3389/fcdhc.2023.1241882
21. Taimur H, Ahmad I, Khan H, Shirayama Y, Okamoto M, Aung MN, Shabbir S, Yuasa M. A scoping review of type 2 diabetes mellitus in Pakistan investigating the status of glycemic control, awareness, treatment adherence, complications and cost. Frontiers in Endocrinology. 2024 Nov 22;15:1441591. https://doi.org/10.3389/fendo.2024.1441591
22. Foss R, Fischer K, Lampman MA, Laabs S, Halasy M, Allen SV, Garrison GM, Sobolik G, Bernard M, Sosso J, Thacher TD. Disparities in diabetes care: differences between rural and urban patients within a large health system. The Annals of Family Medicine. 2023 May 1;21(3):234–9. DOI: https://doi.org/10.1370/afm.2962
23. Bukhsh A, Goh BH, Zimbudzi E, Lo C, Zoungas S, Chan KG, Khan TM. Type 2 diabetes patients' perspectives, experiences, and barriers toward diabetes-related self-care: a qualitative study from Pakistan. Frontiers in Endocrinology. 2020 Nov 27;11:534873. https://doi.org/10.3389/fendo.2020.534873
24. Inbaraj LR, Heylen E, Srinivasan K, Ekstrand ML. Is self-reported adherence a valid measure of glycaemic control among people living with diabetes in rural India? A cross-sectional analysis. Primary Care Diabetes. 2022 Dec 1;16(6):849–52. https://doi.org/10.1016/j.pcd.2022.10.009
25. Gomes MB, Tang F, Chen H, Cid-Ruzafa J, Fenici P, Khunti K, Rathmann W, Shestakova MV, Surmont F, Watada H, Medina J. Socioeconomic factors associated with glycemic measurement and poor HbA1c control in people with type 2 diabetes: the global DISCOVER study. Frontiers in Endocrinology. 2022 Apr 22;13:831676. https://doi.org/10.3389/fendo.2022.831676
26. Dugani SB, Mielke MM, Vella A. Burden and management of type 2 diabetes in rural United States. Diabetes/Metabolism Research and Reviews. 2021 Jul;37(5):e3410. https://doi.org/10.1002/dmrr.3410
27. Fitzgibbons RJ Jr, Forse RA. Clinical practice. Groin hernias in adults. N Engl J Med. 2015;372(8):756-763. doi: 10.1056/NEJMcp1404068
28. Muysoms FE, Miserez M, Berrevoet F, et al. Classification of primary and incisional abdominal wall hernias. Hernia. 2009;13(4):407-414. doi: 10.1007/s10029-009-0518-x
29. Simons MP, Aufenacker T, Bay-Nielsen M, et al. European Hernia Society guidelines on the treatment of inguinal hernia in adult patients. Hernia. 2009;13(4):343-403. doi: 10.1007/s10029-009-0529-7
30. The HerniaSurge Group. International guidelines for groin hernia management. Hernia. 2018;22(1):1-165. doi: 10.1007/s10029-017-1668-x
31. Köckerling F, Simon T. Current concepts of inguinal hernia repair. Visc Med. 2018;34(2):145-150. doi: 10.1159/000487278
32. Amid PK. Lichtenstein tension-free hernioplasty: its inception, evolution, and principles. Hernia. 2004;8(1):1-7. doi: 10.1007/s10029-003-0160-y
33. Luijendijk RW, Hop WC, van den Tol MP, et al. A comparison of suture repair with mesh repair for incisional hernia. N Engl J Med. 2000;343(6):392-398. doi: 10.1056/NEJM200008103430603
34. Awaiz A, Rahman F, Hossain MB, et al. Meta-analysis and systematic review of laparoscopic versus open mesh repair for elective incisional hernia. Hernia. 2015;19(3):449-463. doi: 10.1007/s10029-015-1351-z
35. Bittner R, Schwarz J. Inguinal hernia repair: current surgical techniques. Langenbecks Arch Surg. 2012;397(2):271-282. doi: 10.1007/s00423-011-0875-7
36. Henriksen NA, Montgomery A, Kaufmann R, et al. Guidelines for treatment of ventral and incisional abdominal wall hernias. Br J Surg. 2020;107(3):171-190.
37. Forbes SS, Eskicioglu C, McLeod RS, et al. Meta-analysis of randomized controlled trials comparing open and laparoscopic ventral and incisional hernia repair with mesh. Br J Surg. 2009;96(8):851-858. doi: 10.1002/bjs.6668
38. Patterson TJ, Beck J, Currie PJ, et al. Meta-analysis of patient-reported outcomes after laparoscopic versus open inguinal hernia repair. Br J Surg. 2019;106(7):824-836. doi: 10.1002/bjs.11139
39. Deerenberg EB, Timmermans L, Hogerzeil DP, et al. A systematic review of the surgical treatment of large incisional hernia. Hernia. 2015;19(1):89-101. doi: 10.1007/s10029-014-1321-x
40. Köckerling F, Sheen AJ, Berrevoet F, et al. The reality of general surgery training and increased complexity of abdominal wall hernia surgery. Hernia. 2019;23(6):1081-1091. doi: 10.1007/s10029-019-02062-z
41. Hope WW, Cobb WS, Adrales GL. Contemporary management of inguinal and ventral hernias: balancing recurrence prevention and quality of life. Surg Clin North Am. 2021;101(2):233-248. doi: 10.1016/j.suc.2020.11.004
42. Eklund A, Montgomery A, Bergkvist L, Rudberg C. Chronic pain 5 years after randomized comparison of laparoscopic and Lichtenstein inguinal hernia repair. Br J Surg. 2010;97(4):600-608. doi: 10.1002/bjs.6933
43. McCormack K, Scott NW, Go PM, Ross S, Grant AM. Laparoscopic techniques versus open techniques for inguinal hernia repair. Cochrane Database Syst Rev. 2003;(1):CD001785. doi: 10.1002/14651858.CD001785
44. Sajid MS, Leaver C, Baig MK, Sains P. Systematic review and meta-analysis of laparoscopic versus open preperitoneal mesh repair of inguinal hernia. Br J Surg. 2012;99(10):1467-1477. doi: 10.1002/bjs.8849
45. The HerniaSurge Group. International guidelines for groin hernia management. Hernia. 2018;22(1):1-165. doi: 10.1007/s10029-017-1668-x
46. Awaiz A, Rahman F, Hossain MB, Yunus RM, Khan S, Memon B, et al. Meta-analysis and systematic review of laparoscopic versus open mesh repair for elective incisional hernia. Hernia. 2015;19(3):449-463. doi: 10.1007/s10029-015-1351-z
47. Bittner R, Schwarz J. Inguinal hernia repair: current surgical techniques. Langenbecks Arch Surg. 2012;397(2):271-282. doi: 10.1007/s00423-011-0875-7
48. Eklund AS, Montgomery AK, Moss S, et al. Low recurrence rate and improved quality of life after laparoscopic versus open inguinal hernia repair. Ann Surg. 2006;243(5):653-659. doi: 10.1097/01.sla.0000216786.16856.3a
49. Köckerling F, Simon T. Current concepts of inguinal hernia repair. Visc Med. 2018;34(2):145-150. doi: 10.1159/000487278
50. Köckerling F, Bittner R, Kuthe A, et al. Training and learning curve in laparoscopic hernia surgery. Surg Endosc. 2015;29(8):2170-2177. doi: 10.1007/s00464-014-3921-6
51. Liang MK, Holihan JL, Itani K, et al. Ventral hernia management: expert consensus guided by systematic review. Ann Surg. 2017;265(1):80-89. doi: 10.1097/SLA.0000000000001701
52. Tolver MA, Rosenberg J, Bisgaard T. Early outcomes after laparoscopic versus open inguinal hernia repair: a systematic review. Surg Endosc. 2012;26(7):1847-1856. doi: 10.1007/s00464-011-2111-0.