The study addresses two research questions, namely: 1) Are there relevant significant associations between the age, urban-rural, gender and sex, and the autoimmune diseases’ nosological entity, autoimmune diseases’ nosological sub-entities, and each of the autoimmune diseases? 2) Are there relevant significant associations between the age, urban-rural, gender and sex, and autoinflammatory diseases’ nosological entity, autoinflammatory diseases’ nosological sub-entities, and each of the autoinflammatory diseases?

A cross-sectional study with retrospective data collection was conducted to explore the age, urban-rural, gender, and sex-based differences in autoimmune and autoinflammatory diseases. The study strictly adhered to the cross-sectional reporting guidelines of the Strengthening of Reporting of Observational Studies in Epidemiology (STROBE) [25].

Bamako is the capital and largest city of Mali. Mali is a West African country, comprising 19 administrative regions. Mali is a developing country that is currently 184^th out of 189, according to the United Nations Human Development Index 2019. Mali’s population is estimated at 22,395,485 inhabitants, including 47.2% of young people under the age of 15 and 49.9% of people aged 15 to 64, according to the Fifth General Census of the Population and Housing in Mali. Mali has one of the highest fertility rates in the world, with over six children per woman in 2018. Since January 2016, the guaranteed minimum salary (SMIG) has been 40,000 FCFA (US$ 80). More than 20% of the country’s population live in urban areas and more than 60% live in rural areas. In Mali, health care is organized into three levels: the district health level (community health centers and reference health centers); the second level, which represents the regional hospitals; and the third level, which is anchored by the national hospitals and university hospital centers (UHC). Among the third level is the University Hospital Center of the Point G, where the present study was conducted between January 1, 2005 and December 31, 2019, which is 15 years. It is located in Bamako. This department of internal medicine, known as a center of excellence for diagnosis and management of autoimmune and autoinflammatory diseases, is staffed by educators, researchers, and practitioner internists. A total of 6,383 patients were hospitalized during the study period.

We included in this study all the medical records of patients hospitalized for autoimmune and/or autoinflammatory disease during the study period. We did not include in this study the outpatients, and the patients hospitalized with the diagnosis of autoimmune and/or autoinflammatory diseases in the internal medicine department at the University Hospital Center of the Point G outside the study period.

The independent study variables were the socio-demographic data, which included sex, age, profession, and residency. Autoimmune diseases’ nosological entity, its nosological sub-entities, and each of the autoimmune diseases; and autoinflammatory diseases’ nosological entity, its nosological sub-entities, and each of the autoinflammatory diseases are the dependent (outcome) variables, for which diagnoses were established on the basis of clinical and paraclinical data and/or validated diagnostic criteria according to the type of autoimmune and autoinflammatory diseases.

A pre-established survey form was designed and used to collect data on the sociodemographic and clinical characteristics. Sociodemographic variables such as sex, age, profession/gender-related occupation, residency, and clinical variables such as discharge diagnosis were collected from medical records and registry of hospitalization used for medical record identification.

During this study, we encountered certain problems related to the lack of information in the medical records. Some clinical hypotheses of autoimmune and autoinflammatory diseases were not confirmed because of financial difficulties in performing specific tests.

This was an exhaustive sampling of all cases of hospitalization for autoimmune and/or autoinflammatory diseases during the study period. The sample size was not calculated.

The collected data were entered into SPSS version 22 software for cleaning and analysis. Data cleaning was performed by checking and correcting for duplicates, completing missing data, and correcting outliers. We used Microsoft Excel to generate bar graphs. We conducted statistical analyses using Epi Info version 7.2 and SPSS version 22 software. We conducted univariate analysis to obtain the mean and standard deviation for quantitative data and numbers and percentages for qualitative data. In the bivariate analysis, we calculated odds ratios (OR), 95% confidence intervals (C. I.), and p-values. The outcome variables of interest for bivariate analysis were autoimmune diseases’ nosological entity, autoimmune diseases’ nosological sub-entities, each of the autoimmune diseases and autoinflammatory diseases’ nosological entity, autoinflammatory diseases’ nosological sub-entities, and each of the autoinflammatory diseases. The categorical independent variables of interest were age group, sex, profession, and residency. The sex refers to the biological male/female differences, and the gender refers to social roles typically associated with the sex of a patient like profession/occupation, in other words, the occupational male/female differences. The Chi-square (uncorrected and corrected) and Fisher’s exact tests were used to assess the statistical significance and strength of the associations between the categorical independent variables and the outcome variables. A two-tailed p-value < 0.05 was retained and considered as statistically significant.

According to the Helsinki guideline, research involving human subjects should be conducted ethically, with the well-being of the subject taking priority over scientific or societal interests. Our research study aimed to provide cross-sectional data on the relationship between age group, urban-rural, gender and sex-based differences, and all autoimmune and autoinflammatory diseases in the internal medicine department involving human subjects. However, we used secondary data from patients’ medical records (sociodemographic and clinical data) and did not use biological specimens.

In addition, the study was retrospective, and all data were extracted anonymously from the medical records and registry of hospitalization. Therefore, patients’ informed consent was not required.

Given the nature of the study, formal ethical approval from an ethics committee was not sought. However, formal permission to conduct this study was obtained from the General Director of University Hospital Center of the Point G. The medical records and registry of hospitalization were returned to the archive room immediately after exploitation.

During a fifteen-year study period, 331 cases of autoimmune diseases and autoinflammatory diseases, including 07 cases of associations, were noted from 317 medical records. These were 291 cases of autoimmune diseases and 40 cases of autoinflammatory diseases. Socio-demographic data are illustrated in the supplementary Figure 1. Out of the 317 medical records included in the study, females represented 65.0% of cases with a sex ratio of 0.54. The age group of 20 - 39 years accounted for 50.2% of the study population; the mean age of patients was 35.3 ± 16.3 years; and the extreme ages were 07 and 79 years. Houseworkers were found in 37.2% of cases. Patients came from urban areas in 65.5% of cases.

Figure 1. Distribution of patients according to sociodemographic data such as sex (A), age groups (B), residency (C), and profession (D).

Table 1 summarizes the comparison of patients between the different age groups and the autoimmune and autoinflammatory diseases. The age group of 20 - 39 years was significantly associated with autoimmune diseases (153 cases; OR = 5.02; 95% CI = 2.01 - 12.57; p = 0.000). The age group of 60 - 79 years was significantly associated with autoinflammatory diseases (16 cases; OR = 8.13; 95% CI = 3.75 - 17.61; p = 0.000).

Table 1. Distribution of patients according to age groups and autoimmune and autoinflammatory diseases.

Comparison of patients by sex and profession are mentioned in Supplement Figure 2. The profession of houseworker was exclusive to females (118 cases vs. 0), while farmers and workers were predominantly male (24 cases vs. 0 and 15 cases vs. 1 case, respectively). Table 2 shows the comparison of patients by profession and autoimmune and autoinflammatory diseases. There was no significant correlation between houseworkers and autoimmune diseases (109 houseworkers; OR = 1.58; 95% CI = 0.71 - 3.55; p = 0.261), or between farmers and autoimmune diseases (19 farmers; OR = 0.39; 95% CI = 0.13 - 1.12; p = 0.080). Autoinflammatory diseases occurred more frequently in houseworkers (9 houseworkers; OR = 0.45; 95% CI = 0.21 - 0.98; p = 0.039). Farmers were 2.54 times more likely to develop autoinflammatory diseases than other professions (6 farmers; OR = 2.54; 95% CI = 0.94 - 6.84; p = 0.114).

Table 2. Distribution of patients according to profession and autoimmune and autoinflammatory diseases.

Figure 2. Distribution of patients with autoimmune and autoinflammatory diseases according to sex and profession.

Table 3 summarizes the comparison of patients between the sex and the autoimmune diseases. Systemic autoimmune diseases were more frequently diagnosed in females than in males (63 cases vs. 7 cases; OR = 0.15; 95% CI = 0.07 - 0.35; p = 0.000). The systemic autoimmune diseases found were dominated by systemic lupus erythematosus (3 males vs. 40 females, p = 0.000) and rheumatoid arthritis (3 males vs. 13 females, p = 0.162). Females were 2.45 times more likely than males to have organ-specific autoimmune diseases (90 cases vs. 131 cases; OR = 2.45; 95% CI = 1.41 - 4.27; p = 0.001). Type 1 diabetes (64 males vs. 77 females; OR = 2.28; 95% CI = 1.43 - 3.65; p = 0.001) and Graves’ disease (10 males vs. 38 females, OR = 0.44; 95% CI = 0.20 - 0.92; p = 0.025) were the most common organ-specific autoimmune diseases.

Table 3. Distribution of patients according to sex and autoimmune diseases.

Autoimmune polyendocrinopathies^a: Graves’ disease + Addison’s diseases (2 cases).

Comparison of patients by sex and autoinflammatory diseases is mentioned in Table 4. There were no cases of monogenic autoinflammatory diseases in our study. Males were 2.04 times more likely than females to develop polygenic autoinflammatory diseases (20 cases vs. 20 cases; OR = 2.04; 95% CI = 1.05 - 3.99; p = 0.033). Systemic polygenic autoinflammatory diseases were 3.19 times more likely to be found in males than in females (5 cases vs. 3 cases; OR = 3.19; 95% CI = 0.75 - 13.61; p = 0.134). These systemic polygenic autoinflammatory diseases were dominated by systemic non-autoimmune vasculitis (5 cases vs. 1 case; OR = 9.67; 95% CI = 1.12 - 83.83; p = 0.021). Organ-specific polygenic autoinflammatory diseases were more common in females than in males (15 cases vs. 17 cases; OR = 1.74; 95% CI = 0.83 - 3.63; p = 0.138). Among the organ-specific polygenic autoinflammatory diseases, chronic inflammatory rheumatism (11 cases vs. 11 cases; p = 0.127), gout as microcrystalline arthropathies (8 cases vs. 8 cases; p = 0.197), and spondylarthropathies (2 cases vs. 1 case; p = 0.281), and chronic inflammatory bowel disease (4 cases vs. 6 cases; p = 0.000) were more prevalent.

Table 5 summarizes the comparison of patients between the residency and autoimmune diseases. Systemic autoimmune diseases were more prevalent in patients living in urban areas at the time of diagnosis (44 cases) than in patients living in rural areas (26 cases), (OR =0.77; 95% CI =0.44 - 1.33; p = 0.347). There were no statistically significant differences between patients living in urban areas and patients living in rural areas in regards to systemic autoimmune diseases such as systemic lupus erythematosus (28 cases vs. 15 cases; p = 0.719) and rheumatoid arthritis (11 cases vs. 15 cases; p = 0.913). Patients living in urban areas were more likely than patients living in rural areas to have organ-specific autoimmune diseases (153 cases vs. 68 cases; OR = 1.29; 95% CI = 0.78 - 2.14; p = 0.320). Type 1 diabetes (101 patients living in urban areas vs. 40 patients living in rural areas; p = 0.161) and Graves’ disease (35 patients living in urban areas vs. 23 patients living in rural areas; p = 0.385) were the most common organ-specific autoimmune diseases.

Table 4. Distribution of patients according to diseases, sex, and autoinflammatory.

Table 5. Distribution of patients according to residency and autoimmune diseases.

Sharp syndrome/mixed connective tissue diseases^a: Systemic lupus erythematosus + rheumatoid arthritis (1 case); systemic lupus erythematosus + rheumatoid arthritis + dermato-polymyositis (DPM) + systemic scleroderma (1 case); systemic lupus erythematosus + systemic scleroderma (4 cases); Autoimmune polyendocrinopathies^b: Graves’ disease + Addison’s disease (2 cases).

Comparison of patients between the residency and the autoinflammatory diseases are mentioned in Table 6. Patients living in urban areas were more likely than patients living in rural areas to have polygenic autoinflammatory diseases (25 cases vs. 15 cases; OR = 0.78; 95% CI = 0.39 - 1.54; p = 0.469). There were no statistically significant differences between patients living in urban areas and patients living in rural areas regarding systemic polygenic autoinflammatory diseases (4 cases vs. 4 cases; p = 0.281). These were dominated by systemic non-autoimmune vasculitis (3 patients living in urban areas vs. 3 patients living in rural areas; p = 0.394). Organ-specific polygenic autoinflammatory diseases were more frequent in patients living in urban areas than in patients living in rural areas (21 cases vs. 11 cases; OR = 0.91; 95% CI = 0.42 - 1.97; p = 0.810). Among the organ-specific polygenic autoinflammatory diseases, chronic inflammatory rheumatism (16 patients living in urban areas vs. 6 patients living in rural areas; p = 0.588): gout as microcrystalline arthropathies (11 patients living in urban areas vs. 5 patients living in rural areas; p = 0.913), spondylarthropathies (2 patients living in urban areas vs. 1 patient living in rural areas; p = 1.000), and chronic inflammatory bowel disease (5 patients living in urban areas vs. 5 patients living in rural areas; p = 0.391) were more represented.

Table 6. Distribution of patients according to residency and autoinflammatory diseases.

The result from our 15-year cross-sectional study revealed the existence of age-, urban-rural, gender-, and sex-based differences in autoimmune and autoinflammatory diseases. To our knowledge, it is the first study that has explored these aspects globally and exhaustively.

This study showed that the overall hospital frequency of autoimmune diseases and autoinflammatory diseases was slightly low, females were more represented than males, and study populations were younger. These results are in line with findings from previous studies [26]-[28]. In addition, autoimmune diseases and autoinflammatory diseases are part of rare diseases except for some cases such as rheumatoid arthritis and autoimmune thyroiditis [29][30].

Autoimmune diseases are defined as all pathological manifestations linked to the involvement of effectors of the immune system, B lymphocytes and T lymphocytes, specific to the antigens of the organism to which this system belongs (self-antigens). In contrast, autoinflammatory diseases are due to an abnormality of innate immunity [2][4]. To our knowledge, there are no epidemiological data from published work seeking age group-, urban-rural-, gender-, and sex-based differences in these two major nosological entities and their sub-entities: autoimmune diseases (systemic autoimmune diseases and organ-specific autoimmune diseases) and autoinflammatory diseases (monogenic autoinflammatory diseases and polygenic autoinflammatory diseases), but several pieces of evidence studied individually different autoimmune and autoinflammatory diseases. The age group of 20 - 39 years was significantly associated with autoimmune diseases; this is in concordance with previous studies [8][9][31]. Several studies showed that certain autoinflammatory diseases were more likely to be diagnosed in the elderly than in other age groups, in accordance with our study [13][32]. In contrast, certain autoinflammatory diseases were more often diagnosed in younger old years [14][13].

Gender generally refers to socially constructed roles, behaviors, and identities of women, men, and gender-diverse people that occur in a historical and cultural context and may vary across societies and over time. Gender influences how people view themselves and each other, how they behave and interact, and how power is distributed in society [33].

In our study, the profession of houseworker was exclusively for females, and farmer and worker for males, which strongly indicates that some occupations are devoted either to males or to females. There was a significant correlation between houseworker and autoinflammatory diseases. What are the potential underlying reasons? To determine these potential underlying reasons, such as exposure to specific indoor environmental agents, would provide valuable context and direction for future research. In other words, the houseworker-specific determinants associated with autoinflammatory diseases should be further explored in large cohort studies.

Sex generally refers to a set of biological attributes that are associated with physical and physiological features (e.g., chromosomal genotype, hormonal levels, internal and external anatomy). A binary sex categorization (male/female) is usually designated at birth (“sex assigned at birth”), most often based solely on the visible external anatomy of a newborn [33].

In our study, the systemic autoimmune diseases were more frequently diagnosed in females than in males. The systemic autoimmune diseases found were dominated by systemic lupus erythematosus and rheumatoid arthritis. Multiple studies found that systemic lupus erythematosus and rheumatoid arthritis were more prevalent in females than in males; these results parallel our findings [15][34]-[36]. The females were more likely than the males to have the organ-specific autoimmune diseases. Type 1 diabetes and Graves’ disease were the most common of the organ-specific autoimmune diseases. Males were 2.89 times more likely to develop type 1 diabetes than females in our study, consistent with some previous studies [37][38]. In contrast, Wändell et al. report no significant sex difference in type 1 diabetes in children 0 - 14 years of age [37]. This discrepancy may be explained by methodological approaches, notably the study population is stratified by age group in this study, but not in our studies [37].

In our study, there were no cases of monogenic autoinflammatory diseases. The males were 2.04 times more likely than the females to have polygenic autoinflammatory diseases. Furthermore, males were 3.19 times more likely to develop systemic polygenic autoinflammatory diseases than females, although this increase was not statistically significant. These systemic polygenic autoinflammatory diseases were dominated by systemic non-autoimmune vasculitis. Several epidemiological data found that some systemic non-autoimmune vasculitides, such as giant cell arteritis, are more frequently found in females than in males [39][40]. However, in our study, males were more likely to have had giant cell arteritis compared to females. These differences may reflect the participant inclusion criteria and the site of the study, which were hospital-based, monocentric with exhaustive recruitment of all autoimmune and autoinflammatory cases in our study.

Organ-specific polygenic autoinflammatory diseases were more common in females than in males. Among the organ-specific polygenic autoinflammatory diseases, gout as microcrystalline was the most prevalent. Males were 1.92 times more likely to have gout compared to females, as was found in this study [22].

The systemic autoimmune diseases, in our study, were more prevalent in patients living in urban areas than in patients living in rural. There were no statistically significant differences between patients living in urban areas and patients living in rural areas in regards to systemic autoimmune diseases such as systemic lupus erythematosus and rheumatoid arthritis, these results are not in line with findings from previous studies. Gergianaki et al. showed that the risk of systemic lupus erythematosus in urban areas was 2.08 times higher than in rural areas [41]. Then, Iltchev P et al. found that rheumatoid arthritis was more than twice as common in urban areas as in rural areas [42]. Both studies were population-based studies with large sample sizes, whereas ours was a hospital-based study, which may explain this contrast.

The patients living in urban areas were more likely than the patients living in rural areas to have organ-specific autoimmune diseases. The patients living in urban areas were not more likely to have had type 1 diabetes and Graves’ disease, compared to patients living in rural areas, in accordance with literature data [43][44].

The patients living in urban areas were more likely than the patients living in rural areas to develop polygenic autoinflammatory diseases. Systemic polygenic autoinflammatory diseases were dominated by systemic non-autoimmune vasculitis, among which giant cell arteritis was prevalent. This population-based study found that giant cell arteritis was significantly more frequent in patients living in urban areas than in patients living in rural areas, consistent with our study findings [45].

Organ-specific polygenic autoinflammatory diseases were more frequent in patients living in urban areas than in patients living in rural areas. The organ-specific polygenic autoinflammatory diseases were dominated by chronic inflammatory rheumatism, including gout as microcrystalline disease and chronic inflammatory bowel disease. Dehlin et al. studied the incidence and prevalence of gout in Western Sweden, in which there was no significant difference in the prevalence of gout in rural compared to urban areas; this is in concordance with our study findings [46].

Our study has limitations. Firstly, the patients were followed as outpatients in the internal medicine department, and the patients treated in other departments at the University Hospital Center of the Point G and other health structures for autoimmune and autoinflammatory disease, which may have led to under-ascertainment in the latter. A second limitation is that we had not confirmed the autoimmune and autoinflammatory diseases in certain patients because of socio-economic status. Thus, the patients did not undergo certain specific para-clinical examinations to confirm the diagnosis, which may cause confounding biases. Finally, there is a referral bias because the study was conducted at a single tertiary referral center; the patient population may not be representative of the general population, which may hamper generalizability.

Elsewhere, there was serious difficulty in comparing our findings with others, notably the two heterogeneous nosological entities and their nosological sub-entities. Whereas, it is well known that diseases with similar immune-genetic and pathophysiological characteristics are regrouped as a nosological entity and sub-nosological entities [1]. Similar therapeutic targets, diagnostic and therapeutic tools, diagnostic and therapeutic strategies, and preventive medicine strategies for these nosological entities and sub-nosological entities could be developed. In this context, it is crucial to determine globally the distribution of these two nosological entities and their nosological sub-entities, as well as each of the diseases taken individually, and to also estimate the burden of these diseases in order to adapt public health actions, particularly in low-income countries.

This study contains several strengths. First, it is a long-term observational study with an exhaustive method of sampling of all cases of autoimmune and autoinflammatory diseases. Second, consent procedures were not required for enrollment, therefore minimizing bias and slightly increasing the generalizability of the results. Third, our study demonstrates the scope of the problem regarding age, urban-rural, gender, and sex-based variations in autoimmune and autoinflammatory diseases, and we believe that our data provide sufficient grounds for a reexamination of the correlation between the age-, urban-rural-, gender-, and sex-specific determinants and the autoimmune and autoinflammatory diseases. And finally, our study provided an analytical epidemiological overview of these two heterogeneous nosological entities and their nosological sub-entities, as well as each of the diseases taken individually, which could allow i) health decision-makers to appreciate globally the distribution and to eventually estimate the burden of these diseases, then to adapt preventive medicine strategies by prioritizing the target population by age group, sex, living in urban or rural settings, and gender-related occupation regarding each of the autoimmune and autoinflammatory diseases, and the disease management strategies by prioritizing the acquisition of reference diagnostic tools and reference specific treatments of each autoimmune and autoinflammatory disease, ii) clinicians to integrate these findings into the diagnostic approach, notably anamnestic clues, iii) researchers to pursue the investigation of specific determinants of these socio-demographic factors associated with the autoimmune and autoinflammatory diseases.

The Malian registry of autoimmune and autoinflammatory diseases should receive more funding and be better structured in order to provide a large multi-center cohort study necessary to identify the significant correlations between age group-, urban-rural-, gender-, and sex-based differences, and also extend to ethnicity, seasonality, geographic, and occupational variations in autoimmune and autoinflammatory diseases. In addition, it should allow for more exploration of its specific determinants associated with the development of these autoimmune and autoinflammatory diseases.