1.1. Osteoporosis

Osteoporosis is a bone condition defined as low bone mineral density due to changes in bone microstructure, possibly leading to low-impact, fragility fractures [1–3]. These osteoporotic fractures can cause significant issues such as leading to a prominent decrease in quality of life, increasing morbidity, mortality, and disability [1, 3, 4]. Over two hundred million people have osteoporosis with the incidence rate increasing with age, with more than 70% of individuals over the age of 80 being affected [1, 3]. Millions of individuals are affected worldwide, with around nine million fractures occurring per year due to osteoporosis [1, 4]. Along with the significant effects on quality of life, the increasing age of the American population is estimated to triple the number of osteoporotic fractures [1]. In developed countries, approximately 2%–8% of males and 9%–38% of females are affected by this condition [1]. This condition is common in postmenopausal women, with more than 50% of postmenopausal White women sustaining an osteoporotic-related fracture, with around 33% of senior women being unable to return to independence [1, 3, 4]. Although the risk of an osteoporotic fracture is less in men, around 20%, the 1-year mortality in men who have these fractures is twice that of women [1, 3]. Some risk factors for osteoporosis include medications, preexisting diseases, increasing age, low bodyweight, smoking, family history of osteoporosis, White or Asian race, early menopause, low levels of physical activity, and a personal history of a fracture from a ground-level fall or minor trauma after the age of forty [1, 3, 4].

1.2. Osteomalacia

Osteomalacia, also known as “soft bone disease”, is a metabolic bone disorder that results from insufficient mineralization of bone tissue due to various mineral deficiencies such as vitamin D, calcium, or phosphate [5–7]. Weakened, softened bones, manifesting as bone pain, muscle weakness, and an increased fracture risk is what results from these deficiencies [5–7]. With vitamin D deficiency being the most common nutritional deficiency globally, osteomalacia presents significant concerns regarding bone health worldwide [5, 6]. Debilitating conditions are associated with this disorder, and thus, understanding osteomalacia is critical, as early recognition and appropriate treatment can prevent these issues [5, 7]. The true global incidence of osteomalacia is greatly underestimated as various risk factors exist, predisposing individuals to this condition [5, 7]. Some of these risk factors include dark skinned individuals, a lack of sun exposure, low socioeconomic status, poor diet, and individuals who frequently wear full-body clothing [5–7]. It is important to keep in mind that these risk factors vary depending on geographic location, cultural preferences, and ethnicity [5, 7].

1.3. Rickets

Rickets is a condition, acquired or inherited, that is characterized by a defect in mineralization of the epiphyseal plates [6, 8, 9]. Acquired rickets is the most common cause of rickets worldwide and is most commonly caused by deficiencies in vitamin D, calcium, and phosphorus [6, 8, 9]. The pathophysiology of mineral deficiencies makes it similar to osteomalacia; however, rickets occurs exclusively in children, whereas adults develop osteomalacia after the epiphyseal plate fusion [6, 8]. The condition can present in various ways from being asymptomatic to irritability, growth retardation, and sudden death [6, 8, 9]. The prevalence of rickets has been increasing in both developed and developing countries, although more common in the developing populations [6, 9]. In the United States, there has been a substantial increase in the incidence over the past 20 years, especially in higher latitude regions as restricted sunlight (UVB) exposure is a major factor [6, 8, 9].

1.4. Paget Bone Disease

Paget disease is a multifactorial, skeletal growth disorder with abnormalities such as unusual bone growth [10, 11]. The condition is due to excess osteoclastic activity followed by a compensatory increase in osteoblastic activity, giving rise to the formation of less compact, disorganized bone that is mechanically weaker, highly vascular, and more susceptible to fracture [10, 11]. It generally presents as diffuse pain and radiologically visible bone changes in the musculoskeletal system [10, 11]. It is a localized disease that does not spread to other bones; however, it can deteriorate the affected area and possibly progress to a potentially fatal sarcoma [10]. Paget disease is generally seen in individuals over the age of fifty [10, 11]. The worldwide prevalence has been reported to have decreased over the past 2 decades and is currently between 1.5% and 8.3% [10, 12]. It is common in Caucasians of northern European descent, is equally common in males and females, and affects approximately 1–3 million people in the United States [10, 11].

1.5. Osteonecrosis

Osteonecrosis, also known as avascular necrosis, is a degenerative bone condition characterized by the death of cellular components of the bone due to an interruption of the subchondral blood supply [13, 14]. This condition typically affects the epiphysis of long bones at weight-bearing joints and is most common at the femoral head (most common), knee, talus, and humeral head [13, 14]. If this disease advances, it could result in subchondral collapse, threatening the viability of the joint involved [13, 14]. Some risk factors include repetitive trauma, anatomy, glucocorticoids, alcohol misuse, blood dyscrasias, and autoimmune diseases [13, 14]. Of all hip arthroplasties in the United States, approximately 10 percent are due to osteonecrosis [13]. It generally affects ages 30 to 65, with males tending to be more affected [13, 14].

1.6. Study Significance and Aim

The underserved community that is the Rio Grande Valley (RGV), has a distinct demographic profile, with a high prevalence of chronic conditions such as diabetes and obesity, which warrants investigation of various health issues [15]. This region is also medically underserved, impoverished, and has a large population of undocumented immigrants [16, 17]. Furthermore, the machismo construct is another factor that may contribute to the health of this community as this may deter Hispanic males from seeking medical care due to it being perceived as feminine [18]. To our knowledge, there are no studies that have analyzed all these bone disorders in this medically underserved community. We conducted this study in order to further the knowledge of orthopedics in this unique population. The main objective of our study was to analyze the demographic disparities between osteoporosis, osteomalacia, rickets, Paget bone disease, and osteonecrosis in the RGV. We hypothesized that the frequency of osteoporosis would be the most common bone condition (of those analyzed) in the RGV and that the demographics between the various bone conditions would vary depending on age, sex, BMI, and ethnicity but not on marital status.

2.1. Study Design and Data Collection

This was a retrospective chart review, and IRB approval was obtained prior to starting this study. Data were gathered from the University of Texas RGV (UTRGV) UTHealth electronic database. Data collection included medical charts from January 1, 2017, to January 1, 2024. We collected and analyzed medical charts of individuals who were diagnosed with osteoporosis, osteomalacia, rickets, Paget bone disease, and osteonecrosis. The various disorders were obtained by using ICD-10 diagnosis codes. The codes were as follows (ICD-10 codes): osteoporosis (M80 and M81), osteomalacia (M83), rickets (E55.0), Paget bone disease (M88), and osteonecrosis (M87). For each patient, various demographics were collected, including BMI, sex, age at diagnosis, race/ethnicity, and marital status. Osteomalacia, rickets, and Paget bone disease were categorized together as “other disorders” due to the low frequency of diagnoses.

2.2. Inclusion and Exclusion Criteria

Individuals over the age of 85 were included as a group indicated as > 85 years of age. Individuals who were not seen by a UTRGV-associated institution were not included in the study. If there were duplications of an individual’s medical chart, such as an individual having more than one appointment, the earliest date, the date the patient was diagnosed with any of the analyzed bone disorders, was included. If an individual was diagnosed with more than one of the bone disorders analyzed, all disorders were included and analyzed based on the date of diagnosis and demographics of the patient at the point in time of that specific diagnosis.

2.3. Data Analysis

Bivariate analyses were performed to assess the associations between each variable and the occurrence of any bone disorder (osteoporosis, osteomalacia, rickets, Paget bone disease, and osteonecrosis). For categorical variables (e.g., BMI, gender, and ethnicity), we used Chi-square tests to evaluate statistical independence with bone disorders. Fisher’s exact test was used when expected cell counts were less than five to ensure accurate p value estimation.

To identify factors associated with any type of bone disorder (out of any three bone disorder types) and healthy controls, we performed a binary logistic regression analysis. The binary logistic regression model included having any type of bone disorder as the dependent variable and the demographics as predictors.

The logistic regression model was used to estimate the log odds of having any type of bone disorder, producing coefficients (log odds) for each predictor. Results were reported as odds ratios with 95% confidence intervals. Statistical significance was defined at the 0.05 level.

The binary logistic model fit was evaluated using residual deviance and Akaike information criterion (AIC) values. All analyses were performed with R statistical software (Version: 4.2.2 R Core Team, 2022).

3.1. Osteoporosis

Gender, BMI, ethnicity, and marital status comparison between healthy and osteoporosis individuals (see Figure 1).

3.2. Osteonecrosis

Gender, BMI, ethnicity, and marital status comparison between healthy and osteonecrosis individuals (see Figure 2).

3.3. Other Disorders

Gender, BMI, ethnicity, and marital status comparison between healthy and other bone disorders (see Figure 3).

3.4. Bivariate Analysis

3.5. Age at Diagnosis

Healthy vs. osteoporosis showed a highly significant difference with osteoporosis occurring at later ages (Z = −29.92, adjusted p value (P.adj) < 0.0001). Osteonecrosis vs. osteoporosis showed a strong difference with osteoporosis occurring at a later age (Z = −12.02, P.adj < 0.0001). Healthy vs. other disorders showed a modest significant difference with other disorders occurring at later ages (Z = −2.82, P.adj = 0.028).

The violin plot (Figure 4) shows the distribution of age at diagnosis across four groups based on the “Type” variable: healthy, osteonecrosis, osteoporosis, and other. The shape of each violin indicates the density of data points, with wider sections reflecting higher densities. For the healthy group, the distribution is relatively narrow, with a symmetric shape, as suggested by the alignment of the median and the central region of the violin. In the osteonecrosis group, the violin is narrower, indicating less variation in age, and the median is centrally located, suggesting a balanced distribution. The osteoporosis group displays a wider spread in ages, with the violin shape suggesting potential skewness and a higher median age compared to other groups. For the other group, the violin shows a narrower distribution, with the median centrally positioned, reflecting symmetry in the distribution. Overall, the graph highlights the differences in the central tendencies (median) and variability in the age of diagnosis across the groups.

3.6. Binary Logistic Regression Analysis

When we considered all variables together, we had 2085 subjects (excluding any missing values), and they were used for the binary logistic regression model building purposes. Hispanic or Latino ethnicity showed a decreased risk of bone disorder (estimate = −0.2814), bordering on significance (p = 0.053). Female participants had significantly higher odds of bone disorder compared to males (estimate = 0.7861, p < 0.001). Obese individuals had lower odds (estimate = −0.7837, p < 0.001), and overweight individuals had lower odds (estimate = −0.3328, p = 0.047) of bone disorder, compared to those with a normal BMI. Underweight individuals showed significantly higher odds of bone disorder (estimate = 0.9605, p = 0.002). Age at diagnosis was a strong positive predictor (estimate = 0.0581, p < 0.001), indicating that the odds of bone disorder increased with age.