Abstract
Gene alterations that are essentially present in every cell in the body cause many hereditary diseases. As a result, these ailments frequently impact many bodily systems, and the majority of them are incurable. To treat or manage some of the accompanying symptoms, there might be methods available. When monitored up to the age of 25, it is estimated that 5.3% of babies will experience a genetic disease. In order to build a modified intervention program for people with genetic disorders, this paper reviewed existing programs and interventions for people with genetic disorders. It did this by using information about sickle cell disease. Data was gathered at the sickle cell clinic at the State Hospital, Adeoyo, Ibadan, Oyo state, where all sickle cell patients receive care. 53.3% of the survey participants were male (n=81), whereas 46.7% of the participants were female (n=71). In terms of age, 59.2% (n=90) of the respondents are under 20 years old, 27.6% (n=42) are between 21 and 26 years old, 9.9% (n=15) are between 26 and 30 years old, 1.3% (n=2) are between 31 and 35 years old, and 0.7% (n=1) of the respondents are each in the age brackets of 36 to 40, 41 to 45, and over 45. This demonstrates that the age range between 0 and 30 years old has the highest percentage of respondents, whereas people older than 30 are infrequently found. The respondents' marital status was also taken into account; 91.4% (n=139) of them are single, 7.2% (n=11) are married, and 1.3% (n=2) are separated.
Author Contributions
Copyright© 2023
S. Oluwafemi Oyamakin, et al.
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Introduction
According to Taylor Nigeria has a high prevalence of sickle cell disease (SCD), a genetically based hemologic illness. Dr. JB Herrick discovered it for the first time in the blood of a West Indian medical student who was anemic around 1904. It is unclear precisely when, how, or where the sickle cell gene mutation took place. Nevertheless, it is hypothesized that it evolved among the Veddoids in the Arabian Peninsula's Middle East. From then, the sickle cell gene moved to Southern Europe, India, and Africa Gene alterations, which are essentially present in every cell in the body, are the cause of numerous genetic illnesses. These illnesses frequently impact several bodily systems as a result, and the majority of them have no known cures. To cure or control some of the accompanying signs and symptoms, however, there may be methods available. When followed up until the age of 25, 5.3% of babies are predicted to have a genetic disease. Hemoglobinopathies, glucose-6-phosphate dehydrogenase deficiency, and autosomal recessive diseases are more common in developing nations compared to western nations because of a higher proportion of consanguineous marriages and severe falciparum malaria in the distant past. Gene alterations that are seen in almost every cell in the body cause a number of hereditary diseases. As a result, these ailments frequently impact several bodily systems, and the majority of them cannot be treated. The related indications and symptoms may, however, be amenable to treatment or management. If a newborn is tracked up until the age of 25, it is predicted that 5.3% of them will have a genetic disease. Due to historically severe falciparum malaria, developing nations experience higher rates of hemoglobinopathies and glucose-6-phosphate dehydrogenase deficiency than western nations, and autosomal recessive disorders are more common due to a higher proportion of consanguineous marriages. In order to build a modified intervention program for people with genetic disorders, this paper reviewed existing programs and interventions for people with genetic disorders. It did this by using information about sickle cell disease. John Langdon Down, who documented many of its characteristics in "Observations on ethnic classification of idiots" in 1866, gave the condition, also known as trisomy 21, its name. However, Esquirol identified a person with Down syndrome for the first time in 1838. The relationship between the syndrome and congenital heart problems was originally noted by Garrod in 1894, and the precise association with atrioventricular septal anomalies was clarified by Abbott in 1931. Pamela V. Daniels (2011) used a logistic regression model for binary outcome to study the timely use of prenatal care and its effect on birth outcome in black women of low economic status in the south. Binary logistic regression and ordinary least square regression were used for the Analysis. She compared the models' fit using a secondary data that examines agency factors, structural factor and risk health behavior in predicting timing of prenatal care utilization and compliance and its effect on preterm birth and low birth weight. Her main objective was to investigate why low- income black women still experience loss of prenatal care use and compliance and worse adverse birth outcomes than other racial groups in the south. According to the literature, descriptive statistics, such as means, percentages, and frequency counts, chi-square tests, linear regression techniques, etc. are being used more frequently to analyze student satisfaction in relation to different explanatory variables. To examine the impact of many explanatory variables on dichotomous outcomes, these study methodologies are the most suited and useful approach. Although the use of binary logistic regression in the medical profession is well known, multiple studies on socio-economic data were found that employed binary regression to illustrate the theoretical underpinnings of the mathematical model. Congenital cardiac and gastrointestinal malformations, immunological issues, oncological conditions, increased susceptibility to infections, growth disruption, and other disorders are some of the conditions associated with Down syndrome. Early intervention and educational therapy, therapeutic modalities, medications and dietary supplements, assistive technology, and other measures are used to treat Down syndrome. This part focused on the research design, research settings, target sampling approach, data collection instrument, data collection validation method, and ethical consideration. To ascertain the population proportion of sickle cell disease patients in Adeoyo State Hospital, Ibadan, this study used a cross-sectional design. The State Hospital in Adeoyo, Ibadan, Oyo state, served as the site of this study. The sickle cell clinic, where all sickle cell patients are treated, is where the data was gathered. The final Wednesday of each month is reserved for their monthly check-ups and their weekly clinic day is Friday. According to our analysis of their record units, an average of 70 people visit the weekly check-up, and 150 people attend the monthly check-up. The survey used a sample size of 110 individuals, which was calculated using the cross-sectional study formula developed by Kish and Leslie in 1965. The sample size calculation took into account a 50% estimated proportion, a 95% level of confidence, and a 5% level of precision. The sample size to be used for this study is the Lesly Kish formula: Where: N = Sample size Z = the standard normal deviates corresponding to 95% C.I set at 1.96 P = proportion from previous study (prevalence) d = level of precision (5%) In the Adeoyo State Hospital in Ibadan, a self-administered questionnaire was used to gather information on the numerous factors impacting sickle cell disease. The questionnaire was broken down into four sections: The ramifications of social and economic variables Currently accessible government and multinational programs Potential Solutions Traditionally, either linear discriminant function analysis or ordinary least square (OLS) regression were used to answer these research problems. Due to their strict statistical presumptions — multivariate normally with equal variance and covariances for discriminant analysis, and linearity, normality, and continuous for OLS regression—both techniques were subsequently found to be less than ideal for handling dichotomous outcomes (Cabecca, 1994; Angel Efron, 1975). In the 1960s and early 1970s, logistic regression was put up as a substitute; it first became widely used in statistical software in the early 1980s. Since then, the social sciences have increasingly used logistic regression. Logistic regression usage is still rising as a result of statistical software's accessibility. When attempting to forecast the existence or absence of traits or an outcome based on the results of a predictor variable test, logistic regression might be helpful. Despite resembling a linear regression model, it is more appropriate for models where the dependent variable is dichotomous or polytomous. It can be used in a wider variety of research situations. This study's approach was based on logistic regression. Seven independent/predictor variables (financial constraint, shortage of health care workers, stigmatization, lack of awareness, poor utilization of HR, knowledge about SCD, and) and four dependent/outcome variables (availability of intervention program in respondent's neighborhood, accessibility of people with SCD to intervention program, benefits of people living with SCD to intervention program, and participation of people with SCD in intervention program) were used.
Discussion
According to the demographic data, there were 46.7% (n=71) female survey respondents and 53.3% (n=81) male survey respondents. In terms of age, 59.2% (n=90) of the respondents are under 20 years old, 27.6% (n=42) are between 21 and 26 years old, 9.9% (n=15) are between 26 and 30 years old, 1.3% (n=2) are between 31 and 35 years old, and 0.7% (n=1) of the respondents are each in the age brackets of 36 to 40, 41 to 45, and over 45. This demonstrates that the age range between 0 and 30 years old has the highest percentage of respondents, whereas people older than 30 are infrequently found. The respondents' marital status was also taken into account; 91.4% (n=139) of them are single, 7.2% (n=11) are married, and 1.3% (n=2) are separated. According to the researcher's analysis of the survey respondents' educational backgrounds, 8.6% (n=13) of the participants have completed primary school, 64.5% (n=98) make up the bulk of those who have completed secondary school, and 27.0% (n=41) have completed university education. The survey's results on the participants' occupations show that 14.5% (n=22) of them are employed, 48.0% (n=73) are jobless, 30.9% (n=47) are self-employed, and 6.6% (n=10) are unskilled. The majority of participants are unemployed, as evidenced by this. To adequately account for the participants' earning potential, the researcher calculated the average participant income and divided it into four scales, as shown in The descriptive statistics of the dependent variable parameters for the 152 respondents in the Oyo state survey on sickle cell illness are shown in The lack of HCWs (Health Care Workers) and ignorance of the need for adequate equipment are statistically significant with beta coefficients of (0.4751, 0.5578), respectively, according to the logistic regression model on the effect of intervention program availability on poor implementation of intervention program on sickle cell disease (SCD). The model equation is given as; ????1 = 1.6780 − 0.5329????1 + 0.4751????2+ 0.3374????3 + 0.5578????4 − 0.1145????5 + 0.4291????6 + 0.3122????7 The logistic regression model result on effect of access to intervention program on poor implementation of an intervention program on Sickle Cell Disease shows that shortage of Health Care Worker, lack of awareness and knowledge about Sickle Cell Disease are statistically significant with beta coefficient of (0.6937, -0.5904, 0.6361) respectively. ????2 = −1.3573 − 0.45154????1 + 0.6937????2 + 0.2121????3 − 0.5904????4+ 0.1361 + 0.6361????6 + 0.1989????7 On the beneficiary's effect on intervention program on poor implementation of an intervention program on Sickle Cell Disease, financial constraints, lack of awareness, and inadequate equipment are all significant with beta coefficient (0.7299, -0.6145, 0.5529) respectively. ????3 = 0.7782 + 0.7269????1 + 0.1645????2 − 0.2583????3− 0.6145????4 − 0.1683????5 + 0.2342????6 + 0.5529????7 On the effect of engagement to intervention program on poor implementation of an intervention program on Sickle Cell Disease, financial constraint, stigmatization, knowledge about Sickle Cell Disease, inadequate equipment are statistically significant with a beta coefficient (0.8846, -0.4184, 0.7787, 0.4355) respectively. ????4 = 2.0632 + 0.8846????1 + 0.1098????2 − 0.4184????3 + 0.2222????4 + 0.2019????5 + 0.7787????6 + 0.4355????7
Availability of Intervention programme
46 (30.3)
106 (69.7)
0.3026
0.4609
Access to Intervention programme
69 (45.4)
83 (54.6)
0.4539
0.4995
Benefit from Intervention programme
68 (44.7)
84 (55.3)
0.4473
0.4989
Engagement in Intervention programme
66 (43.3)
86 (56.6)
0.4342
0.4973