Figure 1: Application of the Diagonal Brain Fraction formula to classify brain status
Regions |
||||||
Variable |
Arusha |
Kilimanjaro |
Manyara |
Tanga |
Totals |
P-value |
Gender |
|
|
|
|
|
|
Males (%) |
58.7 |
69.0 |
30.0 |
66.7 |
58.9 |
|
Frequency (n) |
98 |
20 |
3 |
2 |
123 |
0.010c |
Females (%) |
41.3 |
31.0 |
70.0 |
33.3 |
41.1 |
|
Frequency (n) |
69 |
9 |
7 |
1 |
86 |
|
Age (mean±sd) |
11.899±5.115 |
10.910±5.059 |
10.588±4.955 |
8.333±4.725 |
11.648±5.093 |
<0.0001b |
Note: Statistical tests: Chi-square (c); t-test (b) at 5% confidence level
Table 1: The proportion of individuals by age and gender from the four regions
Variable |
Arusha |
Kilimanjaro |
Manyara |
Tanga |
Totals |
P-value |
Age (years) |
|
|
|
|
|
0.00c |
0 – 6 (n, %) |
34, 20.4 |
7, 24.1 |
3, 33.3 |
1, 33.3 |
45, 21.5 |
|
7 – 12 (n, %) |
38, 22.8 |
8, 27.6 |
1, 10.0 |
2, 66.7 |
49, 23.4 |
|
13 – 18 (n, %) |
95, 56.9 |
14, 48.3 |
6, 60.0 |
- |
115, 55.0 |
|
Descriptive statistics are produced (frequency, percentage), Chi-square test used to compare the group difference at 5% confidence level.
Table 2: Distribution of cases by age groups and locations
Regions |
||||||
Variable |
Arusha |
Kilimanjaro |
Manyara |
Tanga |
Totals |
P-value |
DBF Status (Atrophy) |
||||||
Males (%) |
60.4 |
75.0 |
30.0 |
100.0 |
58.9 |
|
Frequency (n) |
55 |
12 |
3 |
1 |
123 |
0.027c |
Females (%) |
39.6 |
25.0 |
70.0 |
- |
41.1 |
|
Frequency (n) |
36 |
4 |
7 |
- |
86 |
|
DBF Status (Normal) |
||||||
Males (%) |
56.0 |
61.5 |
- |
50.0 |
55.83 |
|
Frequency (n) |
42 |
8 |
- |
1 |
51 |
0.173c |
Females (%) |
44.0 |
38.5 |
- |
50.0 |
44.17 |
|
Frequency (n) |
33 |
5 |
- |
1 |
39 |
|
Age (mean±sd) |
11.899±5.115 |
10.910±5.059 |
10.588±4.955 |
8.333±4.725 |
11.648±5.093 |
<0.0001b |
Note: Statistical tests: Chi-square (c); t-test (b) at 5% confidence level
Table 3: Percentage distribution of brain status by gender and regions
Regions |
||||||||||||||||||
Variable |
Arusha |
Kilimanjaro |
Manyara |
Tanga |
Mean |
Total |
Fisher’s test |
P-value |
||||||||||
Birth outside |
67.3 |
13.5 |
4.8 |
1.9 |
21.875 |
87.5 |
1.755 |
0.157 |
||||||||||
Yes |
12 73.1 |
0.5 13.9 |
0 4.8 |
0 1.4 |
3.125 23.3 |
12.5 93.3 |
1.028 |
0.381 |
||||||||||
Yes |
6.2 |
0 |
0 |
0.5 |
1.675 |
6.7 |
|
|
||||||||||
CNS infection |
||||||||||||||||||
No |
57.2 |
10.6 |
3.4 |
1.4 |
19.825 |
79.3 |
0.145 |
0.933 |
||||||||||
Yes |
22.1 |
3.4 |
1.4 |
0.5 |
5.15 |
20.7 |
|
|
||||||||||
Trauma |
|
|
|
|
|
|
|
|
||||||||||
No |
63.5 |
11.1 |
3.4 |
1.9 |
19.975 |
79.8 |
0.533 |
0.660 |
||||||||||
Yes |
15.9 |
2.9 |
1.4 |
0 |
5.05 |
20.2 |
|
|
||||||||||
Metabolic |
|
|
|
|
|
|
|
|
||||||||||
No |
74 |
12.5 |
4.3 |
1.9 |
23.175 |
92.8 |
0.304 |
0.660 |
||||||||||
Yes |
5.3 |
1.4 |
0.5 |
0 |
1.8 |
7.2 |
|
|
||||||||||
Drug Convulsions |
||||||||||||||||||
No |
66.3 |
10.6 |
2.9 |
1.4 |
20.3 |
81.2 |
1.414 |
0.240 |
||||||||||
Yes |
13 |
3.4 |
1.9 |
0.5 |
4.7 |
18.8 |
|
|
||||||||||
SOL & ICP |
|
|
|
|
|
|
|
|
||||||||||
No |
69.2 |
13.5 |
4.8 |
1.9 |
22.35 |
89.45 |
1.340 |
0.262 |
||||||||||
Yes |
10.1 |
0.5 |
0 |
0 |
2.65 |
10.6 |
|
|
||||||||||
Birth injury |
|
|
|
|
|
|
|
|
||||||||||
No |
71.6 |
13 |
3.4 |
1.9 |
22.475 |
89.9 |
1.733 |
0.161 |
||||||||||
Yes |
7.7 |
1 |
1.4 |
0 |
2.525 |
10.1 |
|
|
Table 4: The distribution of the Brain Atrophy determinants across the regions
Variable |
Coefficient (β) |
P-value |
Odds ratio (CI-95%) |
Birth Outside facility |
0.612 |
0.174 |
1.845(0.763-4.459) |
Immaturity |
-1.535 |
0.00 |
0.215(0.103-0.451) |
CNS infection |
1.488 |
0.000 |
4.427(2.129-9.205) |
Malnutrition |
1.092 |
0.102 |
2.981 (0.806-11.02) |
Trauma |
0.796 |
0.034 |
2.216 (1.062-4.624) |
Metabolic |
0.454 |
0.423 |
1.574 (0.518-4.779) |
Drug& Convulsions |
0.804 |
0.038 |
2.234 (1.045-4.778) |
Radiation injury |
-0.274 |
0.847 |
0.761(0.047-12.329) |
Birth injury |
0.981 |
0.066 |
2.667 (0.938-7.580) |
SOL &ICP |
1.353 |
0.018 |
3.870 (1.261-11.874) |
Table 5: Univariate analysis for the determinants of brain atrophy
Univariate regression analysis was done at 5% confidence level
Variable |
Coefficient (β) |
P-value |
Odds ratio (CI-95%) |
Birth injury |
1.248 |
0.029 |
3.483(1.135-10.692) |
CNS infection |
1.551 |
0.000 |
4.717(2.202-10.107) |
Trauma |
0.737 |
0.071 |
2.090(0.939-4.652) |
Drug &Convulsions |
0.264 |
0.542 |
1.302(0.558-3.039) |
SOL& ICP |
1.454 |
0.016 |
4.282(1.306-14.046) |
Table 6: Univariate analysis for the determinants of brain atrophy
Univariate regression analysis was done at 5% confidence level
Variable |
Normal |
Atrophy |
t-value |
P-value |
DBF-value (Mean ± Sd) |
0.79±0.03 |
0.69±0.04 |
19.81 |
0.0001*** |
Table 7: comparison of brain volume score among cases of brain atrophy and normal controls
Descriptive statistics (meanąsd) is computed, the DBF mean differences between normal and effected brain (atrophy) is compared using t-test at 5% confidence level and found that they are mostly significant different.
Figure 1: Application of the Diagonal Brain Fraction formula to classify brain status
CNS infections mark the peak among determinants of brain atrophy. Other conditions that lead to brain atrophy include, space occupying lesions, birth injury, trauma and trivial contribution from drugs and convulsive disorders.
Figure 2: Determinants of Brain Atrophy and their influence
A: Bi-lateral planum sphenoidale meningioma in 16 years boy with extracranial extension through the optic tract resulting into mild cerebral cortical atrophy. B: left frontal scalp abscess with frontal sinusitis with mild cerebral cortical atrophy. C: Left parietal focal brain atrophy in connection with history of trauma. D: White cerebella sign with bitemporal hypoattenuation due to birth asphyxia.
Figure 3: Varying additional determinants of Brain atrophy effects
A: Enlarged lateral ventricles due to severe hydrocephalus with failed shunt. High reading of Evans index >0.3 is evident. B: Brain re-expansion to normal volume or mild atrophy after successful shunt. Borderline sulcal width and ventricular width is shown. C: Severe brain volume loss despite
Figure 4: Hydrocephalus and possible outcomes after interventions ventriculo-peritoneal shunt (VPS) due to delayed intervention. More enlarged cortical sulcal width is shown indicating severe cortical brain atrophy
Figure 5: Principal component analysis of the determinants of brain atrophy and DBF
A: The PCA figure above shows that, Arusha region is affected by all the factors followed by Kilimanjaro region and Manyara. Tanga region is less affected despite its exposure to trivial radiation injury. B: PCA shows that female are exposed more by most contributing factors of brain atrophy compared to male individuals.
Figure 6: Regional and gender distribution of the determinants of brain atrophy
Figure 7: PCA showing distribution of the determinants of brain atrophy among cases and controls The figure above shows exposure distribution of risk factors among the normal and atrophied brain. Most factors point toward the atrophied brain compared to normal brain
Tables at a glance
Figures at a glance