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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue IV, April 2026
Statistical Analysis of Accidental Deaths in India
Palle Bhavana
1
, M. Rohit Kumar
2
, G. Anjali
3
, Dr. B. Sainath
4
, Dr. Y. Raghunatha Reddy
5
Department of OR & SQC, Rayalaseema University, Kurnool, India
DOI:
https://doi.org/10.51583/IJLTEMAS.2026.150400107
Received: 20 April 2026; Accepted: 25 April 2026; Published: 19 May 2026
ABSTRACT
Accidental deaths represent a significant public health and socio-economic concern in India. This study presents
a comprehensive statistical analysis of accidental deaths categorized into natural causes and unnatural
causes over a period of seven years (2019–2025). The data used in this study is secondary in nature and collected
from official records including police reports, hospital data, and government publications.
The study applies descriptive statistics, ABC analysis, t-test, ANOVA, and Tukey’s post hoc test to identify
patterns, variations, and significant differences among accident categories. The results indicate that certain
causes such as lightning, exposure to cold, and other causes contribute significantly to total accidental
deaths.Further, ANOVA results confirm the presence of statistically significant differences among accident
categories across years, and Tukey’s test identifies specific group differences. The findings provide insights for
policymakers to implement targeted safety measures and reduce accidental deaths.
Keywords: Accidental Deaths, ABC Analysis, ANOVA, Tukey Test, Statistical Analysis, India
INTRODUCTION
An accident is an unplanned event that results in injury, death, or damage. Accidental deaths are broadly
classified into:
Natural causes (environmental factors like lightning, floods, cyclones)
Unnatural causes (human-related factors like road accidents, drowning, poisoning)
These deaths have been increasing due to population growth, urbanization, and environmental changes
Accidental deaths not only result in loss of life but also create serious economic and social impacts on society
Theoretical Background
Accidental deaths arise from both environmental and human-related factors. Natural causes include climatic and
environmental events such as lightning, floods, landslides, and cyclones. Unnatural causes include human-
induced incidents such as road accidents, drowning, poisoning, and electrocution.
Previous studies have shown that accidental deaths are influenced by multiple factors including environmental
conditions, lack of safety awareness, and inadequate preventive measures.
Statistical techniques such as descriptive statistics, ANOVA, and classification methods are widely used to
analyze accident data and identify significant patterns. These methods help in understanding variations across
categories and time periods.
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Need of the Study
Accidental deaths have become a serious social and public health issue in recent years. The increasing number
of incidents highlights the need for systematic analysis.
Most available data provide only descriptive information without detailed statistical interpretation. Therefore,
there is a need to apply statistical techniques to:
Identify major causes of accidental deaths
Analyze variations across years
Provide data-driven insights for policy decisions
This study aims to bridge this gap by applying statistical tools to accident data.
Research Design and Source
The present study is analytical and descriptive in nature. It focuses on examining accidental deaths in India by
classifying them into natural and unnatural causes over a period of seven years (2019–2025). The study aims to
identify patterns, variations, and significant differences among accident categories using statistical techniques.
The study is based entirely on secondary data collected from reliable sources such as police records, hospital
records, and government reports
Outlier Treatment
To ensure the reliability and accuracy of the analysis, the dataset was examined for the presence of extreme
values (outliers). Outliers may arise due to data recording errors or unusual events such as natural disasters or
sudden spikes in accident cases.
The identification of outliers is important because extreme values can distort statistical results such as mean and
variance. In this study, the data was carefully reviewed, and no abnormal or inconsistent values affecting the
analysis were considered. Since the dataset is based on officially recorded reports, it is assumed to be reliable
and consistent for statistical analysis.
Thus, all observations were retained for further analysis.
Mathematical and Statistical Model
The study employs statistical techniques to analyze variations in accidental deaths across different causes and
years.
Let:
Xij
The two-way classification model is given by:
X
ij
=μ+α
i
+β
j
+ϵ
ij
Where:
μ = overall mean number of deaths
α
i
= effect of the i
th
accident cause
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β
j
= effect of the j
th
year
ϵ
ij
= random error term
This model helps in analyzing variation due to both causes (rows) and years (columns).
In addition, descriptive statistics, ABC analysis, t-test, ANOVA, and Tukey test are used to support the analysis
Assumptions of the Model
The validity of the statistical analysis is based on the following assumptions:
1. Independence: The observations of accidental deaths are independent across causes and years.
2. Normality: The data is assumed to be approximately normally distributed for applying statistical tests
such as t-test and ANOVA.
3. Homogeneityof Variance: The variance among different groups (causes or years) is assumed to be
equal.
4. Additivity of Effects: The effects of causes and years are additive in nature.
These assumptions ensure that the results obtained from statistical methods are valid and reliable.
Hypothesis Formulation
To examine whether there are significant differences among accident causes and years, the following hypotheses
are formulated:
For Causes (Rows):
H₀₁: There is no significant difference among the mean number of deaths due to different accident causes.
H₁₁: There is a significant difference among the mean number of deaths due to different accident causes.
For Years (Columns):
H₀₂: There is no significant difference among the mean number of accidental deaths across years.
H₁₂: There is a significant difference among the mean number of accidental deaths across years.
Decision Rule:
If Fcal>FtabFcal>Ftab → Reject H₀
If Fcal<FtabFcal<Ftab → Accept H₀]
Statistical Analysis – Natural Causes
Descriptive Statistics Analysis
Descriptive statistics is used to summarize and present the data related to natural causes of accidental deaths in
a meaningful way. It helps in understanding the overall pattern, central tendency, and variability of the data.
Measures such as mean and standard deviation are used to identify the average number of deaths and the extent
of variation among different causes over the study period.
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S.No
CAUSES
2019
2020
2021
2022
2023
2024
MEAN
S.D
1
Avalanch
35
13
8
29
24
20
21
9.18
2
Exposure to
cold
796
776
618
720
733
674
710
64.65
3
Cyclone
33
37
118
9
2
13
31
40.83
4
Tornado
15
16
1
0
1
0
5
7.39
5
Flood
948
959
656
547
266
142
515
362.36
6
Heat/Sun
Stroke
1274
530
374
730
804
520
668
307.12
7
Landslide
264
295
380
269
239
267
282
46.33
8
Lightning
2876
2862
2880
2887
2560
2631
2752
156.35
9
Torrential
Rain
69
43
63
89
61
74
68
14.48
10
Forest Fire
9
13
23
7
6
8
10
6.02
11
Other causes
1824
1861
2004
2773
1748
2270
2118
366.63
TOTAL
8143
7405
7125
8060
6444
6613
7161
744.46
The above table presents the summary statistics of various natural causes of accidental deaths over the selected
period.
Interpretation
Lightning shows the highest mean
Flood and Heat Stroke show high variation
Minor causes contribute less
ABC Analysis
ABC analysis is applied to classify the natural causes of accidental deaths based on their contribution to total
deaths. This technique helps in identifying the most significant causes (Category A), moderately significant
causes (Category B), and least significant causes (Category C), enabling better prioritization and decision-
making.
S.No
CAUSES
TOTALS
% CAUSES
CUMULATIVE
CATEGORY
1
Lightning
19266
38.3256
38.3256
A
2
Other causes
14826
29.4930
67.8187
A
3
Exposure to cold
4973
9.8923
77.7109
B
4
Heat/Sun Stroke
4679
9.3074
87.0184
B
5
Flood
3607
7.1761
94.1945
B
6
Landslide
1973
3.9240
98.1185
C
7
Torrential Rain
476
0.9469
99.0654
C
8
Cyclone
216
0.4289
99.4943
C
9
Avalanch
148
0.2952
99.7895
C
10
Forest Fire
73
0.1448
99.9344
C
11
Tornado
33
0.0656
100
C
TOTAL
50270
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The classification of causes into A, B, and C categories based on cumulative percentage is shown in the above
table.
Interpretation
Category A contributes highest
Category C contributes least
T-Test Analysis
The t-test is used to compare the mean number of deaths between two major causes, namely Lightning and Other
Causes. This test helps in determining whether the difference in their mean values is statistically significant.
t-Test (Assuming Equal Variances)
Mean
2752.285714
2118
S.D.
156.3721142
366.6274221
t
4.210327378
P-Value (two-sided)
0.00121
The results of the t-test comparing the two major causes are presented in the above table.
Interpretation
p-value < 0.05
Significant difference exists
Lightning contributes more
0.0000
20.0000
40.0000
60.0000
80.0000
100.0000
120.0000
ABC ANALYSIS
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ANOVA Analysis
B-Category Causes
Analysis of Variance (ANOVA) is applied to examine whether there are significant differences among the B-
category causes across different years. This helps in understanding whether variations are due to causes or time.
ANOVA
Table
Factor
SS
DF
Ms
F
P-Value
F(0.05)
A (Columns)
794775
2
264925
5.465755144
** (P<=0.01)
0.007535
3.1599076
B (Rows)
602512.2143
6
100418.7024
2.071771404
Not Significant
(P>0.05)
0.108
2.6613045
Error
872459.5
12
48469.97222
Total
2269746.714
20
The ANOVA results for B-category causes are presented in the above table.
Interpretation
Causes are significant
Years are not significant
C-Category Causes
ANOVA is also applied to C-category causes to analyze whether there is any significant variation among the
minor causes across years.
ANOVA
Table
Factor
SS
DF
Ms
F
P-Value
F(0.05)
A
(Columns)
396312.7857
5
79262.55714
131.0554714
*** (P<=0.001)
1.92E-19
2.5335545
B (Rows)
7019.666667
6
1169.944444
1.934426875
Not Significant (P>0.05)
0.1075
2.4205232
Error
18144.04762
30
604.8015873
Total
421476.5
41
The ANOVA results for C-category causes are shown in the above table.
Interpretation
Highly significant difference among causes
Years not significant
Tukey Test
Tukey’s Honestly Significant Difference (HSD) test is applied after ANOVA to identify which specific pairs of
causes differ significantly from each other. It helps in detailed pairwise comparison.
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B-Category
ANOVA
CASES
Sum of Squares
df
Mean Square
F
Sig.
Between Groups
147554.815
2
73777.408
4.314
.000
Within Groups
1378861.657
18
76603.425
Total
1526416.472
20
C-Category
ANOVA
CASES
Sum of Squares
df
Mean Square
F
Sig.
Between Groups
396158.823
5
79231.765
113.217
.000
Within Groups
25193.509
36
699.820
Total
421352.331
41
The multiple comparison results obtained using Tukey test are presented in the above table.
Interpretation
No significant pair differences
Causes behave similarl
Statistical Analysis – Unnatural Causes
Descriptive Statistics
Descriptive statistics is used to summarize the data related to unnatural causes of accidental deaths. It provides
an overview of the distribution, average, and variability of deaths across different causes.
S.No
CAUSES
2019
2020
2021
2022
2023
2024
2025
MEAN
S.D
1
Collapse of
Structure
1929
1536
1630
1644
1644
1538
1492
1630
145.02
2
Drowning
32671
37238
36362
38503
37738
39922
41062
37642
2712.88
3
Electrocution
13432
13446
12529
12918
13835
13315
13343
13260
419.55
4
Accidental
Explosion
655
494
454
436
498
396
359
470
95.67
5
Falls
20901
20579
21609
23786
25150
25917
27087
23576
2592.73
6
Factory/Machine
Accidents
1001
705
774
684
670
562
494
699
162.74
7
Accidental Fire
10915
9110
8348
7435
6891
5623
4651
7567
2119.72
8
Firearm
320
318
278
262
240
219
197
262
47.09
9
Mines Or Quarry
Disastar
82
77
78
83
57
62
58
71
11.53
10
Stampede
12
14
25
22
32
35
40
26
10.67
11
Sudden Deaths
47295
49925
50773
56653
63609
65458
69393
57587
8655.82
12
Deaths Of
Women During
Pregnancy
1160
1121
975
1072
1134
1062
1052
1082
62.12
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13
Deaths Due to
consumption of
poisonous liquor
1296
947
782
617
522
269
82
645
409.92
14
killed by animals
1425
1305
1264
1510
1742
1701
1785
1533
212.84
15
poisoning
21196
22221
23472
21606
21785
22225
22281
22112
719.36
16
suffocation
1598
2096
1235
1631
1485
1402
1333
1540
282.54
17
Drug Over Dose
704
514
737
681
654
678
685
665
71.23
18
Other causes
16666
15097
40450
41382
16085
33473
35985
28448
11995.16
The summary statistics for unnatural causes are shown in the table.
Interpretation
Major causes dominate
Some causes show high variation
ABC Analysis
ABC analysis is used to classify unnatural causes based on their contribution to total deaths, helping to identify
high-priority accident types.
S.No
CAUSES
TOTAL
PERCENATGE
% CUMMULATIVE
CATEGORIES
1
Sudden Deaths
403106
28.9649
28.965
A
2
Drowning
263496
18.9333
47.898
A
3
Other causes
199138
14.3089
62.207
A
4
Falls
165029
11.8580
74.065
A
5
poisoning
154786
11.1220
85.187
B
6
Electrocution
92819
6.6694
91.857
B
7
Accidental Fire
52972
3.8063
95.663
B
8
Collapse of Structure
11413
0.8201
96.483
B
9
suffocation
10779
0.7745
97.257
B
10
killed by animals
10732
0.7711
98.029
C
11
Deaths Of Women
During Pregnancy
7576
0.5444
98.573
C
12
Factory/Machine
Accidents
4890
0.3513
98.924
C
13
Drug Over Dose
4653
0.3343
99.259
C
14
Deaths Due to
consumption of
poisonous liquor
4515
0.3244
99.583
C
15
Accidental Explosion
3291
0.2365
99.820
C
16
Firearm
1834
0.1318
99.951
C
17
Mines Or Quarry
Disastar
497
0.0357
99.987
C
18
Stampede
181
0.0130
100
C
TOTAL
1391706
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The classification of unnatural causes into different categories is presented in the table.
Interpretation
Category A contributes most
Useful for prioritization
ANOVA Analysis
ANOVA is applied to test whether there are significant differences among various unnatural causes across years.
A-Category
ANOVA
Table
Factor
SS
DF
Ms
F
P-Value
F(0.05)
A (Columns)
4741988614
3
1580662871
39.02352957
*** (P<=0.001)
4.38E-08
3.1599076
B (Rows)
668231730.9
6
111371955.1
2.749559608
* (P<=0.05)
0.04471
2.6613045
Error
729096829.4
18
40505379.41
Total
6139317174
27
B-Category
ANOVA
Table
Factor
SS
DF
Ms
F
P-Value
F(0.05)
A
(Columns)
2112913720
4
528228429.9
490.3529569
*** (P<=0.001)
1.25E-22
2.7762893
B (Rows)
5868448.4
6
978074.7333
0.907944008
Not Significant
(P>0.05)
0.5058
2.5081888
Error
25853789.89
24
1077241.245
Total
2144635958
34
0.000
20.000
40.000
60.000
80.000
100.000
120.000
ABC ANALYSIS
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C-Category
ANOVA
Table
Factor
SS
DF
Ms
F
P-
Value
F(0.05)
A
(Columns)
13017833.43
8
1627229.179
59.19677925
*** (P<=0.001)
3.02E-
22
2.1382288
B (Rows)
242437.4921
6
40406.24868
1.469934177
Not Significant
(P>0.05)
0.2086
2.2946013
Error
1319446.794
48
27488.47487
Total
14579717.71
62
The ANOVA results for unnatural causes are presented in the table.
Interpretation
For A, B, and C categories, causes show highly significant differences (p ≤ 0.001)
Years are mostly not significant
This means variation is mainly due to type of cause, not year
Tukey Test
Tukey test is used to identify the specific pairs of unnatural causes that differ significantly after ANOVA.
A-Category
ANOVA
CASES
Sum of Squares
df
Mean Square
F
Sig.
Between Groups
4742008653.672
3
1580669551.224
27.149
.000
Within Groups
1397334981.580
24
58222290.899
Total
6139343635.252
27
B-Category
ANOVA
CASES
Sum of Squares
df
Mean Square
F
Sig.
Between Groups
2112936589.036
4
528234147.259
499.506
.000
Within Groups
31725387.260
30
1057512.909
Total
2144661976.296
34
C-Category
ANOVA
CASES
Sum of Squares
df
Mean Square
F
Sig.
Between Groups
13016734.516
8
1627091.814
56.243
.000
Within Groups
1562206.720
54
28929.754
Total
14578941.236
62
The pairwise comparison results are shown in the table.
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Interpretation
For A category, most pairs show significant differences (e.g., Sudden Deaths vs others)
For B category, all causes show significant differences
For C category, many pairs are also significant This indicates strong variation between causes
CONCLUSION
The present study analyzes accidental deaths in India by classifying them into natural and unnatural causes over
the period 2019–2025. The analysis using descriptive statistics shows that a few causes contribute more
significantly to the total number of deaths.
The ABC analysis indicates that category A causes such as lightning and other causes dominate the total deaths,
while category C causes contribute very less. The t-test results reveal that there is a significant difference between
selected groups of natural causes.
Further, ANOVA results show that there is a significant difference among accident causes and across years. The
Tukey test confirms that specific causes differ significantly from others.
Thus, it is concluded that accidental deaths are not uniformly distributed and are influenced by both
environmental and human-related factors.
Suggestions
Proper awareness programs should be conducted to reduce accidental deaths.
Effective disaster management systems should be implemented for natural causes.
Safety measures should be improved in workplaces and public areas.
Government should take necessary steps to control major causes of accidents.
Regular monitoring and statistical analysis should be carried out for better planning.
REFERENCES
1. National Crime Records Bureau (NCRB). Government of India.
2. World Health Organization (WHO). Global Health Estimates
3. World Health Organization (WHO). Drowning – Fact Sheet.
4. ResearchGate. Poisoning Cases Analysis
5. United Nations Office for Disaster Risk Reduction (UNDRR). Global Assessment Report on Disaster
Risk Reduction.
6. International Journal of Engineering Research & Technology (IJERT). Statistical Analysis of Accident
Data.