1. Introduction

The fundamental aim of most of the themes in Millennium Development Goals was to improve survival chances through improved health and quality of life for both children and adult. The Accident and Emergency Department (AED) in any modern health facility provides initial treatment to patients with a broad spectrum of illnesses and injuries which in most cases are severe and life-threatening. Emergency department was developed during the 20th century in response to an increased need for rapid assessment and management of such critical illness. The AED provides an insight to the quality of care available in the institution. In AED in UCH, the expectation of patients and their relatives are the same as elsewhere, but differ in the manner and extent to which this is met in terms of availability of ‘facilities and care givers’ relative to the number of patients on admission at a particular time. In Nigeria, it has been observed that some patients admitted to AED after trying to have their problem solved Elsewhere - churches, prayer home, traditional healers, drug dealers and self acclaimed pseudo-orthodox medical practitioners. The reasons for this delay in bringing patients to modern health care in Nigeria are multi-dimensional. Some patients would rather go for local medicine instead of coming on time to hospitals because they can’t afford the payments.

However like most public facilities in Nigeria, the AED of most hospitals have their own share in terms of patients neglect due to poor and inadequate health facilities. Patients at times die as a result of inadequate valuable equipments that could be used to resuscitate patients who are in terrible conditions. Also poor electricity supply is another serious problem. In the face of these challenges, the AED in most Nigerian hospitals is striving to meet the health needs of the populace in terms of treatment and care. However, the patients’ family members who are desperate of immediate attention and care for their love ones shift blames on the care givers, believing that they have lackadaisical stance to their problem.

Across all countries, the common cases of emergency admissions include; traumas–bodily injuries from Road Traffic Accident (RTA), fall, assaults, fire arms, cases of domestic accidents, violence and occupational hazards. Others are burns, ingestion of chemicals, acute renal failure, and cardiovascular problems such as heart attack and asthmatic conditions. Obstetrics emergencies, Paediatric (surgical) and Psychiatry emergencies are also reported. All these were assessed together as a variable and as the cases of emergency in this study with the view of observing its seasonal patterns, trends and projection.

Seasonal Variation of emergency admissions in the AED of a hospital will provide information that would assist in decision making by hospital management, health boards and others regarding the future funding and development of AED services. It would provide doctors, nurses and other staff in AED with a valuable tool in structuring their service to meet the needs of patients. This will also help to prepare the medical team toward the peak period of influx of patients. Specific environmental factors underlying seasonal variability in the AED will be more understood for adequate planning strategies for improvement.

This study aims to explore the seasonal variation of total patients in the Accident and Emergency unit of the University College Hospital within 12-year period of study (1995- 2006). It will also describe the seasonal variation separately for the segments 1995-1997, 1998-2000, 2001-2003 and 2004-2006 in attempt to examine whether there have been shifts in the time of maxima over the years.

Emergency medical admissions have risen by 50% since 1984 and now account for almost half of all NHS admissions in England[1]. Over recent years there has been increasing concern that the number of hospital emergency admission is rising, and that this is placing an increasing financial burden on the NHS[2]

Often, adult cases are commonly reported in the in Accident and Emergency unit. Age and deprivation take their toll. People aged over 65 accounts for only 15% of the Scottish population but 37% of emergency admissions[1]. This proportion may have grown because more elderly people live alone as family groups fragment, eroding informal support. Hospitals become "the career of last resort." Socioeconomic deprivation operates across the board, from illness behavior through to use of tertiary services. Deprivation increases emergency admissions, particularly for cardiovascular disease, self poisoning, and asthma. These, along with non- specific conditions, dominate the emergency admission workload[3].

Worldwide, road traffic injuries are on the increase with most of morbidity and mortality occurring in Africa, Asia, the Caribbean and Latin America and the majority of deaths occur among pedestrians, cyclists, users of motorized two-wheelers and occupants of buses and mini buses[4]. Also[5] reported that in Nigeria one out of every three accident victim dies and since civil war, no other pathology or phenomenon has claimed the lives of more Nigerians than RTA.

Injuries from RTA are most significant problem so much so that motor vehicle-related mortality has been described as disease of development[6]. Trading is a central part of Nigerian culture and traders travel large distances to markets, particularly to the inter-kingdom markets[7]. Bearing in mind the poor state of our highways, the deplorable condition of the automobiles on these highways and attitude of the drivers to highways-safety issues, the incidence of Road traffic accident in our environment will be expectably high.

Moreover, in Nigeria, the prevailing economic circumstances have led to an upsurge in the use of motorcycles for private and commercial purpose[8]. Studies have reported that motorcycles users involved in collisions tend to sustain multiple injuries to various parts of the body. However, lower limbs are often injured[9]. Similarly[10] in a community based of commercial motor cyclist in Igbo-Ora, Oyo State reported that about 70% of the crash-involved motor cyclists and 50% of their passengers sustained injuries from the accident injury mainly affected the lower limb, upper limb and the head or face.

Higher incidence of burns have been reported in urban areas than in rural areas, and most of the affected burn injury may occur in the scenario of Road Traffic Accidents, blasts and industrial mishaps at work place. Seasonal variation of burns in Ibadan, Nigeria[11] showed that most patients affected are above 15 years, with most burn occurring in December/January and least incidence is between April and July. The home was the site of injury in most cases (60%), accidents in the street e.g. pedestrian road traffic accidents (30%) and work related burn injury (4%). Similar study by [12] shows that in Malaga, Spain, most cases of burn injury occur in urban environment (89.5%). Most of these accidents occurred at home (65.8%) especially in the kitchen involving hot liquids.

Further studies conducted in Europe suggested that greater number of children is injured during the summer months. Injuries presenting to the Accident and Emergency have shown that paediatrics emergency injuries has its peak during August when Scottish schools were on summer holiday. This is consistent with the findings of other studies. In Toronto, the highest numbers of presentation were recorded in August and September. The phenomenon could be explained by longer playing periods spent in playing unsupervised during holidays, rather than structured supervised activities in school. This seasonal variation suggests that specific injury control initiation should be implemented during school holidays[13].

2. Materials and Methods

2.1. Accident and Emergency Unit, UCH Ibadan

Established in 1984, the AE unit of UCH was established to take care of emergency cases with a broad spectrum of illnesses and injuries. The initial care of the medical team in the unit is aimed at saving life while on-going care focuses on prevention of complication. It is made up of several sections such as Endoscopy, Burns, Traumatology, Orthopaedics, Haematology, Operating theatre, Radiology and Pharmacy. The medical team comprises of Surgeons, Consultants, Doctors, Nurses and Pharmacists that run different shifts.

Individuals with serious illnesses are then seen by a physician more rapidly than those with less severe symptoms or injuries. Patients are not delayed but are given immediate and adequate transfer to the respective places where they will be given further attention may be resuscitation room, wards for admission or to the theatre for operation.

2.2. Data collection and Analysis

The data were obtained from the central medical records in UCH. The data gave the monthly statistics of male and female patients attended to for the period of January 1995 to December 2006. The total number of patients recorded during the 12-years period of study was 79990. A time series data was plotted for the preliminary examination of the linear trend. Auto correlations were calculated and used to examine the existence of serial correlation in the series. The correlogram was also plotted as a preliminary means of investigating whether the series contained a seasonal variation of the sinusoidal type. An appropriate regression technique was then employed to fit a trigonometric curve to the data in order to measure the seasonal variation. We consider a model of the form

(1)

Where:

μ = mean number of casualties per month between January 1995 to December 2006

Y_t = Number of casualties occurring in the t-th ordinal month starting from January 1995

ω = frequency of the periodic variation = 2πf (since f = 1/T and T = 12 months)

t = Time period (in month), a = Amplitude of the data, θ = phase which locates the peak

ε_t = Error or residual term, (ωt + θ) is measured in radians.

Re-parameterization transforms this model into a linear multiple regression of the form

(2)

Where;

The time of the highest variation was obtained by solving the equation

which means

Also,

The extent of the seasonal variation above the mean level was measured by a/u while the highest variation was obtained by solving the equation cos(ωt+θ) = 0 for t. This was then converted to months and days. The above analysis was done for the series as a whole and separately for different time segments 1995-1997, 1998-2000, 2001-2003 and 2004-2006 in attempt to examine whether there have been shifts in the time of maxima over the years.

Seasonal variation of the data was also examined using moving average method based on additive model. The need for additive model was because of the stationary pattern of the data. The adjusted seasonal variations were obtained for each quarter and were later used for projection of the quarters of the year 2011 and 2012.

Table 1. Classification of the emergency cases that are handled at the AED of UCH Surgical Emergency Medical Emergency Gynecology Emergency Psychiatry Emergency Pediatric Surgical Emergency Trauma Cardiovascular or Respiratory problems Antepartum Hemorrhage Violent Behavior Foreign material in the body Acute Blunt abdominal/ chest injuries Diabetic or liver problem Postpartum Hemorrhage Suicidal attempt Chemical ingestion Severe Burn injuries Chronic / Acute renal failure Any reasons for virginal bleeding Unconsciousness of any sort Labour (Normal and abnormal)

3. Results

Figure 1. Monthly Admission of patients in A&E Unit in UCH, Ibadan, 1995-2006

Table 2. Autocorrelation for the Total Patients Lag K Auto correlation Lag K Auto correlation Lag K Auto correlation Lag K Auto Correlation 1 0.324 11 0.054 21 -0.051 31 -0.036 2 0.114 12 -0.019 22 -0.067 32 0.015 3 0.008 13 0.053 23 -0.013 33 -0.022 4 0.000 14 0.058 24 -0.072 34 -0.006 5 0.045 15 0.076 25 -0.104 35 0.0300 6 0.010 16 -0.067 26 -0.001 36 -0.078 7 -0.061 17 0.054 27 0.053 37 -0.077 8 0.060 18 0.04 28 0.077 38 -0.010 9 0.191 19 0.083 29 0.125 39 -0.025 10 0.093 20 0.041 30 0.114 40 -0.105

Autocorrelation is important in identifying whether a time series (X_t) is stationary or not. This is done by using a correlogram which is the plot of r_k against k, i.e the plot of sample of autocorrelation coefficient against lag k.

Figure 2. Autocorrelation and Lagk of number of Admitted patients in A&E Unit in UCH, Ibadan, 1995-2006

Trigonometric Regression (T-R) Analysis

Table 3. Components Of The T-R Of The Total Patients For 1995-2000 T Xt XtSin ωt XtCos ωt T Xt XtSin ωt XtCos ωt 1 592 296.11 512.62 37 615 307.61 532.54 2 604 523.21 301.78 38 529 458.24 264.31 3 606 606.00 -0.38 39 543 543.00 -0.34 4 605 523.69 -302.94 40 482 417.22 -241.35 5 574 286.48 -497.40 41 503 251.04 -435.88 6 548 -0.69 -548.00 42 561 -0.71 -561.00 7 543 -272.19 -469.85 43 464 -232.59 -401.49 8 503 -436.03 -250.77 44 586 -507.98 -292.14 9 454 -454.00 0.86 45 556 -556.00 1.05 10 559 -483.52 280.52 46 623 -538.88 312.64 11 230 -114.54 199.45 47 595 -296.30 515.97 12 896 2.27 896.00 48 519 1.31 519.00 13 673 338.10 581.91 49 282 141.67 243.83 14 398 345.26 197.98 50 359 311.43 178.58 15 399 399.00 -1.26 51 508 508.00 -1.61 16 478 413.15 -240.39 52 276 238.56 -138.81 17 270 134.16 -234.31 53 531 263.85 -460.81 18 576 -2.19 -576.00 54 566 -2.15 -566.00 79 512 -263.35 -439.08 55 595 -306.04 -510.26 20 413 -358.54 -204.99 56 665 -577.30 -330.07 21 522 -521.99 2.31 57 625 -624.99 2.77 22 278 -240.11 140.11 58 613 -529.45 308.96 23 527 -261.28 457.67 59 449 -222.61 389.93 24 592 2.99 591.99 60 626 3.17 625.99 25 483 243.70 417.01 61 699 352.68 603.50 26 572 496.93 283.28 62 815 708.03 403.63 27 602 601.99 -3.43 63 817 816.99 -4.65 28 513 442.75 -259.12 64 611 527.33 -308.62 29 564 279.01 -490.15 64 731 361.62 -635.29 30 503 -3.18 -502.99 66 746 -4.72 -745.99 31 605 -305.92 -521.96 67 638 -322.60 -550.43 32 494 -429.47 -244.11 68 564 -490.33 -278.70 33 557 -556.99 3.87 69 301 -300.99 2.09 34 563 -485.54 284.99 70 616 -531.25 311.81 35 605 -298.63 526.16 71 616 -304.06 535.73 36 640 4.86 639.98 72 238 1.81 237.99 TOTAL 19053 451.49 531.38 TOTAL 20063 -135.40 -473.09

Table 4. Components Of The T-R Of The Total Patients For 2001-2006 T Xt XtSin ωt XtCos ωt T Xt XtSin ωt XtCos ωt 1 527 263.60 456.34 37 493 246.59 426.90 2 853 738.90 426.19 38 538 466.04 268.80 3 1092 1092.00 -0.69 39 494 494.00 -0.31 4 905 783.37 -453.16 40 451 390.39 -225.83 5 765 381.80 -662.91 41 587 292.96 -508.67 6 248 -0.31 -248.00 42 462 -0.58 -462.00 7 498 -249.64 -430.91 43 581 -291.24 -502.73 8 608 -527.06 -303.11 44 542 -469.84 -270.21 9 664 -664.00 1.26 45 485 -485.00 0.92 10 406 -351.18 203.74 46 458 -396.16 229.84 11 590 -293.81 511.64 47 459 -228.58 398.04 12 638 1.61 638.00 48 505 1.28 505.00 13 636 319.51 549.92 49 426 214.01 368.34 14 636 551.73 316.37 50 523 453.70 260.16 15 286 286.00 -0.90 51 523 523.00 -1.65 16 704 608.49 -354.05 52 557 481.43 -280.12 17 702 348.82 -609.20 53 610 303.11 -529.36 18 783 -2.97 -782.99 54 591 -2.24 -591.00 79 674 -346.67 -578.01 55 492 -253.06 -421.93 20 734 -637.20 -364.32 56 489 -424.51 -242.71 21 737 -736.99 3.26 57 461 -461.00 2.04 22 660 -570.04 332.65 58 546 -471.58 275.19 23 456 -226.08 396.01 59 518 -256.82 449.85 24 625 3.16 624.99 60 538 2.72 537.99 25 543 273.97 468.81 61 521 262.87 449.82 26 543 471.73 268.92 62 356 309.28 176.31 27 521 520.99 -2.96 63 518 517.99 -2.95 28 593 511.79 -299.53 64 549 473.82 -277.30 29 611 302.26 -531.00 64 648 320.56 -563.15 30 522 -3.30 -521.99 66 593 -3.75 -592.99 31 609 -307.94 -525.41 67 612 -309.46 -528.00 32 517 -449.47 -255.47 68 511 -444.25 -252.51 33 577 -576.99 4.01 69 615 -614.99 4.28 34 525 -452.77 265.75 70 610 -526.08 308.78 35 434 -214.22 377.45 71 583 -287.77 507.03 36 530 4.02 529.98 72 477 3.62 476.99 TOTAL 21952 853.12 -549.34 TOTAL 18922 -169.53 -607.16

Table 5. Analysis of variance for T-R model

Table 6. Trigonometric Regression Of The Total Patients In A&E Unit, UCH, (1995-2006) Period A P ϴ(degrees) ϴ(radians) M(μ) ti a/μ Estimated Time of Peak 1995-2006 20.62 0.106 42.31 0.7384 555.49 7.5888 0.0485 July 1995-1997 38.74 0.169 139.65 2.4374 529.25 4.3443 0.0732 April 1998-2000 27.33 0.867 164.03 2.8629 557.31 3.5318 0.0490 March 2001-2003 56.36 0.056 57.22 0.9987 609.78 7.0917 0.0924 July 2004-2006 35.02 0.047* 15.61 0.2724 525.61 8.4786 0.0666 August

Table 6 shows the results of the trigonometric regression analysis for the series as a whole and for each of the four time segments after adjustment. In all cases the simple trigonometric model was only significant in the last segment (2004-2006). For the 12-year period of study, the monthly number of patients in the Accident & Emergency varied by about 5% above and below the annual mean with the estimated peak occurring in the month of July. The extent of the seasonal variation above mean level was about 7%, 5%, 9% and 7% in 1995-1997, 1998-2000, 2001-2003 and 2004- 2006 respectively. The estimated month of peak was in April for the first segment (1995-1997), March for the second segment (1998-2000), July for the third segment (2001-2003) and August in the last segment (2004-2006).

3.1. Estimating the Trend

In view of the results of Trigonometry Regression in which the most of the coefficients are not significant, the data was classified to 4quarters in a year (1^st Jan.-Mar.; 2^nd Apr-Jun; 3^rd July-Sept; 4^th Oct.-Dec.). We then estimated the trend of the quarterly data using least square method (Additive approach) and moving average method to estimate the quarter with the highest peak.

Table 7. Quarterly Data Of The Total Number Patients In UCH YEAR Q 1 Q 2 Q 3 Q 4 Total 1995 1802 1470 1657 1687 6616 1996 1149 2331 2472 1558 7510 1997 1607 1525 1472 1395 5999 1998 1727 1324 1580 1546 6177 1999 1373 2088 1918 2189 7568 2000 1726 1500 1758 1790 6774 2001 1500 1447 1656 1606 6209 2002 1885 1503 1770 2145 7303 2003 1703 1608 1442 1738 6491 2004 1685 1397 1808 1737 6627 2005 1688 1470 1634 1741 6533 2006 1489 1422 1602 1670 6183

3.2. Moving Average Method

The moving averages and centered moving averages are needed to eliminate seasonal variations and irregular fluctuations from the data. Here we used a 4-period moving average because the AE time series data are arranged in quarters for easy assessment. The time series is then modeled using additive decomposition model. The model is. Where TR_t, SN_t, CL_t, and IR_t are trend, seasonal, cyclical and irregular variations respectively. Then seasonal factors were obtained by normalizing the values so that the normalized values sum to zero. The normalization is accomplished by subtracting the quantity from each of values. That is the estimate of SN_t is

Table 8. Estimation Of Trend Using Moving Average Method T Xt 4-Quarter Moving Total 4-Quarter Moving Average Trend Line Seasonal Variation 1 1802 - - - - 2 1470 6616 - - - 3 1657 5963 12579 1572 85 4 1687 6824 12787 1598 89 5 1149 7639 14463 1808 -659 6 2331 7510 15149 1894 437 7 2472 7968 15478 1935 537 8 1558 7162 15130 1891 -333 9 1607 6162 13324 1666 -59 10 1525 5999 12161 1520 5 11 1472 6119 12118 1515 -43 12 1395 5918 12037 1505 -110 13 1727 6026 11944 1493 234 14 1324 6177 12203 1525 -201 15 1580 5823 12000 1500 80 16 1546 6587 12410 1551 -5 17 1373 6925 13512 1689 -316 18 2088 7568 14493 1812 276 19 1918 7921 15489 1936 -18 20 2189 7333 15254 1907 282 21 1726 7173 14506 1813 -87 22 1500 6774 13947 1743 -243 23 1758 6548 13322 1665 93 24 1790 6495 13043 1630 160 25 1500 6393 12888 1611 -111 26 1447 6209 12602 1575 -128 27 1656 6594 12803 1600 56 28 1606 6650 13244 1656 -50 29 1885 6764 13414 1677 208 30 1503 7303 14067 1758 -255 31 1770 7121 14424 1803 -33 32 2145 7226 14347 1793 352 33 1703 6898 14124 1766 -63 34 1608 6491 13389 1674 -66 35 1442 6473 12964 1621 -179 36 1738 6262 12735 1592 146 37 1685 6628 12890 1611 74 38 1397 6627 13255 1657 -260 39 1808 6630 13257 1657 151 40 1737 6703 13333 1667 70 41 1688 6529 13232 1654 34 42 1470 6533 13062 1633 -163 43 1634 6334 12867 1608 26 44 1741 6286 12620 1578 163 45 1489 6254 12540 1568 -79 46 1422 6183 12437 1555 -133 47 1602 - - - - 48 1670 - - - -

Table 9. Seasonal variation adjustment

Table 10. Projected Number Of Patients In Accident And Emergency Wards In Uch For The Period, 2011- 2012

Figure 3. Reported and Trend of number of patients in A&E Unit in UCH, Ibadan, 1995-2006

Table 10. Descriptive Statistics of Males and Females in AE unit UCH, Ibadan 1995-2006, Mean N Std. Deviation Std. Error Mean Males 306.63 144 69.555 5.796 Females 248.85 144 65.274 5.439

Table 11. t-test for Males and Females in AE unit UCH, Ibadan, 1995-2006 Variable Mean Sd S.E 95% C.I df Sig.(2-tailed) Male Vs Female 57.78 40.923 3.41 51.037 64.519 143 0.000 Pre Vs Post Military Rule 3.79 114.56 16.54 -29.47 37.06 47 0.229

Figure 4. Number of Admission by sex versus year of admission in A&E Unit in UCH, Ibadan, 1995-2006

4. Discussion

Seasonal index showed that the peak of number of patients admitted was observed in the last quarter of every year. This is an indication that admission occurs mostly during the festive periods where most people travel to their native homes to celebrate with their love ones. Our finding corroborates the findings of[14] where it was reported that “the incidences of the RTA are on the increase and characterized by seasonal factors as can be seen from the high values of the seasonal indices for the months of January, February, May, June, October, November and December”. However, our result is at variance with the findings in [15] and [16] where they claimed that highest number of RTA occurs during the rainy season, whereas our study suggested the peak is during the dry seasons. The estimated adjusted seasonal variations were used to make projections for years 2011 and 2012. The projected quarterly figures for 2011 (Q₁=1488, Q₂=1497, Q₃=1632, Q₄=1634) and 2012 (Q₁=1490, Q₂=1499, Q₃=1634, Q₄=1635).

The number of males patients admitted into AE wards was consistently higher than numbers of females for each of the year. This finding is in agreement with the report made by [15,9,17] where they stated that number of male victims in RTA is higher than that of females. The monthly mean number of admitted males and females were compared using t-test over the periods under study and the analysis revealed that there is significant difference between males andfemales number of admission (p<0.05). The differential can be accounted for going by more exposure of males to occupational and transport hazards than their female counterparts. For instance,[18] reported that accidents resulted from motorbike commercial riders in Nigeria constituted a high proportion of daily reported cases of accident. It is only men that engage in this business and as such, more vulnerable than women. Temporary internal migration was also more common among men than women in Nigeria, this make them more susceptible to accidents than women.

This study also carried out a comparison of the study period divided into period of military rule and democracy. No significant difference existed between number of A&E patients admitted during the two periods (p>0.05). This can be explained by total collapse of socio-economic and health infrastructures including abject poverty that persisted for a long period during military regime which transcends to democracy rule. The dilapidated facilities need many more years for revitalization.

5. Conclusions

Time series analysis is a very important tool in medical outfit like UCH, since the predictions of future number of patients and cases of specific diseases must be incorporated into the decision-making process and planning. This will assist management to make intelligent decision. The foregoing results show that the number of patients seen at the accident and emergency unit of the UCH peaks in the last quarter of every year and are higher in males. The hospital management should engage more care givers and make available more resuscitating medical equipments during the last quarter of the year.

ACKNOWLEDGEMENTS

The authors are grateful to the Director Central Medical Record of the University College Hospital (UCH), Ibadan who gave permission for the use of data set. We also thank Professor Olushola Ayeni for his contributions and technical advice in the course of the paper write-up and those who made an input at one time or the other.