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. 2017 Nov 14;16:127–134. doi: 10.1016/j.dib.2017.11.041

Quantitative evaluation of pregnant women delivery status’ records in Akure, Nigeria

Adebowale O Adejumo a,b, Esivue A Suleiman a, Hilary I Okagbue a,⁎, Pelumi E Oguntunde a, Oluwole A Odetunmibi a
PMCID: PMC5699871  PMID: 29201979

Abstract

In this data article, monthly records (datasets) of total delivery, normal delivery, delivery through Caesarean section and number of still births from pregnant women in Akure, the capital city of Ondo state Nigeria, for a period of ten years, between January 2007 and December 2016 were considered. Correlational and time series analyses were conducted on the monthly records of total delivery, normal delivery (delivery through woman virginal), delivery through Caesarean section, and number of still births, in order to observe the patterns each of these indicators follows and to recommend appropriate model for forecasting their future values. The data were obtained in raw form from State Specialist Hospital (SSH), Akure, Ondo state, Nigeria. A clear description and variation in each of these indicators (total delivery, normal delivery, caesarean section, and still births) were considered separately using descriptive statistics and box plots. Different models were also proposed for each of these indicators using time series models.

Keywords: ARIMA, Caesarean section, Normal delivery, Data, Still birth, Time series, Akure

Specification Table

Subject area Medicine
More specific subject area Child Birth Delivery, epidemiology of delivery patterns, Biostatistics
Type of data Table and figure
How data was acquired Unprocessed secondary data
Data format Processed as Monthly counts from 2007 to 2016 for Four different indicators on Child Birth Delivery
Experimental factors Data obtained from State Specialist Hospital, Akure
Experimental features Computational Analysis: Time Series Analysis, Time plot, ARIMA Models and Correlation Analysis.
Data source location Ondo State Specialist Hospital, Akure, Ondo State, Nigeria
Data accessibility All the data are in this data article
Software R Statistical program and Microsoft Excel
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Value of the Data

  • •

    The data on total delivery is a good indicator to monitor the population growth over the previous years.

  • •

    The data on still birth is a good indicator for the policy makers in the health sector to improve health facilities in the specialist hospitals and encourage pregnant women to attend anti-natal clinic regularly for necessary medical check-up.

  • •

    Data on still birth is also an indicator to create good access to maternal healthcare for all pregnant women at low or no cost.

  • •

    Data on still birth can be used to obtain still birth rate (SBR), post neonatal mortality rate (PNMR) and perinatal mortality rate (PMR) of a state or locality.

  • •

    Data on Caesarea Section is a good indicator for the government to encourage all pregnant women with any form of challenges on normal delivery to opt for Caesarea section with low or no cost in specialist hospitals.

  • •

    The data are for educational purposes and health assessment studies for example gynaecology, obstetrics, nursing and so on.

  • •

    The data on normal delivery can as well give a picture of whether there was improvement in the maternal healthcare in the previous years or not.

  • •

    The data is useful in the study of epidemiology of child delivery, computational gynaecology and public health studies.

  • •

    Several known models for example simple regression and probability fit can be applied to the data which provides alternative to analysis with time series. For example the use of linear, logistic or Poisson regression.

1. Data

The data for this paper was obtained from Ondo State Specialist Hospital, Akure, Ondo State, Nigeria. The data are on monthly total delivery, normal delivery, still birth, and delivery by Caesarean Section of pregnant women in the government owned State Specialist Hospital Akure, the capital city of Ondo State, for ten years; between January 2007 and December 2016.

Statistical summary of the monthly averages for each of the indicators (total delivery, normal delivery, still birth and Caesarean section) from January 2007 to December 2016 was given in Table 1. It was observed that the highest monthly total delivery of 436 were recorded in March 2010, while the highest monthly counts for still birth of 29, were recorded in both January and July 2008. However, in terms of proportion, the highest of 0.08815 (8.82%) were recorded in July 2008. Yearly total still births was 158 in 2007 and reduced to 30 in 2016, which amounts to 81% reduction in ten years. In addition, the highest number of Caesarean section of 64 was recorded in both October 2007 and February 2010.

Table 1.

Summary statistics for the four delivery indicators for pregnant women in Akure.

Indicators Minimum 1st Quartile Median Mean 3rd Quartile Maximum
Total delivery 107.0 236.80 270.00 275.90 303.20 436.00
Normal delivery 90.00 208.00 241.50 242.00 269.80 383.00
Still birth 1.00 2.00 4.50 7.99 12.00 29.00
Caesarean section 7.00 25.00 33.00 33.87 41.00 64.00
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Correlational results were shown in Table 2 and the result of the time series analysis is contained in Table 3, Table 4, Table 5, Table 6.

Table 2.

4×4 correlation matrix for the four indicators.

Indicators Total delivery Normal delivery Still birth Caesarean section
Total delivery 1
Normal delivery 0.98098 1
Still birth 0.62108 0.64250 1
Caesarean section 0.60594 0.43990 0.24032 1
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Table 3.

ARIMA output for total delivery of pregnant women in Akure.

Model ARIMA(0,1,1)
Parameter MA1
Coefficients −0.6238
Standard error 0.0719 RMSE 42.6400
σ2 estimate 1834 Log-likelihood −616.1800
AIC 1236.3700 BIC 1241.9300
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Table 4.

ARIMA output for normal delivery of pregnant women in Akure.

Model ARIMA(0,1,1)
Parameter MA1
Coefficients −0.6222
Standard error 0.0713 RMSE 37.0900
σ2 estimate 1399 Log-likelihood −599.6000
AIC 1203.2000 BIC 1208.7600
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Table 5.

ARIMA output for still birth delivery by pregnant women in Akure.

Model ARIMA(0,1,1)
Parameter MA1
Coefficients −0.6806
Standard error 0.0667 RMSE 4.2200
σ2 estimate 17.9900 Log-likelihood −341.1000
AIC 686.2100 BIC 691.7700
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Table 6.

ARIMA output for delivery of pregnant women through Caesarean section in Akure.

Model ARIMA(3,0,0)
Parameter AR1 AR2 AR3 Mean
Coefficients 0.1208 0.1183 0.2057 33.9664
Standard error 0.0892 0.0935 0.0935 1.9175 RMSE 11.8300
σ2 estimate 144.7000 Log-likelihood −466.8100
AIC 943.6200 BIC 957.5500
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The raw monthly data for the aforementioned indicators are presented in Table 7, Table 8, Table 9, Table 10.

Table 7.

Total monthly delivery of pregnant women between 2007 and 2016.

Month/Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
January 350 342 257 425 259 165 270 232 281 255
February 340 335 240 357 191 245 229 216 202 203
March 306 395 303 436 243 223 299 212 266 238
April 340 379 335 372 229 249 292 290 254 270
May 270 353 305 362 107 278 317 236 291 268
June 287 341 390 286 206 260 255 258 268 270
July 357 329 367 296 206 236 237 276 282 276
August 265 281 302 243 170 210 260 262 262 266
September 370 289 316 256 186 286 268 247 290 275
October 353 357 402 277 213 334 298 286 294 298
November 304 283 357 227 215 259 257 196 225 206
December 301 236 252 196 219 223 182 277 232 251
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Table 8.

Total monthly normal delivery of pregnant women between 2007 and 2016.

Month/Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
January 311 316 208 366 240 150 242 202 257 229
February 277 293 229 293 168 213 208 184 181 182
March 296 355 275 383 208 203 263 174 246 210
April 307 332 293 324 200 224 261 255 219 237
May 245 307 252 305 90 239 277 206 256 231
June 278 312 338 256 168 228 211 229 258 243
July 299 306 316 246 167 215 200 233 257 245
August 256 247 252 205 146 188 220 230 238 234
September 317 266 277 216 160 252 226 219 259 239
October 289 314 352 249 189 293 241 246 255 250
November 259 268 309 189 186 222 223 155 192 173
December 252 220 245 174 203 195 159 248 198 223
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Table 9.

Monthly number of pregnant women still birth between 2007 and 2016.

Month/Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
January 11 29 12 20 7 1 1 9 5 3
February 8 23 8 14 2 3 4 4 3 3
March 18 22 15 17 10 2 4 6 2 3
April 8 22 25 5 7 1 6 1 3 4
May 20 21 18 6 5 8 6 2 3 4
June 12 28 16 12 1 6 1 3 1 1
July 19 29 17 3 3 4 2 1 4 3
August 8 11 26 9 2 4 4 3 1 2
September 8 17 17 2 4 10 1 1 3 2
October 13 26 14 8 8 8 4 1 2 3
November 18 21 15 6 2 1 2 1 1 1
December 15 11 12 2 4 9 1 2 2 1
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Table 10.

Monthly number of pregnant women with Caesarean section between 2007 and 2016.

Month/Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
January 39 26 49 59 19 15 28 30 24 26
February 63 42 11 64 23 32 21 32 21 21
March 10 40 28 53 35 20 36 38 20 28
April 33 47 42 48 29 25 31 35 35 33
May 25 46 53 57 17 39 40 30 35 37
June 9 29 52 30 38 32 44 29 10 27
July 58 23 51 50 39 21 37 43 25 31
August 9 34 50 38 24 22 40 32 24 32
September 53 23 39 40 26 34 42 28 31 36
October 64 43 50 28 24 41 57 40 39 48
November 45 15 48 38 29 37 34 41 33 33
December 49 16 7 22 16 28 23 29 34 28
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The boxplot in Fig. 1 gives the description and variation in each of the indicators examined in this work. It shows that total and normal deliveries are very close to one another, as well as still birth and caesarean section. The boxplot is a chart presentation of Table 1, with extreme cases of delivery, evident from the outliers above and below each box representing the indicators, except for caesarean section (CS), which possesses no outlier.

Fig. 1.

Fig. 1

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Boxplot for the four indicators on delivery of pregnant women in Akure.

Time Plot for each of the indicators in this paper is presented in Fig. 2a, b, c and d. This is designed to reveal the patterns observed in the given time interval.It can be observed from Fig. 2a and c that the total monthly and normal deliveries of pregnant women across the years under consideration were almost the same pattern.

Fig. 2.

Fig. 2

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Time plots showing delivery states of pregnant women in Akure between 2007 and 2016.

The progression of pregnant women having still births, dropped drastically when compared with past years (2007–2009) as shown in Fig. 2a, b, c. The focus is on the trend and not on the year's interval.

Between 2014 and 2016, a steady trend was observed, which was stationary. This obviously resulted to the series being constant over studied time frame (period). In Fig. 2d, a trend surfaces between 2010 and 2016 which declines in the first month of every year. Furthermore, the number of pregnant women who underwent Caesarean section, from 2008 to 2016 is evidently declining, which could likely indicate the increasing fear of pregnant women and most especially the cost of being subjected to such mode of delivery.

It was observed from Fig. 3a, that the proportion of still birth dropped drastically towards year 2016, when compared with the first two or three years under consideration, that is from 2007 to 2009. It was also observed that, the total number of still births in year 2016 (30) was almost the same as the highest monthly (29) earlier recorded in both January and July 2008 respectively. This may be attributed to government efforts in the state to improve maternal and child healthcare is yielding dividends which eventually reduced the rate of monthly still birth in the state to the point of one or even zero as times goes on. The differences in the proportion of pregnant women undergoing Caesarean section across the years under investigation are not significant in pattern as seen in Fig. 3b. Furthermore, the plot showed that within 15.00% to 20.00% of the total number of pregnant women deliver through Caesarean section yearly and within these years drop to as low as 5.00%.

Fig. 3.

Fig. 3

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Monthly proportion for still birth and Caesarean delivery by pregnant women in Akure between 2007 and 2016.

2. Methods and materials

Several studies have been conducted on the issues affecting normal delivery, still birth incidences and epidemiology of Caesarean section child delivery among women in Nigeria [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19]. Similar data articles on medicine that applied statistical tools could be helpful, readers are refer to [20], [21], [22], [23], [24], [25], [26], [27], [28], [29].

Correlation and time series tools are used to explore the data of child delivery in Akure, Nigeria. Pearson correlation coefficients were calculated for the each pairs of total delivery, normal delivery, still birth and Caesarean section. Furthermore, autoregressive integrated moving average (ARIMA) was used in describing and modeling the pattern of child delivery. The correlation was done using the Microsoft Excel while the time series analysis was done with the aid of the R software.

2.1. Correlational study

The correlation coefficient shows the degree of linear relationship that exists between two variables; this was presented in Table 2. There is a very high correlation between total and normal delivery (0.98098), followed by normal delivery and still birth (0.64250), while the least is between Caesarean section and still birth (0.24032).

2.2. Autoregressive integrated moving average (ARIMA)

ARIMA is a time series statistical tool used in describing and modeling the pattern of a given seasonal and non-seasonal time series data. Table 3, Table 4, Table 5, Table 6 present the appropriate ARIMA models for each of the indicators under consideration. It was observed that ARIMA (0, 1, 1) is best for describing and forecasting the future counts for three of the indicators: total delivery, normal delivery and still birth, while ARIMA (3, 0, 0) is most appropriate for the number of delivery through Caesarean section.

Acknowledgements

This work was sponsored by Centre for Research, Innovation and Discovery, Covenant University, Ota, Nigeria. Also the authors thank the management of State Specialist Hospital, Akure, for making the data available.

Footnotes

Transparency document

Transparency data associated with this article can be found in the online version at https://doi.org/10.1016/j.dib.2017.11.041.

Transparency document. Supplementary material

Supplementary material

mmc1.pdf (79.5KB, pdf)

.

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Supplementary Materials

Supplementary material

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