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  • Research article
  • Open Access

Male responsibility and maternal morbidity: a cross-sectional study in two Nigerian states

  • 1Email author,
  • 2,
  • 2,
  • 3,
  • 4,
  • 5,
  • 5,
  • 2,
  • 2, 6,
  • 2,
  • 2 and
  • 2
BMC Health Services Research201111 (Suppl 2) :S7

  • Published:



Nigeria continues to have high rates of maternal morbidity and mortality. This is partly associated with lack of adequate obstetric care, partly with high risks in pregnancy, including heavy work. We examined actionable risk factors and underlying determinants at community level in Bauchi and Cross River States of Nigeria, including several related to male responsibility in pregnancy.


In 2009, field teams visited a stratified (urban/rural) last stage random sample of 180 enumeration areas drawn from the most recent censuses in each of Bauchi and Cross River states. A structured questionnaire administered in face-to-face interviews with women aged 15-49 years documented education, income, recent birth history, knowledge and attitudes related to safe birth, and deliveries in the last three years. Closed questions covered female genital mutilation, intimate partner violence (IPV) in the last year, IPV during the last pregnancy, work during the last pregnancy, and support during pregnancy. The outcome was complications in pregnancy and delivery (eclampsia, sepsis, bleeding) among survivors of childbirth in the last three years. We adjusted bivariate and multivariate analysis for clustering.


The most consistent and prominent of 28 candidate risk factors and underlying determinants for non-fatal maternal morbidity was intimate partner violence (IPV) during pregnancy (ORa 2.15, 95%CIca 1.43-3.24 in Bauchi and ORa 1.5, 95%CI 1.20-2.03 in Cross River). Other spouse-related factors in the multivariate model included not discussing pregnancy with the spouse and, independently, IPV in the last year. Shortage of food in the last week was a factor in both Bauchi (ORa 1.66, 95%CIca 1.22-2.26) and Cross River (ORa 1.32, 95%CIca 1.15-1.53). Female genital mutilation was a factor among less well to do Bauchi women (ORa 2.1, 95%CIca 1.39-3.17) and all Cross River women (ORa 1.23, 95%CIca 1.1-1.5).


Enhancing clinical protocols and skills can only benefit women in Nigeria and elsewhere. But the violence women experience throughout their lives – genital mutilation, domestic violence, and steep power gradients – is accentuated through pregnancy and childbirth, when women are most vulnerable. IPV especially in pregnancy, women's fear of husbands or partners and not discussing pregnancy are all within men's capacity to change.


  • Intimate Partner Violence
  • Eclampsia
  • Maternal Morbidity
  • Female Genital Mutilation
  • Institutional Delivery


Reputedly one of the highest in the world [1, 2], maternal mortality in Nigeria rests on two problems not peculiar to Nigeria, that are easy to state but hard to change. First, as in many countries, maternal health services do not work well. Second, also not specific to Nigeria, maternal deaths follow a life course that puts women at high risk at the time of delivery.

One out of every ten women who attended the Bauchi central referral hospital between 2000-2005 died in relation to childbirth [3]. A review of births over 17 years in neighbouring Plateau State produced much the same figures, indicating the phenomenon is not local [4].

High rates of maternal morbidity and mortality in northern states led some authors to speculate that undervaluing women combines dangerously with harmful traditional medical practices [5]. But studies from the south show very similar pictures of late presentation of morbidity at weak emergency services [610]. North and south, the common morbidities are puerperal sepsis, haemorrhage, abortion complications, eclampsia and prolonged obstructed labour. Several studies have focussed on factors underlying these problems. “Poverty” receives several mentions [1113]; although antenatal and delivery services are officially free at government facilities, in practice almost everyone has some expenditure [14, 15]. A study of maternity staff knowledge in two south-western states of Nigeria showed many maternity unit operatives lack knowledge and skills of emergency management [16].

Bauchi in the north of Nigeria is predominantly Islamic; polygamy is common. Cross River is the south-eastern corner of the country, and the main religion is Christian (Evangelical and Catholic). As part of the five-year Nigeria Evidence-based Health System Initiative (NEHSI) [17], the state governments of Bauchi and Cross River nominated maternal outcomes as their first health priority for study. This article results from a bigger process of building evidence-based planning capacity in the health sector, to improve the public health. This analysis examined actionable risk factors and underlying determinants for reduction of maternal morbidity and, as a result, mortality in these two states.


A cross-sectional survey in 180 sites in a stratified last stage random sample of the recent census enumeration areas (EAs) in Bauchi and Cross River states. In each state, a panel of 60 sites provided state level representation; in addition, 10 sites in each of three randomly selected focus local government authorities (LGA) in each state provided increased sensitivity of local analysis. Local interviewers identified women who had been pregnant in the previous three years and administered a questionnaire in their language of choice. There was no sub-sampling within the enumeration area, or within households.

State planners chose the focus of the survey, and participated in review of existing data, design of instruments, training of fieldworkers, supervision of fieldwork, analysis and development of emerging policy implications.

A household interview provided household characteristics and a questionnaire for women asked if respondents had given birth in the last three years. For those that had done so, we obtained information on the pregnancy and its outcome, surgical intervention during the delivery and the state of the child. We asked simple direct questions about occurrence of complications: During this last pregnancy did you have fits or convulsions? Did the wound open up afterwards or become infected? Did you develop high fever within six weeks after this delivery? Did you develop foul-smelling discharge from vagina within six weeks after this delivery?

The principal analysis addressed all these complications together, under the hypothesis that positive spouse involvement in the pregnancy would be associated with fewer complications. We repeated the analysis separately for specific morbidities: pre-eclampsia and sepsis. We defined pre-eclampsia as two or more of the following during pregnancy: raised blood pressure, swelling of face or hands, fits/convulsions, or upon testing of their urine, they received information that something was wrong.

Table 1 lists the potential risk factors and underlying determinants covered as direct closed questions. Interviewers asked women about female genital mutilation (FGM) in two questions, one specifically about circumcision and another about removal of genital flesh. They asked women about physical intimate partner violence (IPV) in the last year and, separately, during the last pregnancy (In the last year, have you had violent arguments where your partner beat, kicked or slapped you? During the pregnancy, did your partner beat, kick or slap you?).
Table 1

Study population and frequency of maternal knowledge and attitudes in Bauchi and Cross River


Bauchi State

Cross River State

All women interviewed



Women with pregnancy in the last three years




18.2% 1246/7870

30.3% 2617/7759

Any formal education

22.1% 1719/7834

93.6% 7200/7720


97.3% 7653/7860

81.0% 6303/7749

Sufficient food in the last week

89.8% 7072/7845

81.9% 6303/7743

Remunerated employment

48.1% 3746/7809

59.4% 4544/7749

Younger age (lower risk for pregnancy)

82.7% 6577/7854

87.0% 6760/7753

Number of pregnancies (1-3)

48.1% 3786/7749

62.1% 4609/7468

Female headed household

0.6% 61/6975

10.3% 676/6574

Non-crowded household (up to two per room)

34.9% 2547/7428

39.4% 3041/7715






Know any danger in pregnancy (1)


53.7% 4174/7775


63.4% 4909/7746

Know danger signs in childbirth (2)


53.6% 4158/7753


47.3% 3661/7742

Women should give up heavy work in pregnancy


39.0% 3063/7855


38.0% 2946/7756

Believe its not okay for pregnant women to smoke cigarettes


79.7% 6257/7854


89.4% 6924/7746

Believe women without birth problems still need to deliver at a health facility


35.9% 2822/7858


71.3% 5523/7745

If pregnant next year, would give up heavy work


39.1% 2905/7440


48.7% 3771/7747

If pregnant next year, would not smoke cigarettes


98.2% 7707/7847


99.5% 7707/7748

Involved in decisions regarding pregnancy/ childbirth


0.5% 41/7821


25.6% 1982/7735

Say they were never beaten


95.9% 7493/7817


80.1% 6145/7673

Say they were not afraid of their husbands


65.7% 5137/7821


67.5% 5180/7673

No female circumcision or mutilation


89.1% 6265/7028


61.0% 4702/7707

ABOUT THE LAST PREGNANCY (last three years)





Spoke about pregnancy primarily with husband


56.1% 4151/7399


32.9% 2491/7567

Say they were not beaten in pregnancy


97.5% 7406/7600


89.1% 6558/7358

Reduced workload before 3rd trimester


19.1% 1467/7701


52.2% 3524/6754

Four or more antenatal checkups


40.5% 3012/7446


46.4% 3273/7057

Took iron/folate at least one trimester


32.6% 2434/7475


44.7% 2967/6644

Urine checked at antenatal care


39.8% 2955/7432


61.8% 4564/7381

Blood pressure checked at antenatal care


58.7% 4389/7479


73.0% 5382/7372

A qualified person delivered the baby in a health facility


15.4% 1170/7590


45.0% 3198/7107

1. Any of the following responses: pre-eclampsia, eclampsia, fever, bleeding, lap pain, high blood pressure, cord appears, breech/wrong presentation of baby, vomiting, fits/convulsions, uncontrolled urination, baby movements not felt, weakness, anaemia, jaundice, water coming out, malaria

2. Any of the following responses: malposition, premature labour, prolapse, retained placenta, uncontrolled urine, stillbirth, prolonged/obstructed labour, anaemia, weakness, low blood pressure, sepsis, fever, vaginal cut

The survey occurred from May to November 2009. In each state we standardized training in non-sample sites, training 20-30 fieldworkers over one week. Some 140 interviewers aged 20-35 years worked in 12 teams (one man and two women per team), conducting a general household interview (female interviewer), a husband/spouse interview (male interviewer), and an interview with women who had been pregnant in the last three years (female interviewer).

Teams covered each enumeration area moving radially outwards, excluding no households or women in the households. In a second visit, a smaller team conducted focus group discussions separately with women and men, and visited the government health facilities mentioned by household respondents. There were 180 male and 180 female focus groups; each with 7-10 members with a total participation of 1434 men and 1544 women. The team also reviewed government prenatal and delivery services nearest to each cluster, including issues like access to water, privacy and qualifications of health workers.

Preliminary results provided a template for gender-stratified focus group discussions in each of the 180 clusters. Facilitators asked questions and used standardized prompts and monitors recorded male and female discussions about work during pregnancy, safe pregnancy and safe birth, IPV and FGM.

Statistical methods

Different operators entered the data twice with validation to minimize keystroke errors. Analysis relied on CIETmap open-source software [18] that offers a user-friendly interface with the now standard statistical programming language R. We weighted all estimates proportional to population within each state, down-weighting the additional sites in the six focus LGAs.

Sequential bivariate analysis allowed examination of the association of each potential risk factor and underlying determinant in turn with maternal morbidity. To verify that associations of risk factors with maternal morbidity could not be explained by any of the general factors (age, sex, crowding, food security, urban/rural or country) we saturated initial multivariate models with the potential risk factors, then stepped down one variable at a time until only significant associations remained. We followed the same procedure for the Mantel Haenszel procedure and for GEE which accessed Zelig [19], applying an exchangeable correlation structure (logit.gee model, 1000 simulations). We report the adjusted Odds Ratio (ORa) and cluster-adjusted confidence intervals (CIca) using a robust variance estimator to weight the confidence interval around the Mantel Haenszel Odds Ratio for cluster-correlated data [20, 21].

The sample represents only those present at the time of the fieldwork; we have no information on why others were absent. Very few women declined to take the survey and we made no effort to persuade them to do so. More women in Bauchi than in Cross River declined to answer questions about genital mutilation and domestic violence. Clustering effects were different in Bauchi, where polygamy is more widespread and it was more common to have multiple women who gave birth in a single household.


In Bauchi, the Ethics Review Committee of the State Ministry of Health provided general approval in April 2009. The Cross River State Research Ethics Committee approved the methods and survey instruments on 28 August 2009, and the qualitative procedures in January 2010.


Female interviewers administered questionnaires to 25,745 women of a possible 30,918 in the two states; 1.2% declined the interview (345 or 1.8% in Cross River and 37 or 0.3% in Bauchi); a further 15% were not available at the time of the visit (4,213 or 22.2% in Cross River, where more women have formal employment, and 429 or 3.6% in Bauchi). A total of 15,621 women had given birth (7,759 in Cross River and 7,862 in Bauchi) in the last three years.

Table 1 lists the frequency of household characteristics, male knowledge and attitudes, antenatal care, work during pregnancy, IPV and FGM, and female knowledge, attitudes, intentions, and agency. One third lived in urban areas in Cross River, one half of that proportion in Bauchi. Nearly all Cross River women had formal education compared with one in every four Bauchi women.

Reports of pre-eclampsia and eclampsia were comparable in Bauchi (10.3% weighted value of 842/7684) and Cross River (13.0% weighted value of 973/7178). However, post-partum sepsis was much more common in Cross River (30.6% weighted value of 2223/7176), compared with 5.6% (weighted value of 473/7724 in Bauchi). The principal analysis combined pre-eclampsia, sepsis and other complications including excessive bleeding and convulsions as maternal morbidity related to pregnancy, delivery or post delivery: 17.8% of women in Bauchi and 43.9% in Cross River reported one of these.

Table 2 shows the bivariate associations between all potential risk factors and underlying determinants studied and maternal morbidity, indicating a number of promising associations. In addition, in both states, postnatal visits were more common among women who reduced work before the third trimester of pregnancy, who had more antenatal check-ups, who delivered at the health centre, who had healthy attitudes to smoking in pregnancy and who were more likely to know of danger signs in pregnancy. In general, women receiving postnatal visits were better off: they were more likely to have some education, less likely to complain of food insecurity and less likely to live in crowded households.
Table 2

Bivariate associations between maternal morbidity and potential risk factors



Cross River


With problem with factor

With problem without factor

OR (95% CIca)

With problem with factor

With problem without factor

OR (95% CIca)


22.5% 274/1219

18.1% 1155/6378

1.31 (1.00-1.72)

45.7% 1093/2393

43.9% 2097/4777

1.07 (0.90-1.28)

Any formal education

22.5% 374/1660

17.7% 1046/5903

1.35 (1.10-1.66)

44.8% 2971/6637

41.5% 290/496

1.14 (0.94-1.39)


18.7% 1382/7393

22.6% 44/195

0.79 (0.57-1.09)

44.2% 2608/5896

45.6% 577/1264

0.94 (0.82-1.09)

Food security in last week

18.2% 1239/6824

24.8% 186/749

0.67 (0.55-0.81)

42.9% 2502/5835

51.6% 682/1321

0.70 (0.63-0.78)

Remunerated employment

20.7% 753/3629

17.1% 668/3909

1.27 (1.08-1.50)

45.9% 1931/4207

42.5% 1254/2954

1.15 (1.02-1.30)

Low risk age for pregnancy (18-35 yrs)

18.7% 1186/6356

19.7% 242/1226

0.93 (0.79-1.10)

45.1% 2832/6274

40.0% 356/890

1.23 (1.06-1.43)

Times pregnant (1-3 pregnancies)

16.5% 605/3658

20.9% 798/3827

0.75 (0.65-0.87)

44.4% 1945/4385

44.7% 1233/2761

0.99 (0.90-1.08)

Female headed household

25.4% 15/59

18.8% 1251/6672

1.48 (0.73-3.00)

50.6% 159/314

48% 1479/3079

1.16 (0.94-1.42)

Non-crowded households (2/room or less)

19.9% 487/2451

18.4% 870/4719

1.10 (0.94-1.28)

46.7% 652/1395

49.1% 1229/2501

0.86 (0.76-0.97)

Know any danger in pregnancy

20.1% 817/4065

17.3% 596/3441

1.20 (1.06-1.36)

44.5% 2035/4577

44.6% 1151/2583

1.00 (0.90-1.10)

Know danger signs in childbirth

20.2% 817/4042

17.2% 592/3445

1.22 (1.07-1.39)

47.2% 1600/3389

42.0% 1583/3765

1.23 (1.10-1.38)

Believe women should give up heavy work in pregnancy

17.4% 511/2938

19.7% 914/4646

0.86 (0.75-0.98)

44.7% 1217/2720

44.3% 2476/4448

1.02 (0.91-1.14)

Believe it's not okay for pregnant women to smoke cigarettes

18.2% 1100/6046

21.2% 326/1536

0.83 (0.70-0.98)

45.8% 3467/6393

34.1% 261/765

1.63 (1.36-1.95)

Believe women without birth problems still need to deliver at a health facility

19.2% 526/2742

18.6% 899/4843

1.04 (0.89-1.21)

45.2% 2305/5105

42.8% 879/2052

1.10 (0.99-1.22)

Intention: If pregnant next year, would give up heavy work

18.4% 515/2800

19.4% 849/4381

0.94 (0.83-1.06)

45.2% 1573/3477

43.8% 1614/3681

1.06 (0.94-1.19)

Intention: If pregnant next year, would not smoke cigarettes

18.6% 1387/7443

26.3% 35/133

0.64 (0.38-1.07)

44.5% 3170/7120

33.3% (26/39)

1.61 (0.77-3.33)

Involved in decisions regarding pregnancy/childbirth

45.0% 18/40

18.7% 1404/7514

3.56 (1.98-6.39)

46.9% 849/1811

43.7% 2335/5340

1.14 (1.00-1.28)

Spoke about pregnancy primarily with husband

20.1% 802/3956

17.3% 546/3156

1.20 (1.06-1.37)

42.8% 1006/2349

45.4% 2115/4654

0.90 (0.80-1.01)

Say they were not ever beaten

18.4% 1332/7236

28.2% 89/316

0.58 (0.41-0.81)

42.4% 2414/5695

53.1% 745/1403

0.65 (0.58-0.73)

Say they were not beaten in pregnancy

18.9% 1357/7183

24.6% 46/187

0.71 (0.48-1.07)

43.3% 2732/6307

53.8% 415/772

0.66 (0.56-0.77)

Say they were not afraid of their husbands

18.6% 933/5018

19.1% 486/2538

0.96 (0.81-1.15)

43.0% 2062/4795

47.6% 1096/2304

0.83 (0.73-0.94)

Reduced workload before third trimester

22.5% 318/1416

18.0% 1084/6023

1.32 (1.10-1.58)

42.7% 1424/3334

45.9% 1444/3149

0.88 (0.79-0.98)

Had four or more antenatal check-ups

22.0% 650/2952

16.9% 726/4300

1.39 (1.19-1.62)

44.1% 1418/3218

45.4% 1628/3582

0.95 (0.84-1.06)

Took iron-folate at least one trimester

20.8% 496/2385

17.5% 850/4847

1.23 (1.06-1.44)

44.8% 1300/2904

44.8% 1155/3470

1.00 (0.90-1.11)

Urine checked at antenatal clinic

21.4% 619/2891

17.5% 758/4324

1.28 (1.07-1.54)

43.0% 1890/4400

47.1% 1271/2700

0.85 (0.74-0.96)

Blood pressure checked at antenatal clinic

20.8% 892/4294

16.6% 492/2965

1.32 (1.09-1.60)

43.4% 2256/5204

47.6% 898/1885

0.84 (0.72-0.98)

Qualified person at delivery at health facility

28.0% 321/1147

16.8% 1046/6211

1.92 (1.59-2.31)

41.8% 1322/3162

46.8% 1816/3882

0.82 (0.72-0.93)

Did not experience female circumcision

18.5% 1121/6074

23.8% 176/741

0.73 (0.58-0.90)

42.6% 1851/4346

47.6% 1323/2779

0.82 (0.73-0.91)

Table 3 shows the final multivariate models for all complications combined. In Bauchi, initial analysis of non-fatal maternal morbidity (pre-eclampsia, sepsis, excessive haemorrhage) showed marked heterogeneity between the minority of women who had a health check up after delivery and the majority who did not. Among those who received a check up, two factors remained in the final model: FGM (ORa 2.10 95%CIca 1.39-3.17) and four or more pregnancies (ORa 1.48, 95%CIca 1.15-1.90). FGM remained in both models in Cross River.
Table 3

Multivariate analysis of non-fatal maternal morbidity risk factors


OR unadjusted

Mantel Haenszel analysis with cluster adjustment

GEE with exchangeable correlation matrix



Cluster adjusted 95%CI


Robust 95%CI





With check-up after delivery












4+ pregnancies






No check-up after delivery






Did not speak primarily with husband






Physical IPV in pregnancy






Unqualified birth attendant






Insufficient food last week






4+ pregnancies






Less than 4 ANC check-ups






Cross River


With check-up after delivery






IPV last year












Did not speak primarily with husband






Crowded home (>2/room)






Formal employment






No check-up after delivery






IPV last year












Physical IPV in pregnancy






Unqualified birth attendant






Insufficient food last week






Aged 18-35 years






Did not reduce workload






1 Odds Ratio for the association between the variable and maternal morbidity, adjusted for all other variables in the final multivariate model. The initial model was based on the covariates in Table 2

2 An identical modelling process served for GEE

ns = not statistically significant at the 5% level

Physical IPV during pregnancy showed the strongest association with maternal morbidity in all multivariate models except the small group of Bauchi women who had home visits after delivery. This prominent role remained unchanged when we repeated the analysis using GEE.

Among women who had no home visit after delivery, those who had an unqualified birth attendant (most often to a traditional midwife without government approved training, less often to a neighbour or a family member) were more likely to have complications in both states.

We constructed a compound variable of factors related to the role of a husband or partner in the final model: IPV in pregnancy, IPV in the last year, and report that women had not discussed pregnancy with their husband or partner. Women with all three directly husband-related factors were much more likely to report a pregnancy or birth complication than women who had none, one or two of these factors (ORa 2.39, 95%CIca 1.96-2.92, RD 0.207, 222/432 women with all three and 4,397/14,335 who did not). This association was not explained by any of the factors we could take into account in this study.

Table 4 shows the final models for risk factors for pre-eclampsia and sepsis. Both initial models included the risk factors shown in Table 2. As associations with pre-eclampsia were not significantly different in Bauchi and Cross River, we combined the states for analysis of pre-eclampsia. Four variables showed independent associations after adjusting for the others: IPV in the last year, IPV during the pregnancy in question, rural residence and FGM.

In the case of sepsis, the variable “state” modified most bivariate measured associations, so we developed a separate multivariate model for Bauchi and Cross River. In Bauchi, sepsis was independently associated with IPV in the last year, IPV in the last pregnancy, perception of being cared for in pregnancy, age of the mother (younger women more likely to suffer sepsis) and FGM (Table 4). In Cross River, only two variables remained in the final model, IPV in the last year and perception of being cared for during the pregnancy.
Table 4

Multivariate analysis of risk factors for pre-eclampsia and sepsis


Cross River



Cluster adjusted 95%CI


Cluster adjusted 95%CI


Bauchi and Cross River


IPV last year




IPV during this pregnancy




Rural residence










Bauchi n=6992

Cross River n=7671

IPV in last year





IPV in last pregnancy




Did not feel cared for during pregnancy





Age over 30 years








1 Odds Ratio for the association between the variable and maternal morbidity, adjusted for all other variables in the final multivariate model. The initial model was based on the covariates in Table 2

Table 5 shows the low levels of male knowledge of pregnancy and delivery, and the high level of good intentions about maternal risks.
Table 5

Male knowledge and attitudes about pregnancy and childbirth in Bauchi and Cross River States, Nigeria


Bauchi State

Cross River State

Men interviewed








Know any danger in pregnancy (1)


31.0% 706/2276


35.9% 874/2432

Know danger signs in childbirth (2)


41.7% 947/2273


37.8% 921/2435

Agree male health workers can do antenatal checkups


30.4% 714/2352


82.1% 2033/2477

Agree male health worker can do deliveries


23.6% 554/2351


76.1% 1886/2477

Agree it's good pregnant women get together to talk


95.5% 2243/2350


94.4% 2331/2469

Agree that women should give up heavy work in pregnancy


40.2% 944/2351


44.3% 1099/2479

Agree it's not okay for pregnant women to smoke cigarettes


80.3% 1888/2352


90.7% 2249/2480

Agree women with birth complications should deliver at a health facility


98.5% 2313/2348


98.9% 2451/2478

Believe women sometimes deserve to be beaten


8.8% 205/2339


29.2% 723/2475

Believe “in my culture, it is acceptable for a man to beat his wife”


6.9% 161/2342


20.5% 506/2472

Believe violence between a man and a women is private and others should not interfere


45.6% 1069/2344


74.8% 1852/2475

Willing to take time to accompany wife if she had danger in childbirth


68.8% 1531/2226


97.4% 2321/2382

Willing to spend on transport for wife if she had danger in childbirth


96.6% 2209/2288


87.2% 2118/2429

In future, if wife had danger in childbirth, would take her to health facility


96.9% 2203/2273


93.5% 2279/2438

Main source of information on pregnancy and childbirth


Don't get any


4.2% 96/2316


2.6% 63/2468



26.3% 610/2316


24.2% 596/2468



39.1% 905/2316


16.9% 417/2468

Health worker


30.3% 702/2316


54.5% 1344/2468

1. Any of the following responses: pre-eclampsia, eclampsia, fever, bleeding, lap pain, high blood pressure, cord appears, breech/wrong presentation of baby, vomiting, fits/convulsions, uncontrolled urination, baby movements not felt, weakness, anaemia, jaundice, water coming out, malaria

2. Any of the following responses: malposition, premature labour, prolapse, retained placenta, uncontrolled urine, stillbirth, prolonged/obstructed labour, anaemia, weakness, low blood pressure, sepsis, fever, vaginal cut

Male focus groups discussed what men consider when deciding where a woman should deliver her child. Almost all groups recognized a need for skilled birth attendance, and almost all raised economic considerations in taking advantage of this where it was available. “The man considers the weight of his pocket before deciding where to take the woman for delivery”.

Few of the 180 male focus groups saw men as the cause of IPV; nearly all concluded that IPV could be avoided if women prayed, were obedient and patient, and never refused sex. Asked how IPV could be avoided, several groups suggested increasing women's incomes. The focus groups were uniform in the belief that IPV is a private matter, reporting of IPV bringing shame, disgrace and “greater divisions”. In Cross River, men quoted the Bible (“What God has joined together, let no man put asunder”) as the reason for not reporting IPV. In both states, men gave prominence to community leaders and religious leaders to stop the violence. Despite the strong and uniform belief that IPV is a private matter, many male groups were in favour of locally administered punitive schemes, typically a fine for beating one's wife being a goat, or cash ranging from N500 to N10,000 (US$4-70). Asked what men could do themselves, most groups felt they had the power to stop IPV, “As heads of the households, we can do it”.

A clear theme in the 180 female focus groups was self-blame for the IPV (“strong mouth”, disobedient, demanding or refusing sex). Some concluded that men were “naturally violent so there is nothing you can do”. Others said pregnancy was a cause of violence as it made women irritable and too tired to have sex. They saw marital infidelity as a common cause, whether the woman or man was cheating. Across all regions of both states, women saw money as a major cause. According to women in Cross River, “the Bible says that the wife does not have rights over her body, so we should submit our body to our husbands...” and “the Bible says that God created the woman out of Adam’s rib, the woman should be under the man and should be humble to the man’s relatives to avoid being beaten by the man.” In Cross River, women saw IPV as a family matter, to be resolved at home. In clear contrast, no women's focus group in Bauchi reported this view.


Within the constraints of a cross-sectional survey of childbirth survivors, IPV during pregnancy and history of IPV in the last year were the most prominent risk factors or underlying determinants for maternal morbidity in both Bauchi and Cross River.

This study relies on self-reporting of morbidity by survivors of childbirth. Reports of morbidity were quite different between the two states, compatible with different levels of health literacy and the marked differences in women's education between the states. We reduced the effect of this by analysing the two states separately and combining types of maternal morbidity. Despite this reporting difference, spouse-related factors (IPV in the last year, IPV in pregnancy, did not discuss pregnancy primarily with husband) were prominent in both states.

Analysis of individual morbidities (pre-eclampsia and sepsis) showed very much the same picture.

We were initially surprised that women in Cross River reported more delivery complications than women in Bauchi, although many more in River Cross benefited from institutional deliveries. Women in Cross River were also more likely to report IPV and FGM. We do not interpret this to mean these risks are actually higher in Cross River, rather that those women who suffered them were less likely to report them if they were less educated and had less contact with health services. Female education levels were much lower and far fewer women had institutional deliveries in Bauchi than in Cross River. Although we have no detailed information on this from the questionnaires, it is plausible that less educated women considered these problems normal or, having survived, inconsequential. There may also be different social imperatives, interpretations of family pride, between Cross River and Bauchi. This likely under-reporting of complications among women who are at highest risk invalidates unstratified comparison of rates in Bauchi and Cross River. However, it is difficult to compare rates among educated women who have access to care, because there are so few such women in Bauchi.

Associations with maternal morbidity differed between the advantaged women receiving a postnatal home visit, and the majority of women who did not. We offset this by analyzing the groups separately. In both states, those who received a home visit were evidently better off and more engaged with the health services; their risk factors in Bauchi were physical, FGM and multiple (four or more) pregnancies. In all other groups, IPV and socio-economic factors were prominent.

This was a cross-sectional study, with all the usual issues of direction of causality of even the strongest associations. Some spouse-related factors not specific to the pregnancy (IPV in the last year) might be causally related to maternal outcomes or they might result from the maternal outcome or something else shared with the maternal outcome that we neglected to study. It seems likely that the IPV reported during pregnancy preceded the maternal morbidity; it is also possible that women who suffered complications remembered violence differently. Either way, the associations are a cause for concern for pregnant women.

Husband related risk factors and underlying determinants affect many women. Some 45% of women in Bauchi and 68% in Cross River did not say they discussed their pregnancy primarily with their husbands or partners. Only one in five women in Bauchi and one half in Cross River reduced their workload before the third trimester (Table 1). Related to patriarchy though not narrowly to the behaviour of the husband during pregnancy [22], at least one in every ten Bauchi women and four in ten Cross River women entered reproductive life with mutilated genitals.

The protective association between maternal morbidity and the birth attendance by a qualified midwife in both Bauchi and Cross River (Tables3and 4) is especially important given the low level of participation of women in decisions about where the birth should be attended. In Bauchi, only 15.6% of women we interviewed had delivered in a health facility. Although the household survey showed good intentions if little knowledge among male respondents (Table 5), focus groups with men showed a prominent belief that maternal outcomes were a question for health services.

The levels of IPV we detected in the two states are within the range of other studies of IPV in pregnancy in Nigeria [2325]. Associations of maternal morbidity with IPV are well documented in eclampsia [26, 27], pre-term delivery [28, 29], mental health [30, 31], alcohol and tobacco use [32], and health seeking behaviour [3335]. Little is known of the mechanisms underlying these associations with IPV, and our study is not the design to add major insights. Depression [31, 36] and stress [30] are plausible intermediaries. Whatever the mechanism, it is clear that men play an important if not pivotal role – and it is a role they can change. The few calls for men to play a role in favour of prevention of maternal mortality [3739] have not been accompanied by larger scale programmes that address maternal morbidity through working with men.


In this study as in others in other places, violence against women is strongly associated with maternal morbidity. Reduction of these risk factors and underlying determinants involves spouses, independent of the health services. The sample represents the northern Bauchi state and Cross River in the south east of Nigeria. High levels of FGM, maternal mortality and pregnancy complications in the predominantly Christian south contradict any notion that these are limited to the predominantly Muslim north. Across these widely different settings and consistent with existing literature, male responsibility is important in maternal mortality.

Our focus on men in prevention of maternal morbidity does not detract from the good reasons to increase coverage with antenatal care and access to health facilities. Enhancing the clinical protocols and skills of health workers can only be of benefit to women in Nigeria and elsewhere. But, with prominence of men in the strongest risk factors for and underlying determinants of maternal morbidity, efforts to increase coverage and quality of obstetric care should take care not to bolster the male belief that maternal health is not their responsibility.

Our study opens another arena for reduction of maternal morbidity, with men as possible agents for change. The violence women experience throughout their lives – genital mutilation, domestic violence, and steep power gradients – is accentuated through pregnancy and childbirth, when women are most vulnerable. IPV especially in pregnancy, women's fear of husbands or partners and being able to discuss pregnancy with their husbands or partners are all within the male domain.



The Canadian International Development Agency (CIDA) and the International Development Research Centre (IDRC) funded this work as part of a five year Nigeria Evidence-based Health System Initiative (NEHSI) in Bauchi and Cross River states.

The governments of Bauchi and Cross River states created a research-friendly atmosphere that made this work possible. In Cross River, we thank Dr Ekabua, his colleagues at the University of Calabar Teaching Hospital, and the Cross River state Association of Traditional Birth Attendants who gave us insights into maternal care.

This article has been published as part of BMC Health Services Research Volume 11 Supplement 2, 2011: Social audit: building the community voice into health service delivery and planning. The full contents of the supplement are available online at

Authors’ Affiliations

Centro de Investigación de Enfermedades Tropicales, Universidad Autónoma de Guerrero, Calle Pino, El Roble, Acapulco, Mexico
CIET Trust, Calabar and Bauchi, Nigeria
Bauchi State Primary Health Care Development Agency, Nigeria
Bauchi State Ministry of Health, Nigeria
Cross River State Ministry of Health, Nigeria
Institute of Geography, Urban and Regional Planning, University of Peshawar, Pakistan


  1. Hogan MC, Foreman KJ, Naghavi M, Ahn SY, Wang M, Makela SM, Lopez AD, Lozano R, Murray CJ: Maternal mortality for 181 countries, 1980-2008: a systematic analysis of progress towards Millennium Development Goal 5. Lancet. 2010, 375: 1609-23. 10.1016/S0140-6736(10)60518-1.View ArticlePubMedGoogle Scholar
  2. World Health Organisation, UNICEF, UNFPA, the World Bank: Trends in maternal mortality: 1990-2008. 2010, GenevaGoogle Scholar
  3. Mairiga AG, Saleh W: Maternal mortality at the State Specialist Hospital Bauchi, Northern Nigeria. East Afr Med J. 2009, 86 (1): 25-30.View ArticlePubMedGoogle Scholar
  4. Ujah IA, Aisien OA, Mutihir JT, Vanderjagt DJ, Glew RH, Uguru VE: Factors contributing to maternal mortality in north-central Nigeria: a seventeen-year review. Afr J Reprod Health. 2005, 9: 27-40. 10.2307/3583409.View ArticlePubMedGoogle Scholar
  5. Wall LL: Dead mothers and injured wives: the social context of maternal morbidity and mortality among the Hausa of northern Nigeria. Stud Fam Plann. 1998, 29 (4): 341-59. 10.2307/172248.View ArticlePubMedGoogle Scholar
  6. Ozumba BC, Nwogu-Ikojo EE: Avoidable maternal mortality in Enugu, Nigeria. Public Health. 2008, 122: 354-360. 10.1016/j.puhe.2007.04.018.View ArticlePubMedGoogle Scholar
  7. Ibekwe PC, Ibekwe RO: Provision of essential obstetric care (EOC): a sine qua non to reducing maternal mortality rate in Nigeria. Promot Educ. 2008, 15 (4): 50-52. 10.1177/1025382308097698.View ArticlePubMedGoogle Scholar
  8. Chukudebelu WO, Ozumba BC: Maternal mortality at the University of Nigeria Teaching Hospital (UNTH) Enugu, Nigeria: a 10-year survey. Trop J Obstet Gynaecol. 1988, 1: 23-6.PubMedGoogle Scholar
  9. Umeora OUJ, Ejikeme BN: Clinical correlates and trends in hospital mortality in rural Nigeria. J Obstet Gynaecol. 2006, 26 (2): 139-40. 10.1080/01443610500443451.View ArticlePubMedGoogle Scholar
  10. Igberase GO, Ebeigbe PN: Maternal mortality in a rural referral hospital in the Niger Delta, Nigeria. Journal of Obstetrics and Gynaecology. 2007, 27 (3): 275-278. 10.1080/01443610701213687.View ArticlePubMedGoogle Scholar
  11. Lanre-Abass BA: Poverty and maternal mortality in Nigeria: towards a more viable ethics of modern medical practice. International Journal for Equity in Health. 2008, 7: 11-10.1186/1475-9276-7-11.PubMed CentralView ArticlePubMedGoogle Scholar
  12. Harrison KA: Maternal Mortality in Nigeria: The Real Issues. African Journal of Reproductive Health. 1997, 1: 7-13. 10.2307/3583270.View ArticlePubMedGoogle Scholar
  13. Owolabi AT, Fatusi AO, Kuti O, Adeyemi A, Faturoti SO, Obiajuwa PO: Maternal complications and perinatal outcomes in booked and unbooked Nigerian mothers. Singapore Medical Journal. 2008, 49 (7): 526-PubMedGoogle Scholar
  14. Amaghionyeodiwe LA: Determinants of the choice of health care provider in Nigeria. Health Care Management Science. 2008, 11 (3): 215-227. 10.1007/s10729-007-9038-3.View ArticlePubMedGoogle Scholar
  15. Amaghionyeodiwe LA: Government health care spending and the poor: evidence from Nigeria. International Journal of Social Economics. 2009, 36 (3): 220-236. 10.1108/03068290910932729.View ArticleGoogle Scholar
  16. Ijadunola KT, Ijadunola MY, Esimai OA, Abiona TC: New paradigm old thinking: the case for emergency obstetric care in the prevention of maternal mortality in Nigeria. BMC Women's Health. 2010, 10 (6): 10.1186/1472-6874-10-6.Google Scholar
  17. International Development Research Centre: Nigeria Evidence-based Health System Initiative (NEHSI): Implementation. []
  18. Andersson N, Mitchell S: Epidemiological geomatics in evaluation of mine risk education in Afghanistan: introducing population weighted raster maps. International Journal of Health Geographics. 2006, 5: 1-10.1186/1476-072X-5-1.PubMed CentralView ArticlePubMedGoogle Scholar
  19. Lam P: logit.gee: Generalized Estimating Equation for Logit Regression. Zelig: Everyone’s Statistical Software. Edited by: Imai K, King G, Lau O. 2007, []Google Scholar
  20. Lamothe G: Adjusting the Mantel Haenszel test statistic and odds ratio for cluster sampling. BMC Health Services Research. 2011, 11 (Suppl 2): S15 (statistical annex to reference 21)-Google Scholar
  21. Andersson N, Lamothe G: Clustering and meso-level variables in cross-sectional surveys: an example of food aid during the Bosnian crisis. BMC Health Services Research. 2011, 11 (Suppl 2): S15-PubMed CentralView ArticlePubMedGoogle Scholar
  22. Monagan SL: Patriarchy: perpetuating the practice of female genital mutilation. Journal of Alternative Perspectives in the Social Sciences. 2010, 2 (1): 160-181.Google Scholar
  23. Fawole AO, Hunyinbo KI, Fawole OI: Prevalence of violence against pregnant women in Abeokuta, Nigeria. Aust N Z J Obstet Gynaecol. 2008, 48 (4): 405-14. 10.1111/j.1479-828X.2008.00868.x.View ArticlePubMedGoogle Scholar
  24. Ezechi OC, Kalu BKE, Ezechi LO, Nwokoro CA, Ndububa VI, Okeke GCE: Prevalence and pattern of domestic violence against pregnant Nigerian women. J Obstet Gynaecol. 2004, 24 (6): 652-656. 10.1080/01443610400007901.View ArticlePubMedGoogle Scholar
  25. Ezechi OC, Gab-Okafor C, Onwujekwe DI, Adu RA, Amadi E, Herbertson E: Intimate partner violence and correlates in pregnant HIV positive Nigerians. Arch Gynecol Obstet. 2009, 280 (5): 745-52. 10.1007/s00404-009-0956-9.View ArticlePubMedGoogle Scholar
  26. Sanchez SE, Qiu C, Perales MT, Lam N, Garcia P, Williams MA: Intimate partner violence (IPV) and preeclampsia among Peruvian women. Eur J Obstet Gynecol Reprod Biol. 2008, 137 (1): 50-5. 10.1016/j.ejogrb.2007.05.013.View ArticlePubMedGoogle Scholar
  27. Garabedian M, Lain K, Hansen W, Garcia L, Coker A, Crofford L: Intimate partner violence and adverse pregnancy outcomes. American Journal of Obstetrics & Gynecology. 2008, 199 (6): S114-Supp A, []View ArticleGoogle Scholar
  28. Sarkar NN: The impact of intimate partner violence on women's reproductive health and pregnancy outcome. J Obstet Gynaecol. 2008, 28 (3): 266-71. 10.1080/01443610802042415.View ArticlePubMedGoogle Scholar
  29. Campbell JC: Health consequences of intimate partner violence. Lancet. 2002, 359 (9314): 1331-6. 10.1016/S0140-6736(02)08336-8.View ArticlePubMedGoogle Scholar
  30. Tiwari A, Chan KL, Fong D, Leung WC, Brownridge DA, Lam H, Wong B, Lam CM, Chau F, Chan A, Cheung KB, Ho PC: The impact of psychological abuse by an intimate partner on the mental health of pregnant women. BJOG. 2008, 115 (3): 377-84. 10.1111/j.1471-0528.2007.01593.x.PubMed CentralView ArticlePubMedGoogle Scholar
  31. Ludermir AB, Lewis G, Valongueiro SA, de Araújo TV, Araya R: Violence against women by their intimate partner during pregnancy and postnatal depression: a prospective cohort study. Lancet. 2010, 376 (9744): 903-10. 10.1016/S0140-6736(10)60887-2.View ArticlePubMedGoogle Scholar
  32. Fanslow J, Silva M, Robinson E, Whitehead A: Violence during pregnancy: associations with pregnancy intendedness, pregnancy-related care, and alcohol and tobacco use among a representative sample of New Zealand women. Aust N Z J Obstet Gynaecol. 2008, 48 (4): 398-404. 10.1111/j.1479-828X.2008.00890.x.View ArticlePubMedGoogle Scholar
  33. Roelens K, Verstraelen H, Van Egmond K, Temmerman M: Disclosure and health-seeking behaviour following intimate partner violence before and during pregnancy in Flanders, Belgium: a survey surveillance study. Eur J Obstet Gynecol Reprod Biol. 2008, 137 (1): 37-42. 10.1016/j.ejogrb.2007.04.013.View ArticlePubMedGoogle Scholar
  34. Bailey BA, Daugherty RA: Intimate partner violence during pregnancy: incidence and associated health behaviors in a rural population. Matern Child Health J. 2007, 11 (5): 495-503. 10.1007/s10995-007-0191-6.View ArticlePubMedGoogle Scholar
  35. Kendall-Tackett KA: Violence against women and the perinatal period: the impact of lifetime violence and abuse on pregnancy, postpartum, and breastfeeding. Trauma Violence Abuse. 2007, 8 (3): 344-53. 10.1177/1524838007304406.View ArticlePubMedGoogle Scholar
  36. Qiu C, Sanchez SE, Lam N, Garcia P, Williams MA: Associations of depression and depressive symptoms with preeclampsia: results from a Peruvian case-control study. BMC Womens Health. 2007, 7: 15-10.1186/1472-6874-7-15.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Lawoyin TO, Lawoyin OO, Adewole DA: Men's perception of maternal mortality in Nigeria. J Public Health Policy. 2007, 28 (3): 299-318. 10.1057/palgrave.jphp.3200143.View ArticlePubMedGoogle Scholar
  38. Anying JJ: Men can help curb maternal mortality. The Observer, Uganda. 25 August 2010 , []Google Scholar
  39. Liljestrand J: Strategies to reduce maternal mortality worldwide. Curr Opin Obstet Gynecol. 2000, 12 (6): 513-517. 10.1097/00001703-200012000-00010.View ArticlePubMedGoogle Scholar


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