- Email: [email protected]
Armed conflict and maternal health care utilization: Evidence from the Boko Haram Insurgency in Nigeria
Social Science & Medicine 226 (2019) 104–112
Contents lists available at ScienceDirect
Social Science & Medicine journal homepage: www.elsevier.com/locate/socscimed
Armed conflict and maternal health care utilization: Evidence from the Boko Haram Insurgency in Nigeria
T
Adanna Chukwuma∗, Uche Eseosa Ekhator-Mobayode World Bank Group, Washington, DC, 20433, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Nigeria Conflict Maternal health Access Violence Terrorism Health care use
Retention in maternal health care is essential to decreasing preventable mortality. By reducing access to care, armed conflicts such as the Boko Haram Insurgency (BHI), contribute to the high maternal mortality rates in Nigeria. While there is a rich literature describing the mechanisms through which conflict affects health care access, studies that estimate the impact of conflict on maternal health care use are sparse and report mixed findings. In this study, we examine the impact of the BHI on maternal care access in Nigeria. We spatially match 52,675 birth records from the Nigeria Demographic and Health Survey (NDHS) with attack locations in the Armed Conflict Location and Event Dataset (ACLED). We define BH conflict area as NDHS clusters with at least five attacks within 3000, 5000 and 10,000 m of BH activity during the study period and employ difference-indifferences methods to examine the effect of the BHI on antenatal care visits, delivery at the health center and delivery by a skilled professional. We find that the BHI reduced the probability of any antenatal care visits, delivery at a health center, and delivery by a skilled health professional. The negative effects of the BHI on maternal health care access extended beyond the Northeastern region, that is the current focus of humanitarian programs. Systematic efforts to identify and address the mechanisms underlying reductions in maternal health care use due to the BHI, and to target the affected populations, are essential to improving maternal health in Nigeria.
1. Background Approximately 830 mothers die daily from preventable causes related to pregnancy and childbirth worldwide (WHO, 2018). Of these deaths, 145 occur in Nigeria alone, making her the second largest contributor to global maternal mortality (Nigeria Health Watch, 2017). Retaining mothers in antenatal care, skilled birth attendance, and facility delivery is essential to reducing and managing pregnancy complications, and to decreasing preventable maternal mortality (PMNCH, 2006). However, in Nigeria, only 36 percent of births occur in a health facility, 38 percent of deliveries are attended by a skilled health worker, and 51 percent of women receive four antenatal care consultations (National Population Commission of Nigeria, 2014). Gaps in the coverage of essential health services represent missed opportunities to prevent maternal mortality in Nigeria. Access to health care, a proximal determinant of health care use, can be conceptualized in terms of the opportunity to seek and obtain health services, as a function of the attributes of the population and health system (Dutton, 1986; Levesque et al., 2013; Penchansky and Thomas, 1981; Peters et al., 2008). In a seminal article on the concept,
∗
Penchansky and Thomas described five dimensions of access to health care. The availability dimension refers to an adequate supply of health workers, facilities, and services, that is the structural quality of health services being accessed (Campbell et al., 2000). The accessibility dimension accounts for the capacity of the service location to be reached, considering client transportation resources, distance, and travel time to care. The accommodation dimension refers to the perception of appropriateness of service organization including hours of operation and appointment systems. The affordability dimension accounts for ability to pay and financial protection during health care seeking, including income and insurance coverage. The acceptability dimension refers to personal characteristics of the provider and client that may influence health care-seeking behavior, including age, sex, ethnicity, and religious affiliation. The dimensions in the above conceptualization by Penchansky and Thomas provide an organizing framework for the specific determinants that may influence access to and utilization of health care. The impact of armed conflict on health care access, and utilization, can be understood through the mechanisms that influence the above dimensions. Availability of services is curtailed by the destruction or
Corresponding author. World Bank Group, 1818 H Street NW, Washington DC, 20433, USA. E-mail addresses: [email protected] (A. Chukwuma), [email protected] (U.E. Ekhator-Mobayode).
https://doi.org/10.1016/j.socscimed.2019.02.055 Received 13 August 2018; Received in revised form 10 January 2019; Accepted 27 February 2019 Available online 03 March 2019 0277-9536/ © 2019 Published by Elsevier Ltd.
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
Khasholian et al. also described interruptions in maternity care use among 1015 women during the 2006 war in Lebanon (KabakianKhasholian et al., 2013). In Eastern Burma, conflict has been linked to low coverage of skilled birth attendance and antenatal care (Mullany et al., 2008). Secular trends, concurrent changes in other determinants of access, lack of a counterfactual, and a dynamic population due to migration and mortality may confound conclusions on the impact of armed conflict on health care access based on descriptive statistics (DeJong et al., 2017). Hence, other studies have provided associations that adjust for other determinants of health care access or that compare outcomes in groups defined based on exposure to armed conflict. Price and Bohara find a negative correlation between antenatal care coverage and violent incidents in Nepal during the Maoist insurgency, adjusting for other determinants of health care access (Price and Bohara, 2012). Using multinomial logistic regressions, Namasivayam et al. demonstrated negative associations between armed conflict and the use of contraception and birth in health facilities in Uganda (Namasivayam et al., 2017). Their study also reported that skilled birth attendance was higher among women in conflict-affected areas. As the dataset excluded Northern districts that were more seriously affected by conflict, their findings may reflect selection bias (Namasivayam et al., 2017). Using nationally-representative data and difference-in-difference models to construct a credible counterfactual [Anonymous, 2018], found that the BHI does not affect maternal hospital visits (Anonymous, 2018). As up to 50 percent of maternal health care use in Nigeria occurs in primary health care facilities, the impact of conflict on hospital visits may not have captured effects on health facility use as a whole (Okonofua et al., 2018). There are no studies, to our knowledge, that estimate the impact of the BHI on antenatal care and skilled birth attendance. The mixed findings from the literature reflect differences in the mechanisms affecting service delivery in each context and methodological choices, including adjustment for confounders and comparison to a counterfactual. This illustrates the need for studies that quantify the impact of the BHI conflict on maternal health care use in Nigeria specifically, using nationally-representative datasets and adjusting for potential confounders. Thus, in this study, we estimate the impact of the BHI on maternal care utilization in Nigeria. We build on the existing literature by spatially merging data on the specific location of conflict events attributed to BH with nationally-representative surveys of maternal health care use, employing difference-in-difference models to adjust for time-invariant confounders, and controlling for time-varying determinants of access to maternal health care in Nigeria.
shutdown of health facilities, supply chain disruption, and the emigration or attack of health providers (Acerra et al., 2009; Chi et al., 2015; David et al., 2017; Guenther et al., 2012; ICRC, 2011; KabakianKhasholian et al., 2013; McKay, 1998). For example, the Graca Machel Study described the targeting of facilities as a tactic of war in Nicaragua between 1982 and 1987, destroying over 20 percent of health units (McKay, 1998). In Burundi, the targeting of health workers severely constrained the supply of health services (Chi et al., 2015). In Iraq, in addition to targeting medical personnel, the conflict was associated with low enrollments in medical schools affecting the future supply of health workers (Dye and Bishai, 2007). Armed conflict can also affect accessibility, another dimension of access, through insecurity that limits transportation (Bosmans et al., 2008; Chi et al., 2015; McKay, 1998). For example, closures and checkpoints associated with the emergency situation in the Occupied Palestine Territories in 2000, limited access to urban facilities where health infrastructure was predominantly located (Giacaman et al., 2005). Where shorter routes were blocked during the Maoist insurgency in Nepal, mothers opted for longer alternatives to reach health care, increasing travel costs (Price and Bohara, 2012). Fouad et al. have proposed the idea of weaponization of health care to describe the use of large-scale violence to restrict access to health care as described above, through attacking facilities or health workers, shortages of medical supplies, and restrictions in service delivery in besieged areas, exemplified by the Syrian conflict (Fouad et al., 2017). Conflict may also affect access to care through other mechanisms. Accommodation may be affected by irregular opening hours of hospitals due to insecurity during conflict, as in Burundi and Uganda (Chi et al., 2015). Finally, discrimination in health care services during conflict may reduce acceptability of care. Ethnic favoritism in the provision of services during the Burundian civil war and the Maoist insurgency has been linked to reductions in coverage of care during the conflict (Chi et al., 2015; Ghimire and Pun, 2006). The above discussion illustrates the mechanisms through which armed conflicts such as the Boko Haram Insurgency (BHI) may contribute to maternal mortality though reductions in access. Since 2009, violence linked to Boko Haram (BH) activities has persisted in the Northeastern Nigeria states of Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe (AFPC, 2018). BH attacks have been recorded in other states in Northern and Southern Nigeria, including the Ebonyi, Edo, Kogi, Anambra, Niger, and the Federal Capital Territory (CFR, 2018). The BHI has disproportionately affected women (Matfess, 2017). Amnesty International reports that over 2000 women were abducted by BH between 2014 and the spring of 2015 (Amnesty International, 2018). Furthermore, most BH suicide-bombers are female, who are often coerced into their missions (Amnesty International, 2018). The conflict has been associated with the destruction of health facilities and the abduction, killing, and displacement of skilled health workers, limiting the availability of health care services (Patel et al., 2017). By October 2017, 527 of the 755 health centers in Borno state had been closed or destroyed due to the BHI (Siddons, 2018). To our knowledge, there are very few studies that estimate the impact of the BHI on maternal health care utilization (Anonymous, 2018). Studies that assess the impact of armed conflict on maternal health care across contexts report mixed findings. A significant body of research provides descriptive evidence of changes in utilization of care during conflict. Chi et al. provide qualitative evidence that armed conflict in Burundi and Northern Uganda substantially reduces the quality of maternal health care, describing the predominant mechanisms through which armed conflict reduces the availability and accessibility of care (Chi et al., 2015). In a qualitative account of the emergency situation of 2000, Giacaman et al. noted the reductions in access to basic maternity services due to curfews, closures, and the siege (Giacaman et al., 2005). In a review of research conducted on the Syrian conflict, DeJong et al. noted a fall in antenatal care coverage from 87.7 percent to 62 percent and a fall in skilled birth attendance from 96.2 percent to 72 percent (DeJong et al., 2017). Kabakian-
2. Methods 2.1. Study sample The study sample derives from the births recode data files of the 2008 and 2013 Nigerian Demographic and Health Surveys (NDHS), which are nationally-representative cross-sectional household surveys administered to women who are between 15 and 49 years old (National Population Commission (NPC) [Nigeria] and ICF Macro, 2009; National Population Commission of Nigeria 2014). The NDHS provides information on maternal health care use including antenatal care and birth experiences in the five years preceding the survey. The NDHS sample was combined with data from the Armed Conflict Location and Event Dataset (ACLED) (ACLED, 2018). The ACLED contains dates, locations, and actors of events that occur during civil wars and periods of instability, public protest and regime breakdown across Africa, South Asia, South East Asia and the Middle East. The dataset enables the identification of 829 conflicts with BH as an actor that occurred in Nigeria from the beginning of BH violence in 2009 until 2013 to match the NDHS timeframe: 21 in 2009, 37 in 2010, 124 in 2011, 373 in 2012, and 274 in 2013. 105
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
indicator for residence in an urban area, as defined by the National Population Commission, as increased cost of travel to health services correlates with rural dwelling in many African countries (Porter, 2002). This indicator was coded as ‘1’ if the respondent lived in an urban area and ‘0’ if they lived in a rural area. We also included a continuous variable for altitude of the cluster of residence which may influence ease of transportation. Under the affordability dimension, we included a discrete variable for the years of education of the mother, a series of binary variables indicating employment type [coded as ‘1’ if the mother reported the specific employment category and as ‘0’ if otherwise], and a series of binary variables indicating the wealth quintile of the mother's households [coded as ‘1’ if the household belonged to the specific quintile and as ‘0’ if otherwise], which correlate with ability to pay for care and financial protection during health care seeking. Under the acceptability dimension, we included personal characteristics of the population that may influence care-seeking, comprising a discrete variable for household size, a discrete variable for the number of children under five in the household, the age of the mother in years, a series of indicators for religious affiliation [coded as ‘1’ if the mother reported affiliation with a given religion and ‘0’ otherwise], child gender [coded as ‘1’ if the child born is male and ‘0’ otherwise], and a discrete variable for the child birth order. We also included controls for month and year of interview and indicators for the state of residence to adjust for differences due to seasonal variation in health care use and administrative differences across states respectively. Our analysis was restricted to observations for which all the covariates were non-missing. The proportion of observations that were dropped due to missing values was 0.06 for antenatal care outcomes and 0.11 for delivery outcomes. The parameter π3 , measures the effect of the BHI on the maternal healthcare utilization, given that the assumption of parallel trends in the measured outcomes in the absence of the BHI holds. We provide evidence in support of this assumption in the Results section.
2.2. Statistical analysis To examine the effect of the BHI on maternal health care access, we compared trends in outcomes in BH areas and non-BH areas, before and after the onset of the insurgency in 2009. Births that occurred before 2009 were classified as pre-BHI, while births that occurred after 2009 were classified as occurring during the BHI. We excluded births in 2009 to avoid misclassification error in our main models. However, as 10 percent of births occurred in 2009, we conducted robustness checks, including these births in the analysis, as described in the Results section. We spatially matched geocoordinates of clusters in the NDHS sample to events in the ACLED dataset. We calculated the distance between each NDHS cluster to the location of all BH attacks between 2009 and 2013. As there is no agreed definition for exposure to conflict, a NDHS cluster was classified as a BH conflict area if it was located within the catchment area of at least 5 BH attacks. We examined sensitivity of our findings to the number of attacks used to define a BH conflict area, as described in the Results section. There is also no consensus on how to define a radius in which armed conflict may exert significant effects on health care access. An intuitive way to examine this is based on norms for defining access to health services, that is the target maximum distance between an individual's place of residence and the nearest source of health care to enable access to care. An attack within this radius or catchment area would be expected to significantly impact access to health care. There is no official definition of such a catchment area in Nigeria. The distances used vary across similar contexts in Sub-Saharan Africa, from 5000 m in Zambia and 8000 m in Malawi to 10,000 m in Mali (Guenther et al., 2012). Therefore, we varied the catchment area for significant attacks as follows: 3000, 5000 and 10,000 m. We then examined the sensitivity of our findings to further variation in the radius used to define the catchment area, as described in the Results section. The geo-coordinates of the NDHS clusters are randomly displaced to protect the privacy of the respondents (Elkies et al., 2015). While urban clusters are displaced up to 2000 m, rural clusters are displaced up to 5000 m (Perez-Heydrich et al., 2013). The measurement error introduced is likely to lead to attenuation bias in regressions (Skiles et al., 2013). In line with the study conceptual framework, we modelled maternal health care use as a function of the BHI and other determinants of maternal health care access described in the empirical literature, using difference-in-differences (DID) models, specified as follows:
3. Results In Table 1, we present the definitions of outcome and explanatory variables in the empirical models, and descriptive statistics for the study sample. Of the 34,075 birth records that were non-missing antenatal care, 21193 (62%) had at least one antenatal care visit, and 16,985 (50%) had at least four antenatal care visits. Of the 52,765 birth records that were non-missing for the place of delivery, 18240 (35%) were birthed at a health facility. Of the 52,558 birth records that were non-missing for the person who rendered delivery assistance, 19632 (37%) or deliveries were by a skilled health professional. We examine differences in the study outcome variables, before and during the BHI, based on the 5000 m catchment area of BH activity. Overall, the use of essential services was higher in BH catchment areas compared to non-BH catchment areas, before and after 2009. Before 2009, 82% of births to women residing in BHI catchment areas had at least one antenatal visit, and 64% had at least four antenatal visits, compared to 58% and 46% of women residing outside these catchment areas respectively. After 2009, 87% had at least one antenatal care visit in the BH catchment area compared to 62% outside these catchment areas, and 65% had at least four antenatal visits in the BH catchment area relative to 53% outside these catchment areas. Before 2009, 38% of births to women residing in the BH catchment areas occurred in a health center compared to 32% in the non-BH catchment areas. After 2009, 44% of births of women residing in the BH catchment areas were in a health center compared to 37% in the non-BH catchment areas. Finally, before 2009, 43% of births of women residing in the BH catchment areas were attended by skilled health professionals compared to 36% in the non-BH catchment areas. After 2009, 47% of births to women residing in the BH catchment areas were attended by skilled professionals compared to 39% in the non-BH catchment areas area. We reject the F-test of equality of means for BH and non-BH catchment areas for both the period before and after 2009 for all the maternal
Hist = π0 + π1 Post 2009it + π1 BHConflictArea + π3 Post 2009it ∗ BHConfl ictArea + π4 Xist + εist where Hist is a measure of maternal healthcare use for mother i residing in NDHS cluster s at time t. The measures we consider are 4 binary outcomes as follows: any antenatal care visits [coded as ‘1’ if the mother reported attending antenatal care at least once, and ‘0’ otherwise]; 4 or more antenatal care visits [coded as ‘1’ if the mother reported attending antenatal care at least four times, and ‘0’ otherwise]; delivery at health facility [coded as ‘1’ if the mother reported the place of childbirth as a health facility, and ‘0’ otherwise]; and delivery by a skilled health professional [coded as ‘1’ if the mother reported receiving skilled care during delivery from a doctor, nurse, or midwife, and ‘0’ otherwise]. Post2009it is a binary variable equal to 1 if a child's birth was after 2009 and 0 otherwise. BHConflictArea is a binary variable equal to 1 if a mother resides in a BH conflict area and 0 otherwise. All empirical models included respondent sampling weights to ensure representativeness. The models adjusted for time-varying determinants of maternal healthcare access in Nigeria, which increases precision of the estimated effect of the BHI. Xist represents the vector of these control variables. To define Xist , we reviewed the empirical literature on the factors associated with access to and utilization of health care in Nigeria (Babalola and Fatusi, 2009). Under the accessibility dimension, we included an 106
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
Table 1 Descriptive statistics. (1) Variables
(2) Description
1. Outcome Variables Any Antenatal Care Visits
Had at least 1 visit
4 Antenatal Care Visits
Had 4 or more visits
Delivery at Health Center
Delivery was at a public/private hospital or clinic Delivery assistance was given by a doctor or nurse
Delivery by a Skilled Professional 2. Covariates a. Household Characteristics Wealth Lowest quintile of the NDHS wealth index (reference) Second quintile of the NDHS wealth index, Middle quintile of the NDHS wealth index. Fourth quintile of the NDHS wealth index, Highest quintile of the NDHS wealth index, Household size Number of household members
Urban
Number of children under five in household Number of children under five in household square Residence in urban area
Altitude
Altitude of NDHS cluster in meters
Children under-five Children under-five2
b. Mothers Characteristics Occupation Is not employed (reference) Employed in a professional, technical or managerial position, Employed in a clerical position. Employed in sales Self-employed in agriculture. Employed in agriculture Employed in household, and domestic work. Employed in the services industry, Employed as a skilled manual worker. Employed as an unskilled. Other Occupation Age Age
Mother's age in years 2
Mother's age squared
Education
Years of education
Religion
Practices Christianity (reference) Practices Islam Practices other religion
c. Child Characteristics Male
Child is male
Birth order
Order born in the family
Before 2009
After 2009
Total
(3) Non-BH area
(4) BH area
(5) P value
(6) Non-BH area
(7) BH area
(8) P value
(9)
9765/16,864 (58%) 7807/16,864 (46%) 9985/30,804 (32%) 10,995/30669 (36%)
362/444 (82%) 286/444 (64%) 335/874 (38%) 377/871 (43%)
< .0001
10,696/16344 (65%) 8632/16,344 (53%) 7679/20,541 (37%) 8007/20,477 (39%)
370/423 (87%) 260/423 (62%) 241/546 (44%) 253/541 (47%)
< .0001
21,193/34075 (62%) 16,985/34075 (50%) 18,240/52765 (35%) 19,632/52558 (37%)
8216/30,804 (27%) 7411/30,804 (24%) 5974/30,804 (19%) 5080/30,804 (16%) 4123/30,804 (13%) 6.999 (3.566) 2.177 (1.235) 6.263 (7.601) 7947/30,804 (26%) 330.222 (289.495)
4/874 (0.5%) 82/874 (9%)
< .0001
6/546 (1.1%)
< .0001
< .0001
24/546 (4.4%) 92/546 (17%) 251/546 (46%) 173/546 (32%) 8.245 (5.080) 2.341 (1.270) 7.088 (8.247) 546/546 (100%) 543.696 (211.360)
< .0001
237/874 (27%) 316/874 (36%) 235/874 (27%) 8.489 (4.906) 2.539 (1.399) 8.402 (9.967) 756/874 (87%) 477.291 (183.871)
4641/20,541 (23%) 4950/20,541 (24%) 4106/20,541 (20%) 3666/20,541 (18%) 3178/20,541 (15%) 6.926 (3.532) 2.203 (1.224) 6.350 (7.294) 6388/20,541 (31%) 308.547 (227.342)
9352/30,804 (30%) 900/30,804 (3%) 172/30,804 (0.56%) 10,099/30804 (33%) 29/30,804 (0.09%) 5971/30,804 (19%) 3/30,804 (0.01%) 1069/30,804 (3.47%) 3156/30,804 (10%) 46/30,804 (0.15%) 7/30,804 (0.02%) 29.524 (7.090) 921.946 (445.440) 4.340 (5.001) 13,104/30804 (43%) 17,092/30804 (55%) 608/30,804 (2%)
353/874 (40%) 28/874 (3.2%) 10/874 (1.14%) 281/874 (32%) 0/874 (0%) 22/874 (2.52%) 0/874 (0%) 24/874 (2.75%) 156/874 (18%) 0/874 (0%) 0/874 (0%) 29.576 (0.234) 922.656 (439.974) 4.334 (5.273) 112/874 (13%) 760/874 (87%) 2/874 (0.23%)
< .0001
6211/20,541 (30%) 764/20,541 (3.72%) 66/20,541 (0.32%) 7675/20,541 (37%) 161/20,541 (0.78%) 2285/20,541 (11.12%) 23/20,541 (0.11%) 985/20,541 (4.80%) 2336/20,541 (11.37%) 10/20,541 (0.05%) 25/20,541 (0.12%) 28.789 (6.916) 876.657 (422.900) 4.924 (5.320) 8426/20,541 (41%) 11,917/20541 (58%) 198/20,541 (0.96%)
241/546 (44%) 22/546 (4.03) 1/546 (0.18%) 166/546 (30%) 0/546 (0%) 3/546 (0.55%) 2/546 (0.37%) 14/546 (2.56%) 97/546 (18%) 0/546 (0%) 0/546 (0%) 29.654 (6.864) 926.382 (428.278) 6.220 (5.465) 81/546 (15%) 465/546 (85%) 0/546 (0%)
15,679/30804 (51%) 3.961 (2.606)
426/874 (49%) 4.400 (2.847)
10,431/20541 (51%) 3.937 (2.589)
288/546 (53%) 4.333 (2.790)
< .0001 0.0002 < .0001
< .0001
< .0001 < .0001 < .0001 < .0001 < .0001 < .0001 < .0001
0.6259 0.0238 0.6930 0.3642 < .0001 0.7705 0.2473 < .0001 0.2529 0.6558 0.8331 0.963 0.9710 < .0001 < .0001 0.0002
0.2083 < .0001
0.0004 0.0013 0.0003
0.0698 < .0001 < .0001 < .0001 < .0001 0.0201 < .0001 < .0001
< .0001 0.7060 0.5713 0.0009 0.0378 < .0001 0.0883 0.0154 < .0001 0.6061 0.4147 0.0039 0.0067 < .0001 < .0001 < .0001 0.0212
0.3645 0.0004
12,867/52765 (24%) 12,467/52765 (24%) 10,409/52765 (20%) 9313/52,765 (18%) 7709/52,765 (14%) 7.008 (3.605) 2.195 (1.235) 6.341 (7.541) 15,637/52765 (30%) 326.429 (266.579) 16,157/52765 (31%) 1714/52,765 (3.25%) 249/52,765 (0.47%) 18,221/52765 (35%) 190/52,765 (0.36%) 8281/52,765 (16%) 28/52,765 (0.05%) 2092/52,765 (3.96%) 5745/52,765 (11%) 56/52,765 (0.11%) 32/52,765 (0.06%) 29.240 (7.026) 904.373 (437.083) 4.587 (5.147) 21,723/52765 (41%) 30,234/52765 (57%) 808/52,765 (1.53%) 26,824/52765 (51%) 3.963 (2.606)
(continued on next page) 107
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
Table 1 (continued)
(1) Variables
N
(2) Description
Before 2009
Total Sample
After 2009
Total
(3) Non-BH area
(4) BH area
(5) P value
(6) Non-BH area
(7) BH area
(8) P value
(9)
30,804 (97%)
874 (3%)
31,678
20,541 (97%)
546 (3%)
21,087
52,765
Data are N (%), mean(SD) or as indicated. In this analysis, exposure is measured as having at least five attacks within 5000 m of DHS cluster of residence. The pvalues are for the two-sample t-test with equal variances (Ho: Difference in means = 0 and Ha: Difference in means ≠ 0.
corresponding reduction in antenatal care use for the catchment areas of 5000 and 10,000 m are 10.9 and 6.3 percentage points respectively. For the catchment area of 3000 m, the BHI reduces the probability that a woman had at least four antenatal care visits by 22.4 percentage points at the 1% level of statistical significance. The corresponding reduction in having at least four antenatal care visits for the catchment areas of 5000 and 10,000 m are 19.5 and 14.6 percentage points respectively. The estimates also suggest that the BHI reduces the probability that a woman has a delivery at a health center or is assisted by a skilled health professional during delivery for women in the 3000 and 5000 m catchment areas. As with antenatal care visits, the magnitude of the effect is smaller, as the catchment area includes more women that are further away from the attacks. For the catchment area of 3000 m, the BHI reduces the probability that a woman has a delivery at a health center by 11.2 percentage points and reduces the probability that a skilled health professional assists a woman during delivery by 11.1 percentage points at the 1% level of statistical significance. For the catchment area of 5000 m, the BHI reduces the probability that a woman has a delivery at a health center by 8.5 percentage points and reduces the probability that a skilled health professional assists a woman during delivery by eight percentage points at the 1% level of statistical significance. The effect of the BHI on delivery at the health center and delivery assistance by a skilled health professional is not statistically significant for women in the 10,000 m catchment area. The DID estimates in Table 2 assume parallel trends in outcomes for BH and non-BH catchment areas before and after 2009, if in the absence of the BHI, the difference in outcomes between BH and non-BH catchment areas would be constant. To address this concern, we examine an unaffected cohort using placebo treatments by comparing trends in outcomes for births before 2009 (the unaffected cohort) in BH areas and non-BH areas. In this placebo analysis, we assume the BHI began after 2005. Observations of births in 2003, 2004 and 2005 provide data for the control group while observations of births from 2006, 2007 and 2008 provided data for the treatment group. If non-parallel trends exist, we should find spurious significant coefficients otherwise the coefficients should be generally insignificant. All the estimates in the placebo analysis (Table 3) are not statistically significant. The only exception to this is the variable for at least four antenatal visits for the 3000 m catchment area. However, this result does not seem to pose a serious concern as it is only statistically significant at the 10% level and not reinforced by other results from other catchment areas of 5000 and 10,000 m. The evidence suggests that the results of the original analysis are not by chance. In Fig. 1, we provide evidence demonstrating the robustness of our findings to the number of attacks used to define the BH catchment area. For antenatal care, we show that the observed negative impact of the BHI is robust to varying the number of attacks within 5000 m from one to ten. We also show that the observed negative impact of the BHI on use of skilled care at birth and delivery in any facility is robust to varying the number of attacks within 5000 m from four to ten. When there are between one and three attacks within the 5000 m, the estimate is negatively signed, but is not statistically significant at the 5% level. We also examined variation in the estimate of impact of the BHI on
health care access variables at the 1% level. We also examine differences in the explanatory variables, before and during the BHI, based on the 5000 m catchment area of BH activity. Households in BH catchment areas were slightly larger both before and after 2009. Both before and after 2009, households in BH catchment areas have an average of 8 members compared to 7 in the non-BH catchment areas. The number of children under-five in the households is approximately 2 for all study groups. We reject the F-test of equality of means or proportions between BH and non-BH catchment areas for both the period before and after 2009 for all the household characteristics at the 10% level. Thirty-one percent of the births in the sample is by unemployed mothers with the greatest proportion in BH areas in the period before and after the BHI. We are unable to reject the F-test of equality of proportions for 8 of 11 of the classifications for mothers' occupation. Also, we are unable to reject the F-test of equality of means across study groups for mothers' age and education. Mothers practicing Islam account for a greater proportion of births (57%) compared to 41% for Christianity and 1.53% for other religion. The proportion of births for mothers practicing Islam is greater in BH catchment areas before and after the BHI; the reverse is the case for mothers practicing Christianity and other religions. We reject the F-test of equality of proportions for BH and non-BH catchment areas for the period before and after 2009 for mothers’ religion at the 1% level. The difference-in-difference estimates (Table 2) show that the BHI reduces the probability that a woman had any or at least four antenatal care visits. The largest effects are for women in the closest catchment area to the BH attacks. The magnitude of the effects become smaller as the BH catchment area includes women that are further away from the attacks. While the BHI reduces the probability that a woman had any antenatal care visit by 11.1 percentage points at the 1% level of statistical significance for the catchment area of 3000 m, the
Table 2 The effect of the BHI on maternal health care access in Nigeria. Catchment Area (meters) Outcome Variables Any Antenatal Care Visits
4 or More Antenatal Care Visits
Delivery at Health Center
Delivery by a Skilled Health Professional
3000
5000
10,000
N
−0.111*** (0.001) [-0.179 to −0.044] −0.224*** (< 0.0001) [-0.322 to −0.126] −0.112*** (0.004) [-0.188 to −0.037] −0.111*** (0.004) [-0.188 to −0.034]
−0.109*** (< 0.0001) [-0.162 to −0.055] −0.195*** (< 0.0001) [-0.265 to −0.125] −0.085*** (0.002) [-0.138 to −0.031] −0.080*** (0.004) [-0.135 to −0.026]
−0.063*** (0.004) [-0.107 to −0.020] −0.146*** (< 0.0001) [-0.200 to −0.093] 0.005 (0.796) [-0.036 to 0.047] 0.017 (0.429) [-0.025 to 0.059]
34,075
34,075
52,765
52,558
Data are DID estimate (p value) [95% confidence interval]. Estimates are based on robust standard errors. *Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level. 108
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
status. However, we also examine the differential impact of the BHI on states in the Northeastern Nigeria by interacting the DID parameter with residence in Northeastern Nigeria. We find evidence for greater reductions in antenatal care use in Northeastern states relative to other locations following up to five attacks within a 5000 m catchment area of the cluster. However, there is no significant difference in reductions in delivery care use in Northeastern states relative to other locations following attacks (Table 4). In our analysis, we assume that the location of residence reported at the time of survey is the same at the time of birth. The most recent nationally-representative migration surveys indicate that 36% of households have migrants in Nigeria, the majority of whom (62–72%) are male (FAO, 2017). The NDHS birth recode file examines self-reports among females of reproductive age, among whom migration rates are relatively low. However, significant differences in migration rates between BH and non-BH catchment areas may bias results. We restrict the sample to a shorter period over which the probability of migration is lower, to examine the robustness of our findings to migration. This sample includes children born in the year of the survey and the year. That is, children born in 2007 and 2008 from the 2008 NDHS and those born in 2012 and 2013 from the 2013 NDHS, are included. With this restriction, it is more likely that the place of residence at the time of the survey is the same as the place of residence at the time of birth. The estimates for all the maternal health care access variables reinforces the results found for the full sample (Table 5). Finally, our analysis excludes births in 2009 (10% of the study sample) to avoid classification error. We examine if our results are robust to the inclusion of births in 2009, as occurring during the BHI. The results from this analysis (Table 6) reinforces the findings from the original analysis.
Table 3 Parallel trends analysis with placebo BHI. Catchment Area (meters) Outcome Variables Any Antenatal Care Visits
4 or More Antenatal Care Visits
Delivery at Health Center
Delivery by a Skilled Health Professional
3000
5000
10,000
N
−0.080 (0.145) [-0.187 to 0.027] −0.114* (0.079) [-0.241 to 0.013] −0.027 (0.533) [0.114 to 0.059] −0.030 (0.501) [0.119 to 0.058]
−0.030 (0.511) [-0.120 to 0.060] −0.082 (0.135) [-0.190 to 0.026] −0.022 (0.496) [-0.087 to 0.042] −0.011 (0.746) [-0.077 to 0.055]
0.015 (0.734) [-0.073 to 0.103] 0.031 (0.444) [-0.048 to 0.109] −0.035 (0.178) [-0.086 to 0.015] −0.014 (0.608) [-0.066 to 0.038]
17,308
17,308
31,678
31,540
Data are DID estimate (p value) [95% confidence interval]. Estimates are based on robust standard errors. *Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level.
maternal health care use with variation in the radius of the BH catchment area. Increasing the radius of the catchment area progressively includes mothers who live further away from the location of attacks in the intervention group and reducing the difference between exposed and unexposed women in the empirical models. We would then expect the magnitude of the estimate to remain constant or reduce (but not increase) depending on the extent to which differences in access to care between exposed and unexposed groups are reduced. In Fig. 2, we show that our findings are consistent with this rationale. The negative impact of the BHI on health care use is of lower magnitude at higher radii, and for antenatal outcomes, persists on varying the radius of the BH catchment area from 1000 to 10000 m. Attacks linked to the BHI have predominantly occurred in Northeastern Nigeria, with fewer incidents in other parts of the country. Our analysis spatial links the exact locations of attacks across the country to cluster geocoordinates, and accurately assigns exposure
4. Discussion To our knowledge, this study is one of the first to examine the effect of the BHI in Nigeria on maternal health care use, exploiting the proximity of a woman's residence to the location of BH events. Our findings suggest that the BHI has adverse effects on maternal health care use in the antenatal period and during delivery. The magnitude of these events is significant. Relative to the period before 2009 and
Fig. 1. Robustness to number of attacks in 5000 m catchment area. 109
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
Fig. 2. Variation with radius of the BH catchment area.
Table 4 The differential effect of the BHI on maternal health care access in Northeastern Nigeria. Catchment Area (meters)
3000
5000
10,000
Variables
DID Estimate*Northeast
DID Estimate*Northeast
DID Estimate*Northeast
Any Antenatal Care Visits
−0.064** (0.021) [-0.119 to −0.009] −0.126 (0.102) [-0.277 to −0.025] 0.038 (0.590) [-0.099 to 0.174] 0.070 (0.314) [-0.066 to 0.207]
−0.091*** (0.009) [-0.159 to −0.023] −0.172*** (< 0.0001) [-0.255 to −0.089] 0.007 (0.842) [-0.060 to −0.074] 0.022 (0.533) [-0.047 to −0.092]
−0.043 (0.123) [-0.098 to −0.012] −0.117*** (0.001) [-0.183 to −0.051] 0.027 (0.328) [-0.027 to 0.080] 0.047* (0.089) [-0.007 to 0.101]
4 or More Antenatal Care Visits
Delivery at Health Center
Delivery by a Skilled Health Professional
N
34,075
34,075
52,765
52,558
Data are DID estimate (p value) [95% confidence interval]. Estimates are based on robust standard errors. *Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level.
Northeastern region. The adverse effects of the BHI on health care use, however, also impact women outside the Northeast region, and there are no significant regional differences in the negative effects on health care use at delivery. This highlights the importance of reviewing the current strategy of focusing humanitarian efforts exclusively on the Northeast as maternal health care access in other regions may also be affected by increases in transportation costs, irregular facility hours, and other factors. The reductions in skilled care use observed in our analysis also suggest there may be a significant role for task-shifting in ensuring access to care during conflict. The current Plan does not explicitly recognize the role of alternative care settings and providers that are less susceptible to targeting during armed conflict, to maintain access to health care. For example, telemedicine has been identified as a means of providing medical assistance when the supply of health workers is reduced due to conflict (Dye and Bishai, 2007). In Afghanistan, training programs for community midwives and nurses from the local communities closed service delivery gaps due to emigration of skilled health workers (David et al., 2017). Our study extends the existing empirical literature that provides detailed descriptions of trends in coverage of maternal health care and
considering a catchment radius of 3000 m, the BHI reduced the use of any antenatal care by 13%, attending up to 4 antenatal care visits by 35%, deliveries in health facilities by 38%, and skilled birth attendance by 26%. Our study provides evidence that the adverse effect of the BHI is larger the closer a woman is to the BH conflict area. For any antenatal care visits and at four antenatal care visits, the adverse effect persists up to a catchment area of 10,000 m, but the effect size reduces as the catchment area becomes wider, progressively including women who are further away from the conflict. The study findings show that the BHI worsens maternal health care access in Nigeria, which in turn, has potential detrimental effects on maternal and newborn health. Reductions in the availability of care may be an important mechanism underlying decreases in utilization of maternal health care following BH attacks in this study. The 2018 Humanitarian Response Plan for Nigeria notes that the destruction of towns and villages has led to the collapse of public services, including health infrastructure. Approximately 40% of health facilities in Borno State alone were destroyed with mass emigration of health workers (UNOCHA, 2018). Hence, the Plan aims to expand access by rebuilding damaged infrastructure and strengthening sector coordination, with a focus on the 110
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
affects the use of delivery care in health facilities. This study is not without limitations. Firstly, the displacement of cluster geo-coordinates within the Demographic and Health Survey introduces measurement error, which potentially attenuates the regression estimates, thereby underestimating the impact of the insurgency on maternal health care access. Thus, the study findings may represent the lower bound of the effect of the insurgency. The magnitude of the study estimates nevertheless indicate that addressing the impact of conflict on maternal health care access is essential. Also, while we quantify the combined impact of BHI on antenatal and delivery care access, we are unable to examine the relative contribution of mechanisms described in the literature given that primary data was not collected for this study. However, we have shown that the BHI significantly constraints the use of essential health care during pregnancy in Nigeria and provided quantitative estimates of the negative impact. Systematic efforts to identify and address the predominant mechanisms underlying the reductions in maternal health care use associated with the conflict are essential to improving maternal health in Nigeria.
Table 5 The effect of the BHI on maternal health care access in Nigeria: restricted sample considering migration. Catchment Area (meters) Outcome Variables Antenatal Care
4 or More Antenatal Care Visits
Delivery at Health Center
Delivery by a Skilled Health Professional
3000
5000
10,000
N
−0.086* (0.066) [0.178 to 0.006] −0.226*** (0.001) [-0.365 to 0.087] −0.184*** (0.003) [-0.306 to −0.063] −0.172*** (0.007) [-0.296 to −0.047]
−0.093** (0.012) [-0.166 to 0.020] −0.193*** (< 0.001) [0.289 to −0.097] −0.115** (0.013) [-0.205 to −0.024] −0.114** (0.015) [-0.206 to −0.023]
−0.060* (0.053) [-0.120 to 0.001] −0.175*** (< 0.0001) [-0.250 to −0.100] −0.019 (0.586) [-0.088 to 0.050] 0.005 (0.0897) [-0.066 to 0.075]
17,741
17,741
19,117
19,061
Appendix A. Supplementary data
Data are DID estimate (p value) [95% confidence interval]. Estimates are based on robust standard errors. *Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level.
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.socscimed.2019.02.055. References
Table 6 The effect of the BHI on maternal health care access in Nigeria: including births in 2009. Catchment Area (meters) Outcome Variables Any Antenatal Care Visits
4 or More Antenatal Care Visits
Delivery at Health Center
Delivery by a Skilled Health Professional
3000
5000
10,000
N
−0.120*** (< 0.0001) [-0.186 to −0.055] −0.231*** (< 0.0001) [-0.324 to −0.139] −0.105*** (0.003) [-0.176 to −0.035] −0.104*** (0.004) [-0.175 to −0.032]
−0.114*** (< 0.0001) [-0.166 to −0.062] −0.197*** (< 0.0001) [-0.264 to −0.130] −0.076*** (0.003) [-0.126 to −0.026] −0.071*** (0.007) [-0.122 to −0.020]
−0.065*** (0.003) [-0.107 to −0.023] −0.148*** (< 0.0001) [-0.200 to −0.096] 0.010 (0.610) [-0.028 to 0.048] 0.018 (0.373) [-0.021 to 0.057]
35,762
Acerra, John R., Kara, Iskyan, Qureshi, Zubair A., Sharma, Rahul K., 2009. Rebuilding the health care system in Afghanistan: an overview of primary care and emergency services. Int. J. Emerg. Med. 2 (2), 77. ACLED, 2018. Data. https://www.acleddata.com/data/, Accessed date: 4 January 2019. AFPC, 2018. “Boko Haram.” World Almanac of Islamism. http://almanac.afpc.org/bokoharam, Accessed date: 4 January 2019. Amnesty International, 2018. Amnesty International Report 2017/18. Amnesty International Ltd. www.amnesty.org, Accessed date: 4 January 2019. Anonymous, 2018. Details Omitted for Double-Blind Reviewing. Babalola, Stella, Fatusi, Adesegun, 2009. Determinants of use of maternal health services in Nigeria - looking beyond individual and household factors. BMC Pregnancy Childbirth 9 (1), 43. Bosmans, Marleen, et al., 2008. “Palestinian women's sexual and reproductive health rights in a longstanding humanitarian crisis. Reprod. Health Matters 16 (31), 103–111. Campbell, S.M., Roland, M.O., Buetow, S.A., 2000. Defining quality of care. Soc. Sci. Med. 51 (11), 1611–1625. CFR, 2018. Nigeria Security Tracker. https://www.cfr.org/nigeria/nigeria-securitytracker/p29483, Accessed date: 4 January 2019. Che, Chi, Primus, Bulage, Patience, Urdal, Henrik, Sundby, Johanne, 2015. Perceptions of the effects of armed conflict on maternal and reproductive health services and outcomes in Burundi and Northern Uganda: a qualitative study. BMC Int. Health Hum. Right 15 (1), 7. David, Siddarth, et al., 2017. Conflict in South Asia and its impact on health. BMJ 357, j1537. (Clinical research ed.). http://www.ncbi.nlm.nih.gov/pubmed/28400380, Accessed date: 9 January 2019. DeJong, Jocelyn, et al., 2017. Reproductive, maternal, neonatal and child health in conflict: a case study on Syria using countdown indicators. BMJ Global Health 2 (3), e000302. Dutton, Diana, 1986. Financial, organizational and professional factors affecting health care utilization. Soc. Sci. Med. 23 (7), 721–735. Dye, Sarah, Bishai, Linda, 2007. Public Health and Conflict in Iraq: Rebuilding a Nation's Health Panel. Washington, D.C. http://www.usip.org/pubs/usipeace_briefings/ 2007/0726_public_health_iraq.html, Accessed date: 9 January 2019. Elkies, Noam, Fink, Günther, Bärnighausen, Till, 2015. Scrambling’ geo-referenced data to protect privacy induces bias in distance estimation. Popul. Environ. 37 (1), 83–98. FAO, 2017. Evidence on Internal and International Migration Patterns in Selected African Countries. http://www.fao.org/3/a-i7468e.pdf, Accessed date: 4 January 2019. Fouad, Fouad M., et al., 2017. Health workers and the weaponisation of health care in Syria: a preliminary inquiry for the Lancet-American University of Beirut commission on Syria. Lancet (Lond., Engl.) 390 (10111), 2516–2526. http://www.ncbi.nlm.nih. gov/pubmed/28314568, Accessed date: 9 January 2019. Ghimire, Laxmi Vilas, Pun, Matiram, 2006. Health effects of maoist insurgency in Nepal. Lancet 368 (9546), 1494. Giacaman, Rita, Wick, Laura, Abdul-Rahim, Hanan, Wick, Livia, 2005. The politics of childbirth in the context of conflict: Policies or de Facto practices? Health Policy 72 (2), 129–139. Guenther, Tanya, et al., 2012. Beyond distance: an approach to measure effective access to case management for sick children in Africa. Am. J. Trop. Med. Hyg. 87 (5_Suppl), 77–84. ICRC, 2011. Health Care in Danger: A Sixteen-Country Study. www.icrc.org, Accessed date: 9 January 2019.
35,762
58,633
58,394
Data are DID estimate (p value) [95% confidence interval]. Estimates are based on robust standard errors. *Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level.
underlying mechanisms of conflict impacts in Afghanistan, Burundi, India, Iraq, Uganda, Syria, Palestine, Lebanon, Eastern Burma, and Nepal (Acerra et al., 2009; Chi et al., 2015; David et al., 2017; DeJong et al., 2017; Dye and Bishai, 2007; Fouad et al., 2017; Giacaman et al., 2005; Kabakian-Khasholian et al., 2013; Price and Bohara, 2012). The divergence in our study conclusions from the other studies that provide impact estimates reinforces the need for context-specific studies of conflict, as there is a variation in prevailing mechanisms and the magnitude of their combined effects on health care use. For instance, while our estimates of the adverse effect of the BHI on delivery at a healthcare center agree with findings from Namasivayam et al., we find that the BHI had negative effects on skilled birth attendance in Nigeria, whereas Namasivayam et al. find positive effects on the conflict on skilled deliveries in Uganda (Namasivayam et al., 2017). Our findings also diverge from [Anonymous, 2018], who reported a null effect of BHI on hospital visits (Anonymous, 2018). By expanding facility health care use to include primary care, we demonstrate that BHI negatively 111
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
Penchansky, Roy, Thomas, J. William, 1981. The concept of access: definition and relationship to consumer satisfaction. Med. Care 127–140. Perez-Heydrich, Carolina, Warren, Joshua L., Emch, Michael E., 2013. Guidelines on the Use of DHS GPS Data. Spatial Analysis Reports. . www.measuredhs.com, Accessed date: 4 January 2019. Peters, David H., et al., 2008. Poverty and access to health care in developing countries. Ann. N. Y. Acad. Sci. 1136 (1), 161–171. PMNCH, 2006. WHO Opportunities for Africa's Newborns: Practical Data, Policy and Programmatic Support for Newborn Care in Africa. World Health Organization on Behalf of the Partnership for Maternal Newborn and Child Health. https://www. who.int/pmnch/media/publications/africanewborns/en/, Accessed date: 4 January 2019. Porter, Gina, 2002. Living in a walking world: rural mobility and social Equity issues in sub-Saharan Africa. World Dev. 30 (2), 285–300. Price, James I., Bohara, Alok K., 2012. Maternal health care amid political unrest: the effect of armed conflict on antenatal care utilization in Nepal. Health Policy Plan. 28 (3), 309–319. Siddons, Edward, 2018. “Boko Haram's Kidnapped Girls Turn Accused on Returning Home in Nigeria — Quartz Africa.” Quartz Africa. https://qz.com/africa/1220746/ boko-harams-kidnapped-girls-turn-accused-on-returning-home-in-nigeria/, Accessed date: 4 January 2019. Skiles, Martha Priedeman, Burgert, Clara R., Curtis, Siân L., Spencer, John, 2013. Geographically linking population and facility surveys: methodological considerations. Popul. Health Metrics 11 (1), 14. UNOCHA, 2018. Nigeria: Humanitarian Response Plan (January - December 2018). https://reliefweb.int/report/nigeria/nigeria-humanitarian-response-plan-januarydecember-2018, Accessed date: 6 January 2019. WHO, 2018. Maternal Health. WHO. https://www.who.int/maternal-health/en/, Accessed date: 4 January 2019.
Kabakian-Khasholian, Tamar, Shayboub, Rawan, El-Kak, Faysal, 2013. Seeking maternal care at times of conflict: the case of Lebanon. Health Care Women Int. 34 (5), 352–362. Levesque, Jean Frederic, Harris, Mark F., Russell, Grant, 2013. Patient-centred access to health care: Conceptualising access at the interface of health systems and populations. Int. J. Equity Health 12 (1), 18. Matfess, Hillary, 2017. Boko Haram's Campaign against Women and Girls. https://www. futureswithoutviolence.org/wp-content/uploads/FWV_blueprint_8-Boko-Haram.pdf, Accessed date: 4 January 2019. McKay, Susan, 1998. “The effects of armed conflict on girls and women. Peace Conflict: J. Peace Psychol. 4 (4), 381–392. Mullany, Luke C., et al., 2008. Access to essential maternal health interventions and human rights violations among vulnerable communities in Eastern Burma. PLoS Med. 5 (12), e242. Namasivayam, Amrita, González, Pedro Arcos, Delgado, Rafael Castro, Chi, Primus Che, 2017. The effect of armed conflict on the utilization of maternal health services in Uganda: a population-based study. PLoS Curr. Disasters 9 (2017). National Population Commission (NPC) [Nigeria] and ICF Macro, 2009. National Demographic and Health Survey 2008.” Nigerian Demographic Health Survey 2008. National Population Commission of Nigeria, 2014. National Population Commission Nigeria Demographic and Health Survey 2013. Nigeria Health Watch, 2017. Giving Birth in Nigeria: the Staggering Odds Facing Pregnant Women. https://nigeriahealthwatch.com/giving-birth-in-nigeria-thestaggering-odds-facing-pregnant-women/#.XC-gUlxKg2w, Accessed date: 4 January 2019. Okonofua, Friday, et al., 2018. Predictors of women's utilization of primary health care for skilled pregnancy care in rural Nigeria. BMC Pregnancy Childbirth 18 (1), 106. Patel, Preeti, Gibson-Fall, Fawzia, Sullivan, Richard, Irwin, Rachel, 2017. Documenting attacks on health workers and facilities in armed conflicts. Bull. World Health Organ. 95 (1), 79.
112
Contents lists available at ScienceDirect
Social Science & Medicine journal homepage: www.elsevier.com/locate/socscimed
Armed conflict and maternal health care utilization: Evidence from the Boko Haram Insurgency in Nigeria
T
Adanna Chukwuma∗, Uche Eseosa Ekhator-Mobayode World Bank Group, Washington, DC, 20433, USA
A R T I C LE I N FO
A B S T R A C T
Keywords: Nigeria Conflict Maternal health Access Violence Terrorism Health care use
Retention in maternal health care is essential to decreasing preventable mortality. By reducing access to care, armed conflicts such as the Boko Haram Insurgency (BHI), contribute to the high maternal mortality rates in Nigeria. While there is a rich literature describing the mechanisms through which conflict affects health care access, studies that estimate the impact of conflict on maternal health care use are sparse and report mixed findings. In this study, we examine the impact of the BHI on maternal care access in Nigeria. We spatially match 52,675 birth records from the Nigeria Demographic and Health Survey (NDHS) with attack locations in the Armed Conflict Location and Event Dataset (ACLED). We define BH conflict area as NDHS clusters with at least five attacks within 3000, 5000 and 10,000 m of BH activity during the study period and employ difference-indifferences methods to examine the effect of the BHI on antenatal care visits, delivery at the health center and delivery by a skilled professional. We find that the BHI reduced the probability of any antenatal care visits, delivery at a health center, and delivery by a skilled health professional. The negative effects of the BHI on maternal health care access extended beyond the Northeastern region, that is the current focus of humanitarian programs. Systematic efforts to identify and address the mechanisms underlying reductions in maternal health care use due to the BHI, and to target the affected populations, are essential to improving maternal health in Nigeria.
1. Background Approximately 830 mothers die daily from preventable causes related to pregnancy and childbirth worldwide (WHO, 2018). Of these deaths, 145 occur in Nigeria alone, making her the second largest contributor to global maternal mortality (Nigeria Health Watch, 2017). Retaining mothers in antenatal care, skilled birth attendance, and facility delivery is essential to reducing and managing pregnancy complications, and to decreasing preventable maternal mortality (PMNCH, 2006). However, in Nigeria, only 36 percent of births occur in a health facility, 38 percent of deliveries are attended by a skilled health worker, and 51 percent of women receive four antenatal care consultations (National Population Commission of Nigeria, 2014). Gaps in the coverage of essential health services represent missed opportunities to prevent maternal mortality in Nigeria. Access to health care, a proximal determinant of health care use, can be conceptualized in terms of the opportunity to seek and obtain health services, as a function of the attributes of the population and health system (Dutton, 1986; Levesque et al., 2013; Penchansky and Thomas, 1981; Peters et al., 2008). In a seminal article on the concept,
∗
Penchansky and Thomas described five dimensions of access to health care. The availability dimension refers to an adequate supply of health workers, facilities, and services, that is the structural quality of health services being accessed (Campbell et al., 2000). The accessibility dimension accounts for the capacity of the service location to be reached, considering client transportation resources, distance, and travel time to care. The accommodation dimension refers to the perception of appropriateness of service organization including hours of operation and appointment systems. The affordability dimension accounts for ability to pay and financial protection during health care seeking, including income and insurance coverage. The acceptability dimension refers to personal characteristics of the provider and client that may influence health care-seeking behavior, including age, sex, ethnicity, and religious affiliation. The dimensions in the above conceptualization by Penchansky and Thomas provide an organizing framework for the specific determinants that may influence access to and utilization of health care. The impact of armed conflict on health care access, and utilization, can be understood through the mechanisms that influence the above dimensions. Availability of services is curtailed by the destruction or
Corresponding author. World Bank Group, 1818 H Street NW, Washington DC, 20433, USA. E-mail addresses: [email protected] (A. Chukwuma), [email protected] (U.E. Ekhator-Mobayode).
https://doi.org/10.1016/j.socscimed.2019.02.055 Received 13 August 2018; Received in revised form 10 January 2019; Accepted 27 February 2019 Available online 03 March 2019 0277-9536/ © 2019 Published by Elsevier Ltd.
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
Khasholian et al. also described interruptions in maternity care use among 1015 women during the 2006 war in Lebanon (KabakianKhasholian et al., 2013). In Eastern Burma, conflict has been linked to low coverage of skilled birth attendance and antenatal care (Mullany et al., 2008). Secular trends, concurrent changes in other determinants of access, lack of a counterfactual, and a dynamic population due to migration and mortality may confound conclusions on the impact of armed conflict on health care access based on descriptive statistics (DeJong et al., 2017). Hence, other studies have provided associations that adjust for other determinants of health care access or that compare outcomes in groups defined based on exposure to armed conflict. Price and Bohara find a negative correlation between antenatal care coverage and violent incidents in Nepal during the Maoist insurgency, adjusting for other determinants of health care access (Price and Bohara, 2012). Using multinomial logistic regressions, Namasivayam et al. demonstrated negative associations between armed conflict and the use of contraception and birth in health facilities in Uganda (Namasivayam et al., 2017). Their study also reported that skilled birth attendance was higher among women in conflict-affected areas. As the dataset excluded Northern districts that were more seriously affected by conflict, their findings may reflect selection bias (Namasivayam et al., 2017). Using nationally-representative data and difference-in-difference models to construct a credible counterfactual [Anonymous, 2018], found that the BHI does not affect maternal hospital visits (Anonymous, 2018). As up to 50 percent of maternal health care use in Nigeria occurs in primary health care facilities, the impact of conflict on hospital visits may not have captured effects on health facility use as a whole (Okonofua et al., 2018). There are no studies, to our knowledge, that estimate the impact of the BHI on antenatal care and skilled birth attendance. The mixed findings from the literature reflect differences in the mechanisms affecting service delivery in each context and methodological choices, including adjustment for confounders and comparison to a counterfactual. This illustrates the need for studies that quantify the impact of the BHI conflict on maternal health care use in Nigeria specifically, using nationally-representative datasets and adjusting for potential confounders. Thus, in this study, we estimate the impact of the BHI on maternal care utilization in Nigeria. We build on the existing literature by spatially merging data on the specific location of conflict events attributed to BH with nationally-representative surveys of maternal health care use, employing difference-in-difference models to adjust for time-invariant confounders, and controlling for time-varying determinants of access to maternal health care in Nigeria.
shutdown of health facilities, supply chain disruption, and the emigration or attack of health providers (Acerra et al., 2009; Chi et al., 2015; David et al., 2017; Guenther et al., 2012; ICRC, 2011; KabakianKhasholian et al., 2013; McKay, 1998). For example, the Graca Machel Study described the targeting of facilities as a tactic of war in Nicaragua between 1982 and 1987, destroying over 20 percent of health units (McKay, 1998). In Burundi, the targeting of health workers severely constrained the supply of health services (Chi et al., 2015). In Iraq, in addition to targeting medical personnel, the conflict was associated with low enrollments in medical schools affecting the future supply of health workers (Dye and Bishai, 2007). Armed conflict can also affect accessibility, another dimension of access, through insecurity that limits transportation (Bosmans et al., 2008; Chi et al., 2015; McKay, 1998). For example, closures and checkpoints associated with the emergency situation in the Occupied Palestine Territories in 2000, limited access to urban facilities where health infrastructure was predominantly located (Giacaman et al., 2005). Where shorter routes were blocked during the Maoist insurgency in Nepal, mothers opted for longer alternatives to reach health care, increasing travel costs (Price and Bohara, 2012). Fouad et al. have proposed the idea of weaponization of health care to describe the use of large-scale violence to restrict access to health care as described above, through attacking facilities or health workers, shortages of medical supplies, and restrictions in service delivery in besieged areas, exemplified by the Syrian conflict (Fouad et al., 2017). Conflict may also affect access to care through other mechanisms. Accommodation may be affected by irregular opening hours of hospitals due to insecurity during conflict, as in Burundi and Uganda (Chi et al., 2015). Finally, discrimination in health care services during conflict may reduce acceptability of care. Ethnic favoritism in the provision of services during the Burundian civil war and the Maoist insurgency has been linked to reductions in coverage of care during the conflict (Chi et al., 2015; Ghimire and Pun, 2006). The above discussion illustrates the mechanisms through which armed conflicts such as the Boko Haram Insurgency (BHI) may contribute to maternal mortality though reductions in access. Since 2009, violence linked to Boko Haram (BH) activities has persisted in the Northeastern Nigeria states of Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe (AFPC, 2018). BH attacks have been recorded in other states in Northern and Southern Nigeria, including the Ebonyi, Edo, Kogi, Anambra, Niger, and the Federal Capital Territory (CFR, 2018). The BHI has disproportionately affected women (Matfess, 2017). Amnesty International reports that over 2000 women were abducted by BH between 2014 and the spring of 2015 (Amnesty International, 2018). Furthermore, most BH suicide-bombers are female, who are often coerced into their missions (Amnesty International, 2018). The conflict has been associated with the destruction of health facilities and the abduction, killing, and displacement of skilled health workers, limiting the availability of health care services (Patel et al., 2017). By October 2017, 527 of the 755 health centers in Borno state had been closed or destroyed due to the BHI (Siddons, 2018). To our knowledge, there are very few studies that estimate the impact of the BHI on maternal health care utilization (Anonymous, 2018). Studies that assess the impact of armed conflict on maternal health care across contexts report mixed findings. A significant body of research provides descriptive evidence of changes in utilization of care during conflict. Chi et al. provide qualitative evidence that armed conflict in Burundi and Northern Uganda substantially reduces the quality of maternal health care, describing the predominant mechanisms through which armed conflict reduces the availability and accessibility of care (Chi et al., 2015). In a qualitative account of the emergency situation of 2000, Giacaman et al. noted the reductions in access to basic maternity services due to curfews, closures, and the siege (Giacaman et al., 2005). In a review of research conducted on the Syrian conflict, DeJong et al. noted a fall in antenatal care coverage from 87.7 percent to 62 percent and a fall in skilled birth attendance from 96.2 percent to 72 percent (DeJong et al., 2017). Kabakian-
2. Methods 2.1. Study sample The study sample derives from the births recode data files of the 2008 and 2013 Nigerian Demographic and Health Surveys (NDHS), which are nationally-representative cross-sectional household surveys administered to women who are between 15 and 49 years old (National Population Commission (NPC) [Nigeria] and ICF Macro, 2009; National Population Commission of Nigeria 2014). The NDHS provides information on maternal health care use including antenatal care and birth experiences in the five years preceding the survey. The NDHS sample was combined with data from the Armed Conflict Location and Event Dataset (ACLED) (ACLED, 2018). The ACLED contains dates, locations, and actors of events that occur during civil wars and periods of instability, public protest and regime breakdown across Africa, South Asia, South East Asia and the Middle East. The dataset enables the identification of 829 conflicts with BH as an actor that occurred in Nigeria from the beginning of BH violence in 2009 until 2013 to match the NDHS timeframe: 21 in 2009, 37 in 2010, 124 in 2011, 373 in 2012, and 274 in 2013. 105
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
indicator for residence in an urban area, as defined by the National Population Commission, as increased cost of travel to health services correlates with rural dwelling in many African countries (Porter, 2002). This indicator was coded as ‘1’ if the respondent lived in an urban area and ‘0’ if they lived in a rural area. We also included a continuous variable for altitude of the cluster of residence which may influence ease of transportation. Under the affordability dimension, we included a discrete variable for the years of education of the mother, a series of binary variables indicating employment type [coded as ‘1’ if the mother reported the specific employment category and as ‘0’ if otherwise], and a series of binary variables indicating the wealth quintile of the mother's households [coded as ‘1’ if the household belonged to the specific quintile and as ‘0’ if otherwise], which correlate with ability to pay for care and financial protection during health care seeking. Under the acceptability dimension, we included personal characteristics of the population that may influence care-seeking, comprising a discrete variable for household size, a discrete variable for the number of children under five in the household, the age of the mother in years, a series of indicators for religious affiliation [coded as ‘1’ if the mother reported affiliation with a given religion and ‘0’ otherwise], child gender [coded as ‘1’ if the child born is male and ‘0’ otherwise], and a discrete variable for the child birth order. We also included controls for month and year of interview and indicators for the state of residence to adjust for differences due to seasonal variation in health care use and administrative differences across states respectively. Our analysis was restricted to observations for which all the covariates were non-missing. The proportion of observations that were dropped due to missing values was 0.06 for antenatal care outcomes and 0.11 for delivery outcomes. The parameter π3 , measures the effect of the BHI on the maternal healthcare utilization, given that the assumption of parallel trends in the measured outcomes in the absence of the BHI holds. We provide evidence in support of this assumption in the Results section.
2.2. Statistical analysis To examine the effect of the BHI on maternal health care access, we compared trends in outcomes in BH areas and non-BH areas, before and after the onset of the insurgency in 2009. Births that occurred before 2009 were classified as pre-BHI, while births that occurred after 2009 were classified as occurring during the BHI. We excluded births in 2009 to avoid misclassification error in our main models. However, as 10 percent of births occurred in 2009, we conducted robustness checks, including these births in the analysis, as described in the Results section. We spatially matched geocoordinates of clusters in the NDHS sample to events in the ACLED dataset. We calculated the distance between each NDHS cluster to the location of all BH attacks between 2009 and 2013. As there is no agreed definition for exposure to conflict, a NDHS cluster was classified as a BH conflict area if it was located within the catchment area of at least 5 BH attacks. We examined sensitivity of our findings to the number of attacks used to define a BH conflict area, as described in the Results section. There is also no consensus on how to define a radius in which armed conflict may exert significant effects on health care access. An intuitive way to examine this is based on norms for defining access to health services, that is the target maximum distance between an individual's place of residence and the nearest source of health care to enable access to care. An attack within this radius or catchment area would be expected to significantly impact access to health care. There is no official definition of such a catchment area in Nigeria. The distances used vary across similar contexts in Sub-Saharan Africa, from 5000 m in Zambia and 8000 m in Malawi to 10,000 m in Mali (Guenther et al., 2012). Therefore, we varied the catchment area for significant attacks as follows: 3000, 5000 and 10,000 m. We then examined the sensitivity of our findings to further variation in the radius used to define the catchment area, as described in the Results section. The geo-coordinates of the NDHS clusters are randomly displaced to protect the privacy of the respondents (Elkies et al., 2015). While urban clusters are displaced up to 2000 m, rural clusters are displaced up to 5000 m (Perez-Heydrich et al., 2013). The measurement error introduced is likely to lead to attenuation bias in regressions (Skiles et al., 2013). In line with the study conceptual framework, we modelled maternal health care use as a function of the BHI and other determinants of maternal health care access described in the empirical literature, using difference-in-differences (DID) models, specified as follows:
3. Results In Table 1, we present the definitions of outcome and explanatory variables in the empirical models, and descriptive statistics for the study sample. Of the 34,075 birth records that were non-missing antenatal care, 21193 (62%) had at least one antenatal care visit, and 16,985 (50%) had at least four antenatal care visits. Of the 52,765 birth records that were non-missing for the place of delivery, 18240 (35%) were birthed at a health facility. Of the 52,558 birth records that were non-missing for the person who rendered delivery assistance, 19632 (37%) or deliveries were by a skilled health professional. We examine differences in the study outcome variables, before and during the BHI, based on the 5000 m catchment area of BH activity. Overall, the use of essential services was higher in BH catchment areas compared to non-BH catchment areas, before and after 2009. Before 2009, 82% of births to women residing in BHI catchment areas had at least one antenatal visit, and 64% had at least four antenatal visits, compared to 58% and 46% of women residing outside these catchment areas respectively. After 2009, 87% had at least one antenatal care visit in the BH catchment area compared to 62% outside these catchment areas, and 65% had at least four antenatal visits in the BH catchment area relative to 53% outside these catchment areas. Before 2009, 38% of births to women residing in the BH catchment areas occurred in a health center compared to 32% in the non-BH catchment areas. After 2009, 44% of births of women residing in the BH catchment areas were in a health center compared to 37% in the non-BH catchment areas. Finally, before 2009, 43% of births of women residing in the BH catchment areas were attended by skilled health professionals compared to 36% in the non-BH catchment areas. After 2009, 47% of births to women residing in the BH catchment areas were attended by skilled professionals compared to 39% in the non-BH catchment areas area. We reject the F-test of equality of means for BH and non-BH catchment areas for both the period before and after 2009 for all the maternal
Hist = π0 + π1 Post 2009it + π1 BHConflictArea + π3 Post 2009it ∗ BHConfl ictArea + π4 Xist + εist where Hist is a measure of maternal healthcare use for mother i residing in NDHS cluster s at time t. The measures we consider are 4 binary outcomes as follows: any antenatal care visits [coded as ‘1’ if the mother reported attending antenatal care at least once, and ‘0’ otherwise]; 4 or more antenatal care visits [coded as ‘1’ if the mother reported attending antenatal care at least four times, and ‘0’ otherwise]; delivery at health facility [coded as ‘1’ if the mother reported the place of childbirth as a health facility, and ‘0’ otherwise]; and delivery by a skilled health professional [coded as ‘1’ if the mother reported receiving skilled care during delivery from a doctor, nurse, or midwife, and ‘0’ otherwise]. Post2009it is a binary variable equal to 1 if a child's birth was after 2009 and 0 otherwise. BHConflictArea is a binary variable equal to 1 if a mother resides in a BH conflict area and 0 otherwise. All empirical models included respondent sampling weights to ensure representativeness. The models adjusted for time-varying determinants of maternal healthcare access in Nigeria, which increases precision of the estimated effect of the BHI. Xist represents the vector of these control variables. To define Xist , we reviewed the empirical literature on the factors associated with access to and utilization of health care in Nigeria (Babalola and Fatusi, 2009). Under the accessibility dimension, we included an 106
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
Table 1 Descriptive statistics. (1) Variables
(2) Description
1. Outcome Variables Any Antenatal Care Visits
Had at least 1 visit
4 Antenatal Care Visits
Had 4 or more visits
Delivery at Health Center
Delivery was at a public/private hospital or clinic Delivery assistance was given by a doctor or nurse
Delivery by a Skilled Professional 2. Covariates a. Household Characteristics Wealth Lowest quintile of the NDHS wealth index (reference) Second quintile of the NDHS wealth index, Middle quintile of the NDHS wealth index. Fourth quintile of the NDHS wealth index, Highest quintile of the NDHS wealth index, Household size Number of household members
Urban
Number of children under five in household Number of children under five in household square Residence in urban area
Altitude
Altitude of NDHS cluster in meters
Children under-five Children under-five2
b. Mothers Characteristics Occupation Is not employed (reference) Employed in a professional, technical or managerial position, Employed in a clerical position. Employed in sales Self-employed in agriculture. Employed in agriculture Employed in household, and domestic work. Employed in the services industry, Employed as a skilled manual worker. Employed as an unskilled. Other Occupation Age Age
Mother's age in years 2
Mother's age squared
Education
Years of education
Religion
Practices Christianity (reference) Practices Islam Practices other religion
c. Child Characteristics Male
Child is male
Birth order
Order born in the family
Before 2009
After 2009
Total
(3) Non-BH area
(4) BH area
(5) P value
(6) Non-BH area
(7) BH area
(8) P value
(9)
9765/16,864 (58%) 7807/16,864 (46%) 9985/30,804 (32%) 10,995/30669 (36%)
362/444 (82%) 286/444 (64%) 335/874 (38%) 377/871 (43%)
< .0001
10,696/16344 (65%) 8632/16,344 (53%) 7679/20,541 (37%) 8007/20,477 (39%)
370/423 (87%) 260/423 (62%) 241/546 (44%) 253/541 (47%)
< .0001
21,193/34075 (62%) 16,985/34075 (50%) 18,240/52765 (35%) 19,632/52558 (37%)
8216/30,804 (27%) 7411/30,804 (24%) 5974/30,804 (19%) 5080/30,804 (16%) 4123/30,804 (13%) 6.999 (3.566) 2.177 (1.235) 6.263 (7.601) 7947/30,804 (26%) 330.222 (289.495)
4/874 (0.5%) 82/874 (9%)
< .0001
6/546 (1.1%)
< .0001
< .0001
24/546 (4.4%) 92/546 (17%) 251/546 (46%) 173/546 (32%) 8.245 (5.080) 2.341 (1.270) 7.088 (8.247) 546/546 (100%) 543.696 (211.360)
< .0001
237/874 (27%) 316/874 (36%) 235/874 (27%) 8.489 (4.906) 2.539 (1.399) 8.402 (9.967) 756/874 (87%) 477.291 (183.871)
4641/20,541 (23%) 4950/20,541 (24%) 4106/20,541 (20%) 3666/20,541 (18%) 3178/20,541 (15%) 6.926 (3.532) 2.203 (1.224) 6.350 (7.294) 6388/20,541 (31%) 308.547 (227.342)
9352/30,804 (30%) 900/30,804 (3%) 172/30,804 (0.56%) 10,099/30804 (33%) 29/30,804 (0.09%) 5971/30,804 (19%) 3/30,804 (0.01%) 1069/30,804 (3.47%) 3156/30,804 (10%) 46/30,804 (0.15%) 7/30,804 (0.02%) 29.524 (7.090) 921.946 (445.440) 4.340 (5.001) 13,104/30804 (43%) 17,092/30804 (55%) 608/30,804 (2%)
353/874 (40%) 28/874 (3.2%) 10/874 (1.14%) 281/874 (32%) 0/874 (0%) 22/874 (2.52%) 0/874 (0%) 24/874 (2.75%) 156/874 (18%) 0/874 (0%) 0/874 (0%) 29.576 (0.234) 922.656 (439.974) 4.334 (5.273) 112/874 (13%) 760/874 (87%) 2/874 (0.23%)
< .0001
6211/20,541 (30%) 764/20,541 (3.72%) 66/20,541 (0.32%) 7675/20,541 (37%) 161/20,541 (0.78%) 2285/20,541 (11.12%) 23/20,541 (0.11%) 985/20,541 (4.80%) 2336/20,541 (11.37%) 10/20,541 (0.05%) 25/20,541 (0.12%) 28.789 (6.916) 876.657 (422.900) 4.924 (5.320) 8426/20,541 (41%) 11,917/20541 (58%) 198/20,541 (0.96%)
241/546 (44%) 22/546 (4.03) 1/546 (0.18%) 166/546 (30%) 0/546 (0%) 3/546 (0.55%) 2/546 (0.37%) 14/546 (2.56%) 97/546 (18%) 0/546 (0%) 0/546 (0%) 29.654 (6.864) 926.382 (428.278) 6.220 (5.465) 81/546 (15%) 465/546 (85%) 0/546 (0%)
15,679/30804 (51%) 3.961 (2.606)
426/874 (49%) 4.400 (2.847)
10,431/20541 (51%) 3.937 (2.589)
288/546 (53%) 4.333 (2.790)
< .0001 0.0002 < .0001
< .0001
< .0001 < .0001 < .0001 < .0001 < .0001 < .0001 < .0001
0.6259 0.0238 0.6930 0.3642 < .0001 0.7705 0.2473 < .0001 0.2529 0.6558 0.8331 0.963 0.9710 < .0001 < .0001 0.0002
0.2083 < .0001
0.0004 0.0013 0.0003
0.0698 < .0001 < .0001 < .0001 < .0001 0.0201 < .0001 < .0001
< .0001 0.7060 0.5713 0.0009 0.0378 < .0001 0.0883 0.0154 < .0001 0.6061 0.4147 0.0039 0.0067 < .0001 < .0001 < .0001 0.0212
0.3645 0.0004
12,867/52765 (24%) 12,467/52765 (24%) 10,409/52765 (20%) 9313/52,765 (18%) 7709/52,765 (14%) 7.008 (3.605) 2.195 (1.235) 6.341 (7.541) 15,637/52765 (30%) 326.429 (266.579) 16,157/52765 (31%) 1714/52,765 (3.25%) 249/52,765 (0.47%) 18,221/52765 (35%) 190/52,765 (0.36%) 8281/52,765 (16%) 28/52,765 (0.05%) 2092/52,765 (3.96%) 5745/52,765 (11%) 56/52,765 (0.11%) 32/52,765 (0.06%) 29.240 (7.026) 904.373 (437.083) 4.587 (5.147) 21,723/52765 (41%) 30,234/52765 (57%) 808/52,765 (1.53%) 26,824/52765 (51%) 3.963 (2.606)
(continued on next page) 107
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
Table 1 (continued)
(1) Variables
N
(2) Description
Before 2009
Total Sample
After 2009
Total
(3) Non-BH area
(4) BH area
(5) P value
(6) Non-BH area
(7) BH area
(8) P value
(9)
30,804 (97%)
874 (3%)
31,678
20,541 (97%)
546 (3%)
21,087
52,765
Data are N (%), mean(SD) or as indicated. In this analysis, exposure is measured as having at least five attacks within 5000 m of DHS cluster of residence. The pvalues are for the two-sample t-test with equal variances (Ho: Difference in means = 0 and Ha: Difference in means ≠ 0.
corresponding reduction in antenatal care use for the catchment areas of 5000 and 10,000 m are 10.9 and 6.3 percentage points respectively. For the catchment area of 3000 m, the BHI reduces the probability that a woman had at least four antenatal care visits by 22.4 percentage points at the 1% level of statistical significance. The corresponding reduction in having at least four antenatal care visits for the catchment areas of 5000 and 10,000 m are 19.5 and 14.6 percentage points respectively. The estimates also suggest that the BHI reduces the probability that a woman has a delivery at a health center or is assisted by a skilled health professional during delivery for women in the 3000 and 5000 m catchment areas. As with antenatal care visits, the magnitude of the effect is smaller, as the catchment area includes more women that are further away from the attacks. For the catchment area of 3000 m, the BHI reduces the probability that a woman has a delivery at a health center by 11.2 percentage points and reduces the probability that a skilled health professional assists a woman during delivery by 11.1 percentage points at the 1% level of statistical significance. For the catchment area of 5000 m, the BHI reduces the probability that a woman has a delivery at a health center by 8.5 percentage points and reduces the probability that a skilled health professional assists a woman during delivery by eight percentage points at the 1% level of statistical significance. The effect of the BHI on delivery at the health center and delivery assistance by a skilled health professional is not statistically significant for women in the 10,000 m catchment area. The DID estimates in Table 2 assume parallel trends in outcomes for BH and non-BH catchment areas before and after 2009, if in the absence of the BHI, the difference in outcomes between BH and non-BH catchment areas would be constant. To address this concern, we examine an unaffected cohort using placebo treatments by comparing trends in outcomes for births before 2009 (the unaffected cohort) in BH areas and non-BH areas. In this placebo analysis, we assume the BHI began after 2005. Observations of births in 2003, 2004 and 2005 provide data for the control group while observations of births from 2006, 2007 and 2008 provided data for the treatment group. If non-parallel trends exist, we should find spurious significant coefficients otherwise the coefficients should be generally insignificant. All the estimates in the placebo analysis (Table 3) are not statistically significant. The only exception to this is the variable for at least four antenatal visits for the 3000 m catchment area. However, this result does not seem to pose a serious concern as it is only statistically significant at the 10% level and not reinforced by other results from other catchment areas of 5000 and 10,000 m. The evidence suggests that the results of the original analysis are not by chance. In Fig. 1, we provide evidence demonstrating the robustness of our findings to the number of attacks used to define the BH catchment area. For antenatal care, we show that the observed negative impact of the BHI is robust to varying the number of attacks within 5000 m from one to ten. We also show that the observed negative impact of the BHI on use of skilled care at birth and delivery in any facility is robust to varying the number of attacks within 5000 m from four to ten. When there are between one and three attacks within the 5000 m, the estimate is negatively signed, but is not statistically significant at the 5% level. We also examined variation in the estimate of impact of the BHI on
health care access variables at the 1% level. We also examine differences in the explanatory variables, before and during the BHI, based on the 5000 m catchment area of BH activity. Households in BH catchment areas were slightly larger both before and after 2009. Both before and after 2009, households in BH catchment areas have an average of 8 members compared to 7 in the non-BH catchment areas. The number of children under-five in the households is approximately 2 for all study groups. We reject the F-test of equality of means or proportions between BH and non-BH catchment areas for both the period before and after 2009 for all the household characteristics at the 10% level. Thirty-one percent of the births in the sample is by unemployed mothers with the greatest proportion in BH areas in the period before and after the BHI. We are unable to reject the F-test of equality of proportions for 8 of 11 of the classifications for mothers' occupation. Also, we are unable to reject the F-test of equality of means across study groups for mothers' age and education. Mothers practicing Islam account for a greater proportion of births (57%) compared to 41% for Christianity and 1.53% for other religion. The proportion of births for mothers practicing Islam is greater in BH catchment areas before and after the BHI; the reverse is the case for mothers practicing Christianity and other religions. We reject the F-test of equality of proportions for BH and non-BH catchment areas for the period before and after 2009 for mothers’ religion at the 1% level. The difference-in-difference estimates (Table 2) show that the BHI reduces the probability that a woman had any or at least four antenatal care visits. The largest effects are for women in the closest catchment area to the BH attacks. The magnitude of the effects become smaller as the BH catchment area includes women that are further away from the attacks. While the BHI reduces the probability that a woman had any antenatal care visit by 11.1 percentage points at the 1% level of statistical significance for the catchment area of 3000 m, the
Table 2 The effect of the BHI on maternal health care access in Nigeria. Catchment Area (meters) Outcome Variables Any Antenatal Care Visits
4 or More Antenatal Care Visits
Delivery at Health Center
Delivery by a Skilled Health Professional
3000
5000
10,000
N
−0.111*** (0.001) [-0.179 to −0.044] −0.224*** (< 0.0001) [-0.322 to −0.126] −0.112*** (0.004) [-0.188 to −0.037] −0.111*** (0.004) [-0.188 to −0.034]
−0.109*** (< 0.0001) [-0.162 to −0.055] −0.195*** (< 0.0001) [-0.265 to −0.125] −0.085*** (0.002) [-0.138 to −0.031] −0.080*** (0.004) [-0.135 to −0.026]
−0.063*** (0.004) [-0.107 to −0.020] −0.146*** (< 0.0001) [-0.200 to −0.093] 0.005 (0.796) [-0.036 to 0.047] 0.017 (0.429) [-0.025 to 0.059]
34,075
34,075
52,765
52,558
Data are DID estimate (p value) [95% confidence interval]. Estimates are based on robust standard errors. *Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level. 108
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
status. However, we also examine the differential impact of the BHI on states in the Northeastern Nigeria by interacting the DID parameter with residence in Northeastern Nigeria. We find evidence for greater reductions in antenatal care use in Northeastern states relative to other locations following up to five attacks within a 5000 m catchment area of the cluster. However, there is no significant difference in reductions in delivery care use in Northeastern states relative to other locations following attacks (Table 4). In our analysis, we assume that the location of residence reported at the time of survey is the same at the time of birth. The most recent nationally-representative migration surveys indicate that 36% of households have migrants in Nigeria, the majority of whom (62–72%) are male (FAO, 2017). The NDHS birth recode file examines self-reports among females of reproductive age, among whom migration rates are relatively low. However, significant differences in migration rates between BH and non-BH catchment areas may bias results. We restrict the sample to a shorter period over which the probability of migration is lower, to examine the robustness of our findings to migration. This sample includes children born in the year of the survey and the year. That is, children born in 2007 and 2008 from the 2008 NDHS and those born in 2012 and 2013 from the 2013 NDHS, are included. With this restriction, it is more likely that the place of residence at the time of the survey is the same as the place of residence at the time of birth. The estimates for all the maternal health care access variables reinforces the results found for the full sample (Table 5). Finally, our analysis excludes births in 2009 (10% of the study sample) to avoid classification error. We examine if our results are robust to the inclusion of births in 2009, as occurring during the BHI. The results from this analysis (Table 6) reinforces the findings from the original analysis.
Table 3 Parallel trends analysis with placebo BHI. Catchment Area (meters) Outcome Variables Any Antenatal Care Visits
4 or More Antenatal Care Visits
Delivery at Health Center
Delivery by a Skilled Health Professional
3000
5000
10,000
N
−0.080 (0.145) [-0.187 to 0.027] −0.114* (0.079) [-0.241 to 0.013] −0.027 (0.533) [0.114 to 0.059] −0.030 (0.501) [0.119 to 0.058]
−0.030 (0.511) [-0.120 to 0.060] −0.082 (0.135) [-0.190 to 0.026] −0.022 (0.496) [-0.087 to 0.042] −0.011 (0.746) [-0.077 to 0.055]
0.015 (0.734) [-0.073 to 0.103] 0.031 (0.444) [-0.048 to 0.109] −0.035 (0.178) [-0.086 to 0.015] −0.014 (0.608) [-0.066 to 0.038]
17,308
17,308
31,678
31,540
Data are DID estimate (p value) [95% confidence interval]. Estimates are based on robust standard errors. *Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level.
maternal health care use with variation in the radius of the BH catchment area. Increasing the radius of the catchment area progressively includes mothers who live further away from the location of attacks in the intervention group and reducing the difference between exposed and unexposed women in the empirical models. We would then expect the magnitude of the estimate to remain constant or reduce (but not increase) depending on the extent to which differences in access to care between exposed and unexposed groups are reduced. In Fig. 2, we show that our findings are consistent with this rationale. The negative impact of the BHI on health care use is of lower magnitude at higher radii, and for antenatal outcomes, persists on varying the radius of the BH catchment area from 1000 to 10000 m. Attacks linked to the BHI have predominantly occurred in Northeastern Nigeria, with fewer incidents in other parts of the country. Our analysis spatial links the exact locations of attacks across the country to cluster geocoordinates, and accurately assigns exposure
4. Discussion To our knowledge, this study is one of the first to examine the effect of the BHI in Nigeria on maternal health care use, exploiting the proximity of a woman's residence to the location of BH events. Our findings suggest that the BHI has adverse effects on maternal health care use in the antenatal period and during delivery. The magnitude of these events is significant. Relative to the period before 2009 and
Fig. 1. Robustness to number of attacks in 5000 m catchment area. 109
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
Fig. 2. Variation with radius of the BH catchment area.
Table 4 The differential effect of the BHI on maternal health care access in Northeastern Nigeria. Catchment Area (meters)
3000
5000
10,000
Variables
DID Estimate*Northeast
DID Estimate*Northeast
DID Estimate*Northeast
Any Antenatal Care Visits
−0.064** (0.021) [-0.119 to −0.009] −0.126 (0.102) [-0.277 to −0.025] 0.038 (0.590) [-0.099 to 0.174] 0.070 (0.314) [-0.066 to 0.207]
−0.091*** (0.009) [-0.159 to −0.023] −0.172*** (< 0.0001) [-0.255 to −0.089] 0.007 (0.842) [-0.060 to −0.074] 0.022 (0.533) [-0.047 to −0.092]
−0.043 (0.123) [-0.098 to −0.012] −0.117*** (0.001) [-0.183 to −0.051] 0.027 (0.328) [-0.027 to 0.080] 0.047* (0.089) [-0.007 to 0.101]
4 or More Antenatal Care Visits
Delivery at Health Center
Delivery by a Skilled Health Professional
N
34,075
34,075
52,765
52,558
Data are DID estimate (p value) [95% confidence interval]. Estimates are based on robust standard errors. *Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level.
Northeastern region. The adverse effects of the BHI on health care use, however, also impact women outside the Northeast region, and there are no significant regional differences in the negative effects on health care use at delivery. This highlights the importance of reviewing the current strategy of focusing humanitarian efforts exclusively on the Northeast as maternal health care access in other regions may also be affected by increases in transportation costs, irregular facility hours, and other factors. The reductions in skilled care use observed in our analysis also suggest there may be a significant role for task-shifting in ensuring access to care during conflict. The current Plan does not explicitly recognize the role of alternative care settings and providers that are less susceptible to targeting during armed conflict, to maintain access to health care. For example, telemedicine has been identified as a means of providing medical assistance when the supply of health workers is reduced due to conflict (Dye and Bishai, 2007). In Afghanistan, training programs for community midwives and nurses from the local communities closed service delivery gaps due to emigration of skilled health workers (David et al., 2017). Our study extends the existing empirical literature that provides detailed descriptions of trends in coverage of maternal health care and
considering a catchment radius of 3000 m, the BHI reduced the use of any antenatal care by 13%, attending up to 4 antenatal care visits by 35%, deliveries in health facilities by 38%, and skilled birth attendance by 26%. Our study provides evidence that the adverse effect of the BHI is larger the closer a woman is to the BH conflict area. For any antenatal care visits and at four antenatal care visits, the adverse effect persists up to a catchment area of 10,000 m, but the effect size reduces as the catchment area becomes wider, progressively including women who are further away from the conflict. The study findings show that the BHI worsens maternal health care access in Nigeria, which in turn, has potential detrimental effects on maternal and newborn health. Reductions in the availability of care may be an important mechanism underlying decreases in utilization of maternal health care following BH attacks in this study. The 2018 Humanitarian Response Plan for Nigeria notes that the destruction of towns and villages has led to the collapse of public services, including health infrastructure. Approximately 40% of health facilities in Borno State alone were destroyed with mass emigration of health workers (UNOCHA, 2018). Hence, the Plan aims to expand access by rebuilding damaged infrastructure and strengthening sector coordination, with a focus on the 110
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
affects the use of delivery care in health facilities. This study is not without limitations. Firstly, the displacement of cluster geo-coordinates within the Demographic and Health Survey introduces measurement error, which potentially attenuates the regression estimates, thereby underestimating the impact of the insurgency on maternal health care access. Thus, the study findings may represent the lower bound of the effect of the insurgency. The magnitude of the study estimates nevertheless indicate that addressing the impact of conflict on maternal health care access is essential. Also, while we quantify the combined impact of BHI on antenatal and delivery care access, we are unable to examine the relative contribution of mechanisms described in the literature given that primary data was not collected for this study. However, we have shown that the BHI significantly constraints the use of essential health care during pregnancy in Nigeria and provided quantitative estimates of the negative impact. Systematic efforts to identify and address the predominant mechanisms underlying the reductions in maternal health care use associated with the conflict are essential to improving maternal health in Nigeria.
Table 5 The effect of the BHI on maternal health care access in Nigeria: restricted sample considering migration. Catchment Area (meters) Outcome Variables Antenatal Care
4 or More Antenatal Care Visits
Delivery at Health Center
Delivery by a Skilled Health Professional
3000
5000
10,000
N
−0.086* (0.066) [0.178 to 0.006] −0.226*** (0.001) [-0.365 to 0.087] −0.184*** (0.003) [-0.306 to −0.063] −0.172*** (0.007) [-0.296 to −0.047]
−0.093** (0.012) [-0.166 to 0.020] −0.193*** (< 0.001) [0.289 to −0.097] −0.115** (0.013) [-0.205 to −0.024] −0.114** (0.015) [-0.206 to −0.023]
−0.060* (0.053) [-0.120 to 0.001] −0.175*** (< 0.0001) [-0.250 to −0.100] −0.019 (0.586) [-0.088 to 0.050] 0.005 (0.0897) [-0.066 to 0.075]
17,741
17,741
19,117
19,061
Appendix A. Supplementary data
Data are DID estimate (p value) [95% confidence interval]. Estimates are based on robust standard errors. *Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level.
Supplementary data to this article can be found online at https:// doi.org/10.1016/j.socscimed.2019.02.055. References
Table 6 The effect of the BHI on maternal health care access in Nigeria: including births in 2009. Catchment Area (meters) Outcome Variables Any Antenatal Care Visits
4 or More Antenatal Care Visits
Delivery at Health Center
Delivery by a Skilled Health Professional
3000
5000
10,000
N
−0.120*** (< 0.0001) [-0.186 to −0.055] −0.231*** (< 0.0001) [-0.324 to −0.139] −0.105*** (0.003) [-0.176 to −0.035] −0.104*** (0.004) [-0.175 to −0.032]
−0.114*** (< 0.0001) [-0.166 to −0.062] −0.197*** (< 0.0001) [-0.264 to −0.130] −0.076*** (0.003) [-0.126 to −0.026] −0.071*** (0.007) [-0.122 to −0.020]
−0.065*** (0.003) [-0.107 to −0.023] −0.148*** (< 0.0001) [-0.200 to −0.096] 0.010 (0.610) [-0.028 to 0.048] 0.018 (0.373) [-0.021 to 0.057]
35,762
Acerra, John R., Kara, Iskyan, Qureshi, Zubair A., Sharma, Rahul K., 2009. Rebuilding the health care system in Afghanistan: an overview of primary care and emergency services. Int. J. Emerg. Med. 2 (2), 77. ACLED, 2018. Data. https://www.acleddata.com/data/, Accessed date: 4 January 2019. AFPC, 2018. “Boko Haram.” World Almanac of Islamism. http://almanac.afpc.org/bokoharam, Accessed date: 4 January 2019. Amnesty International, 2018. Amnesty International Report 2017/18. Amnesty International Ltd. www.amnesty.org, Accessed date: 4 January 2019. Anonymous, 2018. Details Omitted for Double-Blind Reviewing. Babalola, Stella, Fatusi, Adesegun, 2009. Determinants of use of maternal health services in Nigeria - looking beyond individual and household factors. BMC Pregnancy Childbirth 9 (1), 43. Bosmans, Marleen, et al., 2008. “Palestinian women's sexual and reproductive health rights in a longstanding humanitarian crisis. Reprod. Health Matters 16 (31), 103–111. Campbell, S.M., Roland, M.O., Buetow, S.A., 2000. Defining quality of care. Soc. Sci. Med. 51 (11), 1611–1625. CFR, 2018. Nigeria Security Tracker. https://www.cfr.org/nigeria/nigeria-securitytracker/p29483, Accessed date: 4 January 2019. Che, Chi, Primus, Bulage, Patience, Urdal, Henrik, Sundby, Johanne, 2015. Perceptions of the effects of armed conflict on maternal and reproductive health services and outcomes in Burundi and Northern Uganda: a qualitative study. BMC Int. Health Hum. Right 15 (1), 7. David, Siddarth, et al., 2017. Conflict in South Asia and its impact on health. BMJ 357, j1537. (Clinical research ed.). http://www.ncbi.nlm.nih.gov/pubmed/28400380, Accessed date: 9 January 2019. DeJong, Jocelyn, et al., 2017. Reproductive, maternal, neonatal and child health in conflict: a case study on Syria using countdown indicators. BMJ Global Health 2 (3), e000302. Dutton, Diana, 1986. Financial, organizational and professional factors affecting health care utilization. Soc. Sci. Med. 23 (7), 721–735. Dye, Sarah, Bishai, Linda, 2007. Public Health and Conflict in Iraq: Rebuilding a Nation's Health Panel. Washington, D.C. http://www.usip.org/pubs/usipeace_briefings/ 2007/0726_public_health_iraq.html, Accessed date: 9 January 2019. Elkies, Noam, Fink, Günther, Bärnighausen, Till, 2015. Scrambling’ geo-referenced data to protect privacy induces bias in distance estimation. Popul. Environ. 37 (1), 83–98. FAO, 2017. Evidence on Internal and International Migration Patterns in Selected African Countries. http://www.fao.org/3/a-i7468e.pdf, Accessed date: 4 January 2019. Fouad, Fouad M., et al., 2017. Health workers and the weaponisation of health care in Syria: a preliminary inquiry for the Lancet-American University of Beirut commission on Syria. Lancet (Lond., Engl.) 390 (10111), 2516–2526. http://www.ncbi.nlm.nih. gov/pubmed/28314568, Accessed date: 9 January 2019. Ghimire, Laxmi Vilas, Pun, Matiram, 2006. Health effects of maoist insurgency in Nepal. Lancet 368 (9546), 1494. Giacaman, Rita, Wick, Laura, Abdul-Rahim, Hanan, Wick, Livia, 2005. The politics of childbirth in the context of conflict: Policies or de Facto practices? Health Policy 72 (2), 129–139. Guenther, Tanya, et al., 2012. Beyond distance: an approach to measure effective access to case management for sick children in Africa. Am. J. Trop. Med. Hyg. 87 (5_Suppl), 77–84. ICRC, 2011. Health Care in Danger: A Sixteen-Country Study. www.icrc.org, Accessed date: 9 January 2019.
35,762
58,633
58,394
Data are DID estimate (p value) [95% confidence interval]. Estimates are based on robust standard errors. *Significant at 10% level, ** Significant at 5% level, *** Significant at 1% level.
underlying mechanisms of conflict impacts in Afghanistan, Burundi, India, Iraq, Uganda, Syria, Palestine, Lebanon, Eastern Burma, and Nepal (Acerra et al., 2009; Chi et al., 2015; David et al., 2017; DeJong et al., 2017; Dye and Bishai, 2007; Fouad et al., 2017; Giacaman et al., 2005; Kabakian-Khasholian et al., 2013; Price and Bohara, 2012). The divergence in our study conclusions from the other studies that provide impact estimates reinforces the need for context-specific studies of conflict, as there is a variation in prevailing mechanisms and the magnitude of their combined effects on health care use. For instance, while our estimates of the adverse effect of the BHI on delivery at a healthcare center agree with findings from Namasivayam et al., we find that the BHI had negative effects on skilled birth attendance in Nigeria, whereas Namasivayam et al. find positive effects on the conflict on skilled deliveries in Uganda (Namasivayam et al., 2017). Our findings also diverge from [Anonymous, 2018], who reported a null effect of BHI on hospital visits (Anonymous, 2018). By expanding facility health care use to include primary care, we demonstrate that BHI negatively 111
Social Science & Medicine 226 (2019) 104–112
A. Chukwuma and U.E. Ekhator-Mobayode
Penchansky, Roy, Thomas, J. William, 1981. The concept of access: definition and relationship to consumer satisfaction. Med. Care 127–140. Perez-Heydrich, Carolina, Warren, Joshua L., Emch, Michael E., 2013. Guidelines on the Use of DHS GPS Data. Spatial Analysis Reports. . www.measuredhs.com, Accessed date: 4 January 2019. Peters, David H., et al., 2008. Poverty and access to health care in developing countries. Ann. N. Y. Acad. Sci. 1136 (1), 161–171. PMNCH, 2006. WHO Opportunities for Africa's Newborns: Practical Data, Policy and Programmatic Support for Newborn Care in Africa. World Health Organization on Behalf of the Partnership for Maternal Newborn and Child Health. https://www. who.int/pmnch/media/publications/africanewborns/en/, Accessed date: 4 January 2019. Porter, Gina, 2002. Living in a walking world: rural mobility and social Equity issues in sub-Saharan Africa. World Dev. 30 (2), 285–300. Price, James I., Bohara, Alok K., 2012. Maternal health care amid political unrest: the effect of armed conflict on antenatal care utilization in Nepal. Health Policy Plan. 28 (3), 309–319. Siddons, Edward, 2018. “Boko Haram's Kidnapped Girls Turn Accused on Returning Home in Nigeria — Quartz Africa.” Quartz Africa. https://qz.com/africa/1220746/ boko-harams-kidnapped-girls-turn-accused-on-returning-home-in-nigeria/, Accessed date: 4 January 2019. Skiles, Martha Priedeman, Burgert, Clara R., Curtis, Siân L., Spencer, John, 2013. Geographically linking population and facility surveys: methodological considerations. Popul. Health Metrics 11 (1), 14. UNOCHA, 2018. Nigeria: Humanitarian Response Plan (January - December 2018). https://reliefweb.int/report/nigeria/nigeria-humanitarian-response-plan-januarydecember-2018, Accessed date: 6 January 2019. WHO, 2018. Maternal Health. WHO. https://www.who.int/maternal-health/en/, Accessed date: 4 January 2019.
Kabakian-Khasholian, Tamar, Shayboub, Rawan, El-Kak, Faysal, 2013. Seeking maternal care at times of conflict: the case of Lebanon. Health Care Women Int. 34 (5), 352–362. Levesque, Jean Frederic, Harris, Mark F., Russell, Grant, 2013. Patient-centred access to health care: Conceptualising access at the interface of health systems and populations. Int. J. Equity Health 12 (1), 18. Matfess, Hillary, 2017. Boko Haram's Campaign against Women and Girls. https://www. futureswithoutviolence.org/wp-content/uploads/FWV_blueprint_8-Boko-Haram.pdf, Accessed date: 4 January 2019. McKay, Susan, 1998. “The effects of armed conflict on girls and women. Peace Conflict: J. Peace Psychol. 4 (4), 381–392. Mullany, Luke C., et al., 2008. Access to essential maternal health interventions and human rights violations among vulnerable communities in Eastern Burma. PLoS Med. 5 (12), e242. Namasivayam, Amrita, González, Pedro Arcos, Delgado, Rafael Castro, Chi, Primus Che, 2017. The effect of armed conflict on the utilization of maternal health services in Uganda: a population-based study. PLoS Curr. Disasters 9 (2017). National Population Commission (NPC) [Nigeria] and ICF Macro, 2009. National Demographic and Health Survey 2008.” Nigerian Demographic Health Survey 2008. National Population Commission of Nigeria, 2014. National Population Commission Nigeria Demographic and Health Survey 2013. Nigeria Health Watch, 2017. Giving Birth in Nigeria: the Staggering Odds Facing Pregnant Women. https://nigeriahealthwatch.com/giving-birth-in-nigeria-thestaggering-odds-facing-pregnant-women/#.XC-gUlxKg2w, Accessed date: 4 January 2019. Okonofua, Friday, et al., 2018. Predictors of women's utilization of primary health care for skilled pregnancy care in rural Nigeria. BMC Pregnancy Childbirth 18 (1), 106. Patel, Preeti, Gibson-Fall, Fawzia, Sullivan, Richard, Irwin, Rachel, 2017. Documenting attacks on health workers and facilities in armed conflicts. Bull. World Health Organ. 95 (1), 79.
112