Pre-Pregnancy Intimate Partner Violence and Short Interbirth Interval: The Role of Insurance Status

Abstract

Short interbirth interval (IBI) has serious adverse health consequences, yet has an estimated prevalence of 35% in the United States. Similarly, intimate partner violence (IPV) around time of pregnancy, experienced by approximately 5% of women, is associated with increased risk of poor pregnancy outcomes. IPV might compromise women’s decision-making, contributing to unintended pregnancy and short IBI. This study examines the relationship between pre-pregnancy IPV and short IBI, and whether insurance status moderates this relationship among multiparous women who responded to the 2009–2011 Pregnancy Risk Assessment Monitoring System survey (N = 13,675). Pre-pregnancy IPV (yes; no), insurance status (Private insurance; Medicaid/public insurance; no insurance), and short IBI (yes; no) were examined. Insurance status was identified as an effect modifier (p = .03), and maternal age, maternal and paternal education, marital status, and drinking alcohol were identified as potential confounders. Multiple logistic regression analysis stratified by insurance status provided adjusted odds ratios (aOR) with corresponding 95% confidence intervals (CI). Overall, 4.6% of women reported IPV before pregnancy, and 48% had a short IBI. When stratified by insurance status, the odds of short IBI was about 3 times higher among women with no insurance and women on Medicaid/public insurance who reported IPV compared to women who did not report IPV (aOR = 3.36, 95% CI = [1.02, 8.02], and aOR = 2.50, 95% CI = [1.04, 5.92], respectively). There was no observed significant difference in the likelihood of short IBI by experience of IPV among privately insured women. Findings from this study strengthen the evidence that women who experience IPV before pregnancy are significantly more likely to have short IBI compared to women who do not experience pre-pregnancy IPV. Furthermore, the odds of short IBI is highest among women experiencing pre-pregnancy IPV who are uninsured or on Medicaid/public insurance.

Keywords

intimate partner violence birth intervals insurance coverage

Introduction

Short interbirth interval (IBI) is an important but under addressed public health issue in the United States, where an estimated 35% of the pregnancies are conceived within 18 months of a prior birth (Gemmill & Lindberg, 2013). Short IBI can be associated with unintended pregnancy, and several studies have found unintended pregnancies to be associated with intimate partner violence (IPV) (Campbell et al., 1995; Chu et al., 2010; Gazmararian et al., 1995; Goodwin et al., 2000; Masho et al., 2018; Pallitto et al., 2005; Saltzman et al., 1999). Despite the established relationship between IPV and women’s reproductive outcomes, there is little evidence of moderating factors that could be points of intervention, particularly in regard to health care.

Interbirth interval is defined as the number of months between the birth under study and the mother’s immediately preceding birth (Hailu & Gulte, 2016). Although the optimum IBI varies by individual (Haig, 2014), there is robust evidence that IBIs of less than 36 months are associated with an increased risk of poor perinatal and maternal outcomes (Conde-Agudelo et al., 2006, 2007; Eleje et al., 2011; Grisaru-Granovsky et al., 2009; Lilungulu et al., 2015). The optimum interval, 36 to 59 months between births, is associated with lowest risk for preterm birth, low birthweight or small for gestational age, and maternal complications (Hailu & Gulte, 2016). The 2020 Healthy People objectives call for a 10% reduction of pregnancies in the United States that occur within 18 months of a previous birth (National Center for Health Statistics, 2016).

Intimate partner violence is defined as physical violence, sexual violence, stalking, and/or psychological aggression (including coercive tactics) by a current or former intimate partner (i.e., spouse, boyfriend/girlfriend, dating partner, or ongoing sexual partner) (Breiding et al., 2015). An estimated 32% (95% confidence interval [CI] = [29.9%, 33.2%]) of women in the United States are victims of physical violence by an intimate partner in their lifetime, with 22% (95% CI = [20.8%, 23.9%]) of women experiencing severe physical violence (Breiding et al., 2014). Furthermore, an estimated total of 25% of women in their lifetime experience some form of sexual violence by an intimate partner—9% (95% CI = [7.8%, 9.8%]) are victims of rape and 16% (95% CI = [14.6%, 17.1%]) are victims of other sexual violence (Breiding et al., 2014). Altogether, an estimated 27% (95% CI = [25.8%, 28.9%]) of women have been victims of contact sexual violence, physical violence, or stalking by an intimate partner during their lifetimes (Breiding et al., 2014). Thus, IPV is experienced by up to a third of women in the United States and can result in negative health consequences for victims’ reproductive health.

Previous studies suggest that women experiencing IPV might also experience reproductive coercion, or the male partner’s interference with the woman’s reproductive decisions or contraceptive use (Miller et al., 2014; Miller, Jordan, et al., 2010). Male partners’ interference with women’s decisions about pregnancy and contraception has been acknowledged as a mechanism leading to short IBI in cases of women who experience IPV (Bergmann & Stockman, 2015; Cha et al., 2015; Miller et al., 2014). Furthermore, factors that are associated with IPV are also associated with short IBI (Centers for Disease Control and Prevention, 1998; Kaharuza et al., 2001). For example, a randomized clinical trial found that Medicaid-insured pregnant women had a 3-fold higher incidence of IPV compared to women with private insurance (Bullock et al., 2006). Similarly, an analysis from the state of Utah found that compared to women not insured by Medicaid, Medicaid-insured women were twice as likely to have short interpregnancy intervals (Centers for Disease Control and Prevention, 1998). Other factors that have been found to be associated with both IPV and short IBI are racial/ethnic minority, younger age, low income, unmarried status, and having less education (Bullock et al., 2006; Goodwin et al., 2000; Guo et al., 2004; Pallitto et al., 2005; Saltzman et al., 2003; Silverman et al., 2007).

The association between IPV and IBI is under investigated, yet IPV has been shown to be associated with unintended pregnancies, and consequently IBI (Campbell et al., 1995; Gazmararian et al., 1995; Goodwin et al., 2000; Masho et al., 2018; Pallitto et al., 2005). A study among 1,278 women aged 16 to 29 years found that the combined effect of IPV and a composite variable representing pregnancy coercion and birth control sabotage doubled the likelihood of unintended pregnancy compared to those who were not victims of these experiences (adjusted odds ratio [aOR] = 1.99, 95% CI = [1.11, 3.58]) (Miller, Decker, et al., 2010). This study identified health care settings as an important venue for women to be assessed for history of IPV and various forms of reproductive control. However, the timing and type of prenatal health care sought may be influenced by health insurance status. Little is known about the role of health insurance status in the relationship between pre-pregnancy IPV and IBI. As health insurance status is a modifiable determinant of health, identifying whether it modifies the relationship between IPV and short IBI may facilitate early detection of women at risk through risk profiling for short IBI among IPV victims based on their insurance status and guide intervention strategies in relation to IPV and preconception care. This knowledge may thereby inform policies for reducing adverse pregnancy outcomes in relation to short IBIs.

The association between IPV and short IBI may be moderated by the woman’s health insurance status. Previous studies have reported having Medicaid or lack of health insurance to be associated with IPV (Coker et al., 2000; Masho et al., 2018; Vest et al., 2002) and to increase health consequences of violence (Gonzalez-Guarda et al., 2009). In relation to IBI, having health insurance may improve preconception care among women experiencing IPV, which would ideally increase the likelihood of optimal IBIs. A pilot study conducted among African American women suggested that social support and primary health care for low-income women following a very low birthweight delivery may improve subsequent child spacing and reduce adverse pregnancy outcomes (Dunlop et al., 2008). The objectives of this study are (a) to examine the association between pre-pregnancy IPV and short IBI and (b) to explore the moderating effect of insurance status in the association between pre-pregnancy IPV and short IBI. We hypothesize that women reporting IPV before pregnancy have an increased likelihood of short IBI and that this association varies by health insurance status.

Materials and Methods

Data used for this study come from the 2009–2011 National Pregnancy Risk Assessment Monitoring System (PRAMS) survey. PRAMS is a surveillance program on postpartum women conducted by the Centers for Disease Control and Prevention (CDC) in collaboration with state health departments. The PRAMS survey collects information on maternal attitudes and experiences before, during, and shortly after pregnancy with the aim to improve the health of mothers and infants by reducing adverse outcomes. Each year, between 1,300 and 3,400 women who have had a recent live birth are sampled from birth certificate files of participating states, which would subsequently be linked to their survey responses. A standardized data collection protocol of mailed questionnaires and a telephone survey is employed. The PRAMS survey questionnaire has two parts: core questions administered by all participating states and optional standard questions which vary by state. As survey participants are selected by stratified systematic sampling, an analytic weight is created to account for nonresponse and noncoverage, and to make the study sample representative of the target population. Additional information on PRAMS data collection methodology and questionnaires are available elsewhere (Division of Reproductive Health–National Center for Chronic Disease Prevention and Health Promotion, 2018). The PRAMS study protocol for this study was approved by participating states and the Centers for Disease Control and Prevention Institutional Review Board.

Study Participants

The 2009–2011 PRAMS consisted of 112,328 participants. Multiparous women who provided a valid response to all three key variables—IPV, IBI, and insurance status—either through birth certificate data or on the survey questionnaire were included in our analysis, resulting in a final analytic sample size of 13,675 women. Only multiparous women were included in the analysis to enable the calculation of IBI. Consistent with the PRAMS study design, women from racial minority groups and risk population are oversampled to obtain adequate samples of underrepresented groups. The analytic sample comprised women of the following race and ethnic groups: Hispanic (7.7%), non-Hispanic White (56.3%), non-Hispanic Black (13.5%), and non-Hispanic other race (American Indian, Alaska Native, Asian subgroups, Hawaiian, mixed race, and unknown; 22.5%).

Exposure of Interest

The exposure of interest, pre-pregnancy IPV, was determined based on one assessment of physical IPV on the PRAMS survey questionnaire. Women were asked if either their husband or partner had “pushed, hit, slapped, kicked, choked, or physically hurt them in any way” 12 months before becoming pregnant with their most recent child (Basile et al., 2007). This variable was coded as a dichotomous variable (Yes/No), with “Yes” indicating experience of IPV and “No” indicating no experience of IPV in the 12 months preceding their most recent pregnancy.

Outcome of Interest

The outcome of interest for this study was short IBI, which was defined based on an analytic birth certificate variable for “years since last live birth” of the women in the PRAMS Phase 6 dataset. This continuous variable (in years) was converted to months and then into a binary variable (yes, no) to define short IBI (yes, if IBI < 36 months; no, if IBI ≥36 months). The cut-off point for short IBI was based on previous literature (Hailu & Gulte, 2016), with a binary outcome selected over a continuous measure to define short IBI based on this cut-off.

Potential Confounders and Effect Modifiers

Several additional factors were assessed as potential confounders or effect modifiers in the association between the exposure and the outcome, based on our review of the literature (Breiding et al., 2014; Chu et al., 2010; Coker et al., 2000; Gemmill & Lindberg, 2013; Guo et al., 2004; Hailu & Gulte, 2016; Kaharuza et al., 2001; Saltzman et al., 2003; Vest et al., 2002). The primary potential effect modifier in this analysis was insurance status before pregnancy (private insurance; Medicaid or public insurance; no insurance). Additional covariates were demographic factors including maternal age (<20; 20–24; 25–29; 30–34; 35+ years), maternal race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, and non-Hispanic Other), marital status (married, not married), and location (rural, urban); socioeconomic factors including household income (<US$20,000; US$20,000–US$34,999; US$35,000–US$49,999; US$50,000+) and maternal and paternal education (less than high school, high school, some college, bachelor’s degree or more); reproductive factors including pregnancy intention (intended; unintended); and substance use/behavior including smoking and drinking alcohol 2 years before pregnancy (yes, no).

Statistical Analysis

To examine the distribution of the maternal characteristics of the overall study population and by IPV and short IBI, descriptive analysis was conducted to generate unweighted and weighted frequencies. The association between IPV and short IBI was determined using simple and multiple logistic regression analysis, which generated crude and aORs and corresponding 95% CI. Multicollinearity tests for the covariates revealed a high correlation between household income and insurance before pregnancy; therefore, household income was not included in the final models. Possible effect modifiers were assessed with interaction terms between the IPV and the covariates in bivariate models. Insurance status was found to be a significant effect modifier (p-value = .0138), and consistent with our study objective, the analysis was stratified by insurance status. Maternal race/ethnicity was also found to be a significant effect modifier (p-value <.0001); therefore, maternal race/ethnicity was not included as a confounder in the final analysis. In addition, as previous literature has suggested that pregnancy intention might be on the causal pathway between IPV and short IBI (Campbell et al., 1995; Centers for Disease Control and Prevention, 1998; Kaharuza et al., 2001; Masho et al., 2018; Pallitto et al., 2005), it was not included as a confounder in the final analysis. Parsimonious multivariable logistic regression models were created by including covariates that changed the IPV estimate by ±10% (Maldonado & Greenland, 1993) in the overall (without stratification) and stratified analyses. All analyses were conducted using survey procedures in SAS 9.4 (SAS Institute, Cary, NC), and results were weighted to account for the PRAMS complex sampling design. The Bonferroni correction method for multiple comparison tests was applied, thus the overall adjusted level of significance for the stratified analysis was set to p-value <.02, and to p <.05 for all other tests.

Results

In our study sample (n = 13,675), 4.6% of women reported experience of IPV before their most recent pregnancy. For demographic characteristics, majority of our study sample were age 25 years or older (71.3%), non-Hispanic White (56.3%), married (65.3%), and living in an urban area (72.0%) (Table 1). For socioeconomic factors, majority of the study sample had a high school or greater education level (79.4%), and this was similar for paternal education. A larger proportion of the study sample reported an income of less than US$35,000. Approximately 50% of the women in the sample had private health insurance, 25% had Medicaid or public insurance, and 25% had no insurance. In regard to reproductive history, more than half of the women reported their pregnancy to be unintended (53.1%). Finally, most women in the study sample reported not smoking in the previous 2 years (73.5%), but majority reported drinking alcohol in the same period (59.2%).

Table 1.

Descriptive Characteristics of Women Who Reported Physical Abuse 12 Months Before Pregnancy: Pregnancy Risk Assessment Monitoring System, 2009–2011.

	Total	IPV	Rao-Scott Chi-Square
	(Unweighted, N = 13,675)	(Unweighted, N = 632) (4.6%)	Rao-Scott Chi-Square
Characteristics	%	Prevalence	(p-value)
Demographic factors
Maternal age (years)			76.93 (<.0001)
<20	3.8	13.7
20–24	24.9	7.1
25–29	31.9	3.7
30–34	26.2	1.8
≥35	13.2	2.0
Maternal race/ethnicity			9.60 (0.0223)
Non-Hispanic White	56.3	3.6
Non-Hispanic Black	13.5	6.3
Hispanic	7.7	3.7
Non-Hispanic Other	22.5	4.5
Marital status			78.35 (<.0001)
Not married	34.7	8.1
Married	65.3	2.1
Location			0.22 (.6424)
Rural	28.0	4.2
Urban	72.0	4.7
Socioeconomic factors
Maternal education			31.46 (<.0001)
Less than high school	20.6	5.0
High school	28.8	6.5
Some college	25.4	3.8
Bachelor degree or higher	25.2	1.2
Paternal education (years)			28.66 (<.0001)
Less than high school	19.3	5.8
High school	34.5	3.6
Some college	25.5	2.3
Bachelor degree or higher	20.7	1.0
Household income			73.10 (<.0001)
<US$20,000	40.8	7.6
US$20,000–US$34,999	20.5	4.1
US$35,000–US$49,999	9.8	1.9
US$50,000+	28.9	1.3
Health care access
Insurance status before pregnancy			40.46 (<.0001)
Private insurance	49.9	1.9
Medicaid/public insurance	25.1	7.9
No insurance	25.0	4.8
Reproductive history
Pregnancy intention			31.58 (<.0001)
Intended	46.9	1.9
Unintended	53.1	5.8
Substance use/behavior
Smoked 2 years before pregnancy			52.71 (<.0001)
Yes	26.5	8.2
No	73.5	2.8
Drank alcohol 2 years before pregnancy			8.07 (.0045)
Yes	59.2	5.1
No	40.8	3.0

Note. Bold estimates are significant at p <.05. IPV = intimate partner violence.

For women in the study sample who reported experiencing IPV before pregnancy, Rao-Scott chi-square tests indicated a statistically significant association between pre-pregnancy IPV and maternal age, maternal race/ethnicity, maternal and paternal education, marital status, household income, insurance status before pregnancy, pregnancy intention, smoking, and drinking alcohol before pregnancy (Table 1).

Nearly half of the study sample had a short IBI (47.9%). Table 2 displays the distribution of maternal characteristics by short IBI. Bivariate logistic regression analyses showed statistically significant associations between short IBI and pre-pregnancy IPV, maternal age, maternal race/ethnicity, marital status, maternal education, paternal education, household income, health insurance status before pregnancy, pregnancy intention, and drinking alcohol 2 years before pregnancy.

Table 2.

Factors Associated With Short Interbirth Interval: Pregnancy Risk Assessment Monitoring System, 2009–2011.

Characteristics	Short Interbirth Interval(N = 6,547 (47.9%))	Unadjusted OR (95% CI)
Characteristics	Prevalence	Unadjusted OR (95% CI)
IPV before pregnancy
Yes	63.4	1.96 [1.44, 2.66]*
No	46.9	1.00
Maternal age (years)
<20	88.5	8.94 [5.44, 14.69]*
20–24	60.1	1.76 [1.49, 2.08]*
25–29	46.2	1.00
30–34	37.4	0.70 [0.59, 0.82]*
≥35	35.7	0.65 [0.53, 0.79]*
Maternal race/ethnicity
Non-Hispanic White	45.4	1.00
Non-Hispanic Black	51.9	1.30 [1.10, 1.54]*
Hispanic	47.4	1.08 [0.99, 1.43]
Non-Hispanic Other	49.7	1.19 [0.55, 3.09]
Marital status
Not married	55.5	1.63 [1.43, 1.86]*
Married	43.4	1.00
Location
Rural	49.8	1.04 [0.88, 1.24]
Urban	48.7	1.00
Maternal education (years)
Less than high school	56.6	2.09 [1.73, 2.52]*
High school	51.9	1.73 [1.47, 2.03]*
Some college	44.5	1.29 [1.10, 1.51]*
Bachelor degree or higher	38.4	1.00
Paternal education (years)
Less than high school	54.2	2.00 [1.61, 2.49]*
High school	49.0	1.63 [1.36, 1.95]*
Some college	45.5	1.41 [1.17, 1.71]*
Bachelor degree or higher	37.1	1.00
Household income
<US$20,000	57.2	2.36 [2.02, 2.76]*
US$20–US$34,999	44.6	1.42 [1.18, 1.72]*
US$35–US$49,999	45.9	1.50 [1.21, 1.87]*
US$50,000+	36.1	1.00
Insurance status before pregnancy
Private insurance	39.1	1.00
Medicaid/public insurance	59.7	2.30 [2.00, 2.65]*
No insurance	51.5	1.65 [1.40, 1.95]*
Pregnancy intention
Intended	30.6	1.00
Unintended	64.6	4.15 [3.66, 4.71]*
Smoked 2 years before pregnancy
Yes	48.0	1.02 [0.89, 1.16]
No	47.5	1.00
Drank alcohol 2 years before pregnancy
Yes	43.3	0.65 [0.58, 0.74]*
No	53.9	1.00

Note. OR = odds ratio; CI = confidence interval; IPV = intimate partner violence.

p-value < .05.

The prevalence of short IBI was significantly higher among women who were uninsured (51.5%) or had Medicaid (59.7%) compared to women who had private insurance (39.1%) (p-value = .0010). Figure 1 shows the unadjusted differences in the prevalence of short IBI by pre-pregnancy IPV and insurance status. Prevalence of short IBI was highest in uninsured women reporting pre-pregnancy IPV (69.2%, 95% CI = [55.4%, 82.9%]), and lowest in privately insured women with no history of IPV (38.9%, 95% CI = [36.9%, 40.9%]).

Figure 1.

Prevalence of short interbirth interval by pre-pregnancy intimate partner violence and insurance status.

In unadjusted and unstratified logistic regression analysis, there was a significantly increased likelihood of short IBI for women who reported pre-pregnancy IPV compared to women who did not report experiencing IPV (crude odds ratio [OR] = 1.96, 95% CI = [1.44, 2.66]) (Table 3). After adjusting for maternal age, maternal education, paternal education, and marital status, the likelihood of short IBI was found to be 137% higher for women who reported pre-pregnancy IPV compared to women who did not report IPV (aOR = 2.37, 95% CI = [1.33, 4.23]).

Table 3.

Unadjusted and Adjusted Odds Ratios of IPV and Short Interbirth Interval: Pregnancy Risk Assessment Monitoring System, 2009–2011.

	Crude OR [95% CI]	Adjusted OR (95% CI)
IPV before pregnancy
Yes	1.96 [1.44, 2.66]	2.37 [1.33, 4.23] ^a
No	1.00	1.00

Note. Level of significance p < .05; Bold estimates are significant at p <.05; IPV = intimate partner violence; OR = odds ratio; CI = confidence interval.

Adjusted for maternal age, maternal education, paternal education, and marital status.

In the analysis stratified by insurance status, the unadjusted model showed significant positive associations between pre-pregnancy IPV and short IBI for women on Medicaid or public insurance (OR = 1.28, 95% CI = [1.01, 2.05]), and women who were uninsured (OR = 2.19, 95% CI = [1.13, 4.25]) (Table 4). The associations between pre-pregnancy IPV and short IBI strengthened for women on Medicaid or public health insurance (aOR = 2.50, 95% CI = [1.04, 5.92]) and women who were uninsured (aOR = 3.36, 95% CI = [1.02, 8.02]) after adjusting for potential confounders. There were no observed significant differences in short IBI by experience of pre-pregnancy IPV among privately insured women in either the unadjusted or adjusted analyses.

Table 4.

Crude and Adjusted Analysis of IPV and Short Interbirth Interval, Stratified by Insurance Status: Pregnancy Risk Assessment Monitoring System, 2009–2011.

	Crude OR [95% CI]	Adjusted OR [95% CI]
Privately insured
IPV before pregnancy
Yes	1.62 [0.97, 2.72]	1.34 [0.85, 2.40]^a
No	1.00	1.00
On Medicaid/public insurance
IPV before pregnancy
Yes	1.28 [1.01, 2.05]*	2.50 [1.04, 5.92]^b
No	1.00	1.00
Uninsured
IPV before pregnancy
Yes	2.19 [1.13, 4.25]*	3.36 [1.02, 8.02]^c
No	1.00	1.00

Note. Bold estimates are significant at p < .02. IPV = intimate partner violence; OR = odds ratio; CI = confidence interval.

Adjusted for maternal age, maternal education, paternal education, and marital status. ^bAdjusted for maternal age and paternal education. ^cAdjusted for maternal age, paternal education, and drinking alcohol.

p < .05.

Discussion

This study found that in a diverse, representative national sample of women with live births in the United States, the association between pre-pregnancy IPV and IBI differs by health insurance status. Our findings revealed a statistically significant association between experiencing IPV before pregnancy and short IBI for women on Medicaid and women who were uninsured, even after accounting for confounding factors. Specifically, we found that among women who are on Medicaid or are uninsured, those who experienced pre-pregnancy IPV had a significantly higher likelihood of short IBI compared to those with no experience of IPV. However, there was no association between pre-pregnancy IPV and short IBI among women with private insurance.

To the authors’ knowledge, no previous studies have examined the role of insurance status as an effect modifier in the association between IPV and IBI. The association observed in this study between IPV and short IBI among uninsured women may be partially explained by reproductive coercion. Reproductive coercion and inconsistent contraceptive use is more prevalent among women on Medicaid, those using public assistance, or those with no insurance, in comparison to women with private insurance (American College of Obstetricians and Gynecologists, 2013). Previous studies have evidenced a relationship between IPV and reproductive coercion (Miller et al., 2014; Miller, Jordan, et al., 2010). Victims of IPV may also experience birth control sabotage, pregnancy coercion, inconsistent condom use, and increased risky sexual behavior by abusive partners (Campbell et al., 1995; Coker, 2007; Miller, Decker, et al., 2010; Moore et al., 2010; Raj et al., 2006; Williams & Brackley, 2009). For example, the aforementioned Miller et al. study described how women victimized by an intimate partner face compromised decision-making regarding contraceptive use and family planning, contributing to their increased risk for unintended pregnancy (Miller, Decker, et al., 2010). Several studies have reported an increased risk of unintended pregnancy for women who experience IPV compared to women with no such experiences (Campbell et al., 1995; Chu et al., 2010; Gazmararian et al., 1995; Goodwin et al., 2000; Masho et al., 2018; Pallitto et al., 2013; Saltzman et al., 2003), and unintended pregnancies are closely related to short IBIs. Kaharuza et al. (2001) reported that unintended pregnancies comprise about 40% of all short IBIs. Thus, IPV may be associated with women’s compromised decision-making with regard to family planning and contraception, which may contribute to unintended pregnancy and short IBI. Furthermore, Cha et al. observed that IPV-exposed women are significantly less likely to report contraceptive use after delivery, which may increase their risk of short IBI for the next childbirth (Cha et al., 2015). Future research can quantify the direct and indirect effect of unintended pregnancy in the association between IPV and short IBI, and whether insurance status further modifies this relationship.

Both IPV and unintended pregnancy are more prevalent among women with no insurance or on Medicaid (Bullock et al., 2006; Coker et al., 2000; Cripe et al., 2008; Goodwin et al., 2000; Masho et al., 2018; Mosher et al., 2012; Rahman et al., 2012; Saltzman et al., 2003; Vest et al., 2002). This may explain the findings whereby although the adjusted magnitude of effect for short IBI was highest among women who were uninsured and experienced pre-pregnancy IPV, precision of the estimates was similar between women who were on Medicaid and those who were uninsured (Table 4). Furthermore, absence of insurance may contribute to lack of preconception care and contraceptive counseling for women experiencing IPV and thus may lead to increased risk of short IBI for those women (Dunlop et al., 2008). In contrast, having private insurance may contribute to better quality preconception care and more frequent appointments with health care providers among women experiencing IPV, which could reduce the risk of short IBI.

Another factor that is associated with access to health care is household income, which could also determine whether a person can purchase their own private health insurance plan independent of an employer (Berchick et al., 2018). Household income was found to be highly correlated with insurance status in this study, which is consistent with national trends of insurance coverage increasing with increasing income level (Berchick et al., 2018). In addition, maternal race/ethnicity was found to be a significant effect modifier in the relationship between IPV and IBI. This is consistent with studies that have found minority race women in the United States to have an increased risk of experiencing IPV (Bullock et al., 2006; Goodwin et al., 2000), which could explain why the effect of IPV on IBI varies by race/ethnicity. Estimates of lifetime physical IPV by race indicate that nationally, race/ethnic minorities have higher lifetime physical abuse rates than the national average, with the highest estimates in American Indian/Alaska Native women (52%, 95% CI = [38.1%, 65.0%]), followed by multiracial and non-Hispanic Black women (51.3%, 95% CI = [40.2%, 62.3%] and 41.2%, 95% CI = [36.1%, 46.6%], respectively) (Breiding et al., 2014). Conversely, national level data have shown that compared to non-Hispanic Whites, race/ethnic minorities have longer interpregnancy intervals (Copen et al., 2015).

This study has several strengths. First, we analyzed data from a nationally representative sample of women from 40 states, increasing generalizability of the results. Women from underrepresented racial minority groups are oversampled for PRAMS, thus increasing the racial and ethnic diversity of our study population. In addition, this study revealed findings that have clinical and health policy implications in regard to the potential role of access to health care on IBI among women who experience IPV. Furthermore, the operational definition of IPV used in analysis included abuse by both current and former partners, rather than focusing on just current partners, and also examined abuse specific to intimate partners.

Despite its strengths, this study has some limitations. First, as the IBI calculation required study participants to have had at least two births, all primiparas had to be excluded from the analysis, which reduced our sample size. However, we still had a large enough sample size to conduct stratified analysis by our main effect modifier. Second, it is possible that women in this study under-reported IPV, which might have underestimated the measure of association. However, previous studies have also relied on these self-reported measures to assess IPV (Cha et al., 2015; Goodwin et al., 2000). We also only had a measure of physical violence and may be underestimating IPV overall, including other types of partner violence. Another limitation of this study is the inability to infer temporality between the exposure and outcome due to the cross-sectional design of the survey. In addition, we were unable to assess interpregnancy interval as our outcome, even though it is more inclusive and clinically more preferable. However, the variables necessary to calculate interpregnancy interval were not available in the dataset, thus IBI is an appropriate proxy for this measure. Finally, we analyzed the PRAMS Phase 6 survey data due to the availability of the analytic variable needed to calculate IBI from the birth certificate dataset, which was not provided in later survey phases. However, findings from the 2009–2011 survey are still informative in understanding how the association between IPV and short IBI is modified by insurance, regardless of the time frame.

IPV and short IBI are public health issues in the United States that have serious consequences on both maternal and infant health. Findings from this study provide evidence that women who are on Medicaid or are uninsured who experience pre-pregnancy IPV are more likely to have a short IBI than women who do not experience IPV. This knowledge may facilitate early detection of women at risk for short IBIs and inform interventions in relation to IPV and preconception care ensuring adequate birth spacing. In addition, this study underscores the importance of private health insurance coverage among women experiencing pre-pregnancy IPV in regard to reproductive health outcomes. Policy makers and health care providers need to be aware of this finding and should focus on utilizing available screening tools to assess IPV in Medicaid recipients and women who are uninsured. For women who are identified as survivors of IPV during prenatal or postpartum care, providing additional support services could prevent a potential short IBI, regardless of insurance status. Furthermore, attention to insurance status in this study offers the opportunity to inform social intervention and policy. Tailoring IPV screening among women of reproductive age at the community-level, targeting women who are uninsured and women on Medicaid, and counseling IPV victims about the potential negative consequences of short IBIs may reduce gaps in the current policies and interventions for short IBIs. Screening and counseling, as well as improved contraceptive use, may help to reduce rates of short IBIs and associated adverse birth outcomes. This study further signifies the need of increased access to health insurance for better health outcomes. Future longitudinal studies are recommended to elucidate the role of health insurance status in the association between IPV before pregnancy and short IBI.

Footnotes

Acknowledgments

We would like to acknowledge the Pregnancy Risk Assessment Monitoring System (PRAMS) Working Group for providing the data: Alabama—Tammie Yelldell, MPH; Alaska—Kathy Perham-Hester, MS, MPH; Arkansas—Letitia de Graft-Johnson, DrPH, MHSA; Colorado—Ashley Juhl, MSPH; Connecticut—Jennifer Morin, MPH; Delaware—George Yocher, MS; Florida—Tara Hylton, MPH; Georgia—Florence A. Kanu, PhD, MPH; Hawaii—Matt Shim, PhD, MPH; Illinois—Julie Doetsch, MA; Iowa—Jennifer Pham; Kentucky—Tracey D. Jewell, MPH; Louisiana—Rosaria Trichilo, MPH; Maine—Tom Patenaude, MPH; Maryland—Laurie Kettinger, MS; Massachusetts—Hafsatou Diop, MD, MPH; Michigan—Peterson Haak; Minnesota—Mira Grice Sheff, PhD, MS; Mississippi—Brenda Hughes, MPPA; Missouri—Venkata Garikapaty, PhD; Montana—Emily Healy, MS; Nebraska—Jessica Seberger; New Hampshire—David J. Laflamme, PhD, MPH; New Jersey—Sharon Smith Cooley, MPH; New Mexico—Sarah Schrock, MPH; New York State—Anne Radigan; New York City—Pricila Mullachery, MPH; North Carolina—Kathleen Jones-Vessey, MS; North Dakota—Grace Njau, MPH; Oklahoma—Ayesha Lampkins, MPH, CHES; Oregon—Cate Wilcox, MPH; Pennsylvania—Sara Thuma, MPH; Rhode Island—Karine Tolentino Monteiro, MPH; South Carolina—Kristin Simpson, MSW, MPA; Texas—Tanya Guthrie, PhD; Tennessee—Ransom Wyse, MPH, CPH; Utah—Nicole Stone, MPH; Vermont—Peggy Brozicevic; Virginia—Kenesha Smith, MSPH; Washington—Linda Lohdefinck; West Virginia—Melissa Baker, MA; Wisconsin—Fiona Weeks, MSPH; Wyoming—Lorie Chesnut, PhD; CDC PRAMS Team, Women’s Health and Fertility Branch, Division of Reproductive Health.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Tamala Gondwe

Author Biographies

Sylvia S. Rozario, MBBS, MPH, is an Epidemiology doctoral candidate at Virginia Commonwealth University School of Medicine. Her research focuses on maternal and child health epidemiology, particularly maternal morbidities, mental health, and social determinants of health affecting birth outcome.

Tamala Gondwe, PhD, MPH, is a postdoctoral fellow in the Division of Epidemiology at Virginia Commonwealth University School of Medicine. Her research interests are in the area of Perinatal Epidemiology, with a current focus on racial disparities in perinatal and postpartum adverse maternal outcomes.

Saba W. Masho, MD, DrPH, MPH, was a professor of Epidemiology at Virginia Commonwealth University School of Medicine. Her multidisciplinary research in maternal and child health spanned the areas of intimate partner violence, youth violence, disparities in adverse pregnancy outcomes, and improving postpartum maternal health.

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