RESEARCH ARTICLE


Prevalence of Risk Factors Associated with Intimate Partner Violence in Women of Reproductive Age from South Africa and Uganda



Talani Mhelembe1, *, Shaun Ramroop1
iD
, Faustin Habyarimana1
iD

1 School of Mathematics, Statistics and Computer Science, Pietermaritzburg Campus, University of KwaZulu-Natal, Private Bag X01, Scottsville 3209, South Africa


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Creative Commons License
© 2022 Mhelembe et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Mathematics, Statistics and Computer Science, Pietermaritzburg Campus, University of KwaZulu-Natal, Private Bag X01, Scottsville 3209, South Africa; E-mail: mhelembehht@gmail.com


Abstract

Background:

Intimate partner violence (IPV) remains a primary health concern in both developing and developed countries. Even though some countries are intervening through awareness campaigns to boost IPV knowledge, the high prevalence remains a concern. This study's main objective was to assess IPV factors utilizing the generalized additive mixed model (GAMM).

Methodology:

The current research used datasets from the 2016 South Africa and Uganda Demographics and Health Surveys involving a total of 8514 and 18506 women, respectively. GAMM was used to examine the data. The result assessed if a woman has experienced some form of intimate violence or not.

Results:

This study found that 26.93% and 32.25% of women from South Africa and Uganda, respectively, have experienced intimate partner violence. Additionally, the results from this work using GAMM showed the following independent variables as significant risk factors of IPV: age of the woman's partner, marital status, region where the woman lives, age of the woman, media exposure, size of the family, polygamy, wife-beating attitude, sex of the household head, wealth index, pregnancy termination status, body mass index, marital status, cohabitation duration, partner's desire for children, partner's education level, woman's working status, woman's income compared to partner's income, and the person who usually decide on what to do with the woman's earnings.

Conclusion:

The study shows that socioeconomic and demographic variables are significant factors that can be used in policy development and contingency plans when tackling intimate partner violence. The results of this study can assist general health workers and organizations responsible for gender monitoring in the two countries in developing successful strategies to reduce intimate partner violence against women.

Keywords: Intimate partner violence, GAMM, Women, STIs, Health, Variables.



1. INTRODUCTION

Large numbers of African women live in rural areas and are subject to traditional African law, which reinforces the subordinate place of women within the family [1]. The lifetime prevalence of sexual and physical violence against women of reproductive age in the central Africa regions is high compared to the sub-regions [2]. Gender-based violence (GBV) is a form of human rights infringement, and intimate partner violence (IPV) is a significant aspect of GBV [3, 4]. GBV is usually regarded as the 'tip of the iceberg’ or ‘the silent epidemic' as sufferers are uncertain about disclosing violence incidents due to numerous barriers [5]. Some of the negative consequences of IPV for women involve loss of pregnancy through stillbirths and miscarriages, and acquisition of sexually transmitted infections (STIs) [6]. Sub-Saharan Africa has a high prevalence of IPV compared to the rest of the world [4], with a total prevalence of 36%, compared to the worldwide average of 30% [7].

There are many consequences of IPV that are of concern. Several studies show the association between IPV and a wide range of unfavorable reproductive health results, including unintended pregnancy [8-10]. Other consequences include poor outcomes of pregnancy [11] and gynecological morbidity [12, 13]. Furthermore, women can contract STIs and human immunodeficiency virus (HIV) [9, 14]. There is a strong relationship between socioeconomic status and IPV, with the index of household wealth or the partner's education level being significantly inversely related to the dangers of violence [13].

Habyarimana et al. [15] investigated whether the violent incidents were associated with the female or her partner, husband or partner, and the involvement of community or family members in the matter [15]. The socio-demographic attributes of women were as follows: age group in years, education level attained, employment status, number of sexual partners in the last 12 months, ownership of assets such as a house or land, and the woman's income as compared to that of her partner [15]. The socioeconomic and demographic attributes of the partner that were considered comprised educational level, employment status, alcohol drinking status, polygamy status, and the partner's income [15]. The family or community attributes comprised the number of family members, wealth index, type of residence, province, the person who makes decisions about family visits, significant household expenses, the woman's healthcare, and what to do with the income the partner earns [15].

The lifetime percentage of experiencing violence from an intimate partner was 24.6% in South Africa, whereas in Nigeria, like in many developing countries where domestic violence (DV) commonly occurs, reliable population-based data on violence against women by their partners is scarce [16]. Household violence was associated with lower social class, alcohol drinking status, age difference between partners, and employment status [16]. In a study by Obi and Ozumba [16], about 70% of the male respondents disclosed a history of household violence in their families; in 92% of cases, the female partners were the victims, while 8% of victims were male [16]. Demographic attributes are also remarkable risk factors for IPV, with some researchers having discovered that age disparity and the number of children are associated with a lowered risk of violence [17]. As assessed by their educational achievements, degree of self-governance or authority over resources, women with a well-established status are more protected from the risk of IPV [13]. Some studies in developing countries have also found an association between alcohol consumption status or drugs and the risk of violence [18-20].

2. MATERIALS AND METHODS

The study focused on women between 15-49 years so that the contraceptive use by these women can be investigated as to how it contributes to IPV. Several studies have defined a woman of reproductive age as a woman who is between the ages of 15-51 years [21, 22]. From the DHS datasets for women, the ages are between 15-49 years.

2.1. South Africa

The current scientific study utilized the 2016 South African Demographic and Health Survey (SADHS) data. The 2016 SADHS observes a stratified two-stage sample design with a probability in proportion to size sampling of primary sampling units (PSUs) at the initial stage and systematic sampling of dwelling units (DUs) at the second stage [23]. Seven hundred and fifty PSUs were chosen from 26 sampling strata, resulting in 468 chosen PSUs in urban areas, 224 PSUs in traditional areas, and 58 PSUs in farm areas [23]. Women aged between 15-49 years were selected. The survey utilized questionnaires answered by the women and men of each household [23]. The 2016 SADHS supplied women datasets among others, and we used the dataset in this study.

2.2. Uganda

We used the 2016 Uganda Demographic and Health Survey (UDHS) in this study. The 2016 UDHS sample was stratified and chosen in two stages [24]. In stage one, 697 enumeration areas (EAs) were chosen from the 2014 Uganda NPHC: 162 EAs in urban areas and 535 in rural areas [24]. One cluster from the Acholi sub-region was removed because of the land arguments [24]. Households made up the second stage of sampling [24]. A list of households was put together in each of the 696 accessible chosen enumeration areas from April 2016 to October 2016 [24], with some listings overlapping with the fieldwork [24]. A sample of 20,880 households (30 per EA or EA segment) was chosen at random for the 2016 UDHS [24]. Additionally, all women and men, aged between 15-49 and 15-59 years respectively, were eligible to be interviewed. The survey involved various types of questionnaires, such as for households, men and women. Only the women's data to identify the factors influencing IPV among women in Uganda were used.

3. DATA ANALYSIS

3.1. Dependent Variable

The prevalence of IPV in women aged between 15-49 years was determined using the outcome of the physical, emotional, and psychological violence response from the women respondents. Hence, the response variable was binary, where the woman either experienced IPV (at least one of the responses above was positive) or not (none of the responses were positive).

3.2. Independent Variables

The variables that we utilized in this work were selected after careful consideration of the variables used by other authors. The variables were chosen only if some of the studies were found to be significant [6, 10, 13, 15, 25-27], amongst others. In the current study, the following variables were used: the socio-demographic attributes of the women such as the age of the woman in years, the highest level of education attained, employment status, the person who usually decide on how to spend woman's income, wife-beating attitude, literacy, ever terminated a pregnancy, use of contraceptives, body mass index, and knowledge of STIs [9, 10, 14]. The socioeconomic and demographic attributes of the partner were also considered, such as highest education level, age, employment status, alcohol drinking status, whether there is polygamy, and their desire for children [19]. The community and family characteristics that we investigated include the family size, wealth quintile, type of residence, region or province, sex of the household head, and cohabitation duration. Several studies show the link between household violence and a range of unfavorable reproductive health outcomes; these incorporate non-use of contraception and unintended pregnancy [8, 9]. Other factors that influence IPV are STIs and HIV [9, 14].

3.3. Missing Values

The issue of non-response to more than one question in any survey may cause invalidation when the data is used in regression analysis [28]. Pragmatic and statistically advanced ways have been constructed to deal with missing data issues depending on their nature and proportion [28]. One method is MICE, an acronym for multiple imputations by chained equations. The method has been well presented in a study by van Buuren et al. [29]. Multiple imputations provide a way for dealing with missing values in a dataset [29]. Generating multiple imputations accounts for the statistical unpredictability in the imputations [30]. The MICE perspective is flexible and can manage variables of different types (e.g., continuous or binary) and complexities, such as survey skip patterns [30]. Multiple imputations have a few advantages compared to the other approaches [30]. Multiple imputations involve filling in missing observations multiple times, generating multiple complete datasets, as described by Schafer and Graham [31]. The missing observations are imputed regarding observed values for specific individuals [30, 31]. It is also based on links observed in the data for other individuals, suspecting that the observed variables are incorporated in the imputation model [30]. Multiple imputation techniques, MICE in particular, are flexible and can be utilized in various settings [30, 32]. Multiple imputations involve generating multiple predictions for each missing observation, analyzing the expanded data, accounting for the uncertainty in the imputations, and yielding correct standard errors [30, 31]. In the statistical software suite (SAS), the multiple imputation procedure (Proc mi) is used to carry out multiple imputations [33].

3.4. Descriptive Data Analysis

Descriptive data analysis was conducted to determine the data distribution within the different covariates of interest. The main aim of descriptive data analysis is to look at the data in detail before making any assumptions. The chi-square test for analysis was utilized; this technique reveals the correlation between the response variable and the covariates. The results of this study were analyzed using SAS (version 9.4). The results in Tables 1 and 2 show the frequency distribution for South Africa and Uganda, repectively. A p-value less than 0.05 shows that the covariate correlates to the response variable. A p-value greater than 0.05 shows that the covariate does not correlate to the response variable. The current study considered 8,514 and 18,506 women from South Africa and Uganda, respectively. The overall prevalence of IPV was 26.93% (Table 1) and 32.25% (Table 2) for South Africa and Uganda, respectively.

Table 1. Prevalence of intimate partner violence amongst women of reproductive age by a category indicator variable (South Africa).
Indicator Category Experienced IPV P-value
YES N (%) NO N (%)
IPV 2293(26.93) 6221(73.07)
Respondent current age Continuous Minimum=15
Mean=30.21
Maximum=49
Region Western Cape 141(1.66) 515(6.05) <.0001
Eastern Cape 322(3.78) 719(8.44)
Northern Cape 136(1.60) 582(6.84)
Free State 247(2.90) 607(7.13)
Kwazulu-Natal 314(3.69) 1024(12.29)
North West 270(2.90) 593(6.96)
Gauteng 242(2.84) 621(7.29)
Mpumalanga 313(3.68) 741(8.70)
Limpopo 308(3.62) 797(9.36)
Type of place of residence Rural 1263(14.83) 3542(41.60) 0.1256
Urban 1030(12.10) 2679(31.47)
Woman's education level No education 58(0.68) 132(1.55) 0.3699
Primary 245(2.88) 617(7.25)
Secondary 1745(20.50) 4836(56.80)
Higher 245(2.88) 636(7.47)
Number of household members Less than 5 1774(20.84) 3676(43.18) <.0001
More than or equal to 5 519(6.10) 2545(29.89)
Sex of the household head Male 1090(12.80) 2521(29.61) <.0001
Female 1203(14.13) 3700(43.46)
Literacy Cannot read 100(1.17) 232(2.72) 0.1816
Able to read 2193(25.76) 5989(70.34)
Wife-beating attitude Unacceptable 2067(24.28) 5726(67.25) <.0001
Acceptable 208(2.44) 332(3.90)
I don't know 18(0.21) 163(1.91)
Access to the media Low exposure 325(3.83) 981(11.52) <.0001
Medium exposure 1384(16.26) 3345(39.39)
High exposure 583(6.85) 1886(22.15)
Wealth index combined Poorest 492(5.78) 1271(14.93) <.0001
Poorer 573(6.73) 1292(15.18)
Middle 587(6.89) 1369(16.08)
Richer 435(5.11) 1298(15.25)
Richest 206(2.42) 991(11.64)
Ever had a terminated pregnancy No 1972(23.16) 5763(67.69) <.0001
Yes 321(3.77) 458(5.38)
Contraceptive method used No 1028(12.07) 3461(40.65) <.0001
Yes 1265(14.86) 2760(32.42)
Body Mass Index Underweight 71(0.83) 201(2.36) 0.0806
Healthy 637(7.48) 1676(19.69)
Overweight 559(6.57) 1384(16.26)
Obese 1026(12.05) 2960(34.77)
Current marital status Single 1205(14.15) 4468(52.48) <.0001
Married 637(7.48) 1188(13.95)
Living with partner 451(5.30) 565(6.64)
Number of other wives/partners No other wives 2109(24.77) 5741(67.43) 0.8179
One or more 65(0.76) 178(2.09)
I don't know 119(1.40) 302(3.550
Cohabitation duration 0-4 2059(24.18) 5740(67.42) 0.0003
5-9 234(2.75) 481(5.65)
Partner's desire for children Both want same 1077(12.65) 3434(40.33) <.0001
Partner wants more 453(5.32) 1041(12.23)
Partner wants fewer 129(1.52) 287(3.37)
Don't know 634(7.45) 1459(17.14)
Partner's education level No education 99(1.16) 279(3.28) 0.0130
Primary 258(3.03) 588(6.91)
Secondary 1559(18.31) 4162(48.88)
Higher 364(4.28) 1147(13.47)
Don't know 13(0.15) 45(0.53)
Partner's occupation Employed 1931(22.68) 5363(62.99) 0.0198
Don't know 362(4.25) 858(10.08)
Woman's occupation Unemployed 1261(14.81) 4148(48.72) <.0001
Employed 938(11.02) 1840(21.61)
Don't know 94(1.10) 233(2.74)
Partner's age Less than 25 139(1.63) 756(8.88) <.0001
Between 25 and 34 802(9.42) 2372(27.86)
35 and above 1352(15.88) 3093(36.33)
Woman's earning compared to partner More than him 442(5.19) 1151(13.52) 0.0425
Less than him 1219(14.32) 3441(40.42)
About the same 369(4.33) 998(11.72)
Partner doesn't bring in 209(2.45) 457(5.37)
Don't know 54(0.63) 174(2.04)
Knowledge of Sexually Transmitted Infections (STIs) No 25(0.29) 242(2.84) <.0001
Yes 2268(26.64) 5979(70.23)
The person who usually decide on what to do with the woman's earning Woman alone 723(8.49) 1709(20.07) 0.0011
Woman and partner 1390(16.33) 4013(47.13)
Partner alone 180(2.11) 499(5.86)
Woman's father ever beat her mother No 1699(19.96) 5432(63.80) <.0001
Yes 449(5.27) 551(6.47)
Don't know 145(1.70) 238(2.80)

Table 1 shows that the province with the highest prevalence was Eastern Cape with 3.78%, followed by Kwazulu-Natal, Mpumalanga, Limpopo, Free State, North West, Gauteng, Western Cape, and Northern Cape with 3.69%, 3.68%, 3.62%, 2.90%, 2.90%, 2.84%, 1.66%, and 1.60%, respectively. The results show that the prevalence of IPV for women was high in households where the head of the household was a female, i.e., 14.13%, and it was 12.80% for households with male being the head of the household (p-value < .0001). Women with medium, high and low exposure to the media showed 16.26%, 6.85%, 3.83% prevalence, respectively (p-value < .0001). Women from the poorest, poorer, middle, richer, and richest wealth indexes showed a 5.78%, 6.73%, 6.89%, 5.11%, and 2.42% prevalence of IPV, respectively (p-value < .0001). Women from a household with less than five family members and those with five or more family members showed a 20.84% and 6.10% prevalence, respectively (p-value < .0001). The table also shows that 24.28% and 2.44% of women viewed wife-beating attitudes as unacceptable and acceptable, respectively, while 0.21% were those who did not comment regarding the matter and selected do not know (p-value < .0001). The table reveals that the prevalence of IPV in women who terminated pregnancy was 3.77%, and 23.16% for those who had never terminated a pregnancy (p-value < .0001). Regarding contraceptive use, 14.86% of women were using and 12.07% were not using any contraceptive method (p-value < .0001). Single, married, and those living with a partner exhibited a 14.15%, 7.48%, and 5.30% prevalence of IPV, respectively (p-value < .0001). Women who cohabited with a partner for 0-4 years accounted for 24.18%, and those with 5-9 years accounted for 2.75% prevalence of IPV, respectively (p-value = 0.0003).

Table 1 reveals that women having a partner who wants the same number of children as she does, wants more than her, wants fewer than her, and a woman who does not know her partner's desire for children, showed a 12.65%, 5.32%, 1.52%, and 7.45% of prevalence, respectively (p-value < .0001). Women who had a partner with no education, primary, secondary, higher education, and who did not know regarding her partner's level of education showed a 1.16%, 3.03%, 18.31%, 4.28%, and 0.15% of prevalence, respectively (p-value = 0.0130). Women who had an employed or unemployed partner showed 22.68% and 4.25% of prevalence, respectively (p-value = 0.0198). Employed women, not employed, and those not knowing showed 14.81%, 11.02%, and 1.10% of prevalence, respectively (p-value < .0001). We also investigated the knowledge of STIs; Table 1 shows that women who knew about STIs showed a 26.64% prevalence and those who did not exhibited a prevalence of 0.29% (p-value < .0001). For women with a partner less than 25, between 25 and 34, and 35 years of age and above, reported a prevalence of IPV of 1.63%, 9.42%, and 15.88%, respectively (p-value < .0001). Table 1 also reveals a 8.49%, 16.33%, and 2.11% prevalence of IPV for women deciding what to do with their earnings alone, both the woman and her partner, and the partner by himself, respectively (p-value = 0.0011). The table shows that women with a husband/partner who drinks alcohol, does not drink alcohol, and who do not know, showed a prevalence of 14.34%, 12.50% and 0.09%, respectively (p-value < .0001). Women who had never witnessed their father beat their mother, witnessed, and those who did not know, showed a 19.96%, 5.27% and 1.70% of prevalence of IPV, respectively (p-value < .0001).

Table 2. Prevalence of intimate partner violence amongst women of reproductive age by category of the indicator variable (Uganda).
Indicator Category Experienced IPV P-Value
YES N (%) NO N (%)
IPV 5968(32.25) 12538(67.75)
Respondent current age Continuous Minimum=15
Mean=27.94
Maximum=49
Region Kampala 290(1.57) 1010(5.46) <.0001
South Buganda 471(2.55) 1144(6.18)
North Buganda 459(2.48) 951(5.14)
Busoga 538(2.91) 992(5.36)
Bukedi 441(2.38) 764(4.13)
Busigu 316(1.71) 641(3.46)
Teso 412(2.23) 935(5.05)
Karamoja 275(1.49) 466(2.52)
Lango 402(2.17) 834(4.51)
Acholi 346(1.87) 764(4.13)
West Nile 466(2.52) 815(4.40)
Bunyoro 300(1.62) 913(4.93)
Tooro 419(2.26) 882(4.77)
Ankole 524(2.83) 777(4.20)
Kigezi 309(1.67) 650(3.51)
Type of place of residence Rural 1188(6.42) 3191(17.24) <.0001
Urban 4780(25.83 9347(50.51)
Woman's education level No education 836(4.52) 1235(6.67) <.0001
Primary 3733(20.17 7160(38.69)
Secondary 1093(5.91) 3120(16.86)
Higher 306(1.65) 1023(5.53)
Number of household members Less than 5 3513(18.98) 5577(30.14) <.0001
More than or equal to 5 2455(13.27) 6961(37.61)
Sex of household head Male 4316(23.32) 8035(43.42) <.0001
Female 1652(8.93) 4503(24.33)
Literacy Cannot read 2513(13.58) 3941(21.30) <.0001
Able to read 3455(18.67) 8597(46.46)
Wife-beating attitude Unacceptable 2717(14.68) 6481(35.02) <.0001
Acceptable 3198(17.28) 5770(31.18)
I don't know 53(0.29) 287(1.55)
Access to the media Low exposure 2293(12.39) 4483(24.22) <.0001
Medium exposure 3554(19.20 7436(40.18)
High exposure 121(0.65) 619(3.34)
Wealth index combined Poorest 1504(8.13) 2380(12.86) <.0001
Poorer 1319(7.13) 2321(12.54)
Middle 1152(6.23) 2333(12.61)
Richer 1047(5.66) 2407(13.01)
Richest 946(5.11) 3097(16.74)
Ever had a terminated pregnancy No 4527(24.46) 10651(57.55) <.0001
Yes 1441(7.79) 1887(10.20)
Contraceptive method used No 3836(20.73) 9252(49.99) <.0001
Yes 2132(11.52) 3286(17.76)
Body Mass Index Underweight 536(2.90) 1085(5.86) 0.0588
Healthy 4103(22.17) 8435(45.58)
Overweight 899(4.86) 2029(10.96)
Obese 430(2.32) 989(5.34)
Current marital status Single 967(5.23) 6160(33.29) <.0001
Married 2548(13.77) 3265(17.64)
Living with partner 2453(13.26) 3113(16.82)
Number of other wives/partners No other wives/partners 3957(21.38) 9237(49.91) <.0001
One other wife/partner 1722(9.31) 2883(15.58)
I don't know 289(1.57) 419(2.27)
Cohabitation duration 0-4 4657(25.16) 10583(57.19) <.0001
5-9 1311(7.08) 1955(10.56)
Partner's desire for children Both want same 2092(11.30) 4672(25.25) <.0001
Partner wants more 2109(11.40) 3996(21.59)
Partner wants fewer 579(3.13) 1239(6.68)
Don't know 1188(6.42) 2633(14.23)
Partner's education level No education 516(2.79) 748(4.04) <.0001
Primary 3334(18.02) 6031(32.59)
Secondary 1456(7.87) 3652(19.73)
Higher 542(2.93) 1708(9.23)
Don't know 120(0.65) 399(2.16)
Partner's occupation status Unemployed 216(1.17) 506(2.73) 0.1795
Employed 5752(31.08) 12033(65.02)
Woman's occupation status Unemployed 822(4.44) 3268(17.66) <.0001
Employed 5146(27.81) 9270(50.09)
Partner's age Less than 25 594(3.21) 3410(18.43) <.0001
Between 25 and 34 2102(11.36) 4283(23.14)
35 and above 3272(17.68) 4845(26.18)
Woman earnings compared to partner More than him 568(3.07) 780(4.21) <.0001
Less than him 4271(23.08) 7950(42.96)
About the same 855(4.62) 2695(14.56)
Partner doesn't bring in 101(0.55) 317(1.71)
Don't know 173(0.93) 796(4.30)
Knowledge of Sexually Transmitted Infections (STIs) No 6(0.03) 50(0.27) 0.0006
Yes 5962(32.22) 12488(67.48)
The person who usually decide on what to do with the woman's earnings Woman alone 3439(18.58) 6163(33.30) <.0001
Woman and partner 2093(11.31) 5138(27.76)
Partner alone 436(2.36) 1237(6.69)
Partner drinks alcohol No 3023(16.34) 9396(50.77) <.0001
Yes 2945(15.91) 3142(16.98)
Woman's father ever beat her mother No 3253(17.58 8799(47.55) <.0001
Yes 2450(13.24 3332(18.00)
Don't know 265(1.43) 407(2.20)

Table 2 shows that the region with the highest prevalence is Busonga at 2.91%, followed by Ankole, South Buganda, West Nile, North Buganda, Bukedi, Tooro, Teso, Lango, Acholi, Busigu, Kigezi, Bunyoro, Kampala, and Karamoja regions, at 2.83%, 2.55%, 2.52%, 2.48%, 2.38%, 2.26%, 2.23%, 2.17%, 1.87%, 1.71%, 1.67%, 1.62%, 1.57%, and 1.49%, respectively. The highest prevalence of IPV is in urban parts of the country, with 25.83%, while the prevalence in rural areas is 6.42% (p-value < .000). About 20.17% of the women have experienced IPV with primary education, 5.91% with secondary education, 4.52% with no education, and 1.65% with higher education (p-value < .0001). Women from a household with less than five family members and those with five or more family members showed an 18.98% and 13.27% prevalence rate, respectively (p-value < .0001). The table also shows that 14.68%, 17.28%, and 0.29% of women view wife-beating attitudes as unacceptable, acceptable, and not knowing regarding the matter, respectively (p-value < .0001).

Table 2 also shows that women from a household where the head of the house is a male show a prevalence rate of 23.32%, and where the head of the house is female, 8.93% prevalence rate is observed (p-value < .0001). We can also see that women who can read have an 18.67% prevalence, and those who cannot read 13.58% (p-value < .0001). Women with medium exposure to the media showed a high prevalence at 19.20%, followed by low and high exposure at 12.39% and 0.65%, respectively (p-value < .0001). The results show that women from the poorest, poorer, middle, richer, and richest wealth index have an 8.13%, 7.13%, 6.23%, 5.66%, and 5.11% of prevalence, respectively (p-value < .0001). The prevalence of IPV in women with terminated pregnancy is 7.79% and 24.46% for those who had never terminated a pregnancy (p-value < .0001). Table 2 shows that women who use contraceptive methods have a prevalence of 11.52% and those who do not 20.73% (p-value < .0001). Single, married, and those women cohabiting with a partner have a 5.23%, 13.77%, and 13.26% of prevalence, respectively (p-value < .0001). Women who have been staying with a partner for 0-4 years have a 25.16% prevalence rate and those with a partner for 5-9 years 7.08% (p-value < .0001). Women whose partner has no other wives/partners, one other wife/partner, and those who do not know whether their partner has other wives/partners, showed a prevalence rate of 21.38%, 9.31%, and 1.57%, respectively (p-value < .0001).

Table 2 reveals that women who have a partner who wants the same number of children as she does, wants more than her, wants fewer than her, and those who do not know their partner's desire for children showed an 11.30%, 11.40%, 3.13%, and 6.42% of prevalence, respectively (p-value < .0001). Women who had a partner with no education, primary, secondary, higher education, and who do not know regarding their partner's level of education showed a 2.79%, 18.02%, 7.87%, 2.93%, and 0.65%, respectively (p-value < .0001). Employed and unemployed women showed a prevalence rate of 27.81% 4.44%, respectively (p-value < .0001). Women who earned more than their partner, less than their partner, about the same as their partner, whose partner does not bring anything, and those who did not know what their partner earns showed a 3.07%, 23.085, 4.62%, 0.55%, and 0.93% of prevalence rate, respectively (p-value < .0001). Women with a partner who is less than 25, between 25 and 34, and 35 years of age and above, exhibited a prevalence rate of 3.21%, 11.36%, and 17.68%, respectively (p-value < .0001). Women who decided on their own what to do with their earning, decided along with their partner, and whose partner decided alone displayed a prevalence rate of 18.58%, 11.31%, and 2.36%, respectively (p-value < .0001). We also investigated the knowledge of STIs. The table shows that women who knew about STIs showed a 32.22% prevalence rate and those who did not 0.03% (p-value = 0.0006). Women with a husband/partner who drinks alcohol or does not drink alcohol exhibited a 15.91% and 16.34% prevalence, respectively (p-value < .0001). Table 2 also shows that women whose father never beat their mother or did beat their mother showed a 17.58% and 13.24% prevalence, respectively (p-value < .0001).

4. STATISTICAL ANALYSIS

The results from the bivariate analysis with a p-value of less than 5% level of significance were included in multivariate GAMM analysis.

4.1. Model Formulation

The normality assumption and linearity inherent in linear regression can be relaxed by the generalized additive mixed model [34]. Generalized additive mixed models are suggested for wide-spread and correlated data, which frequently arise in research involving clustered, hierarchical and spatial designs [34]. These models allow pliable practical dependence of an outcome variable on predictors by utilizing non-parametric regression while considering for correlation amongst observations by using random-effects [34, 35]. The pliability of non-parametric regression for constant covariates integrates with linear models for independent variables by the possible means to uncover structures within the data that may be overlooked by linear assumptions [36]. Generalized additive mixed models are perceived as extensions of generalized additive models (GAM) to include random effects or generalized linear mixed models [35, 37]. These models enable the parametric fixed effects to be modeled non-parametrically using smooth additive functions in an equivalent manner [35, 38]. For instance, the observations of the mth of k units comprise a dependent variable ym and p covariates xm = (1,xm1,...,xmp)T associated with fixed effects and q x 1 of covariates zm associated with random effects [35]. Lin and Zhang [34] formulated generalized additive mixed models (GAMM) as follows:

(1)

Where, g(.) is a monotonic differentiable link function [35], µm = E(ym|b), ƒm(.) is a centered twice-differentiable level function, and the random effect b is presumed to be distributed as N {0, K(ϑ)}. Furthermore, ϑ is a c x 1 vector of variance elements [35]. A primary characteristic of GAMM (1) above GAM is that the non-parametric additive functions help model covariate effects [34, 35]. Random effects help model the correlation amongst observations [34, 35]. Provided that ƒm(.) is a linear function, therefore GAMM (1) eases to generalized linear mixed model [35, 37]. For specified variance element ϑ, then the log-quasi-likelihood function of (β0, ƒm, ϑ, m = 1,2,..., k) is given as follows [34, 35]:

(2)

Where, ym = (y1,y2,...,yK) and dm (ym;µm) α - 2 ∫µmymmm (ym-u)/v(u)du define the conditional deviance function of {β0, ƒm(.),ϑ}, provided that b is given. The statistical inferences in GAMM include inferences on the non-parametric functions ƒm(.) that require the estimation of smoothing parameters and inferences on the variance elements ϑ . The linear mixed models and the smoothing spline predictors are closely related [34, 35].

After the derivation of Lin and Zhang [34], with a specified λ and ϑ, the natural cubic smoothing spline predictors of the ƒm(.) maximize the penalized log-quasi-likelihood [35] as

(3)

Where, (st,tt) describes the span of the tth covariates and λt indicates smoothing variables that regulate the tradeoff between the goodness-of-fit and smoothness of the estimated functions [35]. Additionally, ƒm(.) is an rt x 1 unknown vector of the values of ƒm(.), calculated at the rt ordered distinctive values of the xmt(t = 1,2,...,m), and Ht is the complementary non-negative definite smoothing matrix [35-39]. GAMM, given in equation (1), can be formulated in matrix form as

(4)

Where, g(µt) = {g(µ1),g(µ2),...,g(µm)}, 1 is an m x 1 vector of 1's, Mt is an k x rt incident matrix described in the same way similar to that given earlier [35, 40], such that the tth element of Mmƒm is ƒm(xtm) and Zt = (z1, z2,...,zm)T . Numerical integration is required to estimate equation (3) other than the Gaussian outcome [35]. Natural cubic smoothing spline predictors of ƒt(.), evaluated by comprehensive maximization of equation (4), are sometimes demanding [35]. Lin and Zhang [34] suggested the double penalized quasi-likelihood model as a manageable alternative procedure for approximation in the model. Since ƒt is a centered parameter vector, it can be parametrized in terms of βt and αt((rt - 2) x 1) in a one-to-one transformation [34, 35, 41] as

Where, Xt is an rt x 1 vector including the rh centered ordered distinctive values of the xtm(t = 1,2,...,m), and βt = Lt(LttLt)-1, and Lt is an rt x (rt - 2) full rank matrix fulfilling Ht = LtLTt and LTtXt = 0 utilizing the identity ƒTtHtƒt; the double penalized quasi-likelihood concerning (β0, ƒt) and b is given by

(5)

Where, and Γ = diag1I2I,...,τkI) with τi = . A small value of τ = (τ12,...τk)T corresponds to over-smoothing [34, 41].

5. RESULTS

5.1. Model Fitting

The numerous approaches for estimation that have debated for fitting GAMM can be utilized when fitting the semiparametric logistic mixed model [35]. The mgcv library in the R package was utilized to fit the data. The R package has several options for managing the model smoothness, utilizing splines as cubic smoothing splines, locally-weighted running line smoothers, and kernel smoothers [35, 38, 39, 42]. The shrinkage smoothers have numerous advantages, such as bypassing the knot positioning [35]. Additionally, the procedure is built to smooth any covariates [34, 35]. In this work, we considered the primary effect and potential two-way interaction effects, the AIC of each model, the inference of smooth function, and the p-value of the individual smooth term [35]. Lastly, a model with smaller AIC and high statistical significance was selected as follows:

(6)

Where, g(µm) is the logit link function, β's are parametric regression coefficients, are centered smooth functions and b0m is the random effect distributed as N(0,K(ϑ)) [35, 42]. The commonly used methods for estimating additive models incorporate cubic smoothing splines, locally-weighted running line smoothers, and kernel smoothers [35, 38, 42]. The results from the model (6) above are presented under each country section and are interpreted.

5.2. Interpretation of Results

5.2.1. South Africa

Table 3 shows that a woman whose partner drinks alcohol is 2.49 (OR=2.4905, p-value < 2e-16***) times more expected to experience IPV compared to a woman whose partner does not drink alcohol. A woman who does not know if her partner drinks alcohol or not is 5.90 (OR=5.9000, p-value = 0.004250**) times more expected to experience IPV compared to a woman whose partner does not drink alcohol. A woman who has witnessed her father beat her mother is 2.19 (OR=2.1884, p-value < 2e-16***) times more expected to experience IPV compared to a woman who has not witnessed her father beat her mother. A woman who does not know if her father beats her mother is 1.97 (OR=1.9691, p-value = 1.52e-07***) times more expected to experience IPV compared to a woman who has witnessed her father beat her mother. A woman from a household with more than five members is 0.45 (OR=0.4532, p-value < 2e-16***) times less expected to experience IPV compared to a woman from a household with five or fewer members. A woman who sees wife-beating as an acceptable act is 1.90 (OR=1.9049, p-value = 5.64e-09***) times more expected to experience IPV compared to a woman who sees wife-beating as unacceptable. A woman who does not know if the wife-beating is acceptable or not is 0.34 (OR=0.3400, p-value = 0.000142***) times less expected to experience IPV compared to a woman who sees wife-beating as unacceptable. A woman with medium exposure to the media is 1.37 (OR=1.3745, p-value = 0.000327***) times more expected to experience IPV compared to a woman with low exposure to the media.

A woman from the Eastern Cape province is 1.51 (OR=1.5099, p-value = 0.002482**) times more expected to experience IPV compared to a woman from the Western Cape province. A woman from the Free State province is 1.52 (OR=1.5218, p-value = 0.002389**) times more expected to experience IPV compared to a woman from the Western Cape province. A woman from Kwazulu-Natal province is 1.58 (OR=1.5806, p-value = 0.000628***) times more expected to experience IPV compared to a woman from the Western Cape province. A woman from North West province is 1.50 (OR=1.5024, p-value = 0.003626**) times more expected to experience IPV compared to a woman from the Western Cape province. A woman from the Gauteng province is 1.58 (OR=1.5841, p-value = 0.000774***) times more expected to experience IPV compared to a woman from the Western Cape province. A woman from Mpumalanga province is 1.48 (OR=1.4842, p-value = 0.004168**) times more expected to experience IPV compared to a woman from the Western Cape province. A woman from Limpopo province is positively associated with IPV. A woman from Limpopo province is 1.74 (OR=1.7437, p-value = 0.000101***) times more expected to experience IPV compared to a woman from the Western Cape province.

A woman from a household where the head of the house is female is 0.80 (OR=0.8030, p-value = 0.000934***) times less likely to experience IPV compared to a woman from a household where the head of the house is a male. A woman from the richer wealth index class is 0.67 (OR=0.6743, p-value = 0.004953**) times less expected to experience IPV compared to a woman from the poorest wealth index class. A woman from the richest wealth index class is 0.32 (OR=0.3243, p-value = 1.05e-10***) times less expected to experience IPV compared to a woman from the poorest wealth index class. A woman who has terminated pregnancy is 1.37 (OR=1.3695, p-value = 0.000277***) times more expected to experience IPV compared to a woman who has never terminated a pregnancy. A woman who is obese (BMI≥30) is 0.67 (OR=0.6743, p-value = 0.018388*) times less expected to experience IPV compared to an underweight woman (BMI<18). A married woman is 1.47 (OR=1.4650, p-value = 2.44e-06***) times more expected to experience IPV compared to a single woman. A woman living with her partner is 1.57 (OR=1.5717, p-value = 7.58e-07***) times more expected to experience IPV compared to a single woman. A woman with a partner who desires more children compared to her is 1.45 (OR=1.4473, p-value = 1.82e-06***) times more expected to experience IPV compared to a woman whose partner wants the same number of children as her. A woman with a partner who wants fewer children compared to her is 1.51 (OR=1.5140, p-value = 0.001279***) times more expected to experience IPV compared to a woman whose partner wants the same number of children as her. A woman who does not know the number of children her partner wants is 1.40 (OR=1.3964, p-value = 5.77e-07***) times more expected to experience IPV compared to a woman whose partner's desire for children is the same as her.

A woman with an employed partner is 2.17 (OR=2.1655, p-value = 0.000799***) times more expected to experience IPV compared to a woman whose partner is unemployed. An employed woman is 1.21 (OR=1.2122, p-value = 0.001994**) times more expected to experience IPV compared to an unemployed woman. A woman with a partner between 25 to 34 years of age is 0.77 (OR=0.7650, p-value = 0.036368*) times less expected to experience IPV compared to a woman whose partner is less than 25 years old. A woman with a partner equal to or greater than 35 years old is 0.67 (OR=0.6553, p-value = 0.003263**) times less expected to experience IPV compared to a woman whose partner is less than 25 years old. A woman who decides with her partner what to do with her earning is 0.87 (OR=0.8566, p-value = 0.018921*) times less expected to experience IPV compared to a woman who solely decides how to spend her earning. A woman who earns about the same as her partner is 1.30 (OR=1.2988, p-value = 0.007599**) times more expected to experience IPV compared to a woman who earns more compared to her partner. A woman whose partner does not bring in his earnings is 1.34 (OR=1.3494, p-value = 0.013337*) times more expected to experience IPV compared to a woman who earns more compared to her partner. A woman who does not know how much her partner earns is 1.43 (OR=1.4279, p-value = 0.024263*) times more expected to experience IPV compared to a woman who earns more compared to her partner.

Table 3. The parameter estimates of the IPV for the fixed part of GAMM.
Effects Estimate Standard Error t-value P-value Odds Ratio
Intercept -3,315692 0,345442 -9,598 <2e-16*** 0,0363
Partner drinks alcohol (ref=No)
Yes 0,912471 0,056567 16,131 <2e-16*** 2,4905
Don't know 1,774949 0,620662 2,86 0,004250** 5,9000
Woman's father ever beat her mother (ref=No)
Yes 0,783176 0,079147 9,895 <2e-16*** 2,1884
Don't know 0,677596 0,128967 5,254 1,52e-07*** 1,9691
Number of household members (ref=Less compared to 5)
More compared to 5 -0,791347 0,064592 -12,251 <2e-16*** 0,4532
Wife beating attitude (ref=Unacceptable)
Acceptable 0,644407 0,110471 5,833 5,64e-09*** 1,9049
I don't know -1,078901 0,28348 -3,806 0,000142*** 0,3400
Access to the media (ref=Low exposure)
Medium exposure 0,318069 0,088488 3,594 0,000327*** 1,3745
High exposure 0,158442 0,10711 1,479 0,139113 1,1717
Region (ref=Western Cape)
Eastern Cape 0,412044 0,136149 3,026 0,002482** 1,5099
Northern Cape -0,241707 0,150897 -1,602 0,109236 0,7853
Free State 0,419888 0,138212 3,038 0,002389** 1,5218
Kwazulu-Natal 0,457814 0,133845 3,42 0,000628*** 1,5806
North West 0,407086 0,139903 2,91 0,003626** 1,5024
Gauteng 0,459995 0,13677 3,363 0,000774*** 1,5841
Mpumalanga 0,39485 0,137772 2,866 0,004168** 1,4842
Limpopo 0,556036 0,142938 3,89 0,000101*** 1,7437
Type of residence (ref=Rural)
Urban -0,033638 0,072605 -0,463 0,643157 0,9669
Woman's highest education level (ref=No education)
Primary education -0,179293 0,220624 -0,813 0,416434 0,8359
Secondary education -0,018054 0,224895 -0,08 0,936017 0,9821
Higher education -0,002704 0,245996 -0,011 0,99123 0,9973
Sex of household head (ref=Male)
Female -0,219375 0,06626 -3,311 0,000934*** 0,8030
Literacy (ref=Can read)
Cannot read 0,010613 0,166142 0,064 0,949068 1,0107
Wealth index combined (ref=Poorest)
Poorer -0,121217 0,124796 -0,971 0,331416 0,8858
Middle -0,01027 0,127346 -0,081 0,935727 0,9898
Richer -0,394082 0,140201 -2,811 0,004953** 0,6743
Richest -1,126115 0,174102 -6,468 1,05e-10*** 0,3243
Ever had a terminated pregnancy (ref=No)
Yes 0,314463 0,086445 3,638 0,000277*** 1,3695
Contraceptive use (ref=Not using)
Using 0,018269 0,122071 0,15 0,881038 1,0184
Body Mass Index (ref=Underweight(BMI<18))
Healthy (18≤BMI<25) -0,1735562 0,169657 -1,023 0,306328 0,8407
Overweight (25≤BMI<30) -0,247527 0,170754 -1,45 0,147205 0,7807
Obese (BMI≥30) -0,394102 0,167122 -2,358 0,018388* 0,6743
Current marital status (ref=Single)
Married 0,381886 0,080966 4,717 2,44e-06*** 1,4650
Living with partner 0,452133 0,091347 4,95 7,58e-07*** 1,5717
Number of other wives (ref=0)
1 or more -0,092647 0,092647 -0,545 0,586075 0,9115
Don't know -0,017783 0,128194 -0,139 0,889675 0,9824
Cohabitation period (ref=0-4 years)
5-9 years 0,091619 0,114166 0,803 0,422284 1,0959
Partner's desire for children (ref=Both want same)
Husband wants more 0,369722 0,077418 4,776 1,82e-06*** 1,4473
Husband wants fewer 0,414754 0,128733 3,222 0,001279** 1,5140
Don't know 0,333889 0,066741 5,003 5,77e-07*** 1,3964
Partner's education level (ref=No education)
Primary education 0,144755 0,156019 0,928 0,353537 1,1558
Secondary education 0,085726 0,147189 0,582 0,5603 1,0895
Higher 0,200253 0,167645 1,195 0,232314 1,2217
Don't know -0,264443 0,420023 -0,63 0,528979 0,7676
Partner's occupation (ref=Unemployed)
Employed 0,772655 0,230342 3,354 0,000799*** 2,1655
Don't know 0,054147 0,080887 0,669 0,503248 1,0556
Woman's occupation (ref=Unemployed)
Employed 0,192462 0,062245 3,092 0,001994** 1,2122
Don't know -0,022983 0,141177 -0,163 0,870683 0,9773
Partner's age (ref=Less than 25 years)
25 to 34 years -0,267872 0,127977 -2,093 0,036368* 0,7650
35 years and above -0,422714 0,143649 -2,943 0,003263** 0,6553
Person who usually decide on woman's earnings (ref=Woman alone)
Woman and partner -0,154738 0,065915 -2,348 0,018921* 0,8566
Partner alone -0,110197 0,116523 -0,946 0,344326 0,8957
Woman's earnings compared to partner (ref=More compared to him)
Less compared to him 0,12871 0,074828 1,72 0,085457. 1,1374
About the same 0,261424 0,09791 2,67 0,007599** 1,2988
Partner does not bring in earnings 0,299646 0,121062 2,475 0,013337* 1,3494
Don't know 0,356187 0,15807 2,253 0,024263* 1,4279
Knows regarding STIs (ref=No)
Yes 0,772655 0,230342 3,354 0,000799*** 2,1655
Interaction effects
Wealth index combined (ref=Poorest) by contraceptive use (ref=Not using)
Poorer by using contraceptives 0,274623 0,1663 1,652 0,098473. 1,3160
Middle by using contraceptives -0,059718 0,1636 -0,364 0,715679 0,9420
Richer by using contraceptives 0,235772 0,1721 1,367 0,171718 1,2659
Richest by using contraceptives 0,460516 0,2056 2,23 0,025785* 1,5849
Significance codes: ‘***’=0≤p-value<0.001, ‘**’=0.001≤p-value<0.01, ‘*’=0.01≤p-value<0.05, and ‘.’=0.05≤p-value<0.1

A woman who knows about STIs is 2.17 (OR=2.1655, p-value = 0.000799***) times more likely to experience IPV compared to a woman with no knowledge of STIs.

Table 4. Approximation significance of the smooth term.
Smooth Terms Edf F-Value P-Value
s (Woman's current age) 8.702 19.18 <2e-16***

5.3. Interaction Effects

In Fig. (1), we observe that IPV decreases for both women who are not using and those who are using contraceptives from the different wealth index classes, from poorer, middle, richer, and is lowest with the richest class. Those who are using contraceptives have a higher prevalence of IPV for all the wealth index classes compared to those who are not using contraceptives. The main aim of the interaction effects is to determine which women from the different wealth index classes, i.e., who are using or not using contraceptives, are at a high risk of experiencing IPV.

5.4. Approximation Smooth Function

Table 4 shows that the current age of a woman has a significant impact on intimate partner violence. The letter s in Table 4 characterizes the smoothing term, and the value in parenthesis shows the predicted degree of freedom (edf). The test statistic for woman's age (19.18), together with a p-value (<2e-16), shows that there is no linear trend related to IPV. As affirmed in Fig. (2), the trend shows that the effect of age on IPV increases as the woman's age increases up to approximately 20 years and slightly remains constant until about 38 years. It starts to decrease from about 39 to 49 years.

Fig. (1). Predicted probability of experiencing IPV by wealth index class and contraceptive use.

Fig. (2). Smooth function of the age of the woman (V012) (Source: Extracted from RStudio).

5.4.1. Uganda

Table 5 shows that a woman whose partner drinks alcohol is 2.54 (OR=2.5393, p-value < 2e-16***) times more likely to experience IPV compared to a woman whose partner does not drink alcohol. A woman who has witnessed her father beat her mother is 1.77 (OR=1.7736, p-value < 2e-16***) times more likely to experience IPV compared to a woman who has not witnessed her father beat her mother. A woman who does not know if her father beats her mother is 1.84 (OR=1.8360, p-value = 6.25e-11***) times more likely to experience IPV compared to a woman who has not witnessed her father beat her mother. A woman from a household with more than five members is 0.55 (OR=0.5496, p-value < 2e-16***) times less likely to experience IPV compared to a woman from a household with less than five members. A woman who sees wife-beating as an acceptable act is 1.16 (OR=1.1638, p-value = 6.22e-05***) times more likely to experience IPV compared to a woman who sees wife-beating as unacceptable. A woman who does not know if the wife-beating act is acceptable or not is 0.63 (OR=0.6278, p-value = 0.005756**) times less likely to experience IPV compared to a woman who sees wife-beating as an unacceptable act. A woman with high exposure to the media is 0.70 (OR=0.6964, p-value = 0.0005604**) times less likely to experience IPV compared to a woman with low exposure to the media.

A woman from Busoga province is 1.25 (OR=1.2506, p-value = 0.042770*) times more likely to experience IPV compared to a woman from Kampala province. A woman from Teso province is 0.79 (OR=0.7919, pvalue = 0.048915*) times less likely to experience IPV compared to a woman from Kampala province. A woman from Karamoja province is 0.56 (OR=0.564452, p-value = 5.71e-05***) times less likely to experience IPV compared to a woman from Kampala province. A woman from Lango is 0.77 (OR=0.7690, p-value = 0.027850*) times less likely to experience IPV compared to a woman from Kampala province. A woman from Acholi province is 0.72 (OR=0.7178, p-value = 0.006486**) times less likely to experience IPV compared to a woman from Kampala province. A woman from Bunyoro province is 0.68 (OR=0.6776, p-value = 0.000954***) times less likely to experience IPV compared to a woman from Kampala province.

A woman from a household where the head of the house is female is 0.84 (OR=0.8437, p-value = 0.000278***) times less likely to experience IPV compared to a woman from a household with a male as the head of the house. A woman from the richest wealth index class is 0.66 (OR=0.6594, p-value = 1.33e-05***) times less likely to experience IPV compared to a woman from the poorest wealth index class. A woman who has terminated a pregnancy is 1.12 (OR=1.1197, p-value = 0.010853*) times more likely to experience IPV compared to a woman who has never terminated a pregnancy. A woman who uses contraceptives is 1.40 (OR=1.3963, p-value = 0.000286***) times more likely to experience IPV compared to a woman who does not use contraceptives. A woman who is overweight (25≤BMI<30) is 0.82 (OR=0.8237, p-value = 0.012199*) times less likely to experience IPV compared to an underweight woman (BMI<18). A married woman is 2.76 (OR=2.7649, p-value < 2e-16***) times more likely to experience IPV compared to a single woman. A woman living with her partner is 2.81 (OR=2.8123, p-value < 2e-16***) times more likely to experience IPV compared to a single woman.

A woman who has a partner with more than one wife/partner is 1.14 (OR=1.1421, p-value = 0.002137**) times more likely to experience IPV compared to a woman whose partner does not have other wives. A woman who does not know if her partner has other wives or not is 1.51 (OR=1.5089, p-value = 5.54e-06***) times more likely to experience IPV compared to a woman whose partner does not have other wives/partners. A woman who does not know the number of children her partner wants is 0.85 (OR=0.8517, p-value = 0.001363**) times less likely to experience IPV compared to a woman whose partner's desire for children is the same as her. A woman with a secondary education partner is 0.81 (OR=0.8120, p-value = 0.012295**) times less likely to experience IPV compared to a woman whose partner has no education. A woman whose partner has a higher education level is 0.73 (OR=0.7318, p-value = 0.001321**) times less likely to experience IPV compared to a woman whose partner has no education. A woman who does not know her partner's level of education is 0.55 (OR=0.5545, p-value = 2.10e-05***) times less likely to experience IPV compared to a woman whose partner has no education. An employed woman is 1.13 (OR=1.1379, p-value = 0.022295*) times more likely to experience IPV compared to an unemployed woman. A woman who decides with her partner what to do with her earning is 0.83 (OR=0.8266, p-value = 2.98e-06***) times less likely to experience IPV compared to a woman who solely decides how to spend her earnings. A woman whose partner decides how to spend her earnings is 0.70 (OR=0.6984, p-value = 3.95e-07***) times less likely to experience IPV compared to a woman who solely decides how to spend her earnings. A woman who earns about the same as her partner is 0.69 (OR=0.6855, p-value = 1.16e-06***) times less likely to experience IPV compared to a woman who earns more compared to her partner. A woman whose partner does not bring in his earnings is 0.65 (OR=0.6473, p-value = 0.021089*) times less likely to experience IPV compared to a woman who earns more compared to her partner.

Table 5. The parameter estimates of the IPV for the fixed part of GAMM.
Effects Estimate Standard Error t-value P-Value Odds Ratio
Intercept -0,258 0,4997 -5,163 2,46e-07*** 0,7725952
Partner drinks alcohol (ref=No)
Yes 0,9319 0,038 24,524 <2e-16*** 2,5393293
Woman's father ever beat her mother (ref=No)
Yes 0,573 0,03855 14,865 <2e-16*** 1,7735798
Don't know 0,6076 0,09288 6,541 6,25e-11*** 1,8360197
Number of household members (ref=more compared to 5)
Less compared to 5 -0,5985 0,03773 -15,861 <2e-16*** 0,5496355
Wife beating attitude (ref=Unacceptable)
Acceptable 0,1517 0,03789 4,005 6,22e-05*** 1,163811
I don't know -0,4655 0,03789 -2,762 0,005756** 0,6278211
Access to the media (ref=Low exposure)
Medium exposure -0,03716 0,04047 -0,785 0,432592 0,963522
High exposure -0,3618 0,01306 -2,77 0,0005604** 0,6964216
Region (ref=Kampala)
South Buganda -0,01937 0,104 -0,186 0,852171 0,9808164
North Buganda -0,0168 0,1093 -0,154 0,877885 0,9833403
Busoga 0,2236 0,1103 2,026 0,042770* 1,2505707
Bukedi 0,03018 0,1166 0,259 0,795697 1,03064
Busigu -0,1342 0,1208 -1,111 0,266778 0,8744152
Teso -0,2333 0,1185 -1,969 0,048915* 0,791916
Karamoja -0,5719 0,1421 -4,025 5,71e-05*** 0,564452
Lango -0,2627 0,1195 -2,198 0,027950* 0,7689726
Acholi -0,3315 0,1218 -2,722 0,006486** 0,7178462
West Nile -0,06942 0,1172 -0,592 0,553707 0,9329348
Bunyoro -0,3892 0,1178 -3,304 0,000954*** 0,6775987
Tooro -0,01282 0,1135 -0,113 0,910043 0,9872618
Ankole 0,2093 0,1125 1,861 0,062817. 1,2328148
Kigezi -0,1163 0,1221 -0,952 0,34094 0,8902081
Type of residence(ref=Rural)
Urban -0,1122 0,05547 -2,023 0,043052* 0,8938655
Woman's highest education level (ref=No education)
Primary education 0,07356 0,06462 1,138 0,255014 1,0763331
Secondary education 0,09883 0,08421 1,174 0,240579 1,1038786
Higher education 0,08128 0,1171 0,694 0,487742 1,0846746
Sex of household head (ref=Male)
Female -0,1697 0,04667 -3,636 0,000278*** 0,8439
Literacy (ref=Cannot read)
Can read -0,01623 0,04681 -0,347 0,728867 0,9839
Wealth index combined (ref=Poorest)
Poorer 0,02224 0,0684 0,325 0,745029 1,0225
Middle -0,09092 0,07466 -1,218 0,223279 0,9131
Richer -0,1532 0,07973 -1,921 0,054753. 0,8580
Richest -0,4164 0,0956 -4,356 1,33e-05*** 0,6594
Ever had a terminated pregnancy (ref=No)
Yes 0,1131 0,04441 2,548 0,010853* 1,1197
Contraceptive use (ref=Not using)
Using 0,3338 0,09199 3,628 0,000286*** 1,3963
Body Mass Index (ref=Underweight(BMI<18))
Healthy (18≤BMI<25) -0,07095 0,06529 -1,087 0,277192 0,9315
Overweight (25≤BMI<30) -0,194 0,07739 -2,507 0,012199* 0,8237
Obese (BMI≥30) -0,1525 0,09212 -1,655 0,097855. 0,8586
Current marital status (ref=Single)
Married 1,017 0,05666 17,945 <2e-16*** 2,7649
Living with partner 1,034 0,05506 18,787 <2e-16*** 2,8123
Number of other wives (ref=0)
1 or more 0,1329 0,04328 3,071 0,002137** 1,1421
Don't know 0,4114 0,09052 4,545 5,54e-06*** 1,5089
Cohabitation period (ref=0-4 years)
5-9 years 0,06102 0,07522 0,811 0,417251 1,0629
Partner's desire for children (ref=Both want same)
Husband wants more 0,01193 0,04457 0,268 0,788976 1,0120
Husband wants fewer -0,1247 0,06637 -1,88 0,060177. 0,8828
Don't know -0,1605 0,0501 -3,203 0,001363** 0,8517
Partner's education level (ref=No education)
Primary education -0,1445 0,0765 -1,888 0,058979. 0,8655
Secondary education -0,2082 0,08314 -2,504 0,012295* 0,8120
Higher -0,3123 0,1 -3,212 0,001321** 0,7318
Don't know -0,5896 0,1386 -4,255 2,10e-05*** 0,5545
Partner's occupation (ref=Unemployed)
Employed 0,1322 0,1028 1,285 0,198802 1,1413
Woman's occupation (ref=Unemployed)
Employed 0,1204 0,05268 2,285 0,022295* 1,1279
Partner's age (ref=Less than 25 years)
25 to 34 years 0,004632 0,07328 0,063 0,949605 1,0046
35 years and above 0,09827 0,08674 1,133 0,257276 1,1033
The person who usually decide on a woman's earnings (ref=Woman alone)
Woman and partner -0,1904 0,04073 -4,674 2,98e-06*** 0,8266
Partner alone -0,3589 0,07074 -5,073 3,95e-07*** 0,6984
Someone else 0,08827 0,5098 0,173 0,862538 1,0923
Woman's earning compared to partner (ref=More compared to him)
Less compared to him -0,01754 0,06062 -0,289 0,772305 0,9826
About the same -0,3776 0,07762 -4,864 1,16e-06*** 0,6855
Partner does not bring in his earning -0,435 0,1886 -2,307 0,021089* 0,6473
Don't know -0,2642 0,1443 -1,83 0,067213. 0,7678
Knows regarding STIs (ref=No)
Yes 0,2874 0,4959 0,58 0,562199 1,3330
Interaction effects
Wealth index combined (ref=poorest) by contraceptive use (ref=not using)
Poorer by using contraceptives -0,3365 0,1243 -2,708 0,006785** 0,7143
Middle by using contraceptives -0,4226 0,1248 -3,386 0,000711*** 0,6553
Richer by using contraceptives -0,3729 0,1245 -2,996 0,002743** 0,6887
Richest by using contraceptives -0,2243 0,1241 -1,808 0,070664. 0,7991
Significance codes: ‘***’=0≤p-value<0.001(strongly significant), ‘**’=0.001≤p-value<0.01 (moderately significant), ‘*’=0.01≤p-value<0.05(slightly significant), and ‘.’=0.05≤p-value<0.1(slightly insignificant).

5.5. Interaction Effects

In Fig. (3), we observe that IPV decreases with increasing wealth index for women who use and do not use contraceptives. The results indicate that IPV is higher among women using contraceptives compared to women who are not using contraceptives. The main aim of the interaction effects is to determine which women from the different wealth index classes, i.e., who are using or not using contraceptives, are at a high risk of experiencing IPV.

5.6. Approximation Smooth Function

Table 6 shows that the current age of a woman has a significant impact on intimate partner violence. The letter s in Table 6 characterizes the smoothing term, and the value in parenthesis shows the predicted degree of freedom (edf). The test statistics for woman's age (33.92), together with a p-value (<2e-16), shows that there is no linear trend relation with IPV. This is affirmed in Fig. (4), where the trend shows that the age has an effect on an increase in the prevalence of IPV up to approximately 20 years, and thereafter, it remains constant until about 35 years when it starts to decrease until about 40 years; after this age, it starts to show an increase again.

Table 6. Approximation significance of the smooth term.
Smooth Terms Edf F-Value p-Value
s (Woman's current age) 8.232 33.92 <2e-16***

6. DISCUSSION

This work utilized the generalized additive mixed model to explore the risk factors associated with intimate partner violence utilizing nationwide individual women survey data from South Africa and Uganda. Previous studies have utilized parametric models from the GLM family to analyze the data [15, 43, 44]. The parametric models help model the association between a response variable and covariates. The primary idea of non-parametric methods is to enable the data to determine the most appropriate function [36]. Wu and Zhang [41] argue that non-parametric and parametric regression methods are not competitive but complement each other. Combining these two methods may prove to be more potent compared to any single model [36].

The findings of this work show alcohol to be positively associated with IPV. A woman with a partner drinking alcohol had a greater probability of experiencing IPV compared to a woman whose partner did not drink alcohol. The result is consistent with other results from previous works [10, 36, 43, 44]. This may be because alcohol alters cognitive and physical function, minimizes self-control, and leaves individuals incapable of having a non-violent resolution. A woman from a household with more members showed a negative association with IPV. A woman who considered beating from her partner as acceptable showed a higher prevalence of IPV. A woman who regarded beating from the partner as justified showed a higher probability of experiencing IPV. Similar findings were reported by Habyarimana et al. [15]. The findings discussed above are similar in both Uganda and South Africa. A woman who had witnessed her father beat her mother was positively associated with IPV, as she showed a higher prevalence of IPV. This may be because a woman witnessing abuse towards her mother may assume that it is justified for a man to beat a woman.

Fig. (3). Predicted probability of experiencing IPV by wealth index class and contraceptive use.

Fig. (4). Smooth function of the age of the woman (V012) (Source: Extracted from RStudio).

In comparison, the results from South Africa showed a woman more exposed to the media to have a higher probability of experiencing IPV. In contrast, in Uganda, a woman more exposed to the media showed a low probability of experiencing IPV. The region in which the woman lives is statistically significant to IPV, and the result is consistent with other studies [15], amongst others. In both countries, women from a household, where the head of the house was a female, were at a lower risk of experiencing IPV than women from a household where the head of the house was a male. This might be because in a household where the female is the head of the house, her partner is not always in the house. As the wealth index increases from the poorest, poorer, middle, richer, and richest, there is a lower probability of experiencing IPV. Similar results were found by Habyarimana et al. [10]. A woman who had terminated a pregnancy showed a higher prevalence of IPV. In Uganda, a woman who used contraceptives was at a greater risk of experiencing IPV. Similar results were reported by Habyarimana et al. [10].

In South Africa and Uganda, overweight and obese women showed a lower prevalence of IPV. A married woman or a woman living with a partner showed a higher prevalence of IPV. Similar findings were reported by Usta et al. [45]. In Uganda, the polygamy status was statistically significant; a woman whose partner had other wives/partners was at a higher risk of experiencing IPV. Similar findings regarding polygamy were reported by Jewkes et al. [26] and Koeing et al. [13]. In South Africa, a woman whose partner wanted more children was at a high risk of experiencing IPV; this could be because the partner will be persistent in having children even when the woman does not want to have any more children. A woman whose partner wanted fewer children compared to her was at a high risk of experiencing IPV, and also a woman who did not know regarding her partner's desire for children was at a high risk of experiencing IPV. In Uganda, a woman who did not know regarding her partner's desire for children was at a low risk of experiencing IPV. In Uganda, as the education level of a woman's partner increased, the chances of her experiencing IPV decreased. Similar findings were reported by Usta et al. [45] and Habyarimana et al. [10]. A woman from South Africa who had an employed partner was at a high risk of experiencing IPV. Usta et al. reported similar results [45]. In both the countries, an employed woman was at a high risk of experiencing IPV. A woman from South Africa whose partner was 25 years old and above was at a low risk of experiencing IPV.

In both countries, a woman whose expenditure was decided by the woman and the partner together was at a lower risk of experiencing IPV. Additionally, in Uganda, a woman whose spending was decided upon by her partner alone was at a low risk of experiencing IPV. The risk of the woman experiencing IPV increased with an increase in the woman's age. Similar findings were reported by Bonomi et al. [46] and Obi and Ozumba [16]. In South Africa, a woman who earned about the same as her partner, whose partner did not bring in his earnings, and a woman who did not know what her partner earned, were found to be positively associated with IPV. Similar findings have been recorded by Obi and Ozumba [16]. In Uganda, a woman who earned about the same as her partner and the one whose partner did not bring in his earnings were negatively associated with IPV. This implies that these women are at low risk of experiencing IPV. A woman from South Africa who knew regarding STIs was at a high risk of experiencing IPV. This could be due to the woman asking her partner to use protection in order to avoid infections.

CONCLUSION

This study aimed to find factors that influence intimate partner violence in South Africa and Uganda. In this study, the generalized additive mixed model was used. The factors which were found to be associated with IPV are as follows: whether the woman's father beat her mother or not, partner's alcohol consumption, the number of family members, the perception of the woman towards wife-beating attitude, access to the media, the region in which the woman resides, the sex of the household head, the combined wealth index, body mass index of the woman, current marital status, partner's desire for children, the woman's employment status, the person who usually decides on what to do with the woman's earnings, and the woman's earnings compared to her partner. The additional factors that have been reported for Uganda but have not been statistically significant in South Africa are as follows: the use of contraceptives, polygamy status, and the partner's education level. Furthermore, the following factors were statistically significant in South Africa and statistically insignificant in Uganda: the partner's age and women's knowledge of STIs.

The findings of this study could help government officials target specific communities where women are at a high risk of experiencing IPV based on the factors that are more relevant to them. Door-to-door campaigns aimed for women who are at greater risk of experiencing intimate partner violence could provide additional support and advice on how they can avoid or minimize the risk of being subjected to IPV. The study's key findings suggest an urgent need to enlighten the population through workshops, various social media platforms, and in schools or universities on avoiding high-risk factors for intimate partner violence. The study will help the government control and possibly reduce the high prevalence of IPV by advising women of conceptive age of the principal risks of IPV.

STUDY LIMITATIONS

The current work utilized the cross-sectional datasets from SADHS and UDHS; consequently, it could not take causality into account. Therefore, longitudinal studies are suggested to be carried out in order to solve this problem in future studies.

AUTHORS’ CONTIBUTION

T. Mhelembe analyzed and interpreted the data; contributed reagents, materials, analysis tools or data; and wrote the paper. S. Ramroop and F. Habyarimana contributed reagents, materials, analysis tools or data to the study.

ETHICAL STATMENT

The survey protocol was reviewed and approved by the SAMRC Ethics Committee and the ICF Institutional Review Board. The survey protocol, including biomarker collection, was reviewed and approved by the ICF Institutional Review Board.

CONSENT FOR PUBLICATION

Not applicable.

AVAILABILITY OF DATA AND MATERIALS

The data used in this study are second-hand and cross-sectional from the DHS database.

FUNDING

This work has been funded by Absa Bank Limited.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

The authors acknowledge Statistics South Africa, Uganda Bureau of Statistics, and the ICF through the DHS program for providing the data. The data supporting the findings of the article are available in the DHS database at www.dhsprogram.com.

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