1 Applying a Deviance Framework to Understand How Place Shapes

Applying a Deviance Framework to Understand How Place Shapes Modern Contraceptive

Use in Sub-Saharan Africa

Tamar Goldenberg 1, 2 and Rob Stephenson,2, 3

1Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI, 48109

2Center for Sexuality & Health Disparities, University of Michigan, Ann Arbor, 48109

3Department of Health Behavior and Biological Sciences, School of Nursing, University of Michigan, Ann Arbor, MI, 48109

ABSTRACT: Increasing modern contraception use is important for improving maternal and child health and achieving economic growth and development goals. However, high unmet need for modern contraception in Sub-Saharan Africa warrants new understandings of the drivers of modern contraceptive use. Using data from Demographic Health Surveys from 29 Sub-Saharan

African countries, we examine how a woman’s deviation from community norms is associated with modern contraception use. Stepwise analysis was conducted and then separate random- effects logistic regression models were fitted to examine relationships between modern contraception use and positive and negative deviance on socioeconomic characteristics, gender and fertility norms, and health knowledge and media exposure. Results suggest that deviance in all three domains is associated with modern contraception use; however, relationships between deviance and modern contraception use vary by variable and by place. Findings highlight the importance of using context-specific deviance approaches for improving public health interventions to increase modern contraception use.

INTRODUCTION: Increasing the uptake of modern contraception use is an important strategy for improving maternal and child health1,2 and is a pathway to achieving developmental goals.3,4

In many resource-poor settings, unmet need for modern contraception remains high.5 In Sub-

Saharan Africa, rates of modern contraception use have increased; however, the overall modern contraceptive prevalence is still low (at 33% in 2015, compared to 64% worldwide), with a high unmet need (24% in 2015, compared with 12% worldwide).6 While much research has attempted to identify community-level factors associated with modern contraceptive use, the continuing high unmet need for modern contraception in Sub-Saharan Africa warrants the need for new understandings of what facilitates modern contraceptive use in the region.

In recent years, there has been an increased focus on how place shapes modern contraceptive use.7-9 This research has found that community norms, especially norms around fertility and gender, play an important role in facilitating modern contraceptive use. For example,

Elfstrom and Stephenson found that community-level economic prosperity, gender norms, fertility norms, and community health knowledge were associated with modern contraceptive use in 21

African countries and that the effects of these community norms varied by country.9 Perceptions of community norms may also be important indicators of modern contraceptive use. Dynes et al. found that in Ethiopia and Kenya, perceptions of a community’s approval of family planning were an important factor associated with contraceptive use.8 In addition, this study found an association between modern contraceptive use and an individual’s deviation from what they perceived as ideal in the community (e.g., the likelihood of contraceptive use decreased if the perception of a community’s ideal for number of sons was greater than an individual’s actual number of sons). It may not be simply the characteristics of the community environment that shapes an individual’s

3 modern contraceptive use, but also how an individual deviates from their community’s norms and expectations.

Positive deviance is a useful framework that can be used to capture heterogeneity among women in a community, using a strengths-based lens to understand individuals who succeed despite being part of a vulnerable group.10,11 An increased understanding of the relationship between positive deviance and modern contraception use can inform public health programming and public health interventions by providing insight on how to improve modern contraception uptake among other women in the community.12,13 At the same time, understanding the relationship between negative deviance (i.e., women who do not succeed when other women in the community do) and modern contraception use can inform public health programming by highlighting the needs of harder-to-reach women who may be part of a vulnerable group despite living in communities where most women are not. In order to better understand how deviance from one’s community influences modern contraception use, this paper examines the relationships between both positive and negative deviance from community norms and women’s use of modern contraception in 29 Sub-Saharan African countries.

METHODS: The data used in this analysis are from the women’s questionnaire from the

Demographic and Health Surveys (DHS) from 29 countries in sub-Saharan Africa where DHS data were collected in or after 2010. The DHS uses clustered sampling to collect a nationally- representative sample of ever-married women of reproductive age (ages 15-49). The DHS uses a two-stage sampling design, first creating Primary Sampling Units (PSUs) based on the most recent census data and then interviewing 20-30 households from each PSU. All observations with missing data on any of the variables of interest were dropped from the sample.

Measures: Modern contraception use was measured using a dichotomous variable determining whether or not the respondent self-reports currently using a modern method of contraception (pill,

IUD, injections, condom, male or female sterilization, implant, or diaphragm/foam/jelly).

Deviance variables were created based on the difference between a woman’s individual response on a given variable and the aggregate community variable. Since community-level variables are not included in the DHS, we created aggregate variables to describe community effects, with each community being defined as one PSU. This is similar to what other studies have done when using DHS data to examine community effects on reproductive health outcomes.9,14

Aggregate variables were created by taking the mean (for continuous variables) or proportion (for dichotomous variables) for each variable of all of the responses in a given PSU. To calculate an individual’s deviance from her community, two separate strategies were used for continuous and dichotomous variables. For continuous variables, analysts first calculated the difference between each woman’s individual characteristic and her community’s mean for that variable. Then, the difference was standardized into z-scores. Women whose individual z-scores were one standard deviation above or below her community’s mean were considered to be deviant. For dichotomous variables, respondents were defined as deviant if they did not have a specific characteristic and the proportion of women in the community who had that characteristic was greater than or equal to the national average, as determined by the proportion of the entire sample (e.g., women who did not have secondary education, when the proportion of women in the community who had secondary education was greater than or equal to the national average) or if they did have a specific characteristic, when the proportion of women in the community who had that characteristic was less than the national average (e.g., women who had secondary education, when the proportion of

5 women in the community who had secondary education was less than the national average).

Women whose individual response matched the community norm were the reference group (e.g., women who had secondary education, when the proportion of women who had secondary education in the community was greater than or equal to the national average).

For this analysis, deviance variables are described as being positive or negative. Positive deviance is used to describe a respondent who has a “better” outcome than the average woman in her community and negative deviance describes a respondent doing “worse” than the average woman in her community. In some cases, deviation from a variable does not have a value of

“positive” vs. “negative.” For example, having an age at marriage that is much different than the community may have negative consequences, regardless if the woman is much younger or older than the community average. However, in these cases, analysts labeled one direction of deviance as “positive” and the other as “negative” in order to discuss the results succinctly.

In addition to the deviance variables, the analysis also considered individual and community level factors. At an individual level, the analysis controlled for age and setting (urban vs. rural). Additional variables included in the analysis were grouped as follows:

Socioeconomic Characteristics: Previous research suggests that at individual and community levels, increased income and education are associated with increased use of modern contraception 15,16. This may occur through increased knowledge about and access to modern contraception as well as an increase in reproductive decision-making power. A husband/partner’s educational and employment status may also be important, especially in a context in which men often make decisions; in this case, an increase in a husband’s education or employment status may also increase a woman’s autonomy and decision-making power.17,18

Individual-level socioeconomic characteristics included a respondent’s household wealth index score, the woman’s educational attainment, the husband/partner’s educational attainment, the woman’s current employment status, and the husband/partner’s current employment status.

The variable describing a husband/partner’s employment specifically measured whether or not the husband/partner was currently employed in the non-agricultural sector in order to focus on men who were receiving wages. Community-level variables measuring socioeconomic characteristics included the proportion of households in the community with the poorest wealth index, the proportion of women in the community who attained a secondary education or higher, the proportion of husbands/partners in the community who attained a secondary education or higher, the proportion of women in the community who were currently employed, and the proportion of husbands/partners in the community who were currently employed in the non-agricultural sector.

Deviance variables measuring socioeconomic characteristics included each respondent’s deviation from each of the community-level socioeconomic characteristics. For most of the socioeconomic characteristics (secondary education attainment, employment, husband/partner’s secondary education attainment, and husband/partner’s employment), positive deviance was defined as having the characteristic (e.g., being employed) when the proportion of women in the community with that characteristic is less than the national average. For these variables, negative deviance was defined as not having the characteristic (e.g., being unemployed) when the proportion of women who have that characteristic is greater than or equal to the national average.

However, for the variable describing whether or not a woman was in the poorest wealth index, positive deviance was defined as not being in the poorest wealth index when the proportion of women in the community in the poorest wealth index was greater than or equal to the national

7 average and negative deviance was defined as being in the poorest wealth when the proportion of women in the community in the poorest wealth index was less than the national average.

Gender and Fertility Norms: Attitudes towards gender and fertility may be important in determining a woman’s motivation and desire to use modern contraception and agency in accessing care.19,20 At an individual-level, variables for gender and fertility norms measured age at marriage, parity, intimate partner violence justification, the respondent’s ideal number of children, and whether or not the respondent wanted more boys than girls. Violence justification was measured using a five-point scale that is the sum of items asking respondents whether or not they believe violence is justified (0=no, 1=yes) based on five items: going out without telling the husband, neglecting the children, arguing with the husband, refusing sex with the husband, and burning food; lower scores indicate that violence is not justified. Wanting more boys than girls was defined as a respondent indicating that the ideal number of boys was higher than the ideal number of girls. Community-level gender and fertility norm variables included the community’s mean age at marriage, mean parity, mean violence justification score, mean ideal number of children, and the proportion of women who wanted more boys than girls.

For deviance variables, women were considered to be positively deviant if they were below the mean on parity, violence justification, and ideal number of children; women were categorized as negatively deviant if they were above the mean on these variables. For age at marriage, women were categorized as positively deviant if they were above the mean and negatively deviant if they were below the mean. For the proportional variable (wanting more boys than girls), women were considered to be positively deviant if they did not want more boys than girls, when the proportion of women in the community wanting more boys than girls was greater than or equal to the national average. Negative deviance was defined as women wanting more boys than girls when the

8 proportion of women in the community wanting more boys than girls was less than the national average.

Health Knowledge and Health Media Exposure: Knowledge about family planning may facilitate modern contraception use and general health knowledge and health media exposure may be reflective of the healthcare infrastructure in a given community.14 Individual-level variables included whether or not the respondent was visited by a family planning worker, whether or not the respondent had heard family planning messaging through the television, newspaper, or radio, and whether or not the respondent had knowledge of programmatic HIV prevention (including knowledge that using condoms and having fewer sexual partners reduces the risk of HIV transmission). The community-level variables measured the proportion of women in the community who had been visited by a family planning worker, heard family planning messaging, and had HIV knowledge. For all three variables, positive deviance was defined as a respondent reporting that they had the health knowledge or media exposure indicator when the community proportion was less than the national average on these variables. Negative deviance was defined as not having the health knowledge or media exposure indicator when the community proportion was greater than or equal to the national average.

Analysis: Data were analyzed using STATA 14 software package (College Station, Texas).

Twenty-nine separate random-effects logistic regression models were fit (one for each country).

In order to have a nuanced analysis examining which individual, community, and deviance variables were associated with modern contraceptive use across the different countries, we first fit a stepwise logistic regression model (one for each country) using all individual, community, and deviance variables. Then, we fit 29 separate random-effects logistic regression models using only

9 the significant variables for each country. The PSU was included as the random intercept, accounting for the hierarchical structure of the data and producing more accurate standard error estimates, avoiding erroneous results. The sigma-mu measured unobserved heterogeneity that exists after controlling for the variables included in the models. To determine the clustered nature of the data, we first fit multi-level models that only included the outcome and the random intercept; a significant random effect signified that the outcome varied significantly by PSU, illustrating the need to take into account the clustering of the data. A significance level of 0.05 was used to determine statistical significance.

RESULTS: Across the 29 countries, the samples ranged from 2,906 respondents in Comoros to

24,102 in Nigeria (Table 1). Modern contraception use also varied across countries, ranging from

5.25% in Guinea to 56.53% in Namibia. In most countries (excluding the Democratic Republic of Congo, Mali, Niger, Rwanda, and Senegal), deviance variables were statistically significant and were included in each model (in addition to individual-level and community-level variables) for all other countries. Tables 2-4 include the results for the significant deviance variables for all 29 countries; since these variables were categorical, both negative and positive deviance were included in the models, even if only one aspect of deviance was statistically associated with modern contraceptive use. Only statistically significant results are presented here.

Socioeconomic Characteristics: For Eastern Africa (Table 2), being positively deviant on women’s education was associated with an increase in modern contraception use in Tanzania (OR:

2.06, p=0.001), while in Ethiopia (OR: 1.30, p=0.005) and Kenya (1.42, p<0.001) being positively deviant on women’s employment was associated with an increase in modern contraceptive use.

For Central and Southern Africa (Table 3), negative deviance on being in the poorest wealth index was associated with increased modern contraceptive use in Zambia (OR: 1.48, p=0.020), while

10 negative deviance on women’s employment in Zambia was associated with a decrease in modern contraceptive use (OR: 0.84, p=0.017). Zimbabwe had different results, with positive deviance on husband’s employment being associated with a decrease in modern contraceptive use (OR: 0.83, p=0.004). In Mozambique, negative deviance on husband’s education was associated with increased modern contraceptive use (OR: 1.30, p=0.026) and in Congo (OR: 1.25, p=0.013) and

Namibia (OR: 1.25, p=0.031), positive deviance on husband’s education was associated with increased modern contraceptive use. Across the ten countries in Western Africa, three included deviance on husband’s education in their models (Tables 4 and 5). In Benin (OR:1.34, p=0.019),

Nigeria (OR: 1.51, p<0.001), and Sierra Leone (OR: 1.31, p=0.016) having a husband with more education than the community was associated with increased modern contraceptive use.

Gender and Fertility Norms: Six of the seven countries in Eastern Africa had regression models that included deviance variables representing gender and fertility norms. In Burundi (OR:

0.76, p=0.025) and Malawi (OR: 0.84, p=0.001), marrying at a younger age than other women in the community was associated with a decrease in modern contraceptive use, but in Kenya, marrying at a younger (OR: 0.84, p=0.015) and older (OR: 0.81, p=0.005) age than the other women in the community were both associated with a decrease in modern contraceptive use. In

Malawi, wanting more male children than female children when other women in the community did not have this preference was associated with an increase in modern contraceptive use (OR:

1.26, p=0.013), while not having this preference when other women in the community did was associated with a decrease in modern contraceptive use (OR: 0.90, p=0.011). Similarly, in Uganda, not wanting more male children than female children when other women in the community did have this preference was associated with a decrease in modern contraceptive use (0.83, p=0.008).

In addition, in both Comoros (1.80, p=0.001) and Ethiopia (OR: 1.29, p=0.002), justifying violence

11 less than other women in the community was associated with an increase in modern contraceptive use.

Results in Central and Southern Africa were similar to those in Eastern Africa. In Zambia, marrying younger than other women in the community was associated with a decrease in modern contraceptive use (OR: 0.82, p=0.002) and, in Mozambique, justifying violence less than other women in the community was associated with increased modern contraceptive use (OR: 1.42, p=0.013). Zimbabwe, however, had unique results; in this country, wanting fewer children than the community average was associated with a decrease in modern contraceptive use (OR: 0.78, p=0.025).

In Western Africa, having fewer children than other women in the community was associated with a decrease in modern contraceptive use (Sierra Leone OR: 0.62, p=0.023; Togo

OR: 0.52, p=0.009). In Côte d’Ivoire, wanting more children than the other women in the community was also associated with a 50% decrease in modern contraceptive use (OR: 0.51, p<0.001). In Ghana, marrying older than other women in the community was associated with a decrease in modern contraceptive use (OR: 0.77, p=0.020). In addition, in Ghana, justifying violence more than other women in the community was associated with an increase in modern contraceptive use (OR: 1.25, p=0.028). However, in Benin, justifying violence less than other women in the community was also associated with an increase in modern contraceptive use (OR:

1.50, p=0.017). In Benin, having a preference to have more male children than female children when other women in the community do not have that preference was associated with a decrease in modern contraceptive use (OR: 0.70, p=0.038).

Health Knowledge and Health Media Exposure: For countries in Eastern Africa, being positively deviant on hearing family planning messages was associated with an increase in modern

12 contraceptive use by approximately 35% (Ethiopia: p<0.001; Kenya: p<0.001). In addition, in

Ethiopia, having less HIV knowledge than other women in the community was associated with an increase in modern contraceptive use (OR: 1.32, p=0.011), but in Comoros, having more HIV knowledge than other women in the community was associated with an increase in modern contraceptive use (OR: 1.41, p=0.012). In Central and Southern Africa, not having a visit from a family planning worker when other women in the community did was associated with an approximate 20% decrease in modern contraceptive use in Namibia (OR: 0.79, p=0.006). In

Cameroon, being negatively deviant on hearing family planning messages was associated with an increase in modern contraceptive use (OR: 1.42, p=0.012). In addition, in Gabon, being positively deviant on HIV knowledge was associated with an increase in modern contraceptive use (OR:

1.22, p=0.020); however, in Zambia, being positively deviant (OR: 0.89, p=0.044) on this variable was associated with a decrease in modern contraceptive use. For countries in Western Africa, in

Burkina Faso (OR: 1.40, p=0.002), The Gambia (OR: 1.33, p=0.041), and Liberia (OR: 1.31, p=0.008), being negatively deviant on being visited by a family planning worker was associated with a 30-40% increase in modern contraceptive use; however, in Nigeria, being positively deviant on this variable was also associated with an increase in modern contraceptive use (OR: 1.41, p=0.016). In Côte d’Ivoire (OR: 1.84, p<0.001), The Gambia (OR: 1.49, p=0.007) and Liberia

(OR: 1.27, p=0.011), being positively deviant on hearing family planning messages was also associated with an increase in modern contraceptive use, while in Guinea, being negatively deviant on hearing planning messages was associated with a decrease in modern contraceptive use (OR:

0.70, p=0.026). In Guinea, being negatively deviant on HIV knowledge was also associated with a decrease in modern contraceptive use (OR: 0.53, p=0.025), while being positively deviant on

HIV knowledge was associated with an increase in modern contraceptive use (OR: 2.18, p<0.001).

After controlling for all of the variables in the models, the community-level random effects were significant in all countries except for Benin and Guinea.

DISCUSSION: Findings suggest that positive and negative deviance on socioeconomic characteristics, gender and fertility norms, and health knowledge and health media exposure are associated with modern contraception use. The relationship between deviance and modern contraceptive use varies across variables and countries, but can be summarized as occurring because of socioeconomic advantage/disadvantage, women’s autonomy, individual vs. community fertility preferences, and access to healthcare services.

Socioeconomic Advantage/Disadvantage and Women’s Autonomy: In many cases, positive deviance on socioeconomic characteristics was associated with increased use of modern contraception and negative deviance was associated with decreased use. These relationships may be explained by socioeconomic advantage. For example, within an environment where most women do not work, being employed may represent additional socioeconomic privileges that could help to increase a woman’s ability to access modern contraceptives. This socioeconomic advantage may also be related to women’s autonomy and women’s fertility preferences. For example, being positively deviant on women’s employment or women’s education may be associated with an increase in women’s autonomy; this can influence decision-making about modern contraceptive use and access to care.19,21,22 Furthermore, women who work more or who have more education than other women in the community may start childbearing at a later age or may have additional goals for education or employment that may conflict with childbearing; in an environment where

14 most women do not work or have secondary education, there may be fewer opportunities to have children while simultaneously attaining education or working.

The relationship between positive deviance on husband’s education and modern contraceptive use may also be explained by socioeconomic advantage. In addition, husbands may be involved in decision-making around modern contraceptive use23,24 and men who are positively deviant on this variable may be more accepting of women’s decision-making or may be more accepting of modern contraceptive use. At the same time, the significant relationship between negative deviance on women’s employment and decreased modern contraceptive use may be explained by socioeconomic disadvantage, resulting in an inability to access modern contraceptives. In this case, it is not simply being unemployed that decreases use, but rather being unemployed within an environment where it may be more socially acceptable for women to work.

Many of the relationships between gender and fertility norms and modern contraceptive use can also be explained by women’s autonomy. For example, women who justify violence less than other women in their communities or women who do not have a preference to have more boys than girls when other women in the community do may have increased autonomy. The positive relationships between these variables and modern contraceptive use suggest that increased autonomy may influence modern contraceptive use. Previous research already demonstrates that there is a strong relationship between women’s autonomy and modern contraceptive use at an individual level.19,21,22 However, this study suggests that the relationship between women’s autonomy and modern contraceptive use occurs even when women live in places where most women have less autonomy. On the other hand, being negatively deviant on gender and fertility norm variables may be a proxy for reduced autonomy, which may decrease modern contraceptive

15 use. Reduced autonomy may increase a woman’s vulnerability and reduce her ability to make decisions about modern contraceptive use.

Individual vs. Community Fertility Preferences: Some of the relationships between deviance on fertility norms and modern contraception use can be explained by how an individual’s fertility preferences or practices differ from other women in the community. Previous research suggests that women may attempt to match their own fertility ideals to the norms of the places where they live 8. This may explain the relationships between having or wanting fewer children than other women in the community and decreased modern contraceptive use. If a woman has fewer children than other women in her community, she may decrease her use of modern contraception in order to be less deviant from her community. Similarly, if a woman wants fewer children, but lives in a community where women desire to have more children, social norms may encourage not using modern contraceptives. In this case, despite wanting fewer children than other women in the community, a woman may choose to have more children in order to be less deviant from her community. At the same time, individual fertility preferences still play an important role in fertility practices; for example, the significant relationship between wanting more children than other women in the community and decreased modern contraceptive use reflects an individual’s preferences to attain their ideal parity.

A community’s fertility preferences may also influence modern contraceptive use for women who marry older than other women in their community. Marrying older than other women in a community may be associated with decreased modern contraceptive use because these women have a shorter time period during which they are able to have children; this may be especially relevant in communities where women have higher parity.

Access to Care: Access to health care is an important aspect of attaining modern contraceptives, and, depending on the contraceptive method, use sometimes requires continuous access to care.25,26 Being positively deviant on the health knowledge and media exposure variables may suggest an increased ability to access care. Since public health messages and programming are generally distributed at a community level, women who are positively deviant on these variables would need to employ additional effort or use additional resources in order to gain access to family planning or HIV education, messaging, or services. This additional effort or resources may facilitate increased use of modern contraception. On the other hand, negative deviance on these variables may indicate that a woman has especially reduced resources or ability to access health services, which may decrease the ability to access modern contraception.

Unexpected Results: In some cases, results were surprising, with negative deviance on socioeconomic characteristics, gender norms, and health knowledge and media exposure being associated with an increase in modern contraceptive use. This may be explained by endogeneity in which women with background characteristics indicative of low modern contraceptive use (i.e., socioeconomic disadvantage, lower autonomy) actually have high use because these women are specifically targeted by family planning programming.

Another surprising result occurred in Zimbabwe, where being positively deviant on husband’s employment was associated with reduced modern contraceptive use. Women who have husbands with more resources than the average man in the community might typically be expected to have improved access to care and modern contraception. However, it may be that being positively deviant on husband’s employment results in having husbands who spend more time away from the family home, which could influence decisions about fertility and contraceptive use.

Similarly, In Zambia, it is surprising that having more HIV knowledge than other women in the

17 community was associated with a decrease in modern contraceptive use. Having more HIV knowledge would suggest having more access to health education or resources that increase knowledge. Further investigation for these results is warranted.

Not receiving a visit from a family planning worker when other women in the community did was associated with increased modern contraceptive use in three countries (Burkina Faso, The

Gambia, and Liberia); this result is unexpected, since visits from a family planning worker are intended to increase modern contraceptive use. In these cases, it is possible that negative deviance may suggest that a woman does not need to be visited by a family planning worker, depending on the type of modern contraception she is using (e.g., sterilization). Since the data are cross-sectional, it is not possible to test this temporal relationship, but further examination of this relationship could help elucidate this finding.

Patterns Across Places: Three place-specific patterns were found in the results: 1) Results were consistent across countries; 2) Negative and positive deviance were associated with increased modern contraceptive use in some countries and decreased use in others; and 3) Across countries

(or in some cases, within the same country), negative and positive deviance were both associated with increased modern contraceptive use or decreased modern contraceptive use. These patterns suggest that the relationship between deviance and modern contraceptive use is context-specific, likely varying depending on local social norms. Furthermore, cases where negative and positive deviance were both associated with an increase or decrease in modern contraceptive use suggest that, for these variables, it is the experience of being deviant at all that may influence modern contraceptive use.

There were some limitations in this study. These findings were cross-sectional, so no causal pathways could be concluded. It is possible that aggregate variables do not fully capture community effects; however, the DHS does not collect community-level data, so community-level deviance variables were limited to the use of aggregate variables. Furthermore, PSUs were used to define communities. Each PSU consists of 20-30 households in a geographical region, limiting the definition of community. PSUs are artificial sampling units and may not necessarily reflect what a person thinks of as their community. In addition, regions varied in size and some PSUs contained fewer than ten respondents. However, analysis showed that the outcomes did vary significantly by PSU, suggesting these areas have an influence on outcomes, and model checking found no instability due to small number of respondents in some PSUs. Similar methods have been used in other analyses using DHS data to examine community effects.9,27 In addition, even though most DHS questions are consistent across countries, there were some missing variables for four countries (Congo, Gabon, Mali, and Uganda), causing these four models to be slightly different than the others. Furthermore, despite controlling for individual-level, community-level, and deviance variables, there was still unobserved heterogeneity in most of the models. Other important factors not collected by DHS may explain the community-level random effects. Most notably, in most countries, DHS does not collect data on the existence or quality of health services, which may play a very important role in a woman’s ability to access and use modern contraception.

CONCLUSIONS: Taken together, these findings highlight the importance of using a deviance approach when examining women’s use of modern contraception. A deviance lens is important as it examines health outcomes when an individual’s experiences are inconsistent with the experiences of other women in their community. The current study emphasizes the importance of

19 moving beyond examining only individual- and community-level determinants of modern contraception use, and urges programs to understand not only the context in which individuals live, but also the relative position of individuals in their community. An examination of positive and negative deviance allows for an understanding of how women differ from their communities and the role that this plays in modern contraception use. When understood within country-specific contexts, these experiences of deviance can provide insight into additional programmatic needs and can inform interventions that aim to increase the use of modern contraception.

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Table 1: Modern Contraception Use and Total Sample Size across 29 Countries Year Total Sample Size Use of modern Contraception % (n) Eastern Africa Burundi 2012 5,676 17.42% (989) Comoros 2012 2,906 13.66% (397) Ethiopia 2011 9,908 24.17% (2,395) Kenya 2014 9,970 46.18% (4,604) Malawi 2010 17,707 39.54% (7,002) Mozambique 2011 9,741 14.46% (1,426) Rwanda 2014-2015 8,088 42.88% (3,468) Tanzania 2010 7,159 25.03% (1,792) Uganda 2011 6,044 24.09% (1,456) Zambia 2013-2014 10,590 41.41% (4,385) Zimbabwe 2010-2011 6,490 51.83% (3,364) Middle Africa Cameroon 2011 4,414 15.29% (675) Congo 2011-2012 7,120 17.28% (1,230) DRC 2013-2014 11,860 7.32% (868) Gabon 2012 4,514 24.17% (2,395) Southern Africa Namibia 2013 3,039 56.53% (1,718) Western Africa Benin 2011-2012 10,611 8.25% (875) Burkina Faso 2010 13,215 15.58% (2,059) Côte d’Ivoire 2011-2012 5,715 13.04% (745) Gambia 2013 6,633 7.90% (524) Ghana 2014 5,829 22.42% (1,307) Guinea 2012 5,920 5.25% (311) Liberia 2013 6,141 19.92% (1,223) Mali 2012-2013 7,100 12.27% (871) Niger 2012 7,827 14.67% (1,148) Nigeria 2013 24,102 10.89% (2,625) Senegal 2014 4,288 17.65% (757) Sierra Leone 2013 10.012 17.87% (1,789) Togo 2013-2014 6,443 17.04% (1,098)

Table 2: Multilevel Logistic Regression Results in Eastern Africa* Burundi Comoros Ethiopia Kenya Malawi Tanzania Uganda Socioeconomic Characteristics 2.06 PD on women’s education ------(1.37,3.12) 1.30 1.42 PD on women’s employment ------(1.08,1.56) (1.24,1.62) Gender and Fertility Norms 0.76 0.84 0.84 ND on age at marriage ------(0.60,0.97) (0.73,0.97) (0.76,0.93) 0.81 PD on age at marriage ------(0.71,0.94) 1.80 1.29 PD on IPV justification ------(1.28,2.52) (1.10,1.52) ND on fertility preference for 1.26 ------wanting more boys than girls (1.05,1.51) PD on fertility preference for 0.90 0.83 ------wanting more boys than girls (0.83,0.98) (0.72,0.95) Health Knowledge and Health Media Exposure PD on hearing family planning 1.37 1.34 ------messaging (1.15,1.63) (1.17,1.55) 1.32 ND on HIV knowledge ------(1.06,1.63) 1.41 PD on HIV knowledge ------(1.08,1.85) *PD refers to positive deviance and ND refers to negative deviance

Table 3: Multilevel Logistic Regression Results in Central and Southern Africa* Cameroon Congo Gabon Mozambique Namibia Zambia Zimbabwe Socioeconomic Characteristics ND on poorest wealth index 1.48 ------(1.06,2.06) ND on husband’s education 1.30 ------(1.03,1.63) PD on husband’s education 1.25 1.25 ------(1.05,1.50) (1.02,1.53) ND on woman’s employment 0.84 ------(0.72,0.97) PD on husband’s employment 0.80 ------(0.69,0.93) Gender and Fertility Norms ND on age at marriage 0.82 ------(0.71,0.93) PD on IPV justification 1.42 ------(1.08,1.88) PD on ideal number of children 0.78 ------(0.63,0.97) Health Knowledge and Health Media Exposure ND on family planning worker 0.79 ------visit (0.66,0.93) ND on hearing family planning 1.42 ------messaging (1.08,1.86) PD on HIV knowledge 1.22 0.89 ------(1.03,1.44) (0.79,0.99) *PD refers to positive deviance and ND refers to negative deviance

Table 4: Multilevel Logistic Regression Results in Western Africa* Benin Burkina Faso Côte d’Ivoire Gambia Ghana Socioeconomic Characteristics PD on husband’s education 1.34 (1.05,1.72) ------Gender and Fertility Norms PD on age at marriage ------0.77 (0.62,0.96) ND on IPV justification ------1.25 (1.02,1.52) PD on IPV justification 1.50 (1.08,2.08) ------ND on ideal children number of children -- -- 0.51 (0.39,0.67) -- -- ND on fertility preference for wanting more 0.70 (0.50,0.98) ------boys than girls Health Knowledge and Health Media Exposure ND on family planning worker visit -- 1.40 (1.14,1.72) -- 1.33 (1.01,1.76) -- PD on hearing family planning messaging -- -- 1.84 (1.36, 2.49) 1.49 (1.11,1.99) -- *PD refers to positive deviance and ND refers to negative deviance

Table 5: Multilevel Logistic Regression Results in Western Africa, continued* Guinea Liberia Nigeria Sierra Leone Togo Socioeconomic Characteristics PD on husband’s education -- -- 1.51 (1.22,1.87) 1.31 (1.05,1.64) -- Gender and Fertility Norms PD on parity ------0.62 (0.40,0.94) 0.52 (0.32,0.85) Health Knowledge and Health Media Exposure ND on family planning worker visit -- 1.31 (1.07,1.61) ------PD on family planning worker visit -- -- 1.41 (1.07,1.87) -- -- ND on hearing family planning messaging 0.70 (0.48,0.95) ------PD on hearing family planning messaging -- 1.27 (1.05,1.52) ------ND on HIV knowledge 0.53 (0.30,0.92) ------PD on HIV knowledge 2.18 (1.61,2.96) ------*PD refers to positive deviance and ND refers to negative deviance