On condoms in Kenya.

Kenya

 Are condom advertisements targeting widowed Kenyan women?

I recently used the Kenya Demographic Health Survey (a nationally representative data set) and ran some numbers to see how condom marketing influences use amongst women. The findings were pretty bizarre. I was just interested to see if condom marketing actually translates in to condom use. As I was finishing up my study the Kenyan government put out this interesting condom commercial that was censored almost immediately.

Here’s what I found: 

Women, who are widowed, divorced, or not living with their partner and who were exposed to condom messaging in magazines had significantly greater odds of using condoms as a current form of contraception. Exposure to condom advertising on the radio, T.V, and billboards had no significant effects. This study uses a sub-sample of 4 155 Kenyan women who have been sexually active in the past four weeks. Don’t take this at face value though, have a read through my data, definition, and methods (and because this is super academicy, since I wrote it for university, skip to the tables and jump to “explanation of the numbers” : p ).

Where’d I get my data? How are my variables defined?

The data used comes from the 2008-2009 Kenya Demographic and Health Survey (KDHS). This is nationally representative dataset that provides updated information on the health situation in Kenya. The 2008-2009 KDHS is the fifth national survey conducted in Kenya. A sample of 4 155 women, who have been sexually active in the past four weeks is examined.

Outcome variable (Y. Variable)

The outcome variable is a binary variable, of those individuals who have been sexually active in the past four weeks and use a condom (coded 1) and those individuals who have been sexually active in the past four weeks and use other or no contraception (coded 0). This variable is labeled “condomnow”

(1) This variable was created by combining two variables: (a) What is your current contraceptive method? And the responses are: not using, the pill, IUD, injections, condom, female sterilization, male sterilization, periodic abstinence, withdrawal, other, norplant, lactional amenorrhea, and female condom. (b) Recent sexual activity, by timing of last sexual intercourse. And the responses are: never had intercourse, active in last four weeks, have not been active in the last four weeks due to postpartum, and have not been active in the last four weeks.

(2) The variable was created on selecting only those women who have been sexually active in the past four weeks. This was done because the condom-messaging questions ask if the participant has seen the messaging in the past several months. Therefore, this sample of women is selected to account for this exposure.  I am inferring that condoms were used after the participant was exposed to condom messaging.

Predictor Variables (X. Variables)

In addition to the combining and recoding noted below, please note that a new variable was created for each predictor variable that only included the sample of women that have been sexually active in the past four weeks.

(1) Main predictors

Condom advertising is done through four mediums: radio, T.V, magazines, newspapers, and billboards. There are four variables that ask participants if they have been exposed to condom messages through any of these platforms. The four variables are of main interest and will help answer the research question.

(a) Condomrad: heard about condoms on the radio in the past several months. 2 missing values were dropped. Variable was recoded, 0=no and 1=yes.

(b) Condomt: heard about condoms on T.V in the past several months. 2 missing values were dropped. Variable was recoded, 0=no and 1=yes.

(c)Condomag: heard about condoms in the newspaper or magazine in the past several months. 2 missing were dropped values were dropped. Variable was recoded, 0=no and 1=yes.

(d) Condobill: heard about condoms on billboard(s) in the past several months. 2 missing values were dropped. Variable was recoded, 0=no and 1=yes.

(2) Control variables

Five control variables of scientific importance were selected: age, region, education, religion, and marital status.

(a) Age, ranges from 15 – 49. Age was selected since condom use may vary by age. Variable name: v012.

(b) Region, was selected given the differences in HIV prevalence across Kenya. Since condoms provide some protection against HIV, perhaps condoms are used more aggressively in high risk regions. Furthermore, perhaps condom advertising is done more aggressively in high risk regions.

Region was combined in to three categories based on current H.I.V prevalence. The original variable has eight categories. Low risk region, this includes Eastern and North Eastern Kenya where H.I.V. prevalence is low around 0.9-3.5%. Medium risk region,  This includes Rift Valley, Central, Coast, Nairobi, and Western. H.I.V. prevalence ranges from 4.2-7%. Highrisk region, this includes Nyanza which has H.I.V. prevalence of about 14%. (KDHS Fact Sheet). Categories were merged to address the “perfect predictor” error in the logistical regression. After the error was displayed I noticed that certain regions had very few observations. As a result categories were combined and the error was managed. Variable name: region11.

(c) Education is included to control for the impact greater education has on knowledge of contraception. Variable name: educ.

(d) Religion is included to control for the impact religious teachings have on contraceptive use. Typically, Judeo-Christian and Islamic faith advocate against the use of condoms and other safe sex measures.

Religion was recoded in to four categories. “No religion” and “other religion” were combined. The original variable has five categories. The following categories are in the final variable: Catholic, Protestant/Other Christian, Muslim, and No religion/Other. Categories were merged to address the “perfect predictor” error in the logistical regression. After the error was displayed I noticed that certain categories had too few respondents (i.e. no religion and other religion). As a result categories were combined and the error was managed. Variable name: relig.

(c) Marital status, is used as a proxy for: (i) women trying to get pregnant[1] [i.e. not using condoms or other contraception], (ii) women using it as contraception, and (iii) women [or their partner] using it as H.I.V. protection who are having extramarital affairs. To my knowledge there is no question that asks if a woman is trying to conceive. Marital status is used as a proxy.

Marital status was recoded in to three categories. (i) Married and living together were combined to create a new category. (ii) Divorced, widowed, and not living together were combine to create another new category. The original variable had six categories. The following categories are in the final variable: never married, married/living together, divorced, widowed, and not living together. Categories were merged to address the “perfect predictor” error in the logistical regression. After the error was displayed I noticed that certain categories had too few respondents (i.e. not living together, and widowed). As a result categories were combined and the error was managed. Variable name: maritalstatus.

(3) Other variables of interest

(a) Media awareness related variables: The following variables were included to control for the frequency of media encountered. For instance, a person who watches T.V. almost every day has a higher probability of being exposed to condom advertising on T.V.

(i) Frequency of reading newspaper or magazine. This variable has four categories: not at all, less than once a week, at least once a week, and almost every day.  Nine missing values were dropped. Variable name: reading.

(ii) Frequency of listening to radio.  This variable has four categories: not at all, less than once a week, at least once a week, and almost every day.  Nine missing values were dropped. Variable name: listening.

(iii) Frequency of watching television.  This variable has four categories: not at all, less than once a week, at least once a week, and almost every day.  Nine missing values were dropped. Variable name: watching.

(b) Risk related variables: The following variables were included to control for knowledge about H.I.V/AIDS and perceived risk of H.I.V/AIDS.

(i) To avoid H.I.V/AIDS: abstain from sex. Responses: yes, no. Two missing values were dropped. Variable name: abstainhivkn.

(ii) To avoid H.I.V/AIDS: use condoms. Responses: yes, no. Two missing values were dropped. Variable name: condomshivkn.

(iii) To avoid H.I.V/AIDS: limit sex to one partner.  Responses: yes, no. Two missing values were dropped. Variable name: sexparthivkn.

(iv) To avoid H.I.V/AIDS: limit number of sex partners.  Responses: yes, no. Two missing values were dropped. This variable was not used in the final model.

(v) To avoid H.I.V/AIDS: avoid sex with prostitutes. Responses: yes, no. Two missing values were dropped. Variable name: prostithivkn.

(vi)To avoid H.I.V/AIDS: avoid sex with persons who have many partners. Responses: yes, no. Two missing values were dropped. This variable was not used in the final model.

(vii) To avoid H.I.V/AIDS: avoid sex with drug users. Responses: yes, no. Two missing values were dropped.  This variable was not used in the final model.

(viii) Do you think your chances of getting aids are:  small, moderate, great, no risk, and has aids. Variable name: hivrisksr.

Condoms

Methods: What models did I use? What steps did I take to determine which variables I kept and discarded? 

Model One: Logistic Regression The following steps were taken before the main model was ran:

(1) The outcome variable, condomnow, was run individually with all predictor variables.

(2) All predictor variables that had at least one significant category (p <0.1) were kept. The following three variables were not included in the main model since they were insignificant (p >0.1): (i) To avoid H.I.V/AIDS, limit number of sex partners. (ii) To avoid H.I.V/AIDS, avoid sex with persons who have many partners. (iii) To avoid H.I.V/AIDS,  avoid sex with drug users.

(3) A final simultaneous logit model was run.

Model Two: Interaction, condom advertising (magazines)*marital status I was interested in running an interaction model with the main predictor variables and marital status. I hypothesized that marital status may moderate the outcome variable through condom advertising since, despite exposure, those married may not be motivated to use condoms.

(1) I first ran the interaction with all condom advertising variables [condomrad, condomt, condomag, and condobill].

(2) I re-ran the model with only the significance interaction term to see if the significance increases when the other interaction terms were eliminated.

My Findings! 

Table 1. Model One: Logistic Regression

Variable

Odds Ratio

P-value 95

% CI of OR

Age

.9950047

0.674

.9720727    1.018478

Condom advert: radio, “yes”

1.013352

0.961

.5926867    1.732588

Condom advert: T.V, “yes”

.7396809

0.285

.4257157    1.285195

Condom advert: Magazine or Newspapers, “yes”

1.437571

0.173*

.8529596    2.422868

Condom advert: Billboards, “yes”

.954343

0.847

.593152    1.535476

Region (reference: low risk region)
Moderate risk

1.106659

0.652

.7122071    1.719576

High Risk

2.22228

0.004**

1.292429    3.821119

Religion (reference: Catholic)
Christian

1.259974

0.336

7866594     2.01807

Muslim

1.502888

0.291

.706028    3.199124

Other/No Religion

3.245987

0.009**

1.345184    7.832708

Marital Status (reference: never married)
Married

.0937687

0.000**

.0597601    .1471312

Divorced, widowed, not living together

.4332301

0.025**

.2082212    .9013891

Educational Attainment  (reference: no education)
Incomplete

3.684843

0.023**

1.193985    11.37205

Complete Primary

4.38218

0.012**

1.376833    13.94759

Incomplete Secondary

5.368241

0.009**

1.518912     18.9728

Complete Secondary

6.334549

0.003**

1.861473    21.55632

Higher

12.3398

0.000**

3.53051    43.12992

Frequency of reading newspapers or magazines (reference: Not at all)
Less than once a week

.7012988

0.208

.403866     1.21778

At least once a week

.7915805

0.424

.4462527    1.404137

Almost every day

.9971847

0.994

.4858858    2.046525

Frequency of listening to the radio (reference: Not at all)
Less than once a week

.6303513

0.334

.2472805    1.606851

At least once a week

1.087898

0.808

.5507309    2.149003

Almost every day

.7817129

0.447

.414242    1.475165

Frequency of watching T.V. (reference: Not at all)
Less than once a week

1.166324

0.653

.5966818    2.279796

At least once a week

1.451798

0.275

.7433509    2.835428

Almost every day

1.555877

0.135*

.8708003    2.779917

Avoid H.I.V: abstain from sex, yes

.9451957

0.763

.6557427    1.362417

Avoid H.I.V: use condoms, yes

1.869236

0.001**

1.2872    2.714452

Avoid H.I.V: limit sex to one partner, yes

.5447123

0.001**

379951    .7809204

Avoid H.I.V: avoid having sex with prostitute, yes

.5023417

0.254

.1539712    1.638925

Self reported chances of getting aids (reference: small)
moderate

.8710481

0.526

.5683542    1.334951

great

.97287

0.914

.5894395    1.605722

No risk at all

1.006857

0.983

.5405927    1.875278

Has aids

8.229691

0.003**

1.999867    33.86615

Table 2. Model Two: Interaction Term condom advertising (magazine)*marital status

Condom Messaging in Magazines (“yes”) X Marital Status (reference: never married)
Married

1.611478

0.249

.7157756     3.62804

Divorced, Widowed, Not living together

4.573016

0.044**

1.042091     20.0678

*marginally significant

**Significant, p<0.05

KenyaCatholic

(a bucket load of controversy around the above billboard)

Explanation of the Numbers: 

Main Predictor Variables

Exposure to condom advertising on radio, T.V., and on billboards had no significant effect on condom use. Exposure to condom advertising in magazines and newspapers had a marginally significant effect (p=0.173). Those who heard about condoms through magazines and newspapers were had 1.4 X greater odds of using condoms as their current form of contraception relative to those who were no exposed.

Control Variables

Region: Those living in a high risk area [Nyanza] had 2.2X greater odds of using condoms as a current form of contraception relative to those who lived in a safe low risk region. This is significant at the 5% level.

Religion: Those who practice other religions or do not practice any religion have 3.25 greater odds of using a condom as their current form of contraception compared to Catholics. This is significant at the 5% level. Muslims, and Christians also have greater odds of using condoms as a current form of contraception relative to Catholics, however these findings are not significant.

Marital Status: Those who are currently married have 90.6% lesser odds of using a condom as a current form of contraception compared to those who have never been married. Those who are currently divorced, widowed, and not living together have 56.7% lesser odd of using a condom as their current form of contraception compared to those who have never been married. Both of these findings are significant at the 5% level.

Education: Overall more education, systematically increases the odds of the participants of using a condom as a current form of contraception compared to those who have no education. Those who have completed higher than secondary education have 12X greater odds of using a condom as a current form of contraception compared to those who have no education. These findings are all significant at the 5% level.

Other Variables of Interest

Media awareness related variables Those participants who watch T.V almost everyday have 1.6X greater odds of using a condom as their current form of contraception relative to those who do not watch any T.V. This is marginally significant (p=0.135). No other media awareness related variables are significant. Despite this, interestingly, increased frequency of reading newspapers and magazine does not increase the odds of using a condom. This is not a significant finding.

Risk related variables Those who believe that to avoid H.I.V/AIDS “use a condom” have 1.8X greater odds of using a condom as a current method of contraception relative to those who do not believe this. This is significant (p = 0.001). Those who believe that to avoid H.I.V/AIDS you must limit sex to one partner have about 46% lesser odds of using a condom as their current form of contraception. Last, those individuals who currently have aids have 8.2X greater odds of using a condom as their current form of contraception relative those who believe their chances of getting HIV/AIDS is small. This is significant (p=0.003).

Table 2 presents the findings of the interaction term: exposure to condom messaging in magazines X marital status. Those participants that were exposed to condom messaging and are divorced, widowed, and not living together have about 5X greater odds of using a condom as their current form of contraception relative to those who are never married. This is significant at the 5% level.

Discussion

The research question asked at the onset of this study is: does condom messaging influence condom use? And the response is an underwhelming not really. Those who were exposed to condom messages in magazines and newspapers had marginally greater odds to use condoms as a current form of contraception compared to those who were not exposed. One speculation is the private nature of reading. In general magazine and newspaper ads, compared to radio, T.V. and billboard ads, is the only condom messaging consumed in private. You are more likely to watch T.V., listen to the radio, and look at billboards in public with other people, perhaps even with family and friends. Since there is a degree of embarrassment associated with condoms, perhaps “public ads” are ignored. In private, however, the participant may be more likely to thoroughly read the ad and understand the messaging.

Interestingly, the interaction term [condom messaging in magazines X marital status] shows that divorced, widowed, and women not living with their partner have 5X greater odds of using a condom if they were exposed to condom messaging in a magazine or newspaper relative to those never married. This finding reveals that condom messaging in magazines and newspapers moderates the relationship between marital status and condom use. This means that the relationship is different for those who have seen and those who have not seen condom advertising in magazines. This can be for a few reasons: (1) Older and more educated: those individuals, who may be divorced, widowed, or not living together and are able to read magazines may represent a sample of people who are older and more educated. That is, they may be wise, given life experience, and at least somewhat educated since they read magazines and newspapers.

(2) Targeted advertisements: It is nearly impossible to identify which magazine and newspapers messaging the participants were exposed to. Perhaps there is something within this messaging that targets or “hits home” with women who are divorced, widowed, and not living with their partner. (3) Greater caution: the sexual relationships that are being had by women, who are divorced, widowed, and not living with their partner may be done in secret. There is some taboo to being divorced/widowed in Kenya and there is also taboo of having sex outside of a married relationship. Perhaps these women are exercising greater caution to prevent the risk of pregnancy and H.I.V/AIDS by using a condom.

Implications

(1) Condom companies: Arguably, condom messaging in newspapers and magazines is influencing divorced, widowed and separated women to actually use condoms. If the majority of this messaging is coming from condom companies [i.e. not public health] then condom companies within Kenya can target their marketing specifically towards this demographic. This would be a profit-based motive to increase sales.

(2) Public health: If this messaging is coming from public health, then, it is revealing some gaps in their targeting. Those individuals who are young and never married account for nearly half of all new HIV infections (Winksell et al 2011) and the messaging could be better targeted towards these groups. Married couples are also at risk to H.I.V/AIDS if one or both spouses are engaging in extra-marital affairs. The Kenyan government did release a public health commercial specifically encouraging married couples to use condoms, however after religious group backlash the commercial was removed (2013).

(3) Condom messaging is weak: In general the findings confirm that advertisements and public service announcements tend to be the weakest form of promotion that creates behavioral change. Although, such advertisements are necessary for general awareness, they cannot be relied upon absolutely. Advertising works best in addition to other efforts, such as sex education in class and greater opposition to religious groups.

(4) Future studies: in the future, to better quantify the impact of condom advertising, condom sales and number of free-condoms distributed could be recorded post-advertisement.

Limitations

There are a number of limitations in this study. (1) By inferring that condoms were used after condom messaging was seen is a major judgment leap. It fails to account for couples that have may have been using condoms for years and only recently saw condom messaging. (2) Being exposed to written condom advertising assumes a certain level of literacy. Those who use condoms as their current form of contraception and were exposed to magazine, newspaper, and/or billboard advertising may simply represent a portion of the sample that is more educated, and as a result is generally more likely to use condoms. (3) Given the nature of the variables, this study fails to account for the frequency of exposure, i.e. how many times was a condom ad seen. And it fails to account for those individuals who may have been exposed to condom advertising multiple times across 1 or more media platforms.

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