Quantitative analysis plan

Outcome measures

I selected a live birth outcome, and not conception, as the least biased description of the available data. A conception outcome would better to compare behaviours leading to a pregnancy. However,

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bias could be introduced in this analysis because HIV positive women are known to experience more adverse pregnancy outcomes than HIV negative women, and this information was likely not well captured by surveillance. Analyses that estimate a date of conception from birth data would

overlook differences in stillbirths, miscarriages and abortions. Inferences would then risk incorrectly attributing all variance in rates of ‘conception’ to differences in pre-conception behaviour, without accounting for the unknown adverse physiological effects of being HIV positive or taking HIV treatment.

I chose to report the likelihood of live birth because this is an important demographic indicator for population growth that captures the effects of ART on both behavioural and physiological

mechanisms of fertility. Chapter Four presents the results of four demographic surveillance data analyses, using different analytical approaches to report either rates or likelihood of live birth.

I. Annual trends in live birth between 2005 and 2012

II. Incidence and factors associated with risk of live birth amongst women known to be either HIV negative or positive during annual surveillance rounds 2007-2012 (open cohort) III. Birth-free survival in a cohort of women including ‘unknown’ HIV status (closed entry in

2007/2008)

IV. Incidence and factors associated with contraceptive use

Analysis one presents crude annual birth rates to women included in the Africa Centre composite

‘demography’ dataset. It spans Jan 1st, 2005 until Dec 31st, 2012 and excludes women that were not resident or aged 15-49 years old. This analysis refers to the United Nations (UN) definition of Crude Birth Rates (CBR) and Age-specific Fertility Rate (ASFR) as study outcomes:

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Analyses two and three report cohort analyses of likelihood of live birth. They report univariate and multivariable survival analysis of factors associated with likelihood of live birth.

The outcome in analysis four is likelihood of contraceptive use. Contraceptive use is measured as a binary ‘Yes’ or ‘No’ outcome and compared in a univariate likelihood analysis for a range of proximate and distal factors.

Variables for analysis

A woman’s knowledge of own status and awareness of ART were recorded as self-reported categorical variables ‘KnowsHIVStatus’ and ‘UnderstandsBenefitsART’ in HIV surveillance. Also

Crude Birth Rate:

“The number of births over a given period divided by the person-years lived by the population over that period. It is expressed as number of births per 1,000 population”.

- World population prospects glossary. United Nations population division

Age-specific Fertility Rate:

“The annual number of births to women of a specified age or age group per 1,000 women in that age group. Unless otherwise specified, the reference period for the age-specific fertility rate is the calendar year”.

- World Fertility Report 2009

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recorded in HIV surveillance was ‘HIVResult’, the results of a rapid test administered at the household.

Other covariates were added by linking different questionnaire datasets or selected variables using unique woman identifiers in Stata 11 software. Information about contraceptive use, method of contraception chosen, self-reported general health, relationship status and co-residency with a current partner were added to the unique dataset when annual Women’s General Health surveillance datasets were linked. Highest attained level of education was sourced from annual individual household socio-economic status datasets. The delivery date of pregnancies, area of residence and a woman’s parity were sourced from the Africa Centre composite ‘demography’

dataset.

To retrieve clinical data, I submitted a customised query to the data manager for ARTemis. This query returned data from the variables ‘Datelastfollowedup’, ‘DateofDeath’, ‘DateLastART’,

‘DateFirstART’ and ‘Followed’. It included the most recent CD4 count per woman, measured within 6 months before or after an episode start date in the unique dataset. Women known to be using ART during the cohort period, meaning they had both a date of ART initiation and a date last known to be on ART, were recorded as ‘on ART’ in a new variable.

A description of all variables included in the unique cohort dataset, the source of the variable and hypothesis are given in detail in Table 3.2.

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Table 3.2: Description and origin of variables used in each of the four quantitative analysis presented in Chapter 4

Characteristic Description Survey question Survey response Hypothesis/comment Variable in

unique dataset HIV surveillance datasets

HIV Status HIV status determined by testing in annual HIV surveillance. Presented as

‘HIV Result’ in dataset

N/A N/A HIV positive women will have a lower incidence of live

birth compared to HIV negative women.

Categorical Negative / Positive / Unknown Heard of ART Question about awareness

of the benefits of using antiretroviral therapy to

Yes / No / Refused Women will become increasingly aware of ART after 2005 and could result in higher birth rates to ‘replace’

postponed children.

Yes / No / Refused Knowledge of HIV status may influence desire for more children. HIV positive women may wish to limit childbearing compared to HIV negative counterparts if aware of their HIV diagnosis. While some HIV positive women may not be aware of their own status, all women on ART are. An association between HIV diagnosis and childbearing desires may be confounded by perceptions of general health. Use of ART is expected to increase self-perceived health and therefore intention to continue childbearing.

Categorical Yes / No / Unknown

104 Visit date Recorded date of

household visit. Used to calculate two variables for date of cohort entry and year of entry.

Year – Month - Day Date An effect of ART on childbearing decision-making may be most effectual in the earlier years of roll out, i.e.

2007 onwards. Unrelated to roll out of ART, rates of childbearing are likely to follow national fertility decline, or plateau according to a recent reported stall in fertility decline.

Date (Start date)

ART may encourage sexual activity when feeling in good health. This is a proxy measure for sexual activity, and a mechanism through which ART could increase rates of conception. ACDIS collects

information about more than one current partner at a time. This analysis includes information only regarding the status that a woman perceives for her first or ‘main’ partner. before or after the actual

Skip questions 1. ‘Have you ever used contraceptives?’

2. Which method are you currently using

Yes / No / Refused Hlabisa is likely to reflect national trends of injectable contraceptive use. Women on ART may desire more children and modify their use of contraception accordingly. Alternatively, women on ART may have increased access to contraception and choose to use contraception to preserve their own

Categorical

105 date of conception of a

live birth event.

health status. However, there is little evidence so far to suggest that women have changed contraceptive use in response to the HIV epidemic.

Contraceptive Method

Question asking for self-reported method of contraception selected from a list of multiple options. This question is asked only if women respond yes to a filter question, have you ever

HIV positive women may report more dual

protection to avoid HIV transmission and pregnancy at the same time. Women on ART may follow national preferences for injectable methods to prevent an unwanted pregnancy. ART-related side effects may affect preferences for reasons unrelated to childbearing towards barrier methods, which can be less user-efficacious and thereby increase rates of conception.

Women do not commonly co-reside with their partner. This may occur before marriage and typically after initiation of childbearing.

Categorical Yes / No / Unknown

106 Self-reported

health

Question asking about a woman’s self-reported health status, out of three categorical choices.

Health sits on the hypothesised pathway between knowledge of HIV status and childbearing intention.

Women on ART are more likely to feel improved health and those reporting good overall perceived health are more likely to desire a child in the future, resulting in higher rates of conception.

Categorical

Date of Birth Date of birth recorded on individual notification form

Year – Month - Day Date I would expect to see a stronger association between age and fertility than that of HIV status or exposure to ART. Women on ART are likely to be older, already have children and have reached the desired family size. Incidence of live birth will be higher when adjusted for age and parity.

Categorical Education Highest level of education

attained by women over

Women with higher education attainment are more likely to use contraception and reduce rates of conception. Education also influences risk of HIV acquisition and access to treatment.

Categorical

107 Demography composite dataset

Parity Number of live children at start of episode. A sum of answers to four questions asking for a birth who are living with you? 3. Do you have any sons or daughters to whom you have given birth who are alive but do not live with you? 4. Have you ever given birth to a boy or a girl who has been alive but later died?

Numerical Primiparous women are more likely to desire to continue childbearing, increasing rates of conception.

Parity above one may reflect nearing completion of family size and a lower desire for more children, reducing rates of conception. Age will influence a woman’s current parity and so increased rates will been found only at comparable ages.

Categorical 0 / 1 / 2 / 3 / 4 / 5 / 6+

Live birth A new birth reported captured in the Pregnancy Notification Form.

What was the date of birth / did the pregnancy end?

Date Date at which a live birth occurred (outcome event) Date and binary

‘event’ variable

Area of residence Area of residence – is urban or rural?

Women living in rural areas of residence are known to experience higher rates of fertility.

Categorical Rural /

Peri-urban / Urban

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CD4 measured at start of episode

Query for CD4 count closest to the specified episode start date within 6 months.

Numerical count Women with poorer physiological health at start of observation will experience lower rates of fertility

Categorical follow up visits for all women in unique

Women on ART will be more likely to experience a live birth compared to ART-naïve HIV positive women, between comparable age groups and measures of health (CD4 categories or self-reported general heath).

Yes / No

109 3.3.7 The Andersen-Gill model of ordered events

This multiple event analysis defines live births as ordered events: a live birth may occur at multiple times throughout cohort analysis time but more than one event cannot occur to the same individual at the same time (Andersen and Gill, 1982). The Anderson-Gill method assumes that all events are equal and counts the number of events k that occur at time t to calculate time to first event and time to second event. The risk set is defined as the number of women being observed at time t. Each analysis employs the Anderson-Gill method for ordered events to model the hazard that a woman experiences a live birth where multiple events are possible. Women-years provided the denominator to the hazard of birth at time t, assuming that the woman has remained birth-free until the

beginning of the time interval. It transpired that there were relatively few sequential birth events to the same woman but this format also allowed for changes in HIV status at any time relative to an event (Cleaves, 2009).

A woman is also at risk of changing HIV status at any time, if HIV negative or her status is unknown.

Each woman may therefore have multiple observations during which time only one event can occur.

The first observation spans the time from entry in to the study until the time of the event or a change in HIV status, and the second observation spans the time from the event/change in HIV status until the next event or change in status or until the end of follow up.