Young women most likely to be infected by HIV in KwaZulu-Natal

Researchers found a new way to estimate the infection rate of the human immunodeficiency virus (HIV). This incurable disease prevents a person’s body from fighting other illnesses. They suggest we need to focus more on prevention.

We get most of our data on HIV infection from surveys. The easiest number to calculate from survey data is the percentage of the population that has HIV. 

However, it is better to track disease trends by calculating the rate of infection, in other words how quickly a disease is spreading, rather than the percentage of people infected at a certain point in time. 

Calculating the rate of infection using survey data is tricky, and most methods don’t give an accurate estimate. In this study, researchers wanted to find a new, accurate way to estimate HIV infection rates, using surveys and other data from KwaZulu-Natal, South Africa.

The researchers gathered data from a rural area, Mbongolwane, and a large town, Eshowe, in KwaZulu-Natal in 2013. They performed HIV tests, collected blood samples, and gathered information like sexual history. 

They then combined 2 previous methods of estimating new HIV infections, and used their new approach on this data: The first approach uses survey data to estimate infection rate, and the second uses blood samples and HIV tests. 

Thus, their new approach used both these approaches to estimate the HIV infection rate in KwaZulu-Natal.

The researchers’ new method was more accurate and gave more information than the previous methods. For instance, they found that women seem to be infected by HIV 1.3 to 4 times more often than men. In addition, new infection rates are highest for women in their early 20s and men at age 30.

This new method improves on previous ones and will help better manage HIV spread. For instance, these researchers noticed the need for better prevention as 35% of participants did not even know if they had HIV.

This new method is inaccurate for people over 30. The researchers were unsure why but committed to investigating this in future. They also mention that their method will not be accurate if the infection rate changes while conducting the surveys. They plan to conduct more surveys in the area to verify their findings.

These South African and French researchers focused on a developing area in South Africa. Their method seems to work reasonably well in these areas that may need it most.

Abstract

Introduction

There is a notable absence of consensus on how to generate estimates of population-level incidence. Incidence is a considerably more sensitive indicator of epidemiological trends than prevalence, but is harder to estimate. We used a novel hybrid method to estimate HIV incidence by age and sex in a rural district of KwaZulu-Natal, South Africa.

Methods

Our novel method uses an ‘optimal weighting’ of estimates based on an implementation of a particular ‘synthetic cohort’ approach (interpreting the age/time structure of prevalence, in conjunction with estimates of excess mortality) and biomarkers of ‘recent infection’ (combining Lag-Avidity, Bio-Rad Avidity and viral load results to define recent infection, and adapting the method for age-specific incidence estimation). Data were obtained from a population-based cross-sectional HIV survey conducted in Mbongolwane and Eshowe health service areas in 2013.

Results

Using the combined method, we find that age-specific HIV incidence in females rose rapidly during adolescence, from 1.33 cases/100 person-years (95% CI: 0.98,1.67) at age 15 to a peak of 5.01/100PY (4.14,5.87) at age 23. In males, incidence was lower, 0.34/100PY (0.00-0.74) at age 15, and rose later, peaking at 3.86/100PY (2.52-5.20) at age 30. Susceptible population-weighted average incidence in females aged 15-29 was estimated at 3.84/100PY (3.36-4.40), in males aged 15-29 at 1.28/100PY (0.68-1.50) and in all individuals aged 15-29 at 2.55/100PY (2.09-2.76). Using the conventional recency biomarker approach, we estimated HIV incidence among females aged 15-29 at 2.99/100PY (1.79-4.36), among males aged 15-29 at 0.87/100PY (0.22-1.60) and among all individuals aged 15-59 at 1.66/100PY (1.13-2.27).

Discussion

HIV incidence was very high in women aged 15-30, peaking in the early 20s. Men had lower incidence, which peaked at age 30. The estimates obtained from the hybrid method are more informative than those produced by conventional analysis of biomarker data, and represents a more optimal use of available data than either the age-continuous biomarker or synthetic cohort methods alone. The method is mainly useful at younger ages, where excess mortality is low and uncertainty in the synthetic cohort estimates is reasonably small.

Conclusion

Application of this method to large-scale population-based HIV prevalence surveys is likely to result in improved incidence surveillance over methods currently in wide use. Reasonably accurate and precise age-specific estimates of incidence are important to target better prevention, diagnosis and care strategies.

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