Table 2.
How Kenya, Malawi, South Africa, Uganda, and Zimbabwe are monitoring impact of prevention of vertical transmission programmes
| Statistical modelling (such as Spectrum) | Immunisation clinic survey and follow-up | Health and demographic surveillance site (HDSS) | Population based surveys (community based) | Routine monitoring using EID testing data from laboratory | Routine programmatic cross sectional facility based data | Cohort follow-up | ||
|---|---|---|---|---|---|---|---|---|
| Kenya | Used routinely to generate estimates for national and global progress reports | One site, Karonga HDSS, currently exists and participates in the in depth network. Two sites, Nairobi urban cohort study and Kisumu study in Siaya and Rarieda districts of Kenya, participate in the Network for Analysing Longitudinal Population-based HIV/AIDS data on Africa, coordinated by the London School of Hygiene and Tropical Medicine. No data generated yet on prevention of vertical transmission impact from these sites |
Used, but data on children are aggregated into 0-14 years. No data generated yet on prevention of vertical transmission impact from these surveys | A national EID database has been in place for more than 10 years and is used for prevention of vertical transmission effectiveness monitoring | Used to estimate programme coverage along the steps of prevention of vertical transmission care | Used for HIV exposed infants’ cohort monitoring and maternal ART monitoring | ||
| Malawi | Used to measure early prevention of vertical transmission effectiveness in four purposely selected districts out of 28 | Nairobi HDSS, Kilifi HDSS, Mbita HDSS, Kombewa HDSS, and Kisumu HDSS sites currently exist and participate in the in depth network. No data generated yet on prevention of vertical transmission impact from these sites | Routine data are collected quarterly and used for bottleneck analysis and community dialogue to improve systems for retention and care | Used | Paper based systems have been introduced | |||
| South Africa | Used to measure early prevention of vertical transmission impact at 4-8 weeks, three times in three successive nationally representative surveys, June-December 2010, August 2011-March 2012, and October 2012-May 2013, with follow-up of HIV exposed infants until 18 months between October 2012 and September2014 | Dikgale HDSS, Agincourt HDSS, and AHRI HDSS sites currently exist and participate in the in depth network. No data generated yet on prevention of vertical transmission impact from these sites |
Collated annually to monitor PCR positivity from laboratory test results | Used in district implementation plans and for continuous quality improvement | Electronic systems are in the process of being developed | |||
| Uganda | - | Iganga/Mayuge HDSS, Kyamulibwa HDSS, and Rakai HDSS sites currently exist and participate in the in depth network. Data from the Iganga/Mayuge HDSS has been used to estimate prevention of vertical transmission effectiveness and the potential impact of interventions |
EID dashboard used to monitor HIV positivity among tested infants and identify districts with high positivity among infants | Used to monitor maternal and infant outcomes routinely and compare performance across districts | Electronic systems are in the process of being scaled up | |||
| Zimbabwe | Used to measure early prevention of vertical transmission effectiveness in one nationally representative survey, with follow- up of HIV exposed infants until 18 months | - | - | - | Electronic systems are in the process of being developed | |||
| Indications for each method | Consistently used to measure long term prevention of vertical transmission effectiveness | Used for intermittent prevention of vertical transmission impact assessment, to corroborate modelling and laboratory data | To obtain community level cross sectional measurements of prevention of vertical transmission impact | Used to corroborate modelling and immunisation survey data | Used for routine regular programme monitoring | Measures long term maternal and child outcomes in routine programmatic settings | ||
| Lessons learnt | Model assumptions and inputs data need to be updated regularly | Can be expensive to implement in weak health systems where additional staff may be needed or existing staff additionally remunerated | Including prevention of vertical transmission impact monitoring in demographic and health surveys is not always feasible, given the vast focus of HDSS activities | Population based HIV impact assessments an excellent tool but it is not always easy to analyse the 0-14 year age group and relate these data to prevention of vertical transmission impact | Routine laboratory data sources provided similar estimates as surveys and laboratory data on early vertical transmission of HIV | DHIS data quality needs strengthening and improvement. Involvement of communities improves data quality |
Longitudinal monitoring is key to measuring long term prevention of vertical transmission effectiveness but needs unique identifiers and tracking systems within clinics and communities | |
ART, antiretroviral therapy; DHIS, District Health Information System; EID, early infant diagnosis; PCR, polymerase chain reaction.