Assessment of Malawi’s success in child mortality reduction through the lens of the Catalytic Initiative integrated health systems strengthening programme: Retrospective evaluation

Journal of Global Health, Volume 5, No. 2, Year 2015

Interpretation

Background Malawi is estimated to have achieved its Millennium Development Goal (MDG) 4 target. This paper explores factors influencing progress in child survival in Malawi including coverage of interventions and the role of key national policies. Methods We performed a retrospective evaluation of the Catalytic Initiative (CI) programme of support (2007-2013). We developed estimates of child mortality using four population household surveys undertaken between 2000 and 2010. We recalculated coverage indicators for high impact child health interventions and documented child health programmes and policies. The Lives Saved Tool (LiST) was used to estimate child lives saved in 2013. Results The mortality rate in children under 5 years decreased rapidly in the 10 CI districts from 219 deaths per 1000 live births (95% confidence interval (CI) 189 to 249) in the period 1991-1995 to 119 deaths (95% CI 105 to 132) in the period 2006-2010. Coverage for all indicators except vitamin A supplementation increased in the 10 CI districts across the time period 2000 to 2013. The LiST analysis estimates that there were 10 800 child deaths averted in the 10 CI districts in 2013, primarily attributable to the introduction of the pneumococcal vaccine (24%) and increased household coverage of insecticide-treated bednets (19%). These improvements have taken place within a context of investment in child health policies and scale up of integrated community case management of childhood illnesses. Conclusions Malawi provides a strong example for countries in sub- Saharan Africa of how high impact child health interventions implemented within a decentralised health system with an established community-based delivery platform, can lead to significant reductions in child mortality. The analyses undertaken were part of a multi–country retrospective evaluation of the CI programme. The selection of the 10 CI districts for UNICEF support was undertaken jointly by UNICEF and the Ministry of Health (Figure 1). The selected districts reported higher rates of maternal, newborn and child mortality in 2006 [14] compared to national mortality and included remote areas with limited health care access. The CI grant supported both facility and community–based interventions including preventive and curative services (Box 1). This evaluation compared average annual change (AAC) in coverage for key indicators in the 10 CI districts before the CI support began (2000–2006) and during the period of implementation (2007–2013). Expanded Programme on Immunisation: • Catch up immunisation through child health days • Vitamin A supplementation Health system strengthening of the health surveillance assistant (HAS) platform (particularly related to integrated community case management (iCCM) of malaria, pneumonia and diarrhoea): • Communication and social mobilisation on iCCM (through job aids) • Recruitment, selection and training of HSAs • Basic supplies for HSAs (drug box, bicycles, motorcycles for supervision) • Supervision (quarterly mentorship and review meetings on iCCM) • M&E (support to M&E officer at IMCI unit) • Review of health surveillance curriculae to include new competencies Renovation of three training centers: • Purchased sachets of oral rehydration salts (ORS) and zinc tablets, cotrimoxazole, sulfadoxine–pyrimethamine and artemisinin–combination therapies (ACTs) for village clinics Integrated Management of Childhood Illnesses (IMCI): • Training of nurses and clinicians in IMCI Malaria prevention: • Supply and distribution of ITNs for pregnant women and children under five years Health promotion Infant and young child feeding: • Promotion of early initiation and exclusive breastfeeding for six months • Screening for severe and acute malnutrition WASH: • Education on safe water, sanitation and hygiene We used birth and death history data collected from women aged 15 to 49 years in nationally representative surveys: namely the 2000 Demographic and Health Survey (DHS), 2004 DHS, 2006 Multiple Indicator Cluster Survey (MICS), and the 2010 DHS to calculate under–5 mortality. The surveys covered 14 213, 13 664, 30 553, and 24 825 households respectively. For analysis of intervention coverage we used standard indicator definitions [15] for 11 interventions targeted by the CI for tracking progress towards MDG 4 (Table 1). We also captured coverage change for other maternal and contextual indicators. Surveys included in the analysis of intervention coverage were the 2000 DHS, 2006 MICS, 2010 DHS and the 2013 Lot Quality Assurance Survey (LQAS) which sampled in the 10 CI districts only [16,17]. The 2004 DHS did not include disaggregated data for all of the CI districts; therefore it was excluded from the coverage analysis (Section A in Online Supplementary Document(Online Supplementary Document)). All surveys provided cross–sectional data on intervention coverage in their respective years. Full survey data sets with district sampling weights were used for the analysis. For further details on the surveys included in the analysis see Table s1 in Online Supplementary Document(Online Supplementary Document). Adjustments were made to align indicator definitions across the DHS, MICS and LQAS surveys (Section B in Online Supplementary Document(Online Supplementary Document)). Summary of indicator coverage change in the 10 Catalytic Initiative–focus districts IPTp – intermittent preventive treatment of malaria for pregnant women, ITNs – percent of children <5 who slept under an Insecticide Treated Net the previous night, DPT – diphtheria, pertussis and tetanus, ACTs – Artemisinin–combination therapies, ORS – Percentage of children <5 with diarrhoea in the last 2 weeks who received oral rehydration salts *Amongst children aged 12–23 moths. †ACTs were only introduced as first line malaria treatment in 2008. ‡Arrows in the last column indicate whether average annual change in coverage decreased, was stable or increased between period 1 and period 2: ↓ - decrease in AAC between pre–CI (period 1) and during CI (period 2); → – stable AAC between pre–CI (period 1) and during CI (period 2), ↑ – increase in AAC between pre–CI (period 1) and during CI (period 2). Contextual information about child health policies, CI implementation and other relevant child health programmes was obtained through a desk review of documents and databases obtained during a 10–day country visit (August 2013). The information gathered from these sources was used to compile a policy and programme timeline (Figure 2). For further details on the contextual analysis see Panel s1 in Online Supplementary Document(Online Supplementary Document). Major policy changes and programmatic activities related to child survival in Malawi (Catalytic Initiative districts and nationally), 2004 – 2012. RED – Reach Every District Strategy; ACSD – Accelerated Child Survival and Development policy; GoM – Government of Malawi; IMCI – Integrated Management of Childhood Illness; MoH – Ministry of Health; CI – Catalytic Initiative; ACTs – Artemisinin–combination therapies for the treatment of malaria; HSA – Health surveillance assistant; NGO – Non-governmental organisation; ITN – insecticide-treated bed net. We used a direct method for estimating under–5 mortality based on the synthetic cohort approach [18,19]. Under this concept, age–specific mortality probabilities for narrow age ranges and defined periods are calculated using death events and exposures. These probabilities are combined to compute the probability that a child has not died before reaching age 5 years [19]. Five–periods were used beginning with five years before the survey, and survival probabilities were calculated over age ranges; 0, 1–2, 3–5, 6–11, 12–23, 24–35, 36–47, 48–59 months as recommended by DHS (Section C in Online Supplementary Document(Online Supplementary Document)) [19]. The standard errors for the computed mortality estimates were obtained using the Jackknife variance estimation, a repeated sampling method [18]. A series of mortality estimates were obtained by deleting and replacing each primary sampling unit; this produced a sample of under–5 estimates, from which the variance was computed in turn. We also estimated the AAC in mortality using mortality estimates for the periods 1991–1995 and 2006–2010 (Section C in Online Supplementary Document(Online Supplementary Document)). For analysis of intervention coverage, the 10 CI districts were treated as one stratum. We re–calculated all relevant coverage indicators from each survey data set in order to obtain the confidence intervals around the estimates. We then assessed whether there was a significant difference in the AAC in coverage for 11 indicators between the pre–CI period (2000–2006) and the CI implementation period (2006–2013) for the 10 CI districts. The 95% confidence intervals (95% CI) around the AAC on the log scale were based on standard deviations calculated using the delta method for the log function of a proportion. The 95% confidence intervals were used to assess whether the changes were significantly different between pre–CI and CI periods. In order to check the hypothesis that the simultaneous national scale up of iCCM would result in similar coverage change between CI and non–CI districts (supported by other partners), we calculated AAC in intervention coverage in CI and non–CI districts between 2000 and 2010 (data for the non–CI districts was not collected in the 2013 LQAS). To assess the contribution of iCCM by HSAs, data relating to care and treatment sought for fever, suspected pneumonia and diarrhoea by place of treatment were extracted from the available household surveys. The 2006 MICS only collected data on place of treatment for suspected pneumonia but not for diarrhoea or fever [20] and it was therefore not included in this analysis. The sampling design of the household surveys such as regional and rural/urban stratification, clustering at enumeration areas and sampling weights (due to non–proportional sampling) were taken into account. We used Stata (version 12) for these analyses [21]. An attempt to quantify the association between change in contextual factors and intervention coverage with change in under–5 mortality in a multivariate analysis did not yield meaningful results due to the limited number of data points for macroeconomic contextual variables (Section D in Online Supplementary Document(Online Supplementary Document)). We used the Lives Saved Tool (LiST) [22] to forecast child mortality (rates and deaths) in the 10 CI districts in 2013 on the basis of the above measured baseline values of mortality in children younger than 5 years for the period 2006–2010 (Section E in Online Supplementary Document(Online Supplementary Document)) and interpolated changes in coverage from the MICS 2006, DHS 2010 and LQAS 2013. We present the estimates of lives saved in 2013, relative to 2008 when CI implementation began, and used the LiST model to investigate the extent to which the declines in child mortality could be attributed to changes in intervention coverage. We also considered the proportion of deaths averted between 2000 and 2008 using our measured baseline mortality and coverage data from the DHS 2000, MICS 2006 and DHS 2010 to compare results between pre–CI and CI periods. The LiST modelling methods have been widely published, including discussion of the limitations which are particularly related to the lack of population–based coverage data for certain key interventions [22–24]. Specific input values used in this LiST application are available in Table s6 in Online Supplementary Document(Online Supplementary Document). The analysis was done with the computer programme Spectrum/ Lives Saved Tool, version 5.04. The study received ethical approval from the ethics committee of the South African Medical Research Council (EC021–9/2012).

AI Digest

Study Justification:

This study aims to assess the success of Malawi in reducing child mortality through the Catalytic Initiative integrated health systems strengthening program. The study is important because it provides insights into the factors that have influenced progress in child survival in Malawi, including the coverage of interventions and the role of key national policies. Understanding these factors can help inform future strategies and policies to further reduce child mortality in Malawi and other countries in sub-Saharan Africa.

Highlights:

– The study found that the child mortality rate in Malawi decreased rapidly in the 10 Catalytic Initiative (CI) districts from 1991-1995 to 2006-2010.
– Coverage for most child health interventions increased in the CI districts from 2000 to 2013.
– The introduction of the pneumococcal vaccine and increased household coverage of insecticide-treated bednets were identified as key factors contributing to the reduction in child mortality.
– The study highlights the importance of investment in child health policies and the scale-up of integrated community case management of childhood illnesses in achieving significant reductions in child mortality.

Recommendations:

– Continue investment in child health policies and programs to sustain the progress made in reducing child mortality.
– Strengthen the implementation of integrated community case management of childhood illnesses to ensure access to timely and appropriate care.
– Maintain and expand the coverage of key interventions such as the pneumococcal vaccine and insecticide-treated bednets.
– Improve access to and utilization of maternal and child health services, including antenatal care, skilled birth attendance, and postnatal care.
– Enhance health promotion activities to promote early initiation and exclusive breastfeeding, as well as safe water, sanitation, and hygiene practices.

Key Role Players:

– Ministry of Health: Responsible for policy development, planning, and implementation of child health programs.
– Health Surveillance Assistants (HSAs): Community health workers who play a crucial role in delivering integrated community case management services.
– Nurses and clinicians: Responsible for providing training and support in integrated management of childhood illnesses.
– UNICEF: Provides support and funding for child health programs, including the Catalytic Initiative.
– Non-governmental organizations (NGOs): Collaborate with the government and UNICEF to implement child health interventions and provide support at the community level.

Cost Items for Planning Recommendations:

– Recruitment, selection, and training of HSAs.
– Basic supplies for HSAs, such as drug boxes, bicycles, and motorcycles for supervision.
– Supervision and mentorship of HSAs.
– Support to monitoring and evaluation (M&E) activities, including the appointment of an M&E officer.
– Renovation of training centers.
– Purchase of oral rehydration salts (ORS), zinc tablets, and other essential drugs.
– Supply and distribution of insecticide-treated bednets.
– Health promotion activities, including education materials and campaigns.
– Training of nurses and clinicians in integrated management of childhood illnesses.
– Investment in water, sanitation, and hygiene (WASH) infrastructure and education.
Please note that the cost items provided are for planning purposes and do not represent actual costs. The actual budget for implementing the recommendations would depend on various factors, including the scale of implementation and specific context.

Strength of Evidence

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong, as it presents findings from a retrospective evaluation of the Catalytic Initiative (CI) program in Malawi. The study uses multiple population household surveys and the Lives Saved Tool (LiST) to estimate child mortality and assess the impact of interventions. The abstract provides specific data on child mortality rates, coverage indicators, and lives saved in the CI districts. However, to improve the evidence, the abstract could include more details on the methodology used, such as the sampling design of the surveys and the statistical analysis performed. Additionally, it would be helpful to mention any limitations or potential biases in the study.

Abstract

The analyses undertaken were part of a multi–country retrospective evaluation of the CI programme. The selection of the 10 CI districts for UNICEF support was undertaken jointly by UNICEF and the Ministry of Health (Figure 1). The selected districts reported higher rates of maternal, newborn and child mortality in 2006 [14] compared to national mortality and included remote areas with limited health care access. The CI grant supported both facility and community–based interventions including preventive and curative services (Box 1). This evaluation compared average annual change (AAC) in coverage for key indicators in the 10 CI districts before the CI support began (2000–2006) and during the period of implementation (2007–2013). Expanded Programme on Immunisation: • Catch up immunisation through child health days • Vitamin A supplementation Health system strengthening of the health surveillance assistant (HAS) platform (particularly related to integrated community case management (iCCM) of malaria, pneumonia and diarrhoea): • Communication and social mobilisation on iCCM (through job aids) • Recruitment, selection and training of HSAs • Basic supplies for HSAs (drug box, bicycles, motorcycles for supervision) • Supervision (quarterly mentorship and review meetings on iCCM) • M&E (support to M&E officer at IMCI unit) • Review of health surveillance curriculae to include new competencies Renovation of three training centers: • Purchased sachets of oral rehydration salts (ORS) and zinc tablets, cotrimoxazole, sulfadoxine–pyrimethamine and artemisinin–combination therapies (ACTs) for village clinics Integrated Management of Childhood Illnesses (IMCI): • Training of nurses and clinicians in IMCI Malaria prevention: • Supply and distribution of ITNs for pregnant women and children under five years Health promotion Infant and young child feeding: • Promotion of early initiation and exclusive breastfeeding for six months • Screening for severe and acute malnutrition WASH: • Education on safe water, sanitation and hygiene We used birth and death history data collected from women aged 15 to 49 years in nationally representative surveys: namely the 2000 Demographic and Health Survey (DHS), 2004 DHS, 2006 Multiple Indicator Cluster Survey (MICS), and the 2010 DHS to calculate under–5 mortality. The surveys covered 14 213, 13 664, 30 553, and 24 825 households respectively. For analysis of intervention coverage we used standard indicator definitions [15] for 11 interventions targeted by the CI for tracking progress towards MDG 4 (Table 1). We also captured coverage change for other maternal and contextual indicators. Surveys included in the analysis of intervention coverage were the 2000 DHS, 2006 MICS, 2010 DHS and the 2013 Lot Quality Assurance Survey (LQAS) which sampled in the 10 CI districts only [16,17]. The 2004 DHS did not include disaggregated data for all of the CI districts; therefore it was excluded from the coverage analysis (Section A in Online Supplementary Document(Online Supplementary Document)). All surveys provided cross–sectional data on intervention coverage in their respective years. Full survey data sets with district sampling weights were used for the analysis. For further details on the surveys included in the analysis see Table s1 in Online Supplementary Document(Online Supplementary Document). Adjustments were made to align indicator definitions across the DHS, MICS and LQAS surveys (Section B in Online Supplementary Document(Online Supplementary Document)). Summary of indicator coverage change in the 10 Catalytic Initiative–focus districts IPTp – intermittent preventive treatment of malaria for pregnant women, ITNs – percent of children <5 who slept under an Insecticide Treated Net the previous night, DPT – diphtheria, pertussis and tetanus, ACTs – Artemisinin–combination therapies, ORS – Percentage of children <5 with diarrhoea in the last 2 weeks who received oral rehydration salts *Amongst children aged 12–23 moths. †ACTs were only introduced as first line malaria treatment in 2008. ‡Arrows in the last column indicate whether average annual change in coverage decreased, was stable or increased between period 1 and period 2: ↓ – decrease in AAC between pre–CI (period 1) and during CI (period 2); → – stable AAC between pre–CI (period 1) and during CI (period 2), ↑ – increase in AAC between pre–CI (period 1) and during CI (period 2). Contextual information about child health policies, CI implementation and other relevant child health programmes was obtained through a desk review of documents and databases obtained during a 10–day country visit (August 2013). The information gathered from these sources was used to compile a policy and programme timeline (Figure 2). For further details on the contextual analysis see Panel s1 in Online Supplementary Document(Online Supplementary Document). Major policy changes and programmatic activities related to child survival in Malawi (Catalytic Initiative districts and nationally), 2004 – 2012. RED – Reach Every District Strategy; ACSD – Accelerated Child Survival and Development policy; GoM – Government of Malawi; IMCI – Integrated Management of Childhood Illness; MoH – Ministry of Health; CI – Catalytic Initiative; ACTs – Artemisinin–combination therapies for the treatment of malaria; HSA – Health surveillance assistant; NGO – Non-governmental organisation; ITN – insecticide-treated bed net. We used a direct method for estimating under–5 mortality based on the synthetic cohort approach [18,19]. Under this concept, age–specific mortality probabilities for narrow age ranges and defined periods are calculated using death events and exposures. These probabilities are combined to compute the probability that a child has not died before reaching age 5 years [19]. Five–periods were used beginning with five years before the survey, and survival probabilities were calculated over age ranges; 0, 1–2, 3–5, 6–11, 12–23, 24–35, 36–47, 48–59 months as recommended by DHS (Section C in Online Supplementary Document(Online Supplementary Document)) [19]. The standard errors for the computed mortality estimates were obtained using the Jackknife variance estimation, a repeated sampling method [18]. A series of mortality estimates were obtained by deleting and replacing each primary sampling unit; this produced a sample of under–5 estimates, from which the variance was computed in turn. We also estimated the AAC in mortality using mortality estimates for the periods 1991–1995 and 2006–2010 (Section C in Online Supplementary Document(Online Supplementary Document)). For analysis of intervention coverage, the 10 CI districts were treated as one stratum. We re–calculated all relevant coverage indicators from each survey data set in order to obtain the confidence intervals around the estimates. We then assessed whether there was a significant difference in the AAC in coverage for 11 indicators between the pre–CI period (2000–2006) and the CI implementation period (2006–2013) for the 10 CI districts. The 95% confidence intervals (95% CI) around the AAC on the log scale were based on standard deviations calculated using the delta method for the log function of a proportion. The 95% confidence intervals were used to assess whether the changes were significantly different between pre–CI and CI periods. In order to check the hypothesis that the simultaneous national scale up of iCCM would result in similar coverage change between CI and non–CI districts (supported by other partners), we calculated AAC in intervention coverage in CI and non–CI districts between 2000 and 2010 (data for the non–CI districts was not collected in the 2013 LQAS). To assess the contribution of iCCM by HSAs, data relating to care and treatment sought for fever, suspected pneumonia and diarrhoea by place of treatment were extracted from the available household surveys. The 2006 MICS only collected data on place of treatment for suspected pneumonia but not for diarrhoea or fever [20] and it was therefore not included in this analysis. The sampling design of the household surveys such as regional and rural/urban stratification, clustering at enumeration areas and sampling weights (due to non–proportional sampling) were taken into account. We used Stata (version 12) for these analyses [21]. An attempt to quantify the association between change in contextual factors and intervention coverage with change in under–5 mortality in a multivariate analysis did not yield meaningful results due to the limited number of data points for macroeconomic contextual variables (Section D in Online Supplementary Document(Online Supplementary Document)). We used the Lives Saved Tool (LiST) [22] to forecast child mortality (rates and deaths) in the 10 CI districts in 2013 on the basis of the above measured baseline values of mortality in children younger than 5 years for the period 2006–2010 (Section E in Online Supplementary Document(Online Supplementary Document)) and interpolated changes in coverage from the MICS 2006, DHS 2010 and LQAS 2013. We present the estimates of lives saved in 2013, relative to 2008 when CI implementation began, and used the LiST model to investigate the extent to which the declines in child mortality could be attributed to changes in intervention coverage. We also considered the proportion of deaths averted between 2000 and 2008 using our measured baseline mortality and coverage data from the DHS 2000, MICS 2006 and DHS 2010 to compare results between pre–CI and CI periods. The LiST modelling methods have been widely published, including discussion of the limitations which are particularly related to the lack of population–based coverage data for certain key interventions [22–24]. Specific input values used in this LiST application are available in Table s6 in Online Supplementary Document(Online Supplementary Document). The analysis was done with the computer programme Spectrum/ Lives Saved Tool, version 5.04. The study received ethical approval from the ethics committee of the South African Medical Research Council (EC021–9/2012).

Materials

https://efashare.b-cdn.net/materials/fd0302b142bc7e193f7d2187124abb4428a4b1083408f37ab519ed00db715b87/jogh-05-020412-s001.pdf

Innovations

Based on the information provided, here are some potential innovations that could be used to improve access to maternal health:

1. Mobile health (mHealth) technology: Implementing mobile health solutions, such as text message reminders for prenatal care appointments and health education, can help improve access to maternal health services, especially in remote areas with limited healthcare access.

2. Telemedicine: Using telemedicine technology, healthcare providers can remotely provide prenatal care consultations and monitor high-risk pregnancies, reducing the need for pregnant women to travel long distances for medical appointments.

3. Community health workers: Training and deploying community health workers (CHWs) can help improve access to maternal health services, especially in underserved areas. CHWs can provide basic prenatal care, health education, and referrals to healthcare facilities.

4. Maternal health clinics: Establishing dedicated maternal health clinics can provide comprehensive prenatal care, including regular check-ups, screenings, and access to essential medications and treatments.

5. Transportation support: Providing transportation support, such as vouchers or subsidies for pregnant women to access healthcare facilities, can help overcome transportation barriers and improve access to maternal health services.

6. Maternal health hotlines: Setting up toll-free hotlines staffed by healthcare professionals can provide pregnant women with immediate access to medical advice, guidance, and referrals for maternal health services.

7. Maternity waiting homes: Building maternity waiting homes near healthcare facilities can provide a safe and comfortable place for pregnant women to stay before giving birth, especially for those who live far away from healthcare facilities.

8. Financial incentives: Offering financial incentives, such as cash transfers or conditional cash transfers, to pregnant women who attend prenatal care appointments and give birth in healthcare facilities can help improve access to maternal health services.

9. Public-private partnerships: Collaborating with private sector organizations, such as pharmaceutical companies or technology companies, can help leverage their resources and expertise to improve access to maternal health services.

10. Health education and awareness campaigns: Conducting targeted health education and awareness campaigns can help raise awareness about the importance of maternal health and encourage pregnant women to seek timely and appropriate care.

It’s important to note that the specific context and needs of the community should be considered when implementing these innovations to ensure their effectiveness and sustainability.

AI Innovations Description

Based on the provided description, the following recommendation can be developed into an innovation to improve access to maternal health:

1. Strengthening of the health surveillance assistant (HAS) platform: This includes improving communication and social mobilization on integrated community case management (iCCM) through job aids, recruitment, selection, and training of HSAs, providing basic supplies for HSAs, regular supervision, and support for monitoring and evaluation.

Innovation: Develop a mobile application or digital platform that can be used by HSAs to enhance communication, access to information, and reporting. This platform can provide job aids, training materials, and real-time data collection and analysis capabilities. It can also facilitate remote supervision and mentoring through video conferencing or virtual meetings.

Benefits:

– Improved communication and access to information for HSAs, leading to better delivery of maternal health services.
– Real-time data collection and analysis can help identify gaps and trends in maternal health outcomes, allowing for targeted interventions.
– Remote supervision and mentoring can provide ongoing support and guidance to HSAs, even in remote areas with limited access to healthcare.

Implementation:

– Collaborate with the Ministry of Health and UNICEF to develop and pilot the mobile application or digital platform.
– Provide training and technical support to HSAs on how to effectively use the platform.
– Monitor and evaluate the impact of the innovation on access to maternal health services and maternal health outcomes.

Note: This recommendation is based on the provided information and may need to be further tailored and adapted to the specific context and needs of Malawi.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthening health surveillance assistants (HSAs) platform: This can involve improving communication and social mobilization on integrated community case management (iCCM) through job aids, recruiting and training more HSAs, providing basic supplies for HSAs, and enhancing supervision and monitoring of their work.

2. Enhancing the Integrated Management of Childhood Illnesses (IMCI) program: This can include training nurses and clinicians in IMCI to improve the quality of care provided to children and promote early detection and treatment of illnesses.

3. Malaria prevention: Increasing the supply and distribution of insecticide-treated bed nets (ITNs) for pregnant women and children under five years to reduce the risk of malaria infection.

4. Health promotion: Promoting infant and young child feeding practices, such as early initiation and exclusive breastfeeding for six months, and screening for severe and acute malnutrition.

5. Water, sanitation, and hygiene (WASH) education: Providing education on safe water, sanitation, and hygiene practices to prevent the spread of diseases and improve overall health.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could involve the following steps:

1. Collect baseline data: Gather data on key indicators related to maternal health, such as maternal mortality rates, access to antenatal care, skilled birth attendance, and postnatal care.

2. Define intervention coverage: Determine the coverage levels of the recommended interventions in the target population. This can be done through surveys, interviews, or existing data sources.

3. Estimate impact on maternal health outcomes: Use a modeling tool, such as the Lives Saved Tool (LiST), to estimate the potential impact of the recommended interventions on maternal health outcomes. LiST uses input data on intervention coverage, effectiveness, and baseline mortality rates to project the number of lives saved or improvements in health outcomes.

4. Analyze results: Evaluate the simulated impact of the recommendations by comparing the projected outcomes with the baseline data. Assess the extent to which the interventions can contribute to improving access to maternal health and reducing maternal mortality.

5. Refine and adjust interventions: Based on the analysis of the simulation results, refine and adjust the recommended interventions as needed. This may involve prioritizing certain interventions, modifying implementation strategies, or identifying additional interventions that can further improve access to maternal health.

6. Monitor and evaluate: Continuously monitor and evaluate the implementation of the interventions to assess their actual impact on improving access to maternal health. This can involve tracking key indicators, conducting surveys or studies, and gathering feedback from healthcare providers and beneficiaries.

By following this methodology, policymakers and healthcare professionals can gain insights into the potential impact of the recommended innovations on improving access to maternal health and make informed decisions on their implementation.

Author

Dima Health

Statistics:

Citations: 9

Identifiers:

DOI: 10.7189/jogh.05.020412

ISSN: 20472978

Research Areas:

Community Interventions, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants

Study Countries:

Malawi

Study Design:

Cohort Study, Cross Sectional Study, randomised-control-trial

Study Approach:

Quantitative

Participants Gender:

Female