Facility-level characteristics associated with family planning and child immunization services integration in urban areas of Nigeria: a longitudinal analysis

Background: Unmet need for postpartum contraception is high. Integration of family planning with routine child immunization services may help to satisfy unmet need. However, evidence about the determinants and effects of integration has been inconsistent, and more evidence is required to ascertain whether and how to invest in integration. In this study, facility-level family planning and immunization integration index scores are used to: (1) determine whether integration changes over time and (2) identify whether facility-level characteristics, including exposure to the Nigerian Urban Reproductive Health Initiative (NURHI), are associated with integration across facilities in six urban areas of Nigeria. Methods: This study utilizes health facility data collected at baseline (n = 400) and endline (n = 385) for the NURHI impact evaluation. Difference-in-differences models estimate the associations between facility-level characteristics, including exposure to NURHI, and Provider and Facility Integration Index scores. The two outcome measures, Provider and Facility Integration Index scores, reflect attributes that support integrated service delivery. These indexes, which range from 0 (low) to 10 (high), were constructed using principal component analysis. Scores were calculated for each facility. Independent variables are (1) time period, (2) whether the facility received the NURHI intervention, and (3) additional facility-level characteristics. Results: Within intervention facilities, mean Provider Integration Index scores were 6.46 at baseline and 6.79 at endline; mean Facility Integration Index scores were 7.16 (baseline) and 7.36 (endline). Within non-intervention facilities, mean Provider Integration Index scores were 5.01 at baseline and 6.25 at endline; mean Facility Integration Index scores were 5.83 (baseline) and 6.12 (endline). Provider Integration Index scores increased significantly (p = 0.00) among non-intervention facilities. Facility Integration Index scores did not increase significantly in either group. Results identify facility-level characteristics associated with higher levels of integration, including smaller family planning client load, family planning training among providers, and public facility ownership. Exposure to NURHI was not associated with integration index scores. Conclusion: Programs aiming to increase integration of family planning and immunization services should monitor and provide targeted support for the implementation of a well-defined integration strategy that considers the influence of facility characteristics and concurrent initiatives

Development of integration indexes to determine the extent of family planning and child immunization services integration in health facilities in urban areas of Nigeria

Background: Integrating family planning into child immunization services may address unmet need for contraception by offering family planning information and services to postpartum women during routine child immunization visits. However, policies and programs promoting integration are often based on insubstantial or conflicting evidence about its effects on service delivery and health outcomes. While integration models vary, many studies measure integration as binary (a facility is integrated or not) rather than a multidimensional and varying continuum. It is thus challenging to ascertain the determinants and effects of integrated service delivery. This study creates Facility and Provider Integration Indexes, which measure capacity to support integrated family planning and child immunization services and applies them to analyze the extent of integration across 400 health facilities. Methods: This study utilizes cross-sectional health facility (N = 400; 58% hospitals, 42% primary healthcare centers) and healthcare provider (N = 1479) survey data that were collected in six urban areas of Nigeria for the impact evaluation of the Nigerian Urban Reproductive Health Initiative. Principal Component Analysis was used to develop Provider and Facility Integration Indexes that estimate the extent of integration in these health facilities. The Provider Integration Index measures provider skills and practices that support integrated service delivery while the Facility Integration Index measures facility norms that support integrated service delivery. Index scores range from zero (low) to ten (high). Results: Mean Provider Integration Index score is 5.42 (SD 3.10), and mean Facility Integration Index score is 6.22 (SD 2.72). Twenty-three percent of facilities were classified as having low Provider Integration scores, 32% as medium, and 45% as high. Fourteen percent of facilities were classified as having low Facility Integration scores, 38% as medium, and 48% as high. Conclusion: Many facilities in our sample have achieved high levels of integration, while many others have not. Results suggest that using more nuanced measures of integration may (a) more accurately reflect true variation in integration within and across health facilities, (b) enable more precise measurement of the determinants or effects of integration, and (c) provide more tailored, actionable information about how best to improve integration. Overall, results reinforce the importance of utilizing more nuanced measures of facility-level integration

Evaluation of health effects of air pollution in the Chestnut Ridge area : preliminary analysis

This project involves several tasks designed to take advantage of (1) a very extensive air pollution monitoring system that is operating ..n the Chestnut Ridge.region of Western Pennsylvania and (2) -the very well developed analytic dispersion models that have been previously fine-tuned to this particular area.. The major task in this project is to establish, through several distinct epidemiolopic approaches, health data to be used to test hypotheses about relations of air pollution exposures to morbidity and mortality rates in this region. Because the air quality monitoring network involves no expense to this contract this project affords a very cost-effective 6pportunity-for state-of-the-art techniques to be used in both costly areas of air pollution and health -effects data col1 ection. . The closely spaced network of monitors, plus the dispersion modeling capabilities,.allow for the investigation- of health impacts of. various pollutant gradients in neighboring geographic areas, thus minimizing -the confounding effects of social, ethnic, and economic factors. The pollutants that are monitored in this network include total gaseous sulfur, sulfates, total suspended particulates, NOx, NO, ozone/oxidants, and coefficient of haze. In addition to enabling the simulation of exposure profiles between monitors, the air quality2 modeling, along with extensive source and background inventories, will allow for upgrading the quality of the monitored data. as well as simulating the exposure levels for about 25 additional air pollutants. Another important goal of this project is to collect and test the many available models for associating.health effects with air pollution, to determine their predictive validity and their usefulness in the choice and siting of future energy facilities